Láng Ek

G Kröel-Dulay

Simulation of multi-species plant communities in perturbed and nutrientlimited grasslands: development of the growth model ModVege

Keywords: CoSMo, grassland modelling, ModVege, multi-species-grasslands, plant functional types, relative abundance

Grasslands and pastures are herbaceous production systems that cover about 40% of the earth's surface (52.5 million km 2 ). Herbaceous production in these systems is the main source of food for many extensively or semi-extensively farmed herbivore species. Given their complexity, grasslands and pastures are unique ecosystems for studying vegetation fluctuations and provide a conceptual framework for understanding the evolution of these plant communities in terms of feedbacks between natural (environmental) and human (management) components in the soil-vegetation-atmosphere continuum. In chapter 3 we developed a comprehensive understanding of species richness modification due to management (mowing) and climate (warming) variation worldwide. The results show that both mowing and warming modify species richness, which on average increased by c. 32% with once-a-year mowing (against no mowing) and declined by c. 13% with warming (against ambient temperature). Where available, we accounted for harvested biomass as a concomitant variable and we found that overall it decreased by c. 21% (mowing) and increased by c. 11% (warming). In Chapters 4 and 5 we addressed the modelling of fluctuations in the relative abundance of plant species (or species types) on two case studies: a long-term observation site in the French Massif Central and an experiment conducted during the thesis in the island of Sardinia in Italy. In the first case, the ModVege crop model was coupled with CoSMo to simulate the relative abundance of functional types and species.

Three modelling solutions -the ModVege model for biomass estimation and its version coupled to CoSMo (ModVege-CoSMo) for functional types and individual species -were evaluated for biomass production and relative abundance of species types and individual species using multi-year data collected from 2006 to 2018 of both mowed and undisturbed multi-species grasslands. For biomass production, the performance of the CoSMo-based solutions was similar to that of ModVege, with an average difference of <1.2 t ha -1 between simulations and observations. ModVege-CoSMo accurately simulated fluctuations of functional types with sufficient accuracy in all treatments, with relative root mean square errors (RRMSE) rarely >30%. The accuracy in simulating the relative abundance of plant species was also generally satisfactory. In Chapter 5, annual grass species were simulated using the CropSyst crop growth model and its CoSMo-based version. In particular, we dynamically simulated relative species abundance in four pure (two grasses, i.e. spring oat and annual ryegrass, and two legumes, i.

(50:50, 25:75 and 75:25 of grasses and legumes) stands of annual self-reseeding grassland species with two mowing regimes (two or three cuts). The simulation of biomass production was satisfactory, with minimal differences between CropSyst and CoSMo-CropSyst. The accuracy of CropSyst-CoSMo in simulating the relative abundances of the four species in the mixtures was overall satisfactory for high mowing frequency management. By integrating synthesis research (i.e. meta-analysis) and process-based modelling (supported by long-term observations and field experiments), this thesis explored and learned how generic simulation models can be extended to ensure consistency between the growth dynamics of the plant species represented in a community and the biophysical processes in the same community.

Riassunto

Il presente lavoro di dottorato ha avuto per oggetto lo studio della dinamica della diversità vegetale nei prati e nei pascoli in relazione a fattori ambientali (il pedo-clima in cui sono inseriti) e antropici (la gestione agronomica). Anche se i pascoli in senso stretto non sono stati trattati in modo esplicito, le questioni affrontate nella tesi riguardano aspetti comuni ai due sistemi. La ricerca è stata incentrata sull'individuazione delle ipotesi in cui le due categorie di fattori, ambientali e antropici, intervengono nelle comunità erbacee per determinare l'evoluzione della loro composizione floristica. In particolare, l'argomento è stato affrontato dal punto di vista della modellazione esplicita e dinamica della diversità vegetale, al fine di interpretare l'azione esercitata nel tempo dai fattori ambientali e dalla gestione all'interno di comunità erbacee complesse.

La tesi è suddivisa in una parte introduttiva (Cap. 1), in cui si illustra l'importanza dalla modellistica nell'interpretazione delle risposte delle comunità erbacee ai fattori ambientali e antropici, e da un capitolo (Cap. 2) nel quale è illustrata l'ipotesi e presentato il contesto del lavoro scientifico svolto. I tre capitoli principali (3, 4 e 5), sono seguiti da una discussione generale con osservazioni conclusive (capitolo 6).

Nel primo capitolo introduttivo, si è cercato di illustrare l'importanza dei prati e dei pascoli in quanto ecosistemi che forniscono servizi ecosistemici, alla società (oltre la produzione di foraggio per l'alimentazione degli animali), noti come servizi ecosistemici, che a loro volta dipendono dalla diversità vegetale, che varia nel tempo in funzione di fattori antropici e ambientali. La modellazione di questa diversità e della sua evoluzione viene presentata come uno strumento essenziale per governare questi ecosistemi e garantire la continuità nella fornitura dei loro servizi. La diversità vegetale è spesso interpretata nei modelli secondo schemi semplificati che non sono rappresentativi delle effettive a dinamiche reali, oppure troppo complessi per essere utilizzati in applicazioni pratiche a supporto di studi agronomici ed ecologici. Al fine di bilanciare i requisiti di complessità e trattabilità pratica dei modelli, in questa tesi è stato adottato un approccio modellistico che incorpora un insieme di regole per il funzionamento di ecosistemi vegetali multispecie, in modelli di simulazione generici di comunità erbacee. Il capitolo 2, che illustra il (contesto della tesi,) sottolinea anche la complementarità tra la modellistica, che consente una rappresentazione dettagliata dei casi di studio, e la ricerca di sintesi (metanalisi), che fornisce risposte globali. Le due metodologie possono completarsi a vicenda sulla base di ipotesi comuni a entrambe sul ruolo svolto dai fattori ambientali e antropici nelle dinamiche della diversità vegetale.

Dei tre capitoli principali, il capitolo 3 è dedicato a una doppia meta-analisi, pubblicata nel 2021 sulla rivista Agronomy for Sustainable Development, che include rispettivamente 999 e 1793 studi sugli effetti prodotti dallo sfalcio dell'erba (fattore antropico) e dall'innalzamento della temperatura globale (fattore ambientale) sulla ricchezza specifica (come indicatore di biodiversità) delle comunità erbacee. I due fattori studiati e la metrica di biodiversità identificata corrispondono a quanto la letteratura scientifica internazionale offre per una ricerca di sintesi. I capitoli 4 e 5 riguardano la modellazione delle fluttuazioni dell'abbondanza relativa delle specie vegetali (o tipologie di specie) su due casi di studio: un sito di osservazione di lungo periodo nel Massiccio Centrale francese (fattori operanti in un contesto reale) e un esperimento condotto durante la tesi in Sardegna (fattori manipolati). I due capitoli sono stati sviluppati sotto forma di articoli scientifici, uno dei quali (derivato dal capitolo 4) è attualmente in fase di valutazione per la pubblicazione nel quadro della collezione Agroecology for grassland-based farming systems della rivista Agronomy. Un secondo articolo (derivato dal capitolo 5) è in preparazione.

I risultati della metanalisi hanno mostrato l'effetto positivo di una moderata perturbazione (da uno a due sfalci all'anno) sull'impianto e sul mantenimento di prati a flora diversificata di interesse agronomico e ecologico. L'aumento delle temperature, invece, ha mostrato un effetto complessivamente negativo sulla diversità vegetale a fronte di un potenziale aumento della produttività. Questi risultati mostrano una tendenza generale coerente con il principio dell'esclusione competitiva, che prevede una correlazione negativa tra le due variabili di risposta considerate, con un'elevata diversità tassonomica tendenzialmente associata a una bassa produzione di biomassa e viceversa.

Nella tesi si è fatto un approfondimento impiegando modelli di simulazione che combinano i parametri (traits) delle comunità con le proprietà del suolo, il clima e la gestione. Questi modelli sono ampiamente utilizzati per stimare la produttività dei prati e dei pascoli e i cicli biofisici e biogeochimici dell'ecosistema. Nella tesi non sono stati affrontati direttamente i cicli biogeochimici, che potranno essere oggetto di futuri approfondimenti. Infatti, grazie alla capacità di simulare l'evoluzione giornaliera dell'abbondanza relativa delle specie in una comunità erbacea in funzione di una serie di fattori ambientali e gestionali, i modelli di simulazione impiegati offrono ampie possibilità di applicazione su larga scala, riconnettendo i processi biofisici e biochimici dell'ecosistema alla diversità vegetale. La modellazione ha permesso di simulare le dinamiche dell'abbondanza relativa delle specie vegetali e delle caratteristiche della comunità che ne dipendono (traits), con un focus specifico sui prati permanenti del Massiccio Centrale francese, dove erano disponibili database pluriennali dettagliati di misure di produzione e diversità vegetale in situazioni pedo-climatiche contrastanti non ancora considerate in studi precedenti (ad es., l'abbandono).

L'esperimento condotto in Sardegna, limitato a un solo anno, ha permesso di caratterizzare e simulare le dinamiche dei miscugli di specie foraggere annuali tipiche dei pascoli della regione biogeografica mediterranea. Il Community Simulation Model (CoSMo), sviluppato nel 2014 presso il Laboratorio Cassandra dell'Università di Milano (Italia), si è rivelato uno strumento flessibile per integrare regole di dinamica vegetale nella struttura di modelli di simulazione esistenti. I risultati delle prime applicazioni di CoSMo, ottenuto con l'integrazione dei modelli colturali CropSyst e WOFOST, in collaborazione con l'Università di Firenze (Italia) e l'Unité Mixte de Recherche sur l'Écosystème Prairial (UREP) dell'INRAE, hanno mostrato la sua capacità nel rappresentare l'abbondanza relativa delle specie in prati dell'Italia centrale fertilizzati e sfalciati una volta all'anno. In questa tesi si è quindi progettato un ulteriore avanzamento di questo strumento per rispondere alla domanda di ricerca di modelli tecnicamente avanzati capaci di conciliare la biofisica e la biogeochimica da un lato e la biodiversità dall'altro, facilmente applicabili a prati e pascoli. La risposta a questa domanda è stata declinata come proposta di roadmap per lo sviluppo e utilizzazione di modelli per simulare le dinamiche della biodiversità vegetale e gli impatti della biodiversità sui servizi ecosistemici (van Oijen et al 2020), grazie al progetto MODIPRAS (Modelling relationships between species diversity, the functioning of grassland systems and their ability to deliver ecosystem services) del meta-programma ECOSERV (ex-INRA), coordinato dall'UREP. In questa tesi, le potenzialità di CoSMo sono state estese implementando per la prima volta le sue regole per il funzionamento di una comunità vegetale multispecie in un modello di simulazione applicato a prati e pascoli, il modello ModVege sviluppato presso UREP del centro INRAE Clermont-Auvergne-Rhône-Alpes, e valutando questa soluzione modellistica (ModVege-CoSMo) in situazioni di vegetazione caratterizzata da molte specie non solo in condizioni ottimali (prati fertilizzati), ma anche in condizioni di stress nutrizionali (assenza di fertilizzazione) e in presenza di disturbo antropico (sfalci multipli durante l'anno) o in sua assenza (abbandono). Si è giunti così a constatare l'accuratezza di ModVege-CoSMo nel simulare l'evoluzione nel tempo delle abbondanze relative, soprattutto per la simulazione di specie molto presenti come Elymus repens e Alopecurus pratensis, ma anche Festuca arundinacea, Lolium perenne e Poa pratensis, ciò che rende il modello promettente per studi a sostegno di obiettivi agronomici ed ecologici. La qualità delle simulazioni si è rivelata soddisfacente anche nel caso della valutazione di una soluzione modellistica esistente basata su CoSMo (CropSyst-CoSMo) per la simulazione delle fluttuazioni intra-annuali di graminacee e leguminose annuali autoriseminanti in vari miscugli. Tuttavia, l'esperimento che ha permesso di valutare la soluzione modellistica, avviato nell'ambito della tesi nella stagione 2020-2021 e oggetto di questa tesi, ha consentito di far emergere le potenzialità di CoSMo nel contesto dei pascoli mediterranei e ha aperto così la strada per ulteriori approfondimenti.

Il resto di questa sezione riassuntiva illustra gli aspetti salienti della problematica, i metodi e i risultati più significativi conseguiti con la tesi. La letteratura citata si limita ai contributi direttamente collegati a questo lavoro di dottorato, mentre la lista completa della letteratura scientifica consultata è stata riportata nei singoli capitoli della tesi.

Importanza dei prati e dei pascoli

I prati e i pascoli sono sistemi di produzione erbacea che coprono circa il 40% della superficie terrestre (52.5 milioni km 2 ) in tutte le regioni del mondo, tranne l'Antartide e la Groenlandia.

Nello specifico, si tratta di formazioni vegetali prevalentemente erbacee che possono essere utilizzate direttamente dagli animali. Un pascolo è, più particolarmente, un'area di terreno su cui crescono spontaneamente varie specie di piante erbacee, utilizzata per l'alimentazione diretta del bestiame (pascolamento). Un prato, invece, è un'area di terreno in cui possono essere coltivate intenzionalmente o lasciate crescere spontaneamente varie specie di piante erbacee, che vengono poi sfalciate e utilizzate come foraggio fresco o conservato (ad es., fieno o insilato) per gli animali. Una stessa superficie può essere utilizzata a pascolo e a prato in periodi diversi del suo ciclo produttivo (prato-pascolo). La produzione erbacea di questi sistemi è la principale fonte di foraggio per molte specie di erbivori (principalmente ovicaprini, bovini ed equini) allevati in modo estensivo o semi-estensivo. Poiché nella quasi totalità dei casi i pascoli e i prati sono comunità vegetali secondarie, legate cioè alla manipolazione della vegetazione primaria da parte dell'uomo, il loro mantenimento e funzionamento richiede continue pratiche agronomiche efficaci. Sistemi di produzione erbacea migliorati si ritrovano in aree che derivano dalla lavorazione del suolo e dall'impianto di varietà di specie foraggere selezionate anche non autoctone, caratterizzate da un elevato potenziale produttivo. Oltre che per la produzione di foraggio, i prati e i pascoli sono importanti anche perché forniscono una serie di servizi ecosistemici, che possono essere classificati in quattro categorie: di supporto (ad es., la produzione primaria, i cicli dell'acqua e dei nutrienti nel suolo), di approvvigionamento (ad es., la produzione di foraggio), di regolazione (ad es., la regolazione del clima, il controllo dell'erosione, l'impollinazione) e culturali (ad es., l'estetica, la ricreazione, il patrimonio ambientale).

Le specie vegetali presenti in un manto erboso variano notevolmente nelle loro caratteristiche biologiche e morfo-fisiologiche. Anche la composizione delle comunità vegetali varia notevolmente, non solo da regione a regione ma anche nel corso del tempo, e questo influenza la produzione e la qualità del foraggio, nonché la fornitura di altri servizi ecosistemici. I prati e i pascoli sono infatti tra gli ecosistemi più ricchi di specie vegetali al mondo, ma quasi la metà di questi ecosistemi è degradata e quasi il 5% presenta livelli di degrado elevati o estremi. Pertanto, la continuità di queste comunità vegetali e del loro funzionamento è essenziale per garantire la conservazione della loro biodiversità e la fornitura di servizi ecosistemici, tenendo conto del fatto che le dinamiche spazio-temporali delle specie vegetali sono fortemente influenzate dalla loro gestione. Le pratiche agronomiche come l'intensità di pascolamento o la frequenza di sfalcio, così come l'apporto di fertilizzanti, sono fattori importanti nella dinamica della vegetazione e nella distribuzione delle specie, contribuendo alla diversità del paesaggio e alla quantità e qualità del foraggio prodotto. Mentre le pratiche agronomiche (ad es., pascolamento, sfalcio, fertilizzazione, semina di miscugli di specie, controllo degli arbusti) sono i principali fattori che determinano la dinamica della vegetazione, la distribuzione delle specie e la loro diversità, gli effetti combinati delle pratiche di gestione e dei cambiamenti ambientali rimangono un campo di ricerca aperto per garantire il mantenimento del valore dei beni e dei servizi che questi ecosistemi possono fornire in varie condizioni.

Risposte ai disturbi antropici e ai fattori ambientali

La biomassa vegetale dei prati e dei pascoli è una componente rilevante dell'alimentazione degli erbivori domestici nei sistemi di allevamento estensivi e semi-estensivi, e la sua produzione è influenzata dalle pratiche agronomiche come la concimazione, le tecniche di pascolamento o lo sfalcio. La gestione agronomica della vegetazione di prati e pascoli condiziona in modo determinante la diversità delle specie vegetali che a sua volta ha un impatto sulla produttività dei prati e dei pascoli e sulla erogazione di servizi ecosistemici.

Essendo l'azoto uno degli elementi più limitanti la produttività dei prati e pascoli, la concimazione azotata aumenta la produzione di biomassa e ha un effetto negativo sulla diversità delle specie, ma questa risposta varia a seconda del contesto ambientale e del tipo di comunità vegetale. In generale, le graminacee traggono i maggiori benefici dall'aggiunta di N, aumentando così la competitività nei confronti delle leguminose e altre dicotiledoni che sono meno dipendenti dalla disponibilità di N minerale nel suolo. La riduzione dell'apporto di N, per esempio per effetto della ridotta o assente concimazione e il ridotto apporto meteorico può portare a un aumento della diversità vegetale. Anche l'aggiunta di fosforo (P) può causare una riduzione della ricchezza specifica, ma con un effetto minore rispetto all'N e con un vantaggio relativo per le leguminose rispetto alle graminacee. La rimozione della biomassa aerea mediante sfalcio o pascolamento può avere un impatto notevole sulla composizione floristica delle comunità vegetali dei prati e dei pascoli, nonché sulla qualità e sulla resa. Questo impatto dipende principalmente dall'intensità e dalla frequenza delle pratiche di gestione. Ad esempio, rispetto all'abbandono, un singolo sfalcio annuale può avere un effetto positivo sulla ricchezza specifica e negativo sulla produzione di biomassa.

Gli effetti del pascolamento sono generalmente simili a quelli dello sfalcio, ma il pascolamento determina una maggiore eterogeneità spaziale. Un effetto positivo del pascolamento sulla ricchezza floristica si ottiene generalmente con carichi animali bassi o intermedi, mentre con carichi elevati la diversità vegetale può ridursi a poche specie tolleranti o infestanti.

Strettamente legati alle condizioni climatiche, i prati e i pascoli sono altamente esposti ai rischi legati ai cambiamenti climatici, in particolare il cambiamento dei pattern di precipitazione, con una maggiore frequenza di eventi meteorologici estremi (siccità e alluvioni) e con una maggiore variabilità della produzione. L'aumento delle temperature, in zone con clima atlantico in particolare, può favorire l'aumento della produzione di foraggio grazie a una stagione di crescita più lunga e a una maggiore efficienza nell'uso dell'acqua e dei nutrienti, insieme all'effetto fertilizzante dell'aumento della concentrazione di CO2 atmosferica. Tuttavia, l'aumento della temperatura dell'aria ha un impatto anche sulla diversità vegetale, favorendo l'invasione di specie provenienti da habitat più caldi e la perdita di specie native. I cambiamenti climatici minacciano alcune nicchie ecologiche, con la scomparsa di specie che non possono adattarsi all'aumento delle temperature con la stessa rapidità di altre. Il riscaldamento e gli eventi siccitosi spesso agiscono insieme nell'alterare la produttività e le risposte della biodiversità dei prati e dei pascoli. In generale, la siccità esercita effetti negativi sulla produzione di biomassa, che possono essere mitigati quando la diversità vegetale è elevata. Tuttavia, diversi gruppi funzionali di specie presentano risposte diverse alla siccità e la disponibilità di nutrienti può essere un fattore critico nel definire la risposta della vegetazione. Ad esempio, le graminacee se concimate con azoto sono più sensibili alla siccità in termini di biomassa prodotta rispetto alle dicotiledoni.

Importanza dei modelli di simulazione

La ricerca scientifica su sistemi complessi come i prati e i pascoli, le cui comunità vegetali ospitano un'elevata biodiversità, richiede approcci capaci di integrare diverse competenze in ambito ecologico, agronomico, biologico e zootecnico, in modo da poter allineare obiettivi di gestione, strumenti di monitoraggio e sviluppo socio-economico. In particolare, la comprensione e la proiezione delle dinamiche dei prati e dei pascoli nel contesto del cambiamento globale richiedono modelli di simulazione della vegetazione capaci di rappresentare esplicitamente i processi e i fattori che regolano il funzionamento degli ecosistemi. Più specificamente, i modelli basati sui processi, in alternativa a modelli puramente statistici, consentono di integrare le osservazioni di campo, fornendo informazioni aggiuntive sui processi e sui meccanismi dell'ecosistema difficili da misurare o da dedurre da campagne di monitoraggio. I modelli di simulazione, supportati da dispositivi di osservazione a lungo termine e da prove sperimentali, sono strumenti fondamentali per valutare l'impatto combinato dei fattori ambientali (ad es., eventi meteorologici estremi) e della gestione sulla struttura delle comunità vegetali locali e sul loro potenziale produttivo. Il crescente interesse per gli effetti dei cambiamenti climatici sugli agroecosistemi rende l'analisi delle dinamiche delle specie vegetali ancora più rilevante e urgente (Kipling et al., 2016;van Oijen et al., 2018;Ma et al., 2019).

I modelli che simulano la dinamica delle specie in una comunità vegetale sono un potente strumento per studiare l'evoluzione della diversità vegetale in una varietà di condizioni e per supportare le scelte gestionali. In termini di modellazione, la complessità delle dinamiche che caratterizzano questi sistemi suggerisce l'adozione di modelli centrati sul singolo individuo, che simulano cioè le relazioni tra i tratti funzionali delle piante e la loro plasticità in risposta ai cambiamenti ambientali. Questi modelli sono complessi e difficili da inizializzare e parametrizzare per ogni specie della comunità. Al contrario, i modelli che riproducono le caratteristiche morfologiche e fisiologiche medie di una comunità vegetale nel suo complesso tendono a ignorare le abbondanze relative di diverse specie di piante o diversi gruppi funzionali all'interno delle comunità. Questi modelli si basano sulla simulazione del prato o del pascolo come una coltura, con parametri che descrivono le caratteristiche morfologiche e fisiologiche volte a rappresentare i tratti medi della comunità. Questo approccio è oggi considerato troppo semplificato, in quanto (i) non è sempre possibile misurare i parametri che descrivono i tratti delle comunità vegetali, (ii) non è possibile riprodurre la dinamica delle abbondanze delle diverse specie durante la stagione di crescita e (iii) non permette di simulare l'effetto della gestione sulle diverse specie (ad es., la preferenza degli animali e la capacità di recupero dopo lo sfalcio o il pascolamento). Poiché la diversità funzionale è essenziale per la stabilità e la produttività degli ecosistemi, nei modelli dinamici di vegetazione sono stati introdotti approcci basati sulla variabilità temporale dei tratti (parametri) della vegetazione, superando così il paradigma che condensa la diversità dei tipi funzionali con parametri costanti, ma senza una rappresentazione esplicita e dinamica delle abbondanze relative. Altri approcci riducono la complessità del sistema rappresentando una specie principale e, implicitamente, tutte le altre come un'unica specie concorrente. L'approccio adottato in questa tesi per la simulazione di una comunità vegetale è quello proposto da Confalonieri (2014), or CoSMo (Community Simulation Model), in quanto si tratta di un approccio generico (a istanza unica) per descrivere (virtualmente) tutte le specie presenti nelle comunità vegetali.

Inoltre, può essere facilmente implementato per essere accoppiato a un qualsiasi modello generico di simulazione di una copertura vegetale, al fine di calcolare le abbondanze relative delle specie in un determinato passo temporale (ad es., giornalmente). CoSMo richiede informazioni su alcune caratteristiche delle piante, che devono essere specificate per singola specie o gruppo funzionale della comunità, ma si basa sul presupposto che queste caratteristiche, a cui corrispondono i parametri del modello, in un miscuglio possano essere trattate come fossero additive, non affrontando esplicitamente gli impatti non lineari della variazione delle caratteristiche, limitando così la complessità e le richieste computazionali.

Mentre altri modelli simulano la crescita di singole piante che interagiscono tra loro in stretta prossimità spaziale, le soluzioni basate su CoSMo semplificano i singoli processi a favore dell'applicabilità del modello (Movedi et al., 2019) e si iscrivono nella roadmap delineata da van Oijen et al. (2020) per lo sviluppo di modelli che simulano la dinamica delle specie vegetali in prati e pascoli.

Contesto del lavoro di ricerca

Data la loro complessità, i prati e i pascoli sono ecosistemi unici per lo studio delle dinamiche della vegetazione e forniscono un quadro concettuale per comprendere l'evoluzione di queste comunità vegetali in termini di: (1) retroazioni tra le componenti naturali (ambientali) e umane (gestionali) nel continuum suolo-vegetazione-atmosfera e (2) risposta a entrambe le componenti su larga scala.

Per rispondere alle due questioni, una prima ipotesi della tesi è che la dinamica delle specie vegetali in una comunità possa essere governata da due grandi tipologie di fattori: ambientali e gestionali. I fattori ambientali determinano le condizioni di idoneità per la crescita e lo sviluppo delle diverse specie vegetali in una comunità in rapporto ai vincoli biofisici dovuti alle proprietà del suolo e ai fattori climatici, condizioni a loro volta modificate dalle pratiche agronomiche. Una seconda ipotesi della tesi è che le relazioni tra questi fattori e le dinamiche della vegetazione possano essere esplorate attraverso due approcci: la metanalisi della letteratura e la modellazione basata sui processi. Da un lato, una revisione sistematica e una metanalisi della letteratura corrente consentono di confrontare i risultati di diverse prove sperimentali o osservazioni, mentre con la modellazione i processi di funzionamento rilevanti sono inclusi in un modello biofisico della comunità vegetale.

L'integrazione dei due approcci ha il vantaggio di poter affrontare entrambe le questioni (retroazioni tra componenti naturali e antropiche e risposte su larga scala), perché mentre le metanalisi consentono di estrarre risposte generali, i modelli di simulazione basati sui processi, sebbene di natura deduttiva, sono parametrizzati localmente e quindi specifici per le condizioni proprie a specifici casi di studio (Magliocca et al., 2015).

In sintesi:

1) gli studi metanalitici aiutano a identificare profili di riposta generali dell'evoluzione della diversità vegetale (approccio globale);

2) i modelli formalizzano queste risposte attraverso un insieme di regole di funzionamento delle comunità vegetali (approccio a scala locale).

Effetti dello sfalcio e del riscaldamento sulla ricchezza specifica e sulla produzione di biomassa dei prati: metanalisi

Una ricerca per parole chiave in due database bibliografici internazionali, ISI Web of Science e Scopus, ci ha permesso di coprire gli articoli pubblicati dal 1985 al 2020, a cui abbiamo aggiunto altri articoli pertinenti di riviste peer-review di cui eravamo a conoscenza (Gruner et al., 2017). Negli articoli selezionati, gli esperimenti di sfalcio e riscaldamento includevano rispettivamente la temperatura ambiente (senza riscaldamento) e l'abbandono (senza sfalcio) come trattamento di controllo e riportavano gli effetti quantitativi (insieme a una misura di variabilità) dello sfalcio o del riscaldamento sulla ricchezza specifica (SR, metrica di conservazione). Quando disponibili, sono state considerate nelle analisi anche le determinazioni della biomassa raccolta (HB, indicatore del servizio di approvvigionamento).

Un singolo sfalcio annuale è la frequenza di sfalcio più comunemente utilizzata nei prati multispecie ed è stato il regime di sfalcio di riferimento per la metanalisi (indipendentemente dalla sua temporalità nel corso dell'anno). I risultati di esperimenti condotti con regimi di sfalcio più complessi sono stati utilizzati come elemento complementare in sede di discussione.

Gli effetti sperimentali di ogni studio sono stati valutati utilizzando il logaritmo dei rapporti di risposta, calcolati come rapporto tra i valori medi di un trattamento e il suo controllo. Con un modello a effetti casuali si è ottenuta una risposta globale generalizzata, ma poiché è stata rilevata eterogeneità tra gli studi, sono stati utilizzati modelli a effetti misti per spiegare questa eterogeneità con l'uso di variabili moderatrici quando almeno la metà degli studi le riportava.

I moderatori identificati sono i seguenti: dimensioni dell'appezzamento, la durata dello studio, anno di pubblicazione degli articoli, altitudine del sito, temperatura atmosferica media annuale dell'aria e precipitazione totale media annuale. Per l'effetto dello sfalcio, è stata considerata anche l'altezza di taglio mentre la differenza di temperatura tra il trattamento e il controllo e la tecnica di riscaldamento sono state utilizzate come moderatori nella metanalisi sugli effetti del riscaldamento. La selezione dei contributi estratti ha portato a 43 e 34 articoli

(46 e 42 esperimenti) che quantificano gli effetti dello sfalcio e del riscaldamento, rispettivamente, sulla ricchezza specifica (SR). In 16 articoli sull'effetto dello sfalcio ( 18esperimenti) e in 17 articoli (22 esperimenti) sull'effetto del riscaldamento, è stato valutato anche l'effetto degli stessi fattori sulla HB .

Ad eccezione di tre studi, abbiamo ottenuto un effetto significativamente positivo dello sfalcio annuale sulla SR rispetto all'abbandono (circa 32% di aumento medio). Al contrario, lo sfalcio annuale ha un effetto negativo sulla produzione di biomassa (riduzione di circa il 21% in totale). Questi risultati indicano un chiara situazione di compromesso tra un servizio di approvvigionamento (produzione di foraggio) e i servizi ecosistemici mediati dalla biodiversità vegetale (ad es., l'impollinazione, il controllo dei parassiti, la fertilità del suolo e la stabilità delle rese). Solo l'anno di pubblicazione è risultato un moderatore significativo dell'effetto dello sfalcio su HB, avendo rilevato un impatto negativo dello sfalcio più importante negli studi meno recenti (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009).

Il modello a effetti casuali ha indicato un effetto negativo significativo del riscaldamento sulla SR, con una riduzione di circa il 13% per un aumento medio della temperatura di 1,8°C

(nell'intervallo 0,15-4,10°C), riflettendo risposte simili osservate negli ecosistemi terrestri (Gruner et al., 2017). L'anno di pubblicazione è risultato un importante moderatore, con solo otto studi pubblicati prima del 2010, ma soprattutto l'altitudine del sito e le precipitazioni annuali sono emerse come variabili moderatrici significative, spiegando circa il 36% dell'eterogeneità. Gli effetti più deboli del riscaldamento sulla SR (minore perdita di diversità vegetale) tendono a essere associati alle aree più secche (<300 mm di precipitazioni annue), la risposta al riscaldamento essendo più forte quanto più bassa è l'aridità [START_REF] Peñuelas | Response of plant species richness and primary productivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: reductions in primary productivity in the heat and drought year of 2003[END_REF]. Allo stesso modo, abbiamo osservato un declino più limitato della diversità vegetale per i siti al di sotto dei 1000 m di altitudine, dove la SR è generalmente più bassa (Dengler et al., 2014).

In conclusione, la metanalisi ha riunito un gran numero di osservazioni e ha permesso di interpretare in modo esaustivo la risposta delle comunità erbacee in molti studi in diverse regioni del mondo. In primo luogo, una ricchezza di specie vegetali più elevata e una produttività più bassa riscontrate nelle parcelle sfalciate suggeriscono l'importanza di un moderato disturbo (ad es., un singolo sfalcio annuale) per favorire la diversità vegetale. In secondo luogo (e in contrasto con il risultato precedente), la produttività può essere aumentata in presenza di temperature più elevate, ma il riscaldamento tende a ridurre il numero di specie vegetali nella comunità. Queste opposte risposte a stress (riscaldamento) e perturbazioni (sfalcio) suggeriscono possibili meccanismi di esclusione competitiva, che non sono stati analizzati in questo studio. I risultati delle nostre metanalisi confermano l'importanza di considerare la risposta delle specie agli stress ambientali e ai disturbi antropici per prevedere le dinamiche delle comunità vegetali in scenari ambientali e gestionali alternativi. Ulteriori analisi quantitative di queste relazioni possono supportare lo sviluppo di modelli di simulazione delle dinamiche della diversità vegetale in comunità erbacee a flora complessa.

Simulazione di comunità vegetali multispecie in prati disturbati e non fertilizzati: sviluppo del modello di crescita ModVege

ModVege è un modello basato sui processi che stima la quantità e la qualità della biomassa foraggera in prati e pascoli multispecie su base giornaliera (Jouven et al., 2006a, b). Il modello sviluppa l'ipotesi che il comportamento di una comunità erbacea possa essere spiegato dai parametri (traits) medi delle specie graminacee dominanti. In particolare, ModVege si basa su sei tipi funzionali di specie graminacee che possono essere combinati in proporzioni diverse per simulare un'ampia gamma di comunità vegetali, ma non simula le fluttuazioni di queste proporzioni nel tempo. Per questo, ModVege è stato accoppiato con CoSMo (Community Simulation Model), il cui approccio può essere combinato con qualsiasi simulatore generico di crescita della vegetazione per simulare le comunità vegetali attraverso una parametrizzazione media che non rimane costante ma varia in funzione dell'abbondanza relativa di ogni specie o tipologia di specie (Confalonieri, 2014). ModVege si riferisce alla tipologia funzionale elaborata da Cruz et al. (2002) Mentre per alcuni parametri di ModVege sono stati utilizzati valori estratti dalla letteratura, per altri un lavoro di calibrazione è stato effettuato confrontando le stime dei modello con i dati osservati, variando i valori dei parametri all'interno di intervalli plausibili al fine di garantire parametri di vegetazione biologicamente interpretabili. Le simulazioni con CoSMo hanno anche permesso di valutare i valori dei parametri (traits) della comunità, stimati su base giornaliera in funzione dell'abbondanza relativa di specie o tipi. In questo caso, in assenza di osservazioni, i valori di due traits della comunità vegetale (altezza massima e superficie fogliare specifica) sono stati valutati per la loro variazione nel tempo, rispetto ai valori costanti attribuiti ai corrispondenti parametri delle specie o dei tipi funzionali che compongono la comunità.

Per la produzione di biomassa, le prestazioni delle soluzioni basate su CoSMo sono risultate simili a quelle di ModVege, con una differenza media di <1,2 t ha -1 tra simulazioni e osservazioni.

ModVege-CoSMo ha simulato accuratamente le fluttuazioni dei tipi funzionali con sufficiente accuratezza in tutti i trattamenti, con errori medi quadratici relativi Simulazioni soddisfacenti delle abbondanze relative delle singole specie hanno portato a simulazioni realistiche delle dinamiche dei traits della comunità (Louault et al., 2017). Ad esempio, i valori simulati della superficie fogliare specifica (SLA) del trattamento fertilizzato del blocco 1, fluttuanti intorno a ~35 m 2 kg -1 , corrispondono ai valori di SLA assegnati a Poa pratensis e Elymus repens, graminacee di tipo b dominanti nelle parcelle fertilizzate. In questo trattamento, la diminuzione simulata dello SLA della comunità è in gran parte spiegata dalla diminuzione modellata (e osservata) di Taraxacum officinale, una dicotiledone con uno SLA elevato (48 m 2 kg -1 ). Nel trattamento fertilizzato del blocco 2, lo SLA simulato della comunità diminuisce nel tempo come conseguenza del declino simulato (e osservato) di Taraxacum officinale. Per quanto riguarda l'altezza massima della chioma, il suo aumento simulato nell'abbandono del blocco 1 riflette la maggiore abbondanza (simulata e osservata) di due specie graminacee a taglia alta, Alopecurus pratensis (coda di volpe) e Arrhenatherum elatius (avena altissima), le cui altezze massime sono state calibrate a circa 1,1-1,2 m.

In conclusione, i risultati delle simulazioni delle dinamiche vegetali ottenute con ModVege-CoSMo sono incoraggianti perché, sebbene la qualità delle simulazioni della biomassa non sia stata migliorata rispetto a ModVege, non è stata neanche degradata dalla complessità introdotta per simulare la diversità vegetale. Un aspetto importante è che con CoSMo diventa possibile per i modelli di crescita della vegetazione (incluso il modello per prati e pascoli ModVege) ottenere stime della produzione di biomassa rappresentando dinamicamente l'abbondanza relativa di specie o tipologie di specie in comunità miste anche complesse, un risultato utile per dedurre la varietà di servizi ecosistemici forniti da queste comunità e mediati dalla loro diversità floristica.

Simulazione di miscugli di piante erbacee annuali: potenzialità e prospettive dell'utilizzo del modello CropSyst accoppiato a CoSMo

In questo studio, specie foraggere annuali sono state simulate utilizzando il modello di crescita colturale CropSyst (Stöckle et al., 2003) e la sua versione accoppiata a CoSMo.

CropSyst stima la crescita delle piante in base al valore minimo tra la biomassa ottenuta dalla conversione dell'acqua traspirata e quella ottenuta dall'energia radiante intercettata, valori modulati da un fattore di riduzione della temperatura. Il modello simula un prato come una coltura, senza tenere conto della diversità vegetale. Per questo, è stato accoppiato con CoSMo per simulare le comunità vegetali attraverso una parametrizzazione media che non rimane costante ma varia in base all'abbondanza relativa di ogni specie.

In questa tesi, CropSyst-CoSMo è stato valutato su prati mediterranei dominati da specie Paradana) e tre miscugli in diverse proporzioni delle stesse specie (25, 50 e 75% di graminacee) secondo un disegno sperimentale di tipo split-plot in blocchi randomizzati di quattro repliche. Le parcelle principali erano costituite da sette tipi di copertura vegetale: i) specie in purezza di A. sativa, ii) purezza di L. rigidum, iii) purezza di T. michelinaum, iv) purezza di T. subterraneum, v) 50% di graminacee e 50% di leguminose, vi) 25% di graminacee e 75%

di leguminose e vii) 75% di graminacee e 25% di leguminose. Le sottoparcelle erano caratterizzate dalle due opzioni di sfalcio: un singolo sfalcio all'inizio della fioritura (bassa frequenza) e diversi sfalci a intervalli mensili (alta frequenza o pascolo simulato).

Per la scelta delle specie ci si è basati sui loro diversi cicli biologici, sulla loro importanza ecologica ed economica per i sistemi pastorali e silvo-pastorali della Sardegna e sulla disponibilità della semente sul mercato locale. Collezioni di varietà e ecotipi commerciali sono disponibili per Avena sativa (avena comune) e Lolium rigidum (loglio rigido), due specie ampiamente presenti in ambito mediterraneo, a differenza di altre graminacee annuali come, ad esempio, Avena barbata (avena barbata), comune allo stato spontaneo nei pascoli mediterranei. Entrambi i trifogli annuali -Trifolium michelianum (trifoglio micheliano) e T.

subterraneum (trifoglio sotterraneo) -hanno capacità di autorisemina e azoto-fissazione, contribuiscono a una elevata qualità del foraggio e sono spesso impiegati in miscugli con avene, logli e altre graminacee.

Oltre alla produzione di biomassa, sono stati campionati l'indice fogliare (LAI), la superficie fogliare specifica (SLA) e l'altezza delle singole specie e delle comunità, che hanno permesso di caratterizzare sperimentalmente specie annuali di interesse agronomico ed ecologico con parametri di vegetazione specifici per la modellazione. Per simulare la dinamica dell'abbondanza relativa all'interno dei miscugli, è stata eseguita la parametrizzazione di CropSyst e CoSMo per ogni specie, estraendo valori dalla letteratura (incluso il manuale del modello) o, per la superficie fogliare specifica e l'altezza massima (parametri morfologici), utilizzando le determinazioni sperimentali. La calibrazione è stata effettuata confrontando le stime del modello con le osservazioni e aggiustando i valori dei parametri all'interno di intervalli plausibili, garantendo così parametri di vegetazione interpretabili dal punto di vista biologico.

La simulazione della produzione di biomassa è stata soddisfacente, con differenze minime tra CropSyst e CoSMo-CropSyst. Anche i valori di LAI e altezza sono stati simulati all'interno di tendenze generali soddisfacenti.

L'accuratezza di CropSyst-CoSMo nel simulare le abbondanze relative delle quattro specie nei miscugli è stata complessivamente soddisfacente per la gestione ad alta frequenza di sfalcio. Anche se in G25L75, Avena sativa ha raggiunto un valore di RRMSE di ~101%, ma la differenza tra le medie osservate e simulate è stata di ~6%, con un elevato coefficiente di correlazione tra stime e osservazioni (r=0,92). Lolium rigidum, Trifolium michelianum e Trifolium subterraneum hanno mostrato errori medi quadrati relativi (RRMSE) inferiori, compresi tra ~40% e ~18%, con valori di r fino a 0,99 per Trifolium michelianum. In relazione alle abbondanze relative delle specie, CropSyst-CoSMo ha consentito la simulazione di alcuni traits della comunità. Ad esempio, nei miscugli, lo SLA della comunità simulata riflette la crescente proporzione di graminacee (SLA values >20 m² kg -1 ) rispetto alle leguminose (SLA values <20 m² kg -1 ) nella transizione da G25L75 a G75L25. In questo caso, le fluttuazioni simulate mostrano un'ampiezza ridotta rispetto alle osservazioni e le prestazioni del modello tendono a diminuire con l'aumentare della copertura delle graminacee. Risultati simili sono stati ottenuti con la bassa frequenza di sfalcio.

Questo è il primo studio condotto con l'obiettivo di modellare i tratti agronomici di specie erbacee annuali autoriseminanti e le loro abbondanze relative all'interno di comunità

Osservazioni conclusive

Il dottorato si è concentrato principalmente su prati gestiti e non gestiti in condizioni pedoclimatiche contrastanti, a scala di campo, con l'abbondanza relativa di specie (o di tipologie di specie) come metrica di base della diversità vegetale. Lo sviluppo di un approccio modellistico basato sul collegamento di un modulo specifico per la diversità vegetale a modelli di simulazione esistenti si è rivelato promettente per migliorare la capacità di modellazione di comunità multispecifiche. La disponibilità di modelli biofisici ha rappresentato un'opportunità per seguire le dinamiche della diversità vegetale da una prospettiva modellistica, in quanto ha permesso di far emergere gli effetti dei driver ambientali e gestionali sulle dinamiche vegetali in sistemi continentali e mediterranei, senza esplorare, in questa fase, le complesse interazioni con i processi biogeochimici dei cicli del carbonio e dell'azoto.

Seguendo la road map di MODIPRAS, il dottorato (i) ha fatto progredire la comprensione dei fattori ambientali e gestionali che determinano la diversità delle comunità erbacee e il suo impatto sui servizi ecosistemici forniti; (ii) ha esaminato gli studi in corso sulla simulazione delle dinamiche della diversità vegetale in prati e pascoli; e (iii) ha affrontato l'implementazione dei modelli di simulazione a supporto di studi agronomici ed ecologici.

In particolare, il dottorato ha esplorato come i modelli biofisici generici possano essere estesi per garantire la coerenza tra le dinamiche di crescita di ciascuna specie vegetale o tipo funzionale e i flussi biofisici nella comunità. Il lavoro svolto ha considerato la produttività delle comunità erbacee come unico servizio ecosistemico, in quanto è il primo servizio di approvvigionamento dei prati e dei pascoli. Ciò significa che il potenziale di CoSMo o di approcci simili per simulare un insieme più ampio di servizi ecosistemici dovrà essere ulteriormente valutato all'interno di un quadro di simulazione dei processi biogeochimici, poiché è importante migliorare la rappresentazione modellistica degli effetti della diversità delle piante sui cicli del carbonio e dell'azoto, nonché sul ciclo dell'acqua e sul bilancio energetico. Integrando la ricerca di sintesi (cioè la meta-analisi), la modellazione basata sui processi (supportata da osservazioni a lungo termine e prove sperimentali) e le sperimentazioni in campo, in questa tesi è stato dimostrato come il potenziale di applicazione di modelli di simulazione generici possa essere esteso per garantire la coerenza tra le dinamiche di crescita delle specie vegetali rappresentate in una comunità e i flussi biofisici nella stessa comunità. La diversità vegetale influisce infatti sui servizi ecosistemici, il che implica la necessità di introdurre questa diversità -la sua dinamica e i suoi impatti -nei modelli di simulazione al fine di prevedere l'impatto dei cambiamenti ambientali e gestionali sulla fornitura di questi servizi. Sebbene questo lavoro di dottorato abbia affrontato due delle principali sfide di modellazione identificate dalla comunità internazionale, ossia la modellazione dei prati e pascoli multispecie e la modellazione della fornitura di servizi ecosistemici, ampi margini si offrono alla ricerca per far progredire le conoscenze e la modellazione dei servizi ecosistemici di regolazione e di supporto (ad es., la filtrazione dell'ammoniaca e dei nitrati, la regolazione delle acque, la prevenzione dell'erosione, la riduzione delle emissioni, il sequestro del carbonio), più difficili da monitorare rispetto alla produzione.

Questo significa che lo sviluppo dei modelli deve essere accompagnato da una continua produzione di dati di qualità, e dal confronto dei risultati della modellazione con ricche serie 20 di dati di riferimento per diversi servizi ecosistemici entro scenari di produzione alternativi (tra cui crescita potenziale, limitazione idrica, limitazione dei nutrienti, riduzione dei parassiti, pascolamento e sfalcio), in presenza di eventi estremi (abiotici e biotici) e condizioni di stress cronico. La diversità vegetale può agire per stabilizzare i flussi biogeochimici in condizioni estreme e i modelli di simulazione hanno bisogno di dati per verificare la loro capacità di tenerne conto.

I risultati ottenuti con il dottorato aprono quindi nuove prospettive per la ricerca sui prati e i pascoli in diversi contesti ambientali. In questo senso, grazie ai progressi compiuti nell'evidenziare il ruolo dei fattori ambientali e gestionali sull'evoluzione della diversità vegetale in questi sistemi, i risultati della tesi rappresentano un punto di partenza per studi modellistici più avanzati. Des simulations satisfaisantes des abondances relatives des espèces individuelles ont conduit à des simulations réalistes de la dynamique des traits de la communauté (Louault et al., 2017).

Résumé

Par exemple, les valeurs simulées de la surface foliaire spécifique (SLA) du traitement fertilisé du bloc 1, fluctuant autour de ~35 m2 kg -1 , correspondent aux valeurs de SLA attribuées à Chapter 1 Introduction

Importance of grassland ecosystems

Grasslands cover an estimated area of 52.5 million km 2 , accounting for ~40% of the Earth's land surface, occurring in all regions of the globe except Antarctica and Greenland (White et al., 2000). Although alternative classification schemes exist, grasslands are generally defined as terrestrial ecosystems dominated by non-woody vegetation (White et al., 2000[START_REF] Suttie | Grasslands of the world. Food and Agriculture Organization of the United Nations[END_REF]. A grassland is thus any type of predominantly herbaceous plant formation and it could be used directly by animals. They are the primary food source for many herbivore species raised in extensive or semi-extensive farming systems, and effective practices are needed to ensure their continued functioning (EIP-AGRI, 2016). There is a great diversity of grasslands types across the world, such as savannas, shrublands, meadows, scrub and tundra (e.g. [START_REF] Dixon | Distribution mapping of world grassland types[END_REF]. Three main grassland types can be distinguished within agricultural production systems: natural, semi-natural and improved (Hejcman et al., 2013).

Natural grasslands are areas created primarily by processes related to climate, fire and wildlife grazing (Parr et al., 2014), but they are also used by livestock. Semi-natural grasslands are defined as grasslands modified by human activities, requiring livestock grazing or hay-cutting for maintenance, and will generally be colonised by shrubs and trees if not properly managed [START_REF] Hejcman | Farmer decision making and its effect on subalpine grassland succession in the Giant Mts[END_REF]. Improved grasslands are areas resulting from ploughing the soil and sowing allochthonous plant species and varieties with high production potential [START_REF] Hejcman | Response of plant species composition, biomass production and biomass chemical properties to high N, P and K application rates in Dactylis glomerataand Festuca arundinacea -dominated grassland[END_REF]. Furthermore, grasslands are important for providing a variety of ecosystem services, which can be classified into four categories [START_REF] Zhao | Grassland ecosystem services: a systematic review of research advances and future directions[END_REF]: supporting services (e.g. primary production, water cycling, nutrient cycling), provisioning services (e.g. forage production), regulating services (e.g. climate regulation, erosion control, pollination) and cultural services (e.g. aesthetic appreciation, recreation, cultural heritage). Supporting services are necessary for the production of all other ecosystem services, such us primary production and soil formation [START_REF] Mea | Millennium Ecosystem Assessment -Ecosystems and human well-being: Synthesis[END_REF]. Provisioning services refer to any service provided by the ecosystem to ensure human survival and life and include, for instance, food/feed supply, freshwater supply, wildlife habitat supply, genetic resources and fuel supply. Concerning feed supply, grasslands are an important support for domesticated animals such as cattle, sheep and horses, which provide humans with meat, milk, wool and leather products [START_REF] Pogue | Beef production and ecosystem services in Canada's prairie provinces: a review[END_REF]. Grasslands provide numerous regulating services. In fact, they can fix atmospheric nitrogen (N) in the presence of legumes (Carlier et al., 2009) and sequester a high amount of carbon (C) in the soil (Smith et al., 2008). They often contain a high diversity of plants, which is of interest for wildlife conservation, for a diverse forage resource in relation to feed quality and for the resources provided to micro-and macro-fauna (Theau et al., 2017). This biodiversity has positive effects on the productivity of grassland communities and its response to disturbance, mitigating for instance the effects of heavy rainfall and wind speed on soil erosion (Porqueddu et al., 2016) and contributing to the preservation of multifunctionality (Bai et al., 2022). Plant diversity is also a key factor in soil organic C formation and storage, as high plant diversity improves C storage by enhancing belowground C inputs (i.e. root biomass and root exudates) and promoting microbial growth, turnover and burial of necromass [START_REF] Bai | Grassland soil carbon sequestration: Current understanding, challenges, and solutions[END_REF]. Maintaining high and constant levels of biodiversity and root C inputs is thus essential to improve C storage and persistence of grasslands.

Grasslands are among the most species-richness ecosystems in the world (Wilson, 2012).

Extensively managed grasslands, in particular, are recognised for their high biodiversity and social and cultural values. The plant species involved may in fact have high conservation value, with rare species having an effect on ecosystem functioning (Bengtsson et al., 2019).

Plant biodiversity is also known to be closely linked to soil health and functioning, through its beneficial effects on soil microorganisms and other animal species [START_REF] Lange | Plant diversity increases soil microbial activity and soil carbon storage[END_REF].

The plant species present in a grassland cover vary considerably in their biological and morpho-physiological characteristics. The composition of grassland communities also varies considerably from one region to another and influences forage production and quality, as well as the delivery of several ecosystem services. The conservation status of grasslands also varies around the world, but it is generally far from satisfactory [START_REF] Suttie | Grasslands of the world. Food and Agriculture Organization of the United Nations[END_REF]: they have shown signs of degradation, due primarily to overgrazing and other improper management practices linked to climatic issues (e.g. [START_REF] Liu | Changing climate and overgrazing are decimating Mongolian steppes[END_REF][START_REF] Gang | Quantitative assessment of the contributions of climate change and human activities on global grassland degradation[END_REF]. A global estimation (Gang et al., 2012) showed that nearly half of all grassland ecosystems are degraded and that nearly 5% of these grasslands are experiencing high to extreme levels of degradation.

Grassland degradation has multiple causal drivers and the complexity of this process remains largely unexplored, as most studies focus on single drivers of vegetation change, while multiple drivers operate simultaneously and interact under different site-specific conditions (Tiscornia et al., 2019).

The continuity of grassland communities is thus essential to ensure the conservation of biodiversity and the provision of ecosystem services, considering that the spatio-temporal dynamics of plant species are strongly influenced by grassland management. Grazing intensity or mowing frequency, as well as fertiliser inputs, are major forces in vegetation dynamics and species distribution, contributing to landscape diversity and the quantity and quality of forage produced. The ecological mechanisms (including the role of biodiversity)

underlying the resilience of grassland ecosystems and the stable production of quality forage in the face of environmental change are not always fully understood [START_REF] Mayfield | What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change[END_REF].

In fact, while traditional management practices (e.g. grazing, mowing, fertilisation, seeding of species mixtures, shrub control) are major drivers of vegetation dynamics, species distribution and biodiversity, the combined effects of management practices and environmental change remain unclear (Thébault et al., 2014). This means that the extent of of the goods and services that grassland ecosystems can provide under various conditions remains an open field of research of utmost importance, as the diversity and value of grasslands could offer solutions to many of our societal challenges [START_REF] Lopez | The unrecognized value of grass[END_REF].

Grassland ecosystem responses to anthropogenic disturbance

Grassland plant biomass is a key component of food provision for domestic herbivores and its production is usually managed through fertilisation, grazing or mowing. The effect of these management practices on grassland ecosystems will be developed in this section. Land use is a key factor in changing plant species diversity, and changes in plant biodiversity have an impact on biomass productivity. Many studies show that N addition increases biomass production and reduces species diversity (Socher et al., 2013;[START_REF] Tang | The effect of nitrogen addition on community structure and productivity in grasslands: A meta-analysis[END_REF][START_REF] Perring | Understanding context dependency in the response of forest understorey plant communities to nitrogen deposition[END_REF]Zhao et al., 2019) but this response differs depending on the environmental context [START_REF] Limpens | Climatic modifiers of the response to nitrogen deposition in peat-forming Sphagnum mosses: a meta-analysis[END_REF][START_REF] Humbert | Impacts of nitrogen addition on plant biodiversity in mountain grasslands depend on dose, application duration and climate: a systematic review[END_REF][START_REF] Perring | Understanding context dependency in the response of forest understorey plant communities to nitrogen deposition[END_REF], soil pH (Clark et al., 2007) and plant community type (Bai et al., 2009). Previous studies have shown that the effect of N addition is different depending on the botanical composition of the grassland community, which often consist of a mosaic of grasses, legumes and forbs [START_REF] Song | Nitrogen enrichment enhances the dominance of grasses over forbs in a temperate steppe ecosystem[END_REF][START_REF] Song | Impact of nitrogen addition on plant community in a semi-arid temperate steppe in China[END_REF]You et al., 2017). The results of the meta-analysis by [START_REF] Tang | The effect of nitrogen addition on community structure and productivity in grasslands: A meta-analysis[END_REF] conducted on 56 peerreviewed articles showed that grasses benefit more from N addition, while legume biomass decreases and forbs show no significant variation. Some studies also highlighted that reducing N input (e.g. from atmospheric deposition or fertilisers) have a positive response on plant diversity [START_REF] Storkey | Grassland biodiversity bounces back from long-term nitrogen addition[END_REF]. Concerning the effects of adding phosphorus (P) to grasslands, several studies have revealed that this element can also lead to a reduction in species richness [START_REF] Ceulemans | Plant species loss from European seminatural grasslands following nutrient enrichment-is it nitrogen or is it phosphorus?[END_REF][START_REF] Ceulemans | Soil phosphorus constrains biodiversity across European grasslands[END_REF][START_REF] Soons | Nitrogen effects on plant species richness in herbaceous communities are more widespread and stronger than those of phosphorus[END_REF]. Other studies show that P benefits legumes and disadvantages grasses (Xu et al., 2015a). However, a meta-analysis by [START_REF] Soons | Nitrogen effects on plant species richness in herbaceous communities are more widespread and stronger than those of phosphorus[END_REF] concludes that N and NP enrichment generally reduce plant species richness, but P enrichment has a much smaller effect. This suggests that the effects of P enrichment are not uniform.

The removal of aboveground biomass by mowing or grazing clearly has an impact on the botanical composition of the grassland, forage quality and yield. This impact depends primarily on the intensity and frequency of management practices. Existing studies on the effect of mowing give different responses depending on the frequency of mowing. For instance, compared to abandonment, one mowing per year can have a positive effect on species richness (Shao et al., 2012;Milberg et al., 2017;Doležal et al., 2019) and a negative effect on biomass production (Shao et al., 2012;Doležal et al., 2019). Nevertheless, the positive effect of mowing on plant diversity is not the norm: in a mesic hay meadow of western Hungary, the short-term effect of mowing once a year showed a negative impact on species richness (Szépligeti et al., 2018). In fact, in a grassland dominated by Solidago gigantea

Ait. (tall goldenrod), one mown per year was not effective in reducing the presence of this species and providing space and light to new species.

The effects of grazing on grasslands are generally similar to those of mowing, but with greater spatial heterogeneity (Klimek et al., 2007;[START_REF] Marion | How much does grazing-induced heterogeneity impact plant diversity in wet grasslands?[END_REF]. Several studies report a positive effect of grazing on species richness when performed at low or intermediate stocking densities [START_REF] Liu | Impacts of grazing by different large herbivores in grassland depend on plant species diversity[END_REF]Zhang et al., 2015;[START_REF] Tälle | Grazing vs. mowing: A meta-analysis of biodiversity benefits for grassland management[END_REF]. The type of herbivores and the intensity of grazing are factors that affect the impact on plant diversity [START_REF] Olff | Effects of herbivores on grassland plant diversity[END_REF]. For instance, Tóth et al. ( 2014) found lower taxonomic and functional diversity in shortgrass steppes grazed by sheep compared to those grazed by cattle. Large herbivores (e.g. cattle, horses) have a more positive effect on plant diversity when grazing is performed at intermediate stocking rates, while when grazing is perfomed at high densities it can reduce plant diversity as it leaves only a few grazing-tolerant species [START_REF] Zhong | Large herbivores facilitate a dominant grassland forb via multiple indirect effects[END_REF].

Grassland ecosystem responses to environmental factors

Climate-related risks to natural and human systems could increase with warming, to the point of threatening food supply due to changes in rainfall patterns and extreme weather events, leading to greater variability in grassland production (IPCC, 2022). As grassland production is closely linked to climatic conditions, it is highly exposed to climate change hazards. The response of grasslands to climate change is complex and implies interactions with water availability, soil vegetation and management [START_REF] Soussana | Managing grassland systems in a changing climate: the search for practical solutions[END_REF]. Increasing atmospheric CO2 concentration enhances leaf development and plant photosynthesis [START_REF] Lawlor | The effects of increasing CO2 on crop photosynthesis and productivity: a review of field studies[END_REF][START_REF] Kirschbaum | Does enhanced photosynthesis enhance growth? Lessons learned from CO2 enrichment studies[END_REF][START_REF] Moore | The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems[END_REF], while warming can improve primary productivity by: (i) altering growing season length and phenology (Bloor et al., 2010), (ii) increasing the rate of mineralisation and thus access to nutrients (De Boeck et al., 2008), and

(iii) increasing water use efficiency [START_REF] Rötter | Climate change effects on plant growth, crop yield and livestock[END_REF]. Several studies show the positive effect of warming and CO2 increase on biomass production [START_REF] Nowak | Functional responses of plants to elevated atmospheric CO2 -do photosynthetic and productivity data from FACE experiments support early predictions?[END_REF]Cowles et al., 2016;[START_REF] Mcgranahan | Variability in grass forage quality and quantity in response to elevated CO2 and water limitation[END_REF]. However, negative effects of warming on biomass were also obtained, e.g. in a Tibetan alpine steppe (Jingxue et al. 2019), where experimental warming (+2 °C) under ambient precipitation significantly caused reductions in biomass because of induced water deficit. Xu et al. (2015b) also showed in an alpine meadow on the Qinghai-Tibetan Plateau (arid and cold area) how warming and mowing combined negatively affected biomass.

Increasing air temperature also has an impact on plant diversity. For instance, it could lead to the invasion of species from warmer habitats and the loss of species from their native environment [START_REF] Wu | Biogeochemical and ecological feedbacks in grassland responses to warming[END_REF]. Climate change threatens species niches because they cannot adapt as quickly to rising temperatures, as is the case of grasses (Cang et al., 2016). Several studies show how warming can decrease species richness over time (Klein et al., 2004;Gedan and Bertness, 2009;Alatalo et al., 2016;Colins et al., 2017). However, other studies have not shown such a decrease in species richness under warming (Gornish and Miller, 2015;Zhu et al., 2015;Eskelinen et al., 2017).

Warming and drought events often act in concert to determine the productivity and biodiversity responses of grasslands (e.g. [START_REF] Breshears | Regional vegetation die-off in response to global-change-type drought[END_REF][START_REF] Yahdjian | Vegetation structure constrains primary production response to water availability in the Patagonian steppe[END_REF][START_REF] Carlyle | Response of grassland biomass production to simulated climate change and clipping along an elevation gradient[END_REF][START_REF] Magandana | Seasonal herbaceous structure and biomass production response to rainfall reduction and resting period in the semi-arid grassland area of South Africa[END_REF]. Extreme drought events may have a significant effect on C and water cycles [START_REF] Ciais | Europe-wide reduction in primary productivity caused by the heat and drought in[END_REF][START_REF] Hussain | Summer drought influence on CO2 and water fluxes of extensively managed grassland in Germany[END_REF][START_REF] Song | A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change[END_REF], which could be buffered by functional biodiversity [START_REF] Reichstein | Climate extremes and the carbon cycle[END_REF]. Grassland productivity responses to drought events may in fact depend on the composition of plant communities, as different plant functional groups have different responses to drought. For instance, fertilised graminoids are more sensitive to drought than forbs when considering biomass production, and nutrient availability may be a critical factor in defining the vegetation response in terms of biomass production and plant diversity (van Sundert et al., 2021).

Although grassland community structure is relatively resilient to altered rainfall patterns (mediated primarily by altered soil moisture availability and by changing durations and frequencies of dry periods), forb abundance is likely to increase with increased growing season rainfall variability [START_REF] Jones | Altered rainfall patterns increase forb abundance and richness in native tallgrass prairie[END_REF]. In general, drought exerts negative effects on biomass production (Craven et al., 2016;[START_REF] Finn | Greater gains in annual yields from increased plant diversity than losses from experimental drought in two temperate grasslands[END_REF], which can be mitigated when plant diversity is high (Isbell et al., 2015;[START_REF] Boeck | Patterns and drivers of biodiversity-stability relationships under climate extremes[END_REF][START_REF] Finn | Greater gains in annual yields from increased plant diversity than losses from experimental drought in two temperate grasslands[END_REF][START_REF] Grange | Plant diversity enhanced yield and mitigated drought impacts in intensively managed grassland communities[END_REF]. However, drought alters the drivers of the relationships between plant diversity and productivity, which has implications for the stability of ecosystem function and requires a mechanistic understanding in a changing environment [START_REF] Xi | Drought soil legacy alters drivers of plant diversity-productivity relationships in oldfield systems[END_REF].

Importance of grassland modelling

Tools to quantify the resilience of grasslands to disturbance, both in the short term (e.g. a single unfavourable season/year) and in the long term (i.e. climate change), are still poorly developed. The challenge for agro-environmental and ecological research is to propose costeffective and adaptive solutions that evolve with technological and social innovations.

Successful research on a complex system such as species-rich grassland requires several approaches related to agro-ecology, accompanied by management targets, monitoring tools and socio-economic development. In particular, understanding and projecting grassland dynamics under global change requires vegetation models that explicitly capture relevant ecosystems processes and drivers. In this respect, process-based grassland vegetation models can complement field observations by providing additional insights into ecosystem processes and mechanisms that may be difficult to measure in the field or to infer from monitoring campaigns. Supported by long-term observational records and experimental trials, simulation models are important tools for assessing the joint impact of environmental factors (e.g. extreme weather events) and land management on local plant community structure and production potential. The growing interest in the effects of climatic changes on agroecosystems makes the analysis of plant species dynamics even more relevant and urgent (Kipling et al., 2016;van Oijen et al., 2018;Ma et al., 2019).

Grassland simulation models

Models for simulating plant species dynamics in a multi-species plant community are a powerful tool to study the dynamics of plant diversity under a variety of conditions and support grassland management decisions. As such, they can contribute to the implementation of adaptive strategies for grassland management. To this end, existing grassland models differ and range from relatively simple mathematical models (e.g. Tilman, 1988;Kinzing et al., 2002) to more complex ones like rule-based models (e.g. [START_REF] Siehoff | Processbased modeling of grassland dynamics built on ecological indicator values for land use[END_REF] or process-based, individualcentred or individual-based models (Thornley, 1998;[START_REF] Smith | Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space[END_REF][START_REF] May | Reversed effects of grazing on plant diversity: the role of below-ground competition and size symmetry[END_REF][START_REF] Soussana | Gemini: A grassland model simulating the role of plant traits for community dynamics and ecosystem functioning. Parameterization and evaluation[END_REF][START_REF] Taubert | A review of grassland models in the biofuel context[END_REF]Weiss et al., 2015;[START_REF] Rolinski | Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6[END_REF]. They can be defined at a sub-individual level [START_REF] Clark | Shedding light on plant competition: modelling the influence of plant morphology on light capture (and vice versa)[END_REF], and competition between plants can also be represented in three dimensions [START_REF] Munier-Jolain | A 3D model for light interception in heterogeneous crop: weed canopies: model structure and evaluation[END_REF].

As species-rich grasslands are mixtures of plant species, stages of maturity and plant tissues (e.g. [START_REF] Bruinenberg | Factors affecting digestibility of temperate forages from seminatural grasslands: a review[END_REF], their states are continuously changing, which poses a challenge for modelling the interaction processes and feedback mechanisms that occur in the soilvegetation-atmosphere continuum (e.g. Kipling et al., 2016;Brilli et al., 2017). In terms of modelling, complexity can be reduced by representing one main species and, implicitly, all the others as a single competing species [START_REF] Olesen | Simulation of above-ground suppression of competing species and competition tolerance in winter wheat varieties[END_REF] even if the dynamics that characterise this type of systems suggest the adoption of individual-centred models.

Individual-centred models simulate trade-offs among plant functional traits and their plasticity in response to environmental changes (e.g. [START_REF] Maire | Plasticity of plant form and function sustains productivity and dominance along environment and competition gradients. A modeling experiment with Gemini[END_REF], as in the models GEMINI [START_REF] Soussana | Gemini: A grassland model simulating the role of plant traits for community dynamics and ecosystem functioning. Parameterization and evaluation[END_REF] and INTERCOM [START_REF] Schippers | Competition for light and nitrogen among grassland species: a simulation analysis[END_REF], in which plants are well-defined and described by biophysically meaningful parameters that can be directly measured or estimated. This is also the case with GRASSMIND, which simulates the biogeochemical growth of individual plants interacting with each other in their neighbourhood (Taubert et al., 2020). Although strictly adherent to the underlying system, these models are complex and difficult to initialise and parameterise. Significant resources are needed to run simulations in case of large-scale (spatio-temporal) studies on natural or managed grasslands because one instance (model) per species present in the community is required (e.g. [START_REF] Baumann | Analysing crop yield and plant quality in an intercropping system using an eco-physiological model for interplant competition[END_REF]. Inherently resource-intensive, computations focused on plant-plant interactions present scalability issues, as modelling applications target the ecosystem scale, where biophysical and biogeochemical processes (also driven by management) become important and in many cases overwhelm small-scale processes (e.g. [START_REF] Rumpel | The impact of grassland management on biogeochemical cycles involving carbon, nitrogen and phosphorus[END_REF]. Existing modelling solutions that link processes and dynamics of individual plant types to community processes and dynamics are embedded into specific models (each being an instance) for a limited number of species in a community, e.g. the AgPasture module in APSIM (Li et al., 2011), intercrop in STICS (Corre-Hellou et al., 2009) and derived approaches (Jégo et al., 2015;Thivierge et al., 2016). DynaGraM was recently developed (Moulin et al., 2018) to answer some theoretical questions about the response of a grassland species model to climatic, edaphic and management forcing agents as a function of plant community states. Conceived as a mechanistic model of resource competition (Tilman, 1980), it is a stand-alone solution representing the regulation of green biomass and plant competition from the standpoint of resource dynamics (Moulin et al., 2021), integrating eco-physiological and biophysical details inherited from ModVege (Jouven et al., 2006a, b), originally developed to predict vegetation growth from permanent grasslands in central

France. Models reproducing the mean morphological and physiological features of a plant community as a whole tend to ignore the relative abundance of different plant types in mixed communities. They are based on the simulation of the grassland "as a crop", with parameters describing the exposed morphological and physiological characteristics whose values are meant to represent the traits of the community (e.g. LINGRA, [START_REF] Rodriguez | LINGRA-CC: a sink-source model to simulate the impact of climate change and management on grassland productivity[END_REF]. This simulation approach, simpler than those centred on the individual, is often considered too simplified to represent complex systems as multi-species grasslands, because (i) the parameters describing the traits of the plant communities cannot be always measured, (ii) the 2014) is instead implemented generically in a single-instance simulator to describe all species present in plant canopies. This is the approach adopted in this study because it can be easily implemented by coupling an existing grassland model to compute the relative abundances of the species at each time step. CoSMo requires information about plant traits, which needs to be specified for each individual species in the community, but it is based on the assumption that traits (i.e. model parameters) in a mixture can be treated as additive, not explicitly dealing with non-linear impacts of trait variation and thus limiting complexity and computational demand. While other models simulate the growth of individual plants interacting with each other in their neighbourhood (e.g. Taubert et al., 2020), CoSMo-based solutions simplify specific processes in favour of applicability (Movedi et al., 2019) especially when it comes to linking micro-and macro-level modelling efforts. However, such potential synergies are only beginning to be explored for the response of grassland productivity and plant diversity dynamics to environmental and management drivers. In order to create process-based models that can also translate local insights to larger scales, these models need to be designed, parameterised and assessed using data and causal explanations synthesised from a number of local observations to ensure wider applicability beyond the field scale. A conceptual model describes the problem entity, typically in terms of its components and their relations (e.g. variables, processes, system boundaries) that are typically expressed as equations. A conceptual model is inherently a simplification, as it represents the modellers' perception of reality and formalization of selected processes, components and their relationships. Meta-studies can support conceptual modelling by providing a basis for describing the system components a model should include, the range of outcomes the model should produce, defining the scope of the model, and/or supplementing theory or select appropriate elements of theory to inform models. However, both approaches -meta-analysis and process-based modelling -may not adequately represent the huge spatial and temporal diversity of environmental drivers and management decisions.

dynamics
Both lead to biases in favour of particular assumptions or contexts (at the macro and micro levels), and to an interpretation of data that may be overly focused on variables available in the literature (as in meta-analyses), and/or that are regarded as highly uncertain (as in the parameterisation of models).

The rationale of the thesis is that plant competition and changes in the relative abundance of plant species in a grassland community are driven by a set of environmental and management factors. The latter depict the suitability of different plant species to the biophysical constraints due to soil and climate conditions, possibly modified by the agricultural practices.

The hypothesis of this work is that the dynamics of plant species in a grassland community can be explained by a set of environmental and management drivers that can be explored through meta-analysis and explicit modelling fed by field experimental data:

1) Meta-analytical studies help to find general patterns in the evolution of grassland plant diversity (global approach);

2) Models reproduce these patterns through a set of rules formalising the main physical, chemical and biological drivers of grasslands functioning (local-scale approach).

3) Field experiments are the primary source of data for the calibration and validation of models

Objectives and methodology

The objective of this PhD was to explicitly and dynamically represent the evolution of grassland communities (species composition and biomass production) under alternative management practices (mowing vs. abandonment, fertilisation vs. no fertilisation) and climatic regimes (e.g. continental and Mediterranean), and understand the role played by environmental and management drivers on this evolution. The thesis quantitatively reports the effects of environmental and management drivers on species conservation indicators (richness or relative abundance of plant species) and provisioning service metrics (harvested or standing biomass). The doctoral work was not designed to produce outcomes for grazing systems. As well, quantifying components of C and N cycling in grassland systems was out of the scope of this thesis.

With a focus on the plant-environment-management nexus, we used a combination of metaanalytical and modelling approaches, with the support of observational and experimental approaches, to understand plant species dynamics at local and global scales.

First, we studied global response patterns of plant dynamics to understand how plant species richness and biomass respond to two important drivers: cutting (management driver) and warming (environmental driver). This was approached with two meta-analytical studies.

Second, we studied how the relative abundance of plant species in a grassland can be simulated as a response to hierarchically arranged environmental and management drivers (either human-triggered, such as cutting events, or continuous, such as daily weather conditions). This was a modelling activity, supported by:

-observational data collected over multiple years (long-term) for different production situations in the Massif Central of France: potential growth, nutrient-limitation, mowing, abandonment. This study aimed at identifying the drivers of species diversity and quantify the impacts of environmental and management changes on this diversity;

-experimentally collected (short-term) data in the island of Sardinia (Italy), with a degree of manipulation in which we selected and combined different grassland species, to identify the underlying causes of the relationships between species diversity and community performance and to bring out some properties of the system (e.g. plant traits and their representation by modelling).

The modelling study has focused on study-sites belonging to two of the most important (and contrasting) European biogeographical regions (Figure 2.1), which are peculiar zones where climate and land-use changes are strongly influencing ecosystem processes by challenging grassland composition and productivity. The Italian territory is partly in the continental region, where grasslands are subject to mowing and grazing disturbance, with transhumant systems on the Adriatic coast and more intensive management systems in the Po plain. In Italy, continental grasslands are contiguous with those of the Alpine and Mediterranean biogeographical regions (Dibari et al., 2021). The Mediterranean region includes the coastal areas, the south of the Italian peninsula and the islands. The climate is warm, with hot summers and mild winters, and arid conditions increase with water scarcity. In general, July is the warmest month, with daily maximum temperatures often close to 35 °C in many locations, and there is a strong seasonal rainfall pattern, with over 70% of the average annual rainfall between October and March. The Mediterranean biogeographical region accounts for about one third of the agricultural land, including grasslands in the region [START_REF] Dono | The Mediterranean biogeographical region -long influence from cultivation, high pressure from tourists, species rich, warm and drying[END_REF]. Although permanent grasslands cover only about 6% of the total area (and are decreasing), Mediterranean grasslands provide a wide range of supporting, regulating and cultural ecosystem services. The most important are feed supply and biodiversity conservation (Seddaiu et al., 2018;Aguilera et al., 2020), as grasslands are characterised by abundance of self-reseeding annual species (Porqueddu et al., 2016;Pulina et al., 2018) and are adapted to both climatic conditions and anthropogenic management, which have shaped the landscape for centuries (Bagella et al., 2013). Agroforestry areas continue to play an important role locally. However, they used to be much more widespread, as former rainfed grasslands and traditional agroforestry with grazed woodland, such as dehesas (Spain) and montados (Portugal), are declining, with areas converted to intensive agriculture or abandoned to scrubland formation. The region has had a significant part of the grazing land under the transhumance management, where herds of sheep or cattle moved to alternative grazing areas during different seasons (especially in the mountains). With the ongoing depopulation of many rural areas, former grazing regimes are disrupted, and woodlands are replacing grasslands.

In the Mediterranean biogeographical region, we analysed the influence of environmental (dry climate patterns) and management (extensive to intensive mowing) factors on a coastal site in Sardinia (Italy). The area is characterised by commercial farms with grazing livestock -mainly transhumant sheep and beef cattle -within mixed farming including agro-silvo-pastoral systems (wood pastures) and organic production. These areas face low productivity and high inter-annual variability of production , occasional overgrazing, degradation with denuded soil or soil erosion , abandonment with loss of open and wooded grasslands and proliferation of unpalatable plant species. Here, grasslands are mainly composed of annual grasses and legumes, and grazing occurs throughout the year [START_REF] Dono | The Mediterranean biogeographical region -long influence from cultivation, high pressure from tourists, species rich, warm and drying[END_REF].

It is relevant that the two core areas addressed by the PhD were identified as particularly vulnerable to global change in Europe [START_REF] Hamidov | Impacts of climate change adaptation options on soil functions: A review of European case-studies[END_REF].

Abstract

Climate and management affect grassland plant diversity but studies vary regarding the magnitude of changes in plant species richness. Here we develop a comprehensive understanding of species richness modification due to management (mowing) and climate (warming) variation worldwide, and present the results of two meta-analyses from 999 and 1793 records (articles). Recorded articles had at least one experiment with a case-control design. The results show that both mowing (43 articles) and warming (34 articles) modify species richness, which on average increased by c. 32% with once-a-year mowing (against no mowing) and declined by c. 13% with warming (against ambient temperature). Our metaanalysis on the mowing regime supports the humped-back model, with one or two cuts per year being the level of disturbance optimising species richness. We also observed that warming-induced reduction in species richness is lower in dry climates (< 300 mm yr -1 ) and at low-elevations (< 1000 m a.s.l.). Where available, we accounted for harvested biomass as a concomitant variable and we found that overall it decreased by c. 21% (mowing) and increased by c. 11% (warming). The evidence provided of an opposite response of species richness and harvested biomass to disturbance is consistent with the competitive-exclusion hypothesis of negatively correlated patterns between the two outcomes (high taxonomic diversity with low biomass production, and vice versa). The reported difficulties in finding representative studies in previous and the present meta-analyses highlight the need to orient future research towards long-term experiments on the combined effects of mowing and warming for a more robust inference of environmental and management constraints on grassland performance.

Introduction

The intensification of agricultural practices from the 20th century onwards is partly responsible for the reduction of areas covered by grassland ecosystems, notably with a considerable loss of semi-natural grasslands and a decrease of their biodiversity in different regions worldwide (e.g., Fakarayi et al., 2015;Munch et al., 2017;[START_REF] Schirpke | Future impacts of changing land-use and climate on ecosystem services of mountain grassland and their resilience[END_REF][START_REF] Gibson | Future land cover change scenarios in South African grasslands -implications of altered biophysical drivers on land management[END_REF]. Plant species' richness (Figure 3.1) was also observed to decrease with the current warming trend [START_REF] White | Direct and indirect drivers of plant diversity responses to climate and clipping across northern temperate grassland[END_REF], especially in climates that are becoming more arid and less productive [START_REF] Harrison | Climate-driven diversity loss in a grassland community[END_REF]. These changes affect the human population broadly and may actually have a great socio-economic impact [START_REF] Dunford | Ecosystem service provision in a changing Europe: adapting to the impacts of combined climate and socio-economic change[END_REF]. In fact, not only grassland replacement and biodiversity erosion alter the continuity of the forage production supporting livestock agriculture but also the delivery of a broad set of ecosystem services essential to society [START_REF] Loreau | Linking biodiversity and ecosystems: towards a unifying ecological theory[END_REF]Bengtsson et al., 2019) like carbon storage, pollination and the maintenance of the general aesthetic of landscapes (e.g., [START_REF] Oertel | Greenhouse gas emissions from soils -A review[END_REF][START_REF] Tribot | Integrating the aesthetic value of landscapes and biological diversity[END_REF]. These services are related to the plant diversity of grasslands [START_REF] Turnbull | Understanding the value of plant diversity for ecosystem functioning through niche theory[END_REF], whose high biodiversity is not only consisting of plants, but also of mammals, arthropods and microorganisms (Plantureaux et al., 2005;[START_REF] Baur | Effects of abandonment of subalpine hay meadows on plant and invertebrate diversity in Transylvania, Romania[END_REF][START_REF] Van Klink | Effects of large herbivores on grassland arthropod diversity[END_REF]. This biodiversity is recognized as an ecological and evolutionary insurance (after [START_REF] Yachi | Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis[END_REF] thanks to the stabilizing effect of species diversity on aggregate ecosystem properties through fluctuations of component species (e.g., phenotypic changes, Norberg et al., 2001). Different components of plant diversity (e.g., species richness, functional diversity, assemblage structures) would also make grasslands more resilient to hazards and extreme weather events (such as prolonged droughts, e.g., Vogel et al., 2012;Craven et al., 2016) and would be able to stabilize forage production and maintain overall ecosystem services [START_REF] Cleland | Biodiversity and ecosystem stability[END_REF]. It is thus essential to preserve these open spaces in order to preserve their biodiversity and the associated services, but also to study them to better appreciate their evolution under different constraints [START_REF] Zeller | Biodiversity, land use and ecosystem services -An organismic and comparative approach to different geographical regions[END_REF]. In hay meadows, which typically occur where the environmental constraints are less important compared to high-elevation pastures, the management practices and their intensity tend to be the main drivers of plant diversity [START_REF] Pittarello | Environmental factors and management intensity affect in different ways plant diversity and pastoral value of alpine pastures[END_REF], whose changes reflect the evolution of both environmental conditions (pedo-climate) and management practices [START_REF] Lds | Grass strategies and grassland community responses to environmental drivers: a review[END_REF].

While the effects of increased temperature on grassland production are systematically studied and understood (e.g., [START_REF] Parton | Impact of climate change on grassland production and soil carbon worldwide[END_REF][START_REF] Song | A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change[END_REF], the effects of warming on plant diversity is an evolving and multifaceted challenge [START_REF] Cowles | Effects of increased temperature on plant communities depend on landscape location and precipitation[END_REF]. This is because temperature changes are dynamic and their effects on grassland communities depend on a number of other factors like moisture and nutrient availability (e.g., Zavaleta et al., 2013).

Likewise, the effects of cutting events on the botanical composition of a sward are related to environmental conditions (e.g., [START_REF] Wen | Cutting effects on growth characteristics, yield composition, and population relationships of perennial ryegrass and white clover in mixed pasture[END_REF].

Studies that have reported the response of grassland plant diversity to climate and management conditions (e.g., [START_REF] Su | Sensitivity of plant species to warming and altered precipitation dominates the community productivity in a semiarid grassland on the Loess Plateau[END_REF] indicate that the pattern of responses is complex and needs additional analyses based on quantitative assessments. An objective assessment is increasingly important as grasslands continue being vulnerable to warming conditions (e.g., [START_REF] Gao | Quantitative assessment of ecosystem vulnerability to climate change: methodology and application in China[END_REF], and halting grassland abandonment is an emerging topic of interest (e.g., [START_REF] Lasanta | Spacetime process and drivers of land abandonment in Europe[END_REF], especially in mountain regions [START_REF] Haddaway | Evidence on the environmental impacts of farm land abandonment in high altitude/mountain regions: a systematic map[END_REF]. The proportion of grassland plant species tends to decline following abandonment [START_REF] Riedener | Land-use abandonment owing to irrigation cessation affects the biodiversity of hay meadows in an arid mountain region[END_REF] and plant species decline due to abandonment could not easily be reversed (and grasslands restored) by mowing alone (Stampfli and Zeiter, 1999). However, the variability in the reported results is also likely due to the different challenges associated with the quantification of impacts on plant diversity. In particular, there is no standardized mode of conducting the experimental design and setup of control versus the experimental dataset [START_REF] Christie | Simple study designs in ecology produce inaccurate estimates of biodiversity responses[END_REF]. In the wake of diverse findings and conclusions, and because of the availability of an increasing number of peer-reviewed publications as well as the maturity of the results, there are science questions relevant to the issue of plant diversity modifications, e.g., is warming or mowing modifying species richness and, if yes, by what amount and under which conditions? We performed two meta-analyses using species richness as an indicator of plant diversity conditions. In fact, despite the growing knowledge about grassland modifications induced by temperature increase and mowing regime, quantitative assessments and analyses are still limited (e.g., Tӓlle et al., 2016;Gruner et al., 2017). Here, we provide a conceptual framework (Figure 3.2) of the direct and indirect effects of mowing (one cut per year versus abandonment) and climate change (warming) on the grassland ecosystem (after [START_REF] Li | Ecosystem structure, functioning and stability under climate change and grazing in grasslands: current status and future prospects[END_REF], using harvested biomass and species richness as expressions of functioning and stability (e.g., species richness can promote community stability through increases in asynchronous dynamics across species; [START_REF] Zhang | Climate variability decreases species richness and community stability in a temperate grassland[END_REF]. We highlight that the type of inference presented in Figure 3.2 (which represents a simplified view of the grassland ecosystem) depends on the extent to which the meta-analysis can establish causality between the outcomes of interest and the hypothesized related factors. This means that for only a subset of the above questions, it may be possible to find consistency in the set of bibliographic data to code into the state-of-the art literature and develop meta-analyses of the extracted data. Specific objectives were to analyse (1) the mean effect of mowing (first meta-analysis: one mowing event per year versus abandonment) and ( 2) the mean effect of warming (second meta-analysis: warming versus ambient temperature), both conducted on species richness in grasslands (and concomitant harvested biomass when available). In this way, we have pursued standardized meta-analyses to review fragmented results in a common framework. For the impact of mowing on plant diversity worldwide, our study complements

Materials and methods

Literature search method

Our meta-analysis method quantitatively combines and summarizes research results across individual and independent studies performed worldwide and published in peer-review journals (grey literature was not included in our meta-analyses). The first step was to find all the pertinent articles on the topic. We used a keyword search and expert recommendations to find the related articles in two international bibliographic databases. The literature search was initiated using the ISI Web of Science (WoS, (http://apps.webofknowledge.com) with the following topic search terms: We also added other pertinent articles from peer-review journals to the extent that we are aware of them. In particular, for the effect of warming, we used part of the bibliography of a metaanalysis made by Gruner et al. (2017).

Inclusion criteria and data extraction

Care was taken to standardize and document the process of data extraction. The quantitative review followed a structured protocol, which included pre-setting objectives and the inclusion criteria for studies, approach for data collection, and the analyses to be done [START_REF] Pullin | Guidelines for systematic review in conservation and environmental management[END_REF]. To facilitate the capture, organization and elimination of duplicate records from electronic WoS and Scopus databases searching, bibliographic records were imported into EndNote reference manager (https://endnote.com) and outputted in BIBTeX format [START_REF] Lorenzetti | Reference management software for systematic reviews and meta-analyses: an exploration of usage and usability[END_REF]. Data extracted from articles were recorded on carefully designed spreadsheets and accompanying tables with details of the study characteristics, data quality, relevant outcomes, level of replication and variability measures.

Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; [START_REF] Liberati | The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration[END_REF] diagrams depicting the flow of information through the different phases of the literature review, we mapped out the number of records identified, included and excluded, and the reasons for exclusions. Any single article had at least one experiment with a case-control design. The control was defined as being identical to the experimental treatment (case) with regard to all variables apart from the type of factor applied. Here, the mowing and warming experiments included ambient temperature (no warming) and abandonment (no mowing) as controls, respectively. The articles from the literature search were filtered by title and abstract, discarding obviously irrelevant studies (e.g. when species richness referred to other organisms than grassland plant species). After the examination of abstracts, the full text of the remaining articles was examined in detail. Articles that quantitatively reported effects of mowing (or clipping or cutting) or warming on species richness (SR as species conservation metric) were selected. When available, concomitant harvested biomass (HB as provisioning service metric, g DM m -2 ) determinations were also considered in the analyses. Articles had to contain data in the form of experimental determinations together with a measure of variation (e.g., means and variance). Articles with unreported outcomes (e.g., no species richness available), ineligible experimental design (e.g., lack of control) and missing essential statistics (e.g., standard deviations or related variability metrics) were discarded. In our meta-analyses, experiments with and without fertilisation were pooled. We also took into account only the effect of mowing (one cut per year) even if there was a previous grazing period. Mowing once per year is the most commonly used mowing frequency in species-rich grasslands (e.g., Hejcman et al., 2013) and was used as a treatment in all included experiments regardless of the timing of the mowing event during the year. Articles comparing more frequent cuts during the same year were excluded from the meta-analysis, as these comparisons (often using once-a-year mowing, not abandonment, as control) were outside the scope of the present study, but their results were used as complementary elements to improve the discussion of our results.

For the articles that met the inclusion criteria, the sample size, mean and standard deviation (sd) of the response variables were extracted (or calculated where a variability measure other than sd was provided, e.g., standard error). With sample data collected at different dates, mean and sd were used as practical descriptors of time-series central tendency and spread.

Critical appraisals were performed by two authors independently, i.e., the above data were extracted and ~ 50% of the extracted data were randomly cross-checked by another author.

In case of disagreement on data extraction, a consensus was reached through discussion among all authors. As some studies had not reported the exact values for relevant variables and experimental design details, more than 10 disagreements on the most appropriate inference for these missing data were discussed within the team.

Effect sizes

The goal of any meta-analysis is to provide an outcome estimate (or overall effect size) that is representative of all study-level findings. Effect sizes were characterized by the response ratio (RR), which is frequently used to quantify the proportion of changes due to experimental manipulations and thus provide a measure of the experimental effects [START_REF] Hedges | The meta-analysis of response ratios in experimental ecology[END_REF]Nagakawa and Santos 2012). This is calculated as the ratio of the average values of a treatment (𝑋 𝑇 ) and its control (𝑋 𝐶 ). Then, log-response ratio (LRR) values, ln(𝑋 𝑇 /𝑋 𝐶 ), are calculated as these are the size effects used in ecological meta-analyses, primarily because they tend to be normally distributed around zero for small samples. This means that a size effect with a value of zero represents no difference between the groups being compared (treatment vs. control). Meta-analysis, by pooling LRR values from several studies, also assigns a weight to each LRR that is inversely proportional to its sampling variance, equal to var(LRR)=

Meta-analysis models

To perform the two meta-analyses, we referred to the set of dedicated functions of the metafor package [START_REF] Viechtbauer | Conducting meta-analyses in R with the metafor package[END_REF], implemented within the statistical software RStudio (https://www.rstudio.com) for R version 3.5.3 x64. Meta-analysis models determine if an effect (y) is significant or not in a given experiment (i). In mathematical form, this is expressed as 𝑦 𝑖 = 𝜃 𝑖 + 𝑒 𝑖 , where θ and e indicate the unknown true effect and the known sampling error, respectively. Once effect sizes are extracted from the primary studies, they are pooled by applying a fixed-or random-effects model. A random-effects model was used in our metaanalysis, because the fixed-effects model assumes that there is only one underlying population effect size and that the observed effect sizes deviate from this population effect only because of sampling variation (an unrealistic scenario of heterogeneity among the population effect sizes). A random-effects model assumes that each study has its own population effect, i.e., effect sizes vary due to sampling variation and also due to systematic differences among studies. In this model, not only is the combined effect size estimated, but also the variance of the overall effect among studies. The mixed-effects model was also applied to explain heterogeneity in the data with the use of moderators (covariates). In this case, it is a challenge for the meta-analyst to find moderating variables (moderator) that explain the variation in effect sizes among studies. Mixed-effects analyses were only conducted if at least half of the studies reported information on moderators.

The Q-statistic (or multiple significance testing across means; weighted squared deviations)

was used to evaluate heterogeneity through 0 < I 2 < 100, which quantifies the proportion of total variability that is due to heterogeneity rather than sample variations: I 2 > 75% means high heterogeneity; values between 50 and 75% are considered as moderate heterogeneity; if the I 2 is between 25 and 50%, it is considered as low heterogeneity; below 25%, it is considered as no heterogeneity (e.g., [START_REF] Gianfredi | Rectal cancer: 20% risk reduction thanks to dietary fibre intake. Systematic review and meta-analysis[END_REF]. When p-values for the Q-test and effect sizes (random-effect model) were less than 0.1, homogeneity and no-effect assumptions were considered invalid. After quantifying variation among effect sizes beyond sampling variation (I 2 ), we examined the effects of moderators (covariates) that might explain this additional variation. The significance of moderators was tested using the probability (P) of an omnibus test (i.e., the Qm statistic). For that, in addition to SR and HB determinations, we recorded information on moderators that may affect the response variables -from the k articles (k ≤ n) and experiments (j) for which this information was available. Plot size (S, m 2 ), duration of the study (D, number of years), year of publication of articles (Y), site elevation (E, m a.s.l.) and two site-specific climatic variables (mean annual air temperature: T, °C; mean annual precipitation total: R, mm) were chosen as moderators of the SR and HB responses in the mixed-effects model. Year of publication of the studies can be a potential source of bias because changes in study methods and characteristics occurring over time can correlate to effect sizes (e.g., [START_REF] Jennions | Relationships fade with time: a meta-analysis of temporal trends in publication in ecology and evolution[END_REF]. As well, since vegetation within smaller plots tends to be more homogeneous than within larger plots, plot size may influence the number of recorded species and the estimate of SR [START_REF] Chytrý | Phytosociological data give biased estimates of species richness[END_REF]. Some authors also found that as the duration of the study increased so did the plant species diversity and productivity (e.g., [START_REF] Cardinale | Impacts of plant diversity on biomass production increase through time because of species complementarity[END_REF][START_REF] Pallett | Changes in plant species richness and productivity in response to decreased nitrogen inputs in grassland in southern England[END_REF]. Then, plant community composition can change along elevation gradients (e.g., [START_REF] Ohdo | Plant species richness and community assembly along gradients of elevation and soil nitrogen availability[END_REF], with global warming pushing species towards higher elevations (e.g., [START_REF] Engler | Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?[END_REF], and temperature and rainfall are the climatic variables most used to explore the relationships between climate and plant community data (e.g., [START_REF] Harrison | Climate and plant community diversity in space and time[END_REF]. For the effect of mowing, the cutting height (H, cm) was also considered because it can affect the community characteristics and biomass production (e.g. Wan et al., 2016). The temperature difference between control and warming treatments (ΔT, °C) was instead used as moderator in the meta-analysis of the effects of warming because divergent effects may be due to different warming treatments among experiments and different temperature sensitivities of various plant species (e.g., [START_REF] Llorens | Effects of an experimental increase of temperature and drought on the photosynthetic performance of two ericaceous shrub species along a north-south European gradient[END_REF] generally relatively small (i.e. ≤ 3 m in diameter), allowing the establishment of a wellcontrolled and essentially homogeneous environment (e.g., [START_REF] Cunningham | Large-eddy simulations of air flow and turbulence within and around low-aspect-ratio cylindrical open-top chambers[END_REF].

Potential data analysis bias and results

Possible publication biases were tested, either visually by means of funnel plots, which show the observed effect sizes on the x-axis against a measure of precision (standard error) of the observed effect sizes on the y-axis, or statistically by means of the test for plot asymmetry [START_REF] Egger | Bias in meta-analysis detected by a simple, graphical test[END_REF]. The results of meta-analysis were displayed in forest plots for each Table S1 and Table S2 show the characteristics of the articles included in the meta-analysis on the effects of mowing and warming, respectively. The current literature did not provide a robust sample of articles and quantitative results corresponding to different subclasses (e.g., abandonment versus management with two, three, etc. mowing events associated with fertiliser supply gradients; warming under gradients of atmospheric CO2 concentration and water status levels). The included studies report on grassland research conducted in 46 mowing experiments in 18 countries from Asia, Europe, North America and Oceania, and 42 warming experiments in nine countries from Asia, Europe and North America (Figure 3.4). Using the Köppen-Geiger climate classification [START_REF] Peel | Updated world map of the Köppen-Geiger climate classification[END_REF], our research shows an uneven geographical distribution of the selected studies for the effect of mowing (Table S1), with most articles focusing on temperate-oceanic (44%) and warm-or hot-summer continental (37%) climate zones of the northern hemisphere (with the exception of one study from the southern hemisphere in the temperature-oceanic climatic zone of Australia). Studies from cold (12%), Mediterranean (2%) and subtropical (5%) areas remain rare. Climate zones only in part reflect the distinctive characteristics of grassland systems, which varied widely in environmental conditions, mowing regimes and experimental settings. All recorded articles on the effect of warming document studies carried out on grasslands in the northern hemisphere (Table S2)

Potential data analysis bias

The statistical distributions of LRR values were determined to be nearly normal according to quantile plots (Figure S1).

For mowing, high heterogeneity was found with both SR (I 2 = 92%; Q = 499, p < 0.01) and HB (I 2 = 66%; Q = 55, p < 0.01) determinations. However, no evidence of publication bias was found in our meta-analysis for the effect of mowing on SR and HB that would reflect bias toward not reporting small positive or negative effect sizes, as demonstrated by the substantial symmetry of the funnel plots (SR: z = -1.06, p > 0.10; HB: z = -2.00, p = ~ 0.05).

The points falling outside both funnels (Figure S2

Effect of mowing on SR and HB

A forest plot for all 43 recorded articles combined (46 experiments) indicates a significantly positive effect of mowing (one cutting event per year) on SR compared to abandonment (Figure 3.5a): pooled LRR = 0.28 (c. 32% increase), 0.95 confidence interval from 0.19 to 0.37 (p < 0.01). There are however three studies, which showed an opposite effect. This was distinctly observed in Finnish meadow patches (LRR = -0.50, Huhta and Rautio, 1998),

where an increase in SR due to a successional change may have only been apparent, plausibly related to short-term effects and creating (according to authors) the illusion that abandonment is more desirable than management. In fact, early succession was characterized by a transient loss of plant species diversity (Velbert et al., 2017) in wet meadows of northwest Germany (LRR = 0.22). While in the Qinghai-Tibetan plateau (Xu et al., 2015a) SR was observed not to be sensitive to the short-term effects of mowing (LRR = -0.02), in a mesic hay meadow of Western Hungary (near the Slovenian border), Szépligeti et al. (2018) noted that mowing once a year may not be efficiently preventing (LRR = -0.15) the spread of tall goldenrod (Solidago gigantea Ait.) and control native competitive species (which hinder the growth of rare and less competitive species).

Without including the unmanaged option in their analysis, Tälle et al. (2018) observed small differences in the effects of different mowing intensities on the SR of European semi-natural grasslands (LRR < 0.13, with 0.1 representing the difference between a SR of two communities consisting of 10 and 11 species, respectively). The authors highlighted that while lower and higher mowing frequency can be expected to have both positive and negative effects on plant diversity at the same time, they concurred with other authors (e.g., [START_REF] Batáry | Effect of conservation management on bees and insect-pollinated grassland plant communities in three European countries[END_REF]Tóth et al., 2018) that any kind of management which is actually applied tends to be more important than the intensity of the management itself. We show that the difference can indeed be high when moving from abandoned fields to once-a-year mowing. The highest estimated mean effect size of LRR = 1.53 (wet experiment from Truus and Puusild, 2009), in particular, reflects the substantial decrease in SR on long-abandoned floodplain grasslands, which is likely a consequence of increased light competition and the accumulation of dense litter layers, as several low-growing plant species are outcompeted by strong competitors during succession or germination and establishment are inhibited by litter layers.

In addition to the results of our meta-analysis, some results by individual studies were also informative on the effect of alternative mowing schemes on SR. Figure 3.6 shows the changes in the SR of grassland plants under combinations of mowing frequency beyond one cut per year versus no cut, which were identified in the systematic review and in additional sources (section "References of the review on the effect of different mowing regimes"), and not included in the meta-analysis. Overall, it appears that a moderate mowing intensity of one or two cuts per year is positive for maintaining or enhancing a high plant SR. With two cuts per year over abandonment, we observe a similar mean response (LRR = 0.28) but greater variability across studies compared to just one cut (that is the core of this metaanalysis), likely associated with the influence of varying situations of soil fertility. In fact, the more a grassland is harvested, the more intensively it must be fertilised to remain productive.

Significant interactions are often observed between cutting and fertilisation treatments, with maximum counts detected in unfertilized plots and the total number of plant species decreasing along with increasing fertilization rates (e.g., [START_REF] Hejcman | The Rengen grassland experiment: Plant species composition after 64 years of fertilizer application[END_REF]. The addition of nitrogen, in particular, significantly influences the increase of plant biomass and height, leading to a decrease in species diversity (Tilman 1987;[START_REF] Silvertown | The Park Grass Experiment 1856-2006; its contribution to ecology[END_REF]. Then, the potential benefits of mowing on SR are progressively lost with more frequent cuts (i.e., three to four cutting events per year compared to one cut). It is known that regular disturbance by mowing can trigger niche partitioning, leading to higher species diversity (e.g., [START_REF] Mason | Niche overlap reveals the effects of competition, disturbance and contrasting assembly processes in experimental grassland communities[END_REF], but too frequent harvests may threaten the long-term survival of certain plant species (e.g., [START_REF] Loydi | Effects of litter on seedling establishment in natural and semi-natural grasslands: a meta-analysis[END_REF] by suppressing their seed stock. Our results can be interpreted in terms of the humped-back model [START_REF] Huston | A general hypothesis of species diversity[END_REF], a dynamic equilibrium model predicting that taxonomic richness may be greatest at intermediate biomass production and at intermediate levels of available resources (stress) and disturbance factors [START_REF] Pierce | Implications for biodiversity conservation of the lack of consensus regarding the humped-back model of species richness and biomass production[END_REF]. In fact, a hump-shaped relationship between vegetation biomass and SR, based on the balance between competition and abiotic stress, has been found in a large number of case studies [START_REF] Van Klink | No detrimental effects of delayed mowing or uncut grass refuges on plant and bryophyte community structure and phytomass production in low-intensity hay meadows[END_REF], and with SR likely peaking at intermediate productivity levels [START_REF] Boch | Threatened and specialist species suffer from increased wood cover and productivity in Swiss steppes[END_REF]. Consistently with the pattern predicted by the intermediate disturbance hypothesis, SR may be maintained by extensive agricultural practices [START_REF] Uchida | Biodiversity declines due to abandonment and intensification of agricultural lands: patterns and mechanisms[END_REF]. By alleviating understory light limitation through the removal of plant biomass, both mowers and grazers play an important role in maintaining plant diversity in grassland ecosystems, where they increase ground-level light availability [START_REF] Borer | Herbivores and nutrients control grassland plant diversity via light limitation[END_REF].

However, even if mowing frequency only marginally affecting plant diversity measures like SR might still affect the species composition in a grassland and, considering that mowing is costly, it is important to find a balance between mowing frequency and conservation benefits beyond SR (Tälle et al., 2018). The most suitable mowing frequency can be highly site-specific because the mechanisms linking mowing to conservation value are complex, and there is often no need or no resource for a second cut (beneficial for the feeding of herbivores), or weather conditions may make hay making difficult in autumn (Szépligeti et al., 2018). The level of detail of the present study, aiming at assessing the overall SR, does not allow to refer to the richness (and abundance) of plant species of nature conservation interest (which would be a more valuable indicator than the overall richness). Studying the effects of disturbances requires measures of species abundance, rather than just their presence, and an experimental approach to complete the understanding of the mechanisms involved (e.g., Debussche et al., 1996). For instance, it is possible that the abundance of each plant species decreases or the plant species turnover increases while the SR remains the same. In this case, different results would be expected when assessing biodiversity outcomes taking species abundance into account, e.g., Shannon diversity, which is calculated on the proportion of each species relative to the total number of species (Milberg et al., 2017).

A possibly important factor not taken into account in the present study is the timing of mowing during the year (either this information was not available for some included studies or too small subgroups would have been created by including this factor). In fact, the effects can be different depending on whether the harvest occurs early or late in the growing season.

Early mowing can have negative effects on plant species with late seed-setting. In combination with more frequent harvesting this can affect the ability of species to re-grow back (e.g., [START_REF] Humbert | Does delaying the first mowing date benefit biodiversity in meadowland?[END_REF]. Then, the two American studies of the review (Dickson and Foster, 2008;Foster et al., 2009) in which fertilisers were used during the study period, were also combined in the meta-analysis.

In the study of Lanta et al. (2009), the estimate of LRR = 0.14 was obtained with a wide confidence interval (from -1.58 to 1.86), likely due to the wide variation in the original dataset.

We also note that five experiments showed effects that are about three-to five-fold higher (LRR from 0.71 to 1.53) than the average. Fenner and Palmer (1998) In the mixed-effects model, planned moderators were mostly not significant (p > 0.10). When a grassland is abandoned, changes in SR can be expected as a function of time since abandonment (vegetation succession; e.g., Tasser and Tappeiner, 2002) but we could not confirm an effect of the duration of the experiment. Only the year of publication was a significant moderator (p < 0.05) of the effect of mowing on HB (k = 16, j = 18) when this covariate was assessed alone (with more negative LRR values observed in the oldest experiments, i.e., mean LRR of about -0.20 in 2010-2019 and -0.31 in 1993-2009). The covariate explained ~ 33% of the heterogeneity (Table S3) but the effect was not significant (p> 0.05) when different moderators were assessed together. are to the right. The reference articles are listed in the section "References of the review on the effect of different mowing regimes".

Effect of warming on SR and HB

A forest plot for all 34 recorded articles combined (42 experiments) indicates a significantly negative effect of warming (different treatments) on SR compared to control (Figure 3.7a):

pooled LRR = -0.14 (c. -13%), 0.95 confidence interval from -0.21 to -0.06 (p < 0.01). The decline in SR, observed here for an average temperature increase of 1.8 ± 0.9 °C (range: 0.15 to 4.10 °C), is consistent with the response of terrestrial ecosystems (-10.5% of SR) as observed by Gruner et al (2017) for an average warming of 3 °C. It cannot be excluded that short-term simulation of warming, without considering temporal adaptation, has exacerbated the warming effect on terrestrial ecosystems [START_REF] Leuzinger | Do global change experiments overestimate impacts on terrestrial ecosystems?[END_REF].

The results of the mixed-effects model showed that SR was somewhat significantly moderated by the year of publication (p ~ 0.05) when this moderator, which explained only ~ 9% of the heterogeneity (Table S3), was assessed alone (k = 34, j = 42). We note that more recently published studies were more numerous and yielded larger effect sizes, with an imbalance with only eight studies published prior to 2010 (giving an average LRR of -0.05). on the grassland response to warming since SR changes slowly (e.g., [START_REF] Galvánek | Changes of species richness pattern in mountain grasslands: abandonment versus restoration[END_REF]), but we have no confirmation of these effects in our study.

Site elevation (p < 0.05) and annual rainfall (p < 0.01) emerged as significant moderators when all moderators were included in the mixed-effects model (p< 0.05; k = 22, j = 30). The latter explained ~ 36% of the heterogeneity (Table S3). We note that smaller size effects of warming on SR (lesser plant diversity loss) tend to be associated with dry areas (< 300 mm precipitation per year, with LRR of about -0.01 on average). In fact, the response of SR to warming was observed to be stronger the lower the aridity (e.g. [START_REF] Peñuelas | Response of plant species richness and primary productivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: reductions in primary productivity in the heat and drought year of 2003[END_REF].

Similarly, less negative LRR values (i.e., more limited decline in plant diversity) were found for grassland sites below 1000 m a.s.l. (about -0.06 on average), where SR is generally lower (e.g., Dengler et al., 2014). The more pronounced decline of SR in high-elevation grasslands may reflect that plant species that are adapted to cold areas tend to be more sensitive to warming. It can be assumed that the thermal niche of plant species may be narrower than at low altitudes, which considerably hinders adaptation/acclimation in the short-term (e.g., [START_REF] Löffler | Thermal niche predictors of alpine plant species[END_REF]. While changes in species cover and the composition of plant communities indicate an acceleration of the transformation towards more heat-demanding vegetation, this colonisation process could take place at a slower pace than the continued decrease in cryophilic species, thus favouring periods of accelerated species decline [START_REF] Lamprecht | Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps[END_REF].

Of the few experiments in which LRR > 0 (i.e., increased SR under higher temperatures), the one from Zhu et al. (2015), with LRR = 0.15, is consistent with the situation of a meadow steppe dominated by a perennial rhizome grass species -Leymus chinensis (Trin.) Tzvelev (Chinese ryegrass) -which is the first to germinate each year. A higher accumulation of plant community biomass in the warmed plots leads to more plant litter, which suppresses the germination and regrowth of L. chinensis, reducing its dominance and allowing other species who, in a meta-analysis, found that across warming experiments conducted worldwide, warming overall had positive effects on plant photosynthetic rates of terrestrial plants, with varying effects depending on plant functional types. Similarly, [START_REF] Wu | Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation[END_REF] reported in a meta-analysis that experimental warming has led to an overall increase in aboveground biomass production of terrestrial ecosystems. However, plant gas exchanges can be constrained by other environmental factors (e.g., water availability) that can inhibit photosynthetic and transpiration rates [START_REF] Song | Precipitation regulates plant gas exchange and its long-term response to climate change in a temperate grassland[END_REF]. In fact, warming-induced soil water deficit indirectly affects biomass production by decreasing soil moisture availability (e.g., [START_REF] Wagle | Confounding effects of soil moisture on the relationship between ecosystem respiration and soil temperature in switchgrass[END_REF]. In Hoeppner and Duke (2009), the maximum HB was obtained with +2.7 °C (LRR = 0.30), while with ΔT = +4.0 °C the increase in HB was smaller (LRR = 0.22). The mixed-effects model with year of publication, site elevation and heating method as moderators (k = 15, j = 20) was significant (p < 0.05) and explained ~ 63% of the heterogeneity (Table S3), but only year of publication was significant (p < 0.05). In fact, negative effects of warming on HB were mostly observed in experiments conducted after 2015, reducing the LRR values for the period 2015-2019 (which nevertheless remained positive on average, i.e., ~ 0.04). In our meta-analysis, the most negative effect of warming on HB (LRR = -0.20) was obtained in a Tibetan alpine steppe (Jingxue et al., 2019), where experimental warming (+2 °C) under ambient precipitation significantly caused reductions in biomass because of induced water deficit. Xu et al. (2015) also showed in an alpine meadow on the Qinghai-Tibetan Plateau (arid and cold area) how warming and mowing combined (a treatment not included in our meta-analysis) negatively affected HB (LRR = -0.08) and positively affected SR (LRR = 0.04), indicating the dominant role of management (which tends to favour SR and limit HB) over an environmental change (which, conversely, is supposed to favour HB and limit SR). Higher biomass production under warming conditions could explain the decline in SR, through competitive exclusion, for which all environmental conditions likely to favour high levels of HB could lead to a decline of SR.

However, as warming increases evapotranspiration, greater drought conditions could dampen the biomass response, thus reducing competitive exclusion, which favours the stability of SR. In fact, the adverse effects of warming caused by increased heat stress and water scarcity could, in the long term, counteract the positive effect of mowing on SR, especially in temperate grasslands (e.g., De Boeck et al., 2008).

Conclusion

Using a meta-analytical methodology, we generated an integrated analysis of a large amount of observation data from different regions of the world, which better reflect the general patterns of grassland response than several fragmented studies performed so far. First, we found higher SR and lower HB in plots that were mown, suggesting the importance of management practices based on the application of disturbances such as prescribed mowing to enhance plant species diversity. Second (and opposite to the first result), we found that HB can be higher in plots that are exposed to higher temperatures while warming tends to decrease the number of plant species. The opposite responses of SR and HB to disturbances in the two meta-analyses suggest possible competitive exclusion mechanisms, which have not be investigated in this study. This is supported by the importance of site elevation (narrow thermal niche preventing plant species from adapting quickly at high altitudes) and annual rainfall (competitive exclusion in humid areas) in explaining the response of SR to warming. However, the present results of meta-analyses have some limitations. First, SR and

(b)

HB are kinds of ecosystem response influenced by multiple factors and there are complex interactions between them that we have not considered due to lack of adequate data. Second, even if publication bias was substantially avoided, we have no access to unpublished researches or studies published in other language than English, which may have influenced our results. Despite some limitations, the present meta-analyses provide the latest evidence regarding the positive effect of moderate physical disturbance (i.e., limited mowing) on the creation and maintenance of highly diverse, ecologically and agriculturally valuable grasslands. In parallel to that, our results confirm the importance of considering plant

Introduction

Grasslands often contain a high diversity of plants, which is of interest for wildlife conservation, for a diverse forage resource in relation to feed quality and for the resources provided to micro-and macro-fauna (Theau et al., 2017a;Bengtsson et al., 2019). This plant diversity can be described in different ways and studied using different approaches (e.g. Perrone et al., 2014). For instance, the number of individual species present in a grassland (species richness) informs about the ecological or patrimonial status of the communities.

However, despite experimental evidence of causal relationships between species number, ecosystem productivity and carbon sequestration (e.g. Tilman et al., 1997Tilman et al., , 2006)), species richness is not an accurate indicator of the agronomic value of grasslands (e.g. productivity, quality, precocity). For that, herbaceous species are often classified into three taxonomic groups, namely grasses, legumes and forbs, the latter two playing a decisive role in the overgrowth of mixtures [START_REF] Marquard | Positive biodiversity-productivity relationship due to increased plant density[END_REF]. Both species richness and taxonomic diversity do not take into account whether plant species or groups of species are similar or differ in their attributes (or traits). Taxonomic-based analyses have thus evolved to explore species coexistence [START_REF] Kraft | Plant functional traits and the multidimensional nature of species coexistence[END_REF] and ecosystem functioning based on species traits (functional diversity). Functional groups highlight the traits of plant species to identify the main types of plant species present in a grassland based on biological characteristics (functional traits) that correspond to similar functioning or strategies [START_REF] Roscher | Trait means, trait plasticity and trait differences to other species jointly explain species performances in grasslands of varying diversity[END_REF].

In fact, plant functional traits mediated by plant species composition affect most key ecosystem properties, depending on the relative contribution of a given species to the total vegetation biomass (e.g. [START_REF] Chapin | Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change[END_REF][START_REF] Ansquer | Functional traits as indicators of fodder provision over a short time scale in species-rich grasslands[END_REF]Zheng et al., 2015). As such, functional diversity characterises the agronomic value of grasslands, e.g. a grassland with high functional diversity can be exploited for its resilience to extreme weather events (Isbell et al., 2015), and functional diversity can be an indicator of grassland ecosystem services (e.g. [START_REF] Díaz | Incorporating plant functional diversity effects in ecosystem service assessments[END_REF][START_REF] Klumpp | Using functional traits to predict grassland ecosystem change: a mathematical test of the response-and-effect trait approach[END_REF].

The concept of functional traits (or plant functional diversity), which provides a generic approach to characterise vegetation types (e.g. [START_REF] Lavorel | Plant functional effects on ecosystem services[END_REF], is attractive as a tool for inferring ecosystem processes (e.g. plant growth) through aggregated traits of dominant species other than weather, soil and management factors [START_REF] Klumpp | Using functional traits to predict grassland ecosystem change: a mathematical test of the response-and-effect trait approach[END_REF].

Plant traits and trait-based plant classifications thus provide a sound scientific basis for reckoning the provision of ecosystem services and guiding grassland management (e.g. [START_REF] Quétier | Plant-trait-based modeling assessment of ecosystem-service sensitivity to land-use change[END_REF][START_REF] Lamarque | Plant trait-based models identify direct and indirect effects of climate change on bundles of grassland ecosystem services[END_REF]. This can be done through a typology, i.e. a set of rules to define the characteristics of an observed system (here the kind of grassland), which facilitates its classification and the assessment of the ecosystem services provided [START_REF] Carrère | Une typologie multifonctionnelle des prairies des systèmes laitiers AOP du Massif Central combinant des approches agronomiques et écologiques[END_REF]. Cruz et al. (2002) conceived a grass species typology with the aim to provide support to grassland advisors via a generic method facilitating the manipulation of complex information, namely on the linkage between biodiversity and grassland performance (e.g. grassland types for modelling purposes, aimed at predicting the dynamics of herbage biomass, structure and digestibility as a function of management practices and climate (Jouven et al., 2006a, b). Simulation models combining community traits with soil, climate and management are widely used to predict grassland productivity and biophysical/biogeochemical cycles (e.g. Ehrhardt et al., 2018;Sándor et al., 2020). In these models, plant traits are generally considered as static inputs (i.e. model parameters) that characterise the mean vegetation of the community, which become specific and dynamic when plant diversity is taken into account. Modelling solutions linking the processes and dynamics of plant types to the processes and dynamics of communities are mostly integrated to specific models and for a limited number of species in a community (e.g. Li et al., 2011;Corre-Hellou et al., 2009;Jégo et al., 2015;Thivierge et al., 2016). A high detail of plant interactions can be achieved at the expense of detailed process descriptions, while niche which implies that aspects related to plant diversity cannot be ignored in modelling studies (van Oijen et al., 2020). Designed as a re-usable component, CoSMo was already coupled with generic crop models (CropSyst, Stöckle et al., 2003;WOFOST, van Keulen and Wolf, 1986) to explicitly and dynamically simulate the relative abundance of plant species in grasslands, and satisfactorily applied to annually mown grasslands in central Italy (Movedi et al., 2019). Here, the CoSMo suitability functions are exploited as a means of improving the grassland-specific model ModVege. A first approach, consistent with the level of complexity of the generic grassland simulator, dynamically models the relative abundance of functional types in a grassland community. A second approach adds complexity to ModVege to represent the dynamics of individual plant species in the community. The assessment of three modelling solutions -a grassland model in standalone, coupled versions for functional groups and individual species -was thus carried out for aboveground biomass and relative abundance of plant groups/species using multi-year field data from mown (fertilised or not)

and undisturbed (abandoned) multispecies grasslands in the Massif Central of France.

Materials and methods

The modelling framework

Generic grassland model (ModVege)

ModVege is a relatively simple process-based model that estimates herbage quantity and quality in managed, multi-species grasslands. It implements the conceptual framework originally proposed by [START_REF] Carrère | Design of a spatial model of a perennial grassland grazed by a herd of ruminants: the vegetation submodel[END_REF][START_REF] Carrère | Validation of a model simulating grassland vegetation dynamics using plant traits measured along a gradient of disturbance[END_REF] to calculate the mass flow in four structural compartments of aboveground biomass on a daily time-step (Supplementary material, Figure A). The model development is based on the assumption that community behaviour can be explained by the mean traits of dominant grasses. The model addresses six basic functional groups of grasses [START_REF] Graux | Plant trait-based assessment of the Pasture Simulation model[END_REF] that can be combined in different proportions to simulate a wide range of grassland communities (Jouven et al., 2006a). Environmental constraints are associated with water scarcity, high radiation levels and temperature extremes, within a purely source-driven model, where potential growth is expressed as a function of the photosynthetic active radiation intercepted [START_REF] Schapendonk | LINGRA, a sink/source model to simulate grassland productivity in Europe[END_REF]. The processes contributing to biomass turnover are growth, senescence and abscission. The onset of growth is assumed to occur when the cumulative thermal time since 1 January exceeds 200 °C-d for the first time. Water availability is related to the fractional water content,

128

W=WR/WHC, where WR is the current water reserve and WHC is the water holding capacity. A simple bucket approach is used to evaluate WR, where precipitation is considered as an input and actual evapotranspiration (AET) and drainage as outputs. Drainage is assumed to occur whenever the difference between precipitation and AET exceeds the soil's absorption capacity, given by the difference between WHC and WR. Herbage growth is further controlled by the overall nutrient availability through a nutrient index, i.e. a relative measure of the overall nutritional status of the canopy, which can range from 0.35 in nutrient deficiency to 1.20 in heavily fertilised systems (Bélanger et al., 1992). As there is no model component that simulates the nitrogen (N) cycle dynamically, the nutrition index is considered a site-specific parameter that can be reduced over time to represent the progressive depletion of N availability in unfertilised fields. Despite its simplifications, ModVege has been shown to perform well in a variety of environmental contexts and management regimes (e.g. [START_REF] Hurtado-Uria | Evaluation of three grass growth models to predict grass growth in Ireland[END_REF][START_REF] Calanca | Testing the ability of a simple grassland model to simulate the seasonal effects of drought on herbage growth[END_REF], and is being developed [START_REF] Ruelle | Development of the Moorepark St Gilles grass growth model (MoSt GG model): A predictive model for grass growth for pasture based systems[END_REF] for application as a decision-support system [START_REF] Ruelle | La prévision de la croissance d'herbe en Irlande : une information attendue, de l'éleveur au gouvernement[END_REF].

Modelling component for plant diversity (CoSMo)

CoSMo simulates plant communities through a mean parameterisation based on the relative abundance of each group/species. Its approach can be coupled with any generic grassland simulator. The relative abundance of plants species (or groups of species) depends on the hierarchical aggregation of several drivers (each one assuming values between 0 and 1), which are estimated to characterise the suitability and competitiveness of each group/species in a given context. The generic simulator community parameterisation (ycommunity) is updated at a daily time-step, for each simulator parameter (y) based on the relative abundance (SCP) of each group/species (i) of n simulated groups/species in a mixed cover, as follows:

𝑦 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 = ∀ 𝑦 ∑ 𝑛 𝑖=1 (𝑦 𝑖 ⋅ 𝑆𝐶𝑃 𝑖 ) (1)
where the universal quantifier is encoded as ∀ ("for all").

𝑆𝐶𝑃 𝑖 is derived at each time step (dt), dSCPi(t), as follows:

𝑑𝑆𝐶𝑃 𝑖 (𝑡) 𝑑𝑡 = ( 𝑆𝑓𝑠 𝑖 (𝑡)-𝑆𝑓𝑠(𝑡) 𝐼 ) (2)
where 𝑆𝑓𝑠 𝑖 (𝑡) is the suitability factor for group/species i, 𝑆𝑓𝑠(𝑡) is the mean suitability factor for all groups/species and 80≤I≤120 is an inertial replacement coefficient (suggested to be set at 100).

The term Sfsi(t) is calculated from the hierarchical suitability function of driver q for the species i, 𝐻𝑆𝑓 𝑖,𝑞 (𝑡):

𝑆𝑓𝑠 𝑖 (𝑡) = ∑ 𝑧 𝑞=1 (𝐻𝑆𝑓 𝑖,𝑞 (𝑡)) (3)
where z is the number of drivers. In the current version, six drivers are hierarchically arranged: 1, management (cutting/grazing); 2, phenology; 3, air temperature; 4, light interception; 5, water availability; 6, N availability. They are hierarchically arranged as follows:

𝐻𝑆𝑓 𝑖,𝑞 (𝑡) = {𝑆𝑓 𝑖,𝑞 (𝑡) √ 𝐻𝑆𝑓 𝑖,𝑞-1 (𝑡) • 𝑆𝑓 𝑖,𝑞 (𝑡) 𝑞 = 1 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (4)

Applying Eq. ( 3) to the case of z=6, the result is:

𝐻𝑆𝑓 𝑖,1 (𝑡) = 𝑆𝑓 𝑖,1 (𝑡) 𝐻𝑆𝑓 𝑖,2 (𝑡) = √𝑆𝑓 𝑖,1 (𝑡) • 𝑆𝑓 𝑖,2 (𝑡) 𝐻𝑆𝑓 𝑖,3 (𝑡) = √𝑆𝑓 𝑖,1 (𝑡) 4 • √𝑆𝑓 𝑖,2 (𝑡) • 𝑆𝑓 𝑖,3 (𝑡)
𝐻𝑆𝑓 𝑖,4 (𝑡) = √𝑆𝑓 𝑖,1 (𝑡) (5)

The methods for estimating the suitability functions of these drivers are described in Confalonieri (2014) and Movedi et al. (2019). The application within ModVege required some adaptations of the CoSMo suitability functions to the features of the generic simulator.

The ModVege-based implementation of the CoSMo suitability functions is described in the Supplementary material (section 2).

Study-site and experimental design

We refer to the long-term observational system of Theix (45° 43' N, 03° 01' E, 880 m a.s.l.), located in the Massif Central of France (Supplementary material, Figure B). A designed experiment was established in 2005 on an area of ~3 ha, with the aim of analysing the response of permanent grasslands to changes in management practices (Louault et al., 2017).

The site is equipped with a meteorological station, which provides hourly values of global radiation, air temperature and precipitation, as well as soil temperature. The climate is semicontinental with a mean annual temperature of 8.7 °C and a mean yearly rainfall of 770 mm.

It is essentially humid or sub-humid, according to the De Martonne-Gottmann aridity index [START_REF] De Martonne | Nouvelle carte mondiale de l'indice d'aridité[END_REF], calculated annually as 𝑏 = Management prior to 2003 consisted of a mixed regime with one to two cuts for silage or hay, with applications of mineral and organic fertilisers, followed by grazing in autumn. In the two years prior to the implementation of the experiment (2003)(2004), three annual cuts were applied without fertilisation. Since 2005, treatments were applied, distinguishing between i) level of herbage utilisation by rotational grazing of cattle without fertilisation (i.e.

disturbance gradient; three treatments) and ii) level of fertilisation under a fixed mowing regime (i.e. nutrient availability gradient; three treatments). Each treatment was repeated twice in each block, resulting in 28 plots. Cattle grazing treatments (not used in this study)

were applied on 2200 m 2 plots while smaller plots (350-400 m 2 ) were either abandoned (Ab)

or used for fertilisation treatments. The fertiliser application was split in early spring, after the first and after the second mowing. Fertilisation treatments were: (1) NPK: annual addition of mineral nitrogen, phosphorus and potassium (on average 264 kg N ha -1 , 33 kg P ha -1 and 189 kg K ha -1 ); (2) PK: annual addition of mineral phosphorus and potassium (on average 26 kg P ha -1 and 143 kg K ha -1 ); (3) Null: no fertilisation. More details are in Louault et al. (2017). For the modelling, the data corresponding to the NPK, Null and Ab treatments were used as a block mean (the PK treatment was not used).

Experimental data

Aboveground standing biomass and relative abundance of plant species were determined experimentally in mown and unmown plots. Biomass was harvested from the unmown (i.e. abandoned) plots for research purposes. Aboveground biomass (AGB hereafter) was determined in four sampling areas per plot (0.3 x 0.6 m 2 in the abandonment and 0.6 x 0.6 m 2 otherwise) at ~0.055 m height, harvested up to three times a year in May, July and October, after a cleaning cut at the end of winter. The sampling area was moved within the plot at each cutting date and the aboveground biomass was removed at the beginning of the regrowth period. Herbage samples were oven dried (60 °C, 48 h). For mown plots, annual herbage production was calculated as the sum of the three sampling dates.

The relative abundance of plant species in each treatment was determined from 2006 to 2018 (all years except 2009), using 40 pinpoints regularly spaced along two to three fixed transects.

Presence/absence of species was recorded at each of the 40 pinpoints. The relative abundance of each species was calculated at the plot level [START_REF] Grant | Resource description: vegetation and sward components[END_REF], normalised to the sum of presences of all species and expressed as a percentage.

The total number of plant species recorded at least once in blocks 1 and 2 during the study period of ~50 in the Null treatments, compared to ~35-40 with full fertilisation or abandonment (Supplementary material, Table A), reflects the positive effect of cutting and no N input on species richness (e.g. [START_REF] Piseddu | Mowing and warming effects on grassland species richness and harvested biomass: meta-analyses[END_REF]. In this study (and in agreement with Movedi et al., 2019), we based the modelling work on the relative abundances of dominant species or functional groups. In order to limit the uncertainties related to the initialisation and parameterisation of minor groups/species, a practical threshold of 0.04 (4%) of the mean relative abundance during the simulated period was set in order to exclude groups/species present with an abundance lower than this threshold. For modelling purposes, the relative abundances of the dominant types/species were then recalculated by relating the relative abundance of each species (Table 2) or functional types (Table 3) to the sum of the abundances.

For instance, Lolium perenne was excluded from the list of dominant species in the block 1 treatments, with 2.0% (NPK) and 2.8% (Null), and in the block 2 abandonment, with 0.3%.

On the other hand, it was included in the simulated species of the NPK and Null treatments in block 2, where its relative abundance is ~9%. Similarly, Holcus lanatus was excluded from the NPK treatment in block 1, where it is present at ~1%, while its presence amounts to ~8% in the same treatment in block 2, and is below the critical threshold in all other treatments except the Ab treatment in block 2, where it is at the 4% abundance threshold.

We also underline that, when present, Stellaria media is at the 4% abundance threshold in the fertilised treatment of both blocks. It can also be seen that Poa pratensis is the only species present in all situations, while occasional forbs such as Galium aparine (block 1) and Urtica dioica (both blocks) are only present in the abandoned plots (about 4-7% on average and mostly absent in the first years; Table D in Supplementary material). One only legume species, Trifolium repens, is present above the 4% threshold (10-11%) in both the unfertilised mown treatments. The selected species have a total abundance ≥70%, and even ≥80% in the NPK treatments (Table 4.2). In terms of functional grass typologies, type C was only marginally present in the unfertilised and abandoned treatments of block 2 (or absent for the rest), where the dominating groups A, B and b covered nearly 100% of abundances (Table 4.3). C andD of Supplementary material, respectively.

Simulation design and model evaluation

Simulations were designed and model evaluation carried out to determine whether (i)

CoSMo-based ModVege solutions simulated the relative abundance of plant (grass) functional types and species, and (ii) differences in simulated AGB were caused by the explicit simulation of plant diversity, by comparing the CoSMo-based and stand-alone ModVege solutions. Standalone (ModVege) and coupled (ModVege-CoSMo) modelling solutions were configured using daily weather data retrieved from the CLIMATIK database (through the INRAE portal, https://www6.paca.inrae.fr/agroclim/Les-outils) and management data as determined at the experimental site, and parameterised for each treatment of both blocks (Supplementary material). In particular, simulations were performed with common sets of ModVege and CoSMo parameters (Table E andTable H) and type-and plant-specific parameters for either ModVege (Tables F andG) or CoSMo (Tables I andJ). Some ModVege parameters were extracted from published literature for (grass-based) functional types (Jouven et al., 2006a). Otherwise, the datasets described in Section 2.3 were used for the calibration of models parameters. ModVege stand-alone was first run for each functional type separately (A, B and b) and then the daily community AGB was obtained as a weighted mean of the three functional types, using for each treatment the mean abundances of each type over the period 2006-2018 (Table 3) as a weighting factor.

The calibration work was carried out through a trial-and-error process comparing the model estimates with observational data while ensuring biologically interpretable plant parameters by modifying their values within plausible ranges. For instance, the parameters ST1=800 °Cd and ST2=1200 °C-d of Galium aparine (Table G) were adapted from the ranges of values provided by Theau et al. (2017b), and a similar approach was adopted for the other species.

For the functional types, the maximum leaf area index values (LAImax>9 m 2 m -2 ; Table I) were calibrated to the upper limit of the LAI of grasslands, which is rarely higher than 10 [START_REF] Jackson | A global budget for fine root biomass, surface area, and nutrient contents[END_REF]. As well, as the canopy height varies by several folds, mainly from 0.25 to 1.50 m (Sun and Frelich, 2011), the maximum plant height (MaxHeight) was calibrated below the upper limit of 2.0 m (e.g. [START_REF] Zhang | Climate variability decreases species richness and community stability in a temperate grassland[END_REF]. For specific leaf area (SLA), we used (Table G) the values provided by Bourdôt (1980) for Achillea millefolium (0.017 m 2 g -1 ) and by Ianovici et al. (2015) for Taraxacum officinale (0.048 m 2 g -1 ), while the SLA value used for Urtica dioica (0.023 m 2 g -1 ) is the one provided by Gulías et al. (2003) for Urtica atrovirens, and for We also evaluated the mean values of grassland community traits as estimated on a daily basis with CoSMo-based solutions according to the relative abundance of species/types. In the absence of observations, the values of three canopy traits (maximum height, specific leaf area and maximum leaf area index) were appreciated for their variation over time, compared to the constant values attributed to the corresponding parameters of the species/types composing the community.

(

Results

Results are presented via a set of performance metrics calculated separately by output, block and treatment for the whole simulation period (Supplementary material). For AGB, we compared standalone ModVege and its CoSMo-based solutions (Table K). The estimates of relative plant abundances obtained with the two CoSMo-based modelling solutions were evaluated for both functional types (Table L) and species (Table M), either taken individually or aggregated by taxonomic groups and for the whole community.

Evaluation of modelling solutions for grassland biomass production

CoSMo-based and standalone modelling solutions performed similarly (Supplementary material, Table K) with <1.2 t ha -1 AGB difference between simulations and observations on average (RRMSE~70%, RMAE~54% on average). In most cases, CRM values were negative, indicating a general model overestimation of the observed AGB (with the exception of CoSMo-based versions in the abandoned plots, with CRM from 0.07 to 0.12). The unfertilised (stressed and perturbed) plots were the most difficult to simulate (RRMSE>100%). Although error amounts were lower for abandoned plots (RRMSE~40%, RMAE<40%), with no substantial differences between modelling solutions, simulated and observed data were less correlated (R~0.4) under these stressed (unperturbed) conditions, reflected in a limited fit (R 2 ≤0.20) as data points do not line up around the 1:1 identity line F-J).

(Figures C, D

Relative abundance of plant species

The accuracy of ModVege-CoSMo in simulating the relative abundance of plant species was generally satisfactory (Table M

Discussion

Plant biomass simulation

Our results show that there is not always an adequate estimate of AGB by ModVege under different treatments, which also holds true with CoSMo-based modelling solutions. This indicates that the investigated grassland model is not yet sufficiently developed to capture the complexity of interactions between weather fluctuations, soil properties, floristic composition and the resilience of grassland communities to environmental stresses and management factors. However, discontinuous biomass measurements contain rather large uncertainties, mainly due to the spatial heterogeneity of grassland covers (Snow et al., 2014), which make model evaluation difficult (Vuichard et al., 2007;[START_REF] Gomára | Influence of climate variability on the potential forage production of a mown permanent grassland in the French Massif Central[END_REF]. Even in this study, it is worth examining some issues with the experimental data, which may explain some of the discrepancies observed between the modelling solutions and the measurements.

In 2008, the first biomass determination was postponed to the end of June (instead of the normal end of May; [START_REF] Sándor | Plant acclimation to temperature: Developments in the Pasture Simulation model[END_REF]. Thus, biomass production was lower than in other years in the first determinations of 2011, which is also one of the warmest year in the time-series, with three days with maximum air temperature >35 °C detected at a nearby station (Zwicke et al., 2013). This likely also reflects the lasting effects of the sequence of heatwaves that had occurred since 2009 [START_REF] Sándor | Plant acclimation to temperature: Developments in the Pasture Simulation model[END_REF].

Most problematic was the simulation of biomass in the abandonment treatment, a condition for which ModVege was not explicitly developed. Abandoned grasslands have complex vegetation structures that share environmental conditions with both open habitats and forests. At the study site, the abandoned grasslands are in an early succession stage, their vegetation structure is still more similar to open habitats than to forests, and we hypothesised that abandoned plots partly may act as managed grasslands. However, community similarity was relatively low, indicating that the dense sward found in abandoned plots created different environmental conditions (e.g. shade limiting light availability for emerging plants) compared to the mown grasslands. In particular, the vegetation cut at ground level included much of dead biomass components, which have increased along the years. The biomass sampled in summer (in July or August) was more similar to that of the mown grasslands, as it did not contain much dead biomass from the previous year's growth or that of the current growing season (which is more present in the autumn sample). The assessment at the summer sampling date (Table 4) indicates that, on average, the three modelling solutions tended to converge with the observations: <1.0 t ha -1 deviation down to ~0.1 t ha -1 with the CoSMobased functional group solution in block 2. The simulations continue to indicate difficulties for all three modelling solutions to capture interannual variability in AGB. Deviations from observations of up to ~3.7 t ha -1 with ModVege standalone in 2018 in block 1 were however mitigated by the CoSMo-based solutions to ~2.8 t ha -1 . As well, the maximum deviation of ~3.0 t ha -1 observed in block 2 in 2018 with ModVege standalone was reduced to ~2.0 t ha - 1 with CoSMo-based solutions. Beyond the observational issues, the low modelling performance is not surprising, as simulations for grassland biomass are generally less accurate compared to cereal crops (e.g. [START_REF] Kollas | Crop rotation modelling -a European model intercomparison[END_REF]. It is also a fact that the simulated biomass dynamics are essentially dissimilar between different grassland models, with shortcomings in the estimation of soil water content dynamics (Sándor et al., 2017). As well, ModVege simplifies plant growth simulation, with constant ratios of the above-to below-ground biomass allocation and plant water availability linked to fractional water content through a simple bucket approach used to assess the water reserve [START_REF] Calanca | Testing the ability of a simple grassland model to simulate the seasonal effects of drought on herbage growth[END_REF]. Taken together, these simplifications affect the accuracy of simulations of transpiration and soil water content. Consequently, to improve the performance of plant water demand and consumption simulation, a more detailed hydrological model should be integrated with the growth model.

Another important limitation of ModVege is that it assumes fixed values of the nutrition index, whereas in reality the availability of N and other nutrients varies with season, environmental conditions and management. In the context of the present study, the issue is relevant because N availability is highly dependent on management. The scope of the model thus needs to be extended to include important processes that determine the nutritional status of the plant community, such as a root compartment, components to simulate mineral and organic N cycling in the soil, and a description of fertilisation [START_REF] Durand | How much do water deficits alter the nitrogen nutrition status of forage crops?[END_REF]. Since legume fixers represent an important component of grassland communities, contributing to maintaining high productivity under low N-fertiliser supply, consideration should also be given to including a model component to simulate symbiotic N fixation [START_REF] Fuchs | Evaluating the potential of legumes to mitigate N2O emissions from permanent grassland using process-based models[END_REF].

In addition, drought can cause changes in grassland dynamics through physiological responses [START_REF] Chaves | How plants cope with water stress in the field: photosynthesis and growth[END_REF]. The ability of some species to resist or avoid water stress through enhanced water uptake at low soil water potentials (Volaire and Lelièvre, 2001;Volaire, 2008), which is not currently taken into account in ModVege, may be important for understanding the disparity of responses observed in field studies.

Beyond eco-physiological responses, long-term changes related to grassland-community composition should be considered (e.g. [START_REF] Gilgen | Competitive advantage of Rumex obtusifolius L. might increase in intensively managed temperate grasslands under drier climate[END_REF][START_REF] Mariotte | Subordinate plant species enhance community resistance against drought in semi-natural grasslands[END_REF]. Here, the aim was not merely to assess the quality of AGB simulations by ModVege, but to evaluate this grassland model in comparison with solutions that introduce complexity in the modelling structure to represent plant dynamics (either species or functional types). The results are encouraging because, although the quality of the biomass simulations did not improve, it was not degraded by the complexity introduced. A pertinent point is that with CoSMo, it becomes possible for grassland simulators to obtain estimates of AGB while dynamically representing the relative abundance of plant species/types in mixed communities, a useful outcome for obtaining valuable information on a variety of ecosystem services provided by grasslands (e.g. [START_REF] Weisser | Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: Patterns, mechanisms, and open questions[END_REF]. The use of a simplified plant-growth model structure, in conjunction with a plant dynamics module, proved useful as it allowed the properties of CoSMo to emerge, without interactions with the detailed biogeochemical processes of C and N cycles. CoSMo-like approaches could thus be more effective in simulating grassland biomass than in this study, as biophysical and biogeochemical issues are resolved.

Relative abundance simulation

The accuracy of ModVege-CoSMo in simulating the relative abundance of plant species in a community (RRMSE in the range ∼28-52%; Supplementary material, Table M) was similar to that found by Movedi et al. (2019) with CropSyst-CoSMo (∼28-64%) and Soussana et al.

(2012) with GEMINI (51%), the latter (individual-centred model) being much more complex than CoSMo. The performances are also comparable to those of GRASSMIND (individualbased biogeochemical model; Taubert et al., 2020), which, however, was only applied to represent the vegetation cover a limited number of species (mixture of two grass and one forb species).

With a total of 17 species simulated (up to eight per treatment, Table 4.2), this study goes beyond Movedi et al. (2019), where a maximum of seven species were simulated together in a simplified grassland system (once a year mowing). In our study, seven CoSMo parameters (Table I in Supplementary material) were added to 40 vegetation parameters in ModVege (24 functional type-specific parameters and 16 plant species-specific parameters; Tables F andG in Supplementary material). In comparison, the generic crop model WOFOST (van Keulen and Wolf, 1986) and the grassland model GEMINI [START_REF] Soussana | Gemini: A grassland model simulating the role of plant traits for community dynamics and ecosystem functioning. Parameterization and evaluation[END_REF] would need ∼100 parameters to characterise each species, and rapidly become increasingly complex as the number of species in the community increases, which obscures their interpretability and ultimately limits the applicability of the model.

Although limited to a single site, this study covers a variety of contrasting situations. They

were not simulated with the same accuracy, which allows some strengths and weaknesses of the modelling framework to be identified. The satisfactory simulation of abundant grass species like Elymus repens and Alopecurus pratensis, but also Festuca arundinacea, Lolium perenne and the ubiquitous Poa pratensis, is important in view of modelling studies in support of agronomic and ecological objectives. Despite its classification as a weedy perennial grass for row crops, Elymus repens (couch grass) has in fact characteristics (erect stem growth habit, high feeding quality, vegetative reproduction through rhizomes, adaptation to a wide range of soil fertility and long seasonal growing periods) that make it an acceptable species for pastures (e.g. [START_REF] Amiaud | After grazing exclusion, is there any modification of strategy for two guerrilla species: Elymus repens (L.) Gould and Agrostis stolonifera (L.)?[END_REF], which may play a role for forage production and erosion control in northern temperate climates (e.g. [START_REF] Ringselle | Under-sown cover crops and post-harvest mowing as measures to control Elymus repens[END_REF]. Its seeds are also eaten by several species of grassland birds (Snow and Perrins, 1998). Alopecurus pratensis (meadow foxtail) is also a pasture grass, usually mown before flowering to preserve its feed value, and used by some lepidopterans and other insects as a food plant (e.g. [START_REF] Ružičková | Morava River alluvial meadows on the Slovak-Austrian border (Slovak part): plant community dynamics, floristic and butterfly diversity -threats and management[END_REF].

Festuca arundinacea (tall fescue) is also an important forage crop under various fertility conditions, whose beneficial attributes for low-fertility soils are the result of a symbiotic association with an endophytic fungus, which increases soil C storage by limiting microbial and macro-faunal activity to decompose endophyte-infected organic matter inputs and by increasing C inputs through plant production (e.g. [START_REF] Franzluebbers | Soil carbon and nitrogen pools under low-and high-endophyte-infected tall fescue[END_REF]. Lolium perenne (perennial ryegrass) is an important perennial grass for turf grasses, globally one of the most important forage grasses, forming the basis of grassland production in temperate pastures as it has a low, dense vegetation with good regrowth and tillering [START_REF] Mcdonagh | Genetic gain in perennial ryegrass (Lolium perenne) varieties 1973 to 2013[END_REF]. The forage yield of Poa pratensis (bluegrass) is lower compared to other temperate grasses, but it has a good regrowth capacity, responds well to intensive grazing, has good nutritional quality, is a food plant for various insects and is used in mixtures with taller species [START_REF] Martin | Role of ley pastures in tomorrow's cropping systems. A review[END_REF].

The performance indices highlighting some inaccuracies in the estimates for two minor edible wild forbs, Stellaria media (common chickweed) and Galium aparine (cleavers), which are often considered as weeds, and thus of no interest for fodder crops (e.g. Weiss et al., 2010).

For instance, we obtained RRMSE values >100% (Table M in Supplementary material) for

Stellaria media in both fertilised plots and for Galium aparine in the abandoned plot of block 1, which can be explained by the presence of a few data points deviating (with small absolute differences) from the general observed trend. In fact, although the performance metrics are not entirely satisfactory, the visualisation of the simulated species dynamics shown in the time-series plots is generally consistent with that of the observations (Figures K, L andO).

Our study also confirms other findings of a change in the abundance of some dominant species as an effect of the abandonment (e.g. Pavlů et al., 2011;[START_REF] Jernej | Impact of land-use change in mountain semi-dry meadows on plants, litter decomposition and earthworms[END_REF]. For instance, this shift of species can be seen in the abandoned block 1 plot with a decline of important grass species (i.e. Elymus repens, Festuca arundinacea, Poa pratensis) and an increase of the grass Alopecurus pratensis and forbs (i.e. Galium aparine, Urtica dioica). Here, the important point is that the model was able to reproduce these dynamics (Figure O). For the one legume species (white clover), present in unfertilised mown treatments, the spikes in the simulated lines (Figures M-N) indicate that the N provided by the symbiotic fixation option of ModVege-CoSMo coupling needs to be refined.

These satisfactory simulations also support the expression of the distribution of plant trait values at the community level by averaging the trait values weighted by the respective abundance of each species (community weighted mean by Garnier et al., 2004), which functionally characterises plant communities in different environments in order to better understand community assemblages (e.g. [START_REF] Dubuis | Predicting current and future spatial community patterns of plant functional traits[END_REF]. For the SLA, for instance, the simulated trends (here estimated from the dominant species) reflect those determined by Louault et al. (2017) up to 2012 for the entire set of species present in the canopy.

Conclusions

Based on the suitability-function rules of the Community Simulation Model (CoSMo), this study is one of the few to consider explicit and dynamic modelling of the relative abundance of plants in grassland communities. Although specific to the grassland model ModVege, it holds potential for extension to other vegetation models of similar structure, as CoSMo provides a framework for estimating the response of grassland systems with biophysical models that is more robust and comprehensive for complex vegetation covers than the mean parameterisation-based modelling paradigm. The substantial agreement between the simulations and the experimental data, obtained in a set of environmental and management options at a representative site of semi-natural grasslands of central France, demonstrates that CoSMo is an appropriate approach for estimating the dynamic behaviour of plant species (or functional or taxonomic groups) in a community. This is important because

CoSMo has fewer parameters than other modelling approaches representing plant abundance, is relatively easy to calibrate and allows the use of a single instance of a generic vegetation model. We explored the parameter space of ModVege and its CoSMo-based solution under the specific climatic conditions of a single study site, under varying soil and management situations, with manual calibration to obtain a set of reference parameter values reflecting expert knowledge and simple adjustment strategies. The calibration work carried out through a trial-and-error process allowed the model estimates to be compared with observed data, and the solution obtained constitutes a satisfactory performance for two output variables (aboveground biomass and relative abundances) according to a set of metrics. By contrasting trait values for individual species to aggregated values at the community level, dynamically estimated functional traits can support predictions of community composition along environmental gradients and provide a greater understanding of changing ecosystem functioning. However, the site-specific study and the limited number of situations used for modelling raises the need for broader model evaluation work. First, this means that CoSMo-based ModVege may potentially be suitable for applications at other grassland sites if the model parameters are documented for those sites other than the one investigated here. Then, it is desirable to better understand the evolution of ecosystem functioning in the current context of global change in order to investigate whether measures of aggregate traits at the community level can be modelled at local scales, projected over the long term and upscaled across regions.

In conclusion, this study confirms that CoSMo-based solutions for grassland modelling can be easily implemented, although some background knowledge is required to parameterise each individual species/type in a grassland community. As the biophysical structure of CoSMo reflects important determinants of the functioning of grassland systems, we thus advocate its use to predict the behaviour of relevant grassland outputs, with three caveats.

We first recommend further testing with grazing animals. Further tests are also required at a variety of sites, given the need to evaluate the CoSMo approach in contrasting biogeographic regions (e.g. the Mediterranean region where annual self-seeding species are dominant).

Then, the present study highlights the need to orient grassland research towards the framing of dedicated functional trait measurements in the context of plant diversity and productivity observations for the CoSMo-based modelling purposes. More research on these lines can be seen as the natural evolution of this study, with the aim of developing scalable solutions open to a wide range of grassland types, while addressing the issues of re-use and interconnection of model components.

Introduction

Grassland modelling has distinct features in the Mediterranean biogeographical region (Pulina et al., 2018), where dry conditions prevail and where secondary grasslands are mainly composed of self-reseeding annual plant species that avoid the summer drought period in the form of seeds protected by the germination during occasional summer rains through various mechanisms that ensure the survival of the reseeding species for many years after seed production (e.g. [START_REF] Cosentino | European grasslands overview: Mediterranean region[END_REF]Porqueddu et al., 2016). Annual grasslands are characterized by a seasonal and multi-year dynamism, adapting to different seasonal and environmental conditions and micro-gradients -niche diversification -through competition for light, nutrients and water (e.g. [START_REF] Mamolos | Plant species abundance and tissue concentrations of limiting nutrients in low-nutrient grasslands: a test of competition theory[END_REF][START_REF] Reynolds | Soil heterogeneity and plant competition in an annual grassland[END_REF][START_REF] Whittaker | Scale and species richness: towards a general hierarchical theory of species diversity[END_REF]. The structure and composition of grasslands are determined by soil and climatic patterns. In the Mediterranean, in particular, the most peculiar factors shaping grasslands are the intense summer drought and the wide intra-and inter-annual fluctuations in rainfall, in addition to the influence of human activities such as ploughing and livestock farming, soil variability related to micro-topography and plant interactions [START_REF] Peco | Modelling Mediterranean pasture dynamics[END_REF][START_REF] Brooker | Facilitation in plant communities: the past the present and the future[END_REF]. The growing season of Mediterranean grasslands usually starts in the autumn with seed germination and ends in late spring, when the most annual species disseminate and dry up. During the growing season, which ranges between 180 and 270 days, mainly depending on rainfall distribution and altitude, the growth rate curve is typically characterized by two maxima: the first occurs in mid-autumn and the second in April [START_REF] Cavallero | Caratterizzazione della dinamica produttiva di pascoli naturali italiani[END_REF]. A variety of traits ensure plant persistence through self-reseeding under varying rainfall and temperature patterns [START_REF] Roggero | The agronomic purpose of evaluation: Relating nurseries to field situations[END_REF]Bagella et al., 2013;Porqueddu et al., 2016). However, plants are notably subjected to low rainfall during the spring-summer period (two to six months), and a water deficit of up to 1000 mm negatively affects plant survival and fodder yield [START_REF] Volaire | Summer drought survival strategies and sustainability of perennial temperate forage grasses in Mediterranean areas[END_REF]. Annual herbage production is thus strongly constrained by the duration of summer drought and, to a greater extent, by the variability of autumn and spring rainfall regimes (Gea- [START_REF] Gea-Izquierdo | Changes in limiting resources determine spatio-temporal variability in tree-grass interactions[END_REF][START_REF] Golodets | Climate change scenarios of herbaceous production along an aridity gradient: Vulnerability increases with aridity[END_REF]. In this context, special emphasis is put on drought escape in annual species [START_REF] Kooyers | The evolution of drought escape and avoidance in natural herbaceous populations[END_REF] and seed bank management [START_REF] Traba | From what depth do seeds emerge? A soil seed bank experiment with Mediterranean grassland species[END_REF], as well as drought survival in perennial grasses [START_REF] Porqueddu | Drought survival of some perennial grasses in Mediterranean rainfed conditions: agronomic traits[END_REF]Lelievre et al., 2011). The agronomic role and importance of legumes in grassland mixtures also point to interactions between grasses and legumes [START_REF] Nichols | Improvement of pasture and forage legumes and grasses for Mediterranean climate zones[END_REF].

Modelling can be a valuable complement to experimental research to improve understanding of the mechanisms underlying plant species dynamics and to assess the impact of management practices in grassland systems. This is especially relevant for Mediterranean grassland-based farming systems, which are no longer considered exclusively as livestock production enterprises, but as multiple-use systems with important environmental implications [START_REF] Dumont | A meta-analysis of climate change effects on forage quality in grasslands: specificities of mountain and Mediterranean areas[END_REF]Dibari et al., 2021). Indeed, Mediterranean grasslands provide a wide range of supporting, regulating and cultural ecosystem services, of which feed provision and biodiversity conservation are among the most important (Seddaiu et al., 2018;Aguilera et al., 2020;[START_REF] Bagella | Patchy landscapes support more plant diversity and ecosystem services than wood grasslands in Mediterranean silvopastoral agroforestry systems[END_REF]. Therefore, aspects related to plant diversity cannot be ignored in modelling studies (van Oijen et al., 2020).

The Community Simulation Model (CoSMo) dynamically represents the relative abundance of different plant types in a community by coupling with generic simulation models that reproduce (at plot scale) soil and vegetation biophysical processes in interaction with management practices (Confalonieri, 2014). By explicitly accounting for changes in the botanical composition in mixed stands, CoSMo can simulate the performance of multispecies grassland systems, extending the potential of generic simulation models to complex vegetation covers. To explore its properties and assess the sensitivity of the simulated community dynamics to initial conditions and changing climate, CoSMo was initially coupled (Confalonieri, 2014) with the generic crop simulator CropSyst (Stöckle et al., 2003). Movedi

Material and methods

The modelling framework

We simulated annual plant species using the crop growth model CropSyst and its CoSMobased solution. CropSyst (Stöckle et al., 2003; Supplementary material, (Confalonieri, 2014) simulates plant communities through a mean parameterization based on the relative abundance of each plant species (or group of species). Its approach can be coupled with any generic crop simulator. The relative abundance of plant species depends on the hierarchical aggregation of several drivers (each one assuming values between 0 and 1), which are estimated to characterize the suitability and competitiveness of each species in a given context. The generic simulator community parameterization is updated at a daily time-step for each simulator parameter based on the relative abundance of each species in a mixed stand.

Study-site and experimental design

The field experiment was carried out in 2020-2021 at the Santa Lucia experimental station (39° 56' N, 08° 41' E, 15 m a.s.l.) of the University of Sassari, in Sardinia, Italy (Supplementary material, Figure B). The climate is typically Mediterranean, with a long-term mean annual rainfall of 561 mm, mainly concentrated between October and April, and a mean annual temperature of 16.2 °C. The soil is clay loam about 2 m deep, and the pH is 8.2. During the experiment, local weather conditions (rainfall, maximum and minimum air temperatures, maximum and minimum air relative humidity and wind speed) were recorded daily, while global solar radiation was derived from the transmittance t=K•ΔT0 .5 , i.e. the fraction of solar radiation at the top of the Earth's atmosphere. According to [START_REF] Hargreaves | Estimating potential evapotranspiration[END_REF], ΔT (°C) is the range of mean air temperature, and K=0.170 °C-0.5 is a coefficient calibrated between the values originally set at 0.162 for inland sites and 0.190 for coastal sites.

In November 2020, four pure stands were sown (Lolium rigidum var. Nurra, Avena sativa var.

Prevision, Trifolium subterraneum var. Antas and Trifolium michelianum var. Paradana), and three mixtures in different proportions of the same species (25, 50 and 75% grasses). The choice of these species was based on their contrasting biology, their ecological and economic relevance to Mediterranean pastoral and silvo-pastoral systems, and the seed availability of commercial varieties [START_REF] Bagella | Contrasting land uses in Mediterranean agro-silvo-pastoral systems generated patchy diversity patterns of vascular plants and below-ground microorganisms[END_REF][START_REF] Bagella | Patchy landscapes support more plant diversity and ecosystem services than wood grasslands in Mediterranean silvopastoral agroforestry systems[END_REF][START_REF] Rossetti | Isolated cork oak trees affect soil properties and biodiversity in a Mediterranean wooded grassland[END_REF]. For Avena sativa (common oat) and Lolium rigidum (annual ryegrass), widely present in Mediterranean annual grasslands, commercial varieties and a collection landraces is available, in contrast to other spontaneous annual grasses such as, for instance, Avena barbata (slender wild oat), a common grassland oat dominating Mediterranean pastures (e.g. [START_REF] Roggero | Un archivio dati di indici specifici per la valutazione integrata del valore pastorale[END_REF]. In Mediterranean grasslands, annual grasses play a fundamental role in providing high quality forage during the cool period in late autumn and winter, as they are particularly tolerant to low temperatures. The complex and variabile germination biology of annual ryegrass is adapted to grow in the winter and escape drought in summer after abundant seed set. It is considered a common weed in wheat and rapeseed fields in Australia, recently extending its growth habit to the irrigated fields in summer (Thompson and Chauhan, 2022). Its genetic diversity, prolific seed production, widespread dispersal, flexible germination requirements and competitive growth habit make this species of great concern as a weed (Bajwa et al, 2021) but of interest for improving semi-arid Mediterranean permanent grassland systems, given its ability to persist by maintaining viable seeds in the seed bank for many years.

Common oat is grown as an autumn-spring short-term forage crop, to be grazed in the winter and then preserved from grazing to be cut for hay in spring or as a grazing crop. Farmers appreciate its slow decrease in forage quality during senescence, which allows relatively good quality hay even when cut late because of unfavorable weather.

The two annual clovers considered in this study have self-reseeding capacity, provide N fixation and are characterized by good forage quality. In the Mediterranean grassland systems these species are sown in mixtures with annual grasses such as oats, annual or Italian ryegrass.

Balansa clover (Trifolium michelianum L.) is a small seeded cross-pollinated forage legume with abundant inflorescences mainly positioned at the top of the canopy. Subclover (Trifolium subterraneum L.) is a self-pollinated with inflorescences positioned at ground level which are able to bury the seeds in the ground at maturity. Hence subclover is generally able to selfreseed even under heavy grazing, while Balansa clover requires careful management to prevent flowers to be damaged either by cutting or grazing [START_REF] Conlan | Effect of grazing intensity and number of grazings on herbage production and seed yields of Trifolium subterraneum, Medicago murex, and Ornithopus compressus[END_REF]. Their

adaptive traits (e.g. tolerance to soil acidity, long-term persistence, tolerance to pests and pathogens) make them suited to Mediterranean annual grasslands and have been a target for breeding investments for pasture improvement programmes (Porqueddu et al., 2016).

The seeding and fertilization rates adopted in the field experiment are described in Table A and Table B (Supplementary material). A ripper ploughing (about 40 cm) was carried out at the beginning of October 2020. Soon after ploughing, phosphoric fertilization was performed using single superphosphate (50 kg ha-1 P2O5). A disk harrowing was carried out to prepare the seedbed soon before seeding. Nitrogen fertilizer was used only in plots sown with mixtures and pure grasses using ammonium nitrate by supplying about 40% of the annual N supplementation the day after sowing and about 60% top-dressing at the end of winter.

The experimental design was a split plot, in randomized complete blocks with four replications. (Supplementary material, Figure C). The main plots were the seven combinations of species mixtures: i) pure stands of A. sativa, ii) pure L. rigidum, iii) pure T. michelinaum, iv) pure T. subterraneum, v) 50% grasses and 50% legumes, vi) 25% grasses and 75% legumes, and vii) 75% grasses and 25% legumes. In each mixture, the seed proportion of each grass or legume species was 50% in weight. The sub-plots were two cutting management options: low mowing frequency (one cut at the beginning of flowering) and high mowing frequency (i.e. 2 cuts at monthly intervals). All plots were cut at the end of the winter, simulating grazing of the autumn-winter forage production.

The size of the main plots was 31.5 m 2 (4.5 m x 7.0 m) and they were split lengthwise according to the two management options. In this way, the least perturbed management was compared to a highly perturbed one, with frequent mowing virtually simulating the dynamics of grazing management.

Experimental data

The aboveground biomass (AGB) was measured by cutting the herbage with electric grass shears in a 0. Sward height was measured with a Hill Farm Research Organization (HFRO) sward stick [START_REF] Bircham | Herbage growth and utilisation under continuous management[END_REF] attributing each height measurement to each of the plant species. Sward height was measured in the whole subplot (25 drops), in the sampling area for the LAI-SLA measurement (12 drops), and in the sampling area for AGB determination (24 drops). The drops were performed along a W-shaped path.

The soil physical chemical characteristics of the experimental field (Table 5.1) revealed a clayey soil, with sub-alcaline pH and a relatively good total available water and low saturated hydraulic conductivity. 2019) for some forage species (e.g. for critical temperature thresholds) was used as the basis for the parameterization of CropSyst (Table E) and CoSMo (Table F). The basic concept of CropSyst is that some minimum temperature thresholds, or base temperatures (Tb), are to be exceeded for plant development and growth to occur. We matched estimates of base temperatures for plant growth and development as they are usually not significantly different (e.g. [START_REF] Ong | Response to temperature in a stand of pearl millet (Pennisetum typhoides, S. & H.): II. Reproductive development[END_REF][START_REF] Van Der Werf | The effect of temperature on leaf appearance and canopy establishment in fibre hemp (Cannabis sativa L.)[END_REF]. The Tb=1 °C set for Avena sativa is within limits for common oat varieties (e.g. from 0 °C by López-Castañeda et al., 1996 to 5° C by Rajala andPeltonen-Sainio, 2011) and is not of the AGB of mixed stands with the stand-alone version of the model (Supplementary material, Table G).

The agreement between simulated and observed AGB data and the relative abundance of plant species was assessed by three frequently used performance metrics: the relative root mean square error (optimum, 0≤RRMSE (%)<∝), the relative mean absolute error (0≤RMAE (%)<∝) and the coefficient of residual mass (-∝<CRM<∝; if positive, it indicates an underestimation, if negative, an overestimation).

We also assessed the dynamics of grassland community traits, i.e. specific leaf area (with observations), maximum canopy height and maximum LAI (in the absence of observations), as simulated daily in the mixed stands with the CoSMo-based CropSyt solution as a function of the estimated relative abundance of species.

Results

Results are illustrated via a set of performance metrics calculated separately by output, For the low mowing frequency (Figure 5.2), the performance of the standalone version was acceptable for each individual species, except for the underestimation of AGB (CRM=0.30)

with the second mowing of annual ryegrass. However, this result is uncertain due to a high degree of missing data on that mowing date (i.e. lack of replicates with loss of samples due to technical issues that occurred in the sample processing).

As well, the underestimates (G75L25) and overestimates (G25L75) of AGB at the second mowing date for the mixed stands should be taken with caution because of the poor keeping quality of the samples (which were lost for G50L50), with similar performance between the two modelling solutions.

Leaf area index

In the high mowing frequency management, the LAI was generally well simulated (Figure 5.3). The seasonal dynamics of the observed data was well reproduced by the models for common oat, ryegrass, subterranean clover and G75L25 (0.88≤R≤~1.00). The high standard errors in the observations may explain the limited ability of the model to capture the observed monotonic decreasing trend of the LAI (with the highest value at the first mowing date) in the pure balansa clover stand, and in the mixed stands G25L75 and G50L50. grasses were present, the higher the value because higher Hmax values were assigned for grass species (1.5 m for Avena sativa and 1.2 m for Lolium rigidum; Table E).

Discussion and concluding remarks

Questions have been raised about the suitability of crop growth models for use in simulating the relative abundances of plant species in Mediterranean grasslands. The study presented here is the first of its kind, aiming to reproduce the seasonal dynamics of the agronomic traits of four selected annual self-reseeding grassland species and their relative abundances within mixed canopies under Mediterranean environment.

The manual calibration, which was carried out reflecting expert knowledge and simple strategies for adjustment to the site-specific conditions, provided a practical solution to obtain satisfactory estimates of multiple outputs (AGB, LAI and sward height with both modelling solutions) and relative abundances and canopy traits (with CropSyst-CoSMo). The obtained parameterizations can thus be considered the first adaptation of CropSyst and CropSyst-CoSMo to the specific environmental conditions of Mediterranean annual grasslands.

The CropSyst model performed reasonably well for the variables considered in this study.

The simulations of biomass growth and relative abundances of plant species were fairly well matched to measurements made during a growing season with two mowing regimes, with a view to using CoSMo-based solutions for scenario analysis and predictive purposes. While overestimation or underestimation of aerial biomass for some species and mixed stands can be found with any grassland model (e.g. Sándor et al., 2017), the main issue concerns the potential of the model (and its limitations) in matching traits, e.g. canopy height, leaf area index or specific leaf area. These canopy traits are related to the simulation of relative abundances and support the expression of the distribution of plant trait values at the community level by averaging the trait values weighted by the respective abundance of each species (e.g. Garnier et al., 2004).

Regarding the SLA, for instance, CropSyst-CoSMo simulations at the canopy level suggest the limited sensitivity of the model to the environmental and ecological drivers that shape the fluctuations of this trait (e.g. [START_REF] Tardieu | Modelling leaf expansion in a fluctuating environment: are changes in specific leaf area a consequence of changes in expansion rate?[END_REF]. The discrepancies observed are not dramatic but require further study because the modelled variability does not reflect the patterns observed within the season. SLA influences canopy expansion and growth through its effect on total leaf area, affecting light interception and light use efficiency (e.g. [START_REF] Kumar | Effect of climate change factors on processes of crop growth and development and yield of groundnut (Arachis hypogaea L.)[END_REF]. Our parameterization was designed for grass communities under non-limiting soil nutrient content, which tend to result into taller species with higher SLA values, particularly competitive for the light resource (Perronne et al., 2014). Taller communities of fast-growing annual species with higher SLA values benefit from N inputs promoting the development and growth, which also allow the overtopping of underlying vegetation, a process associated with shade avoidance mechanisms [START_REF] Gubsch | Differential effects of plant diversity on functional variation of grass species[END_REF]. While our overestimates of SLA and canopy height somewhat reflect an overestimate of oat abundance, the results show that a more consistent performance appears in modelling SLA dynamics with a limited presence of grass species in the stand, i.e. 25% in a mixed stand with 75% legume species. With higher proportions of grasses (i.e. 50% and 75%), we observed an overestimation of SLA associated with overestimation of canopy height, indicating that modelling of N-cycling processes of plant nutritional status (with external fertilization and symbiotic fixation) needs to be refined.

The dependence of canopy-trait variations on plant species abundance dynamics of CoSMobased models is well illustrated by the estimated LAImax. The key point is that the model was able to reproduce the dynamics of annual ryegrass, which was also observed to increase in abundance over the season. In the absence of observed LAImax data, it is logical to expect that the correct simulation of the species with the highest LAImax parameter (i.e. 6 m 2 m -2 ) would be reflected in the simulated LAImax values of the canopy, which also increase during the season. In general, LAI is a rather difficult variable to simulate in grasslands. Some studies (e.g. Ben [START_REF] Touhami | Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress[END_REF]Moulin et al., 2018) have indicated the need to consider the species composition of grassland vegetation to estimate it. We provided evidence for that by comparing LAI estimates and observations of mixed stands. Then, our study indicates the possibility of estimating LAImax as a community trait, which contains information on the water status of the canopy. In fact, LAI is an important factor controlling crop evapotranspiration as it affects the exchange of water vapour between the canopy and the atmosphere (e.g. [START_REF] Barbu | Assimilation of soil wetness index and leaf area index into the ISBA-A-gs land surface model: grassland case study[END_REF]. In general, it increases with soil moisture, considering that some species may have a tolerance threshold for soil moisture and do not persist if the threshold is exceeded, which may facilitate species change resulting in varying LAI values for a particular site, due to differences in vegetation morphology, physiology and development rate. Shifts in the dominant vegetation can also have an impact on LAI as different vegetation varies in the length of the growing season, which can influence the timing and extent of maximum LAI (e.g. [START_REF] Carey | Growing season energy and water exchange from an oil sands overburden reclamation soil cover, Fort McMurray, Alberta, Canada[END_REF].

This evidence provides clues for further studies, as the experimental data did not allow a detailed assessment of the water status of the canopy. Considering that, overall, CropSyst-CoSMo performances observed throughout a grassland growing season at the studied site were encouraging. The understanding emerging from this analysis can provide a basis for developing further long-term experiments and simulations to better explore the effect of seasonal climate variability and the impact of crop management on the evolution of canopy traits. While biophysical and biogeochemical factors, other than those included in the model, limiting the simulation in response to environmental and management drivers may need to be improved, a critical point specific to grassland systems dominated by self-reseeding annual species is the modelling of the dynamic of seed bank processes (e.g. [START_REF] Gaba | Response and effect traits of arable weeds in agroecosystems: a review of current knowledge[END_REF]. In fact, at plant maturity, seeds are added to the soil seed bank and must be considered in longterm grassland modelling to estimate plant species abundances seed dormancy. For instance, hardseededness (or seed coat impermeability) is the main seed dormancy mechanism that regulates germination of annual pasture legumes both within and between years [START_REF] Taylor | Hardseededness in Mediterranean annual pasture legumes in Australia: a review[END_REF]. Issues of this type are fundamental and can best be examined by conducting multiyear experiments, which are necessary to assess carry-over effects from one year to the next that may alter the starting conditions of subsequent stands.

Chapter 6 General discussion and conclusions

Introduction

The diversity is a functionally meaningful descriptor that helps assessing the performance and composition of a community as a result of the action of such a variety of environmental factors and management factors, as well as plant-plant interactions [START_REF] Montalvo | Species diversity patterns in Mediterranean grasslands[END_REF][START_REF] Hacker | Some implications of direct positive interactions for community species diversity[END_REF]. Any changes in species diversity thus change the nature of species interactions, thereby altering the ecology of the plant community as a whole.

Using grassland simulation models assess grassland ecosystem services

Simulation models are widely used to simulate growth and productivity of agricultural crops and grasslands and, if they have a biogeochemical structure, they are also often used to quantify carbon (C) and nitrogen (N) exchanges in agro-ecosystems (e.g. Ehrhardt et al., 2018;Sándor et al., 2020). More or less simplified, they are dynamic process-based models based on C, nutrient and water stocks and fluxes through soil-vegetation-atmosphere systems (Brilli et al., 2017). Many of the ecosystem services provided by grasslands, including Grassland simulation models can help to explain observed variations of grassland ecosystem services, as well as to infer responses of ecosystem services to environmental change.

However, most models do not represent plant diversity dynamics (Figure 6.2, top) or they do so for a small range of plant species or functional types (e.g. Taubert et al., 2020). This limits their ability to explain or predict the impact of environmental changes on those ecosystem services whose response is modulated by plant biodiversity. The PhD developed this view, referring to a narrow definition of plant biodiversity, pertaining the higher (vascular) plant species of grassland communities. It then referred mainly to two biodiversity metrics, namely species richness in the research synthesis (Chapter 3) and relative abundance in the modelling (the latter being developed in two options: functional types and individual species), and only marginally to trait diversity (Chapter 4 and Chapter 5), reflecting different features of plant biodiversity [START_REF] Balent | Pratiques de gestion, biodiversité floristique et durabilité des prairies[END_REF].

Grassland simulation models are simplified models, in which plant diversity is at best reduced to a simplistic schematisation of interactions between plant species or functional types (Ma et al., 2019). The study of the relationship between grassland biodiversity and ecosystem services requires convergent ecological and biophysical/biogeochemical approaches that take into account agricultural practices [START_REF] Tixier | Modelling interaction networks for enhanced ecosystem services in agroecosystems[END_REF]van Oijen et al., 2018). Ecological models that simulate plant biodiversity dynamics and their relationships to ecosystem functioning tend to be mathematically elegant and focus on biotic interactions, ignoring or greatly simplifying biophysical/biogeochemically processes (e.g. De [START_REF] De Mazancourt | Predicting ecosystem stability from community composition and biodiversity[END_REF].

As a result, the interactions between biophysics/biogeochemistry and biodiversity, and their interdependent effects on ecosystem services, have not been yet well represented and there is a need for improved simulation models, which the PhD work sought to elucidate.

The wealth of knowledge gained from empirical studies formed the basis of this PhD, while drawing on the insights of modellers and experimenters in grassland agronomy and ecology.

The integration of these views into a conceptual framework was based on an understanding of the controls exerted by environmental and management factors on the provision of biodiversity-mediated ecosystem services. Simulation models need to address how environmental and management changes affect grassland biodiversity, how plant biodiversity is linked to ecosystem services, and how changes in this biodiversity and ecosystem functioning affect microclimate and soil conditions. The PhD focussed mainly on managed and unmanaged grasslands across contrasting soils and climates, at field scale (not landscape or regional scales, which would require upscaling), with relative abundance of higher plant species or types as a metric of biodiversity. The development of a modelling approach linking a biodiversity module to existing simulation models (Confalonieri, 2014) has offered one promising way to improve modelling capacity in relation to multi-species swards. The availability of biophysical models represented an opportunity to address plant diversity dynamics from a modelling perspective, as it enabled the effects of environmental and management drivers on plant composition dynamics in continental and Mediterranean systems without exploring, at this stage, the complex interactions with the biogeochemical processes of C and N cycles.

In the roadmap for model development and data use proposed by van Oijen et al. (2020), the PhD (i) reviewed the current understanding of the impact of grassland biodiversity on ecosystem services, and the drivers of biodiversity itself; (ii) reviewed the state of the art of models for grassland simulation, and of models for biodiversity dynamics; and (iii) discussed how grassland models can be modified to simulate both the dynamics of biodiversity itself and the impacts of biodiversity on ecosystem services. Then, the PhD explored how generic biophysical models can be extended to ensure consistency between the growth dynamics of each plant species or functional type and the biophysical fluxes in the community. Drivers of interest included climatic variables and agricultural practices, namely mowing and N fertilisation, while the extension to grazing systems has just started [START_REF] Piseddu | Development of grassland models for the simulation of multi-species plant communities[END_REF] and can be seen as a natural evolution of the current work. The PhD considered grassland productivity as the only ecosystem service, as it is the first provisioning service of grassland systems that has already been intensively studied, both in modelling and experimentally. This means that the potential of CoSMo-like approaches to simulate grassland plant diversity and a broader set of ecosystem services will have to be further assessed within a biogeochemical modelling structure, as it is important to improve the representation of the effects of plant diversity on C-N cycles, as well as on water cycling and energy balance.

Taking all the above limitations into account, the framework adopted ensured consistency between the meta-analyses and the modelling carried out, as the same types of drivers were considered in both approaches, even though the level of detail of the meta-analyses (focusing on species richness) did not allow for reference to plant species (or plant type) abundance.

The latter is a more valuable biodiversity metric than overall richness to understanding of the mechanisms involved (Debussche et al., 1996;Milberg et al., 2017).

Why considering plant diversity in grassland simulation models?

"The use of functional traits of plants, for which plant species are classified into functional types based on vegetative and reproductive traits … can be a promising avenue for future research … to draw conclusions about the relationship between model parameters and phenotypic traits" (Ma et al., 2015, p. 368) "… modeling multi-species swards, grassland quality and the impact of management changes requires further development" (Kipling et al., 2016a, p. 24) "… further development is needed for uses that require characterisation beyond the definition of an average vegetation, for example in relation to the simulation of changes in sward composition" (Kipling et al., 2016b, p. 855) "Common feature of these models is that they were designed for application to single species swards, or to simple legume-grass mixes. As a result, they are limited in their ability to characterise interactions in multi-species swards" (Sándor et al., 2017, p. 25) "… the challenges that grassland systems are facing today imply that aspects related to plant diversity cannot be ignored in modelling studies" (van Oijen et al., 2018, p. 2) "Plant diversity or invasion under climate change or alternative management is also generally poorly simulated" (Ma et al., 2019, p. 200)"

Modelling plant dynamics in grasslands, as concluded by many authors listed above, is important but short on development. The scientific community claims its importance in improving the representation of the effects of plant diversity on the mass and energy fluxes that drive the provision of grassland ecosystem services. This thesis has taken up the challenge of providing robust evidence on the role played by environmental and management factors in driving grassland plant dynamics (Chapter 3, Chapter 4 and Chapter 5). In particular, it has integrated synthesis research (i.e. meta-analysis) and process-based modelling, and (supported by long-term observational data and field experimental evidence)

has explored and learned lessons on how generic simulation models can be extended to ensure consistency between the growth dynamics of plant species represented in a grassland community and the biophysical fluxes in the same community.

Lessons learnt, need and scope for introducing biodiversity in grassland modelling

Plant biodiversity affects the provision of ecosystem services by grasslands, which implies the need to introduce this biodiversity -both its dynamics and impacts -into simulation models if the impact of environmental and management changes on the provision of ecosystem services by grasslands is to be predicted. This PhD work has delved into two of the grassland modelling challenges identified by Kipling et al. (2016b), namely 'modelling multi-species swards' and 'modelling the provision of ecosystem services'. However, there are still considerable knowledge gaps, which may hinder the development and testing of models. The literature on grassland ecosystem services is dominated by studies on productivity, while the impacts of biodiversity on other ecosystem services are less studied (e.g. [START_REF] Bagella | Vegetation series: A tool for the assessment of grassland ecosystem services in Mediterranean large-scale grazing systems[END_REF]. This can be explained both by the traditional dominance of economic considerations and by the fact that various ecosystem services, such as regulating and supporting services (e.g. filtering of ammonia and nitrate, water regulation, erosion prevention, emission reduction, C sequestration), have been more difficult to measure. Many of these ecosystem services are expressions of grassland biogeochemical cycles and are thus amenable to modelling, but the knowledge base still needs to be expanded to be able to do so reliably. The assessment of the ecosystem services associated with each plant community represents a focal point, and knowledge of plant community dynamics provides additional support for the definition of possible scenarios under different management regimes (Bagella et al., 2013). This exercise should include ecological, vegetation, hydrological and soil models to reveal not only capacity gaps, but also areas where models from these different disciplines could be used for integrated assessments of grassland systems that encompass the valuation of non-market services.

The evidence provided by this thesis (limited to grassland productivity) and by other studies (covering a broader spectrum of ecosystem services) shows that it is not straightforward to quantify the effects of environmental and management drivers of grassland biodiversity, nor the impact of grassland biodiversity on the provision of ecosystem services. Multiple environmental drivers affect both grassland biodiversity and the ecosystem services that grasslands provide, so the relationships between biodiversity and ecosystem services may be correlative rather than causal. To facilitate the detection of causal pathways, grassland experiments have been carried out in which different species compositions were imposed and the resulting differences in sensitivity to environmental perturbation were quantified. Chapter 4) and by designing a dedicated field experiment (study of causality; Chapter 5). The latter, carried out in Sardinia (Italy), which has shown that there is a trade-off between the realism of an experiment and its ability to detect causality, has nevertheless provided patterns of response that models must be able to explain and predict to be useful.

Another aspect of grassland models is the quantity of data required to run and evaluate them.

In fact, they are considerable because the properties of all community components have to be specified. For some model parameters, modellers can take advantage of trait databases such as TRY [START_REF] Kattge | TRY -A global database of plant traits[END_REF] or other databases such as e-FLORA-sys (http://eflorasys.univ-lorraine.fr) and LEDA (https://uol.de/en/landeco/research/leda/data-files), and other initiatives like the 'leaf economic spectrum' [START_REF] Wright | The worldwide leaf economics spectrum[END_REF], which have also been referred to initially in this study. However, trait databases are not always useful because the expression of traits can be highly plastic, with a complex dependence on the overall community composition [START_REF] Weisser | Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: Patterns, mechanisms, and open questions[END_REF]. Thus, the bottom-up approach to parameter measurement could be complemented by top-down approaches to derive probability distributions of trait values from measurements of fluxes and other model output variables. This, in turn, requires the availability of reliable benchmarking data from sites where biophysics/biogeochemistry and biodiversity have been studied together, and suggests that model design, parameterisation and testing would benefit from a broader empirical knowledge base, including both manipulation studies and long-term observations. Addressing the modelling of plant diversity to unravel the relationships between this diversity and grassland functioning can provide answers of direct interest to farm managers and policy makers to maintain or improve certain ecosystem services. For instance, the identification of mixtures to optimise biomass production is a field of study that aims to improve the potential of multi-species grasslands for high-quality livestock production. In this perspective, the mobilisation of models simulating several species can help to guide management towards the maintenance of balanced mixtures under changing conditions (Porqueddu et al., 2016).

Final remarks

Explicit and dynamic representation of plant diversity can also help to manage grasslands, e.g. to optimise the proportion of legume species in the plant community (which indirectly determine the protein content of the forage and the quality of diets; [START_REF] Capstaff | Improving the yield and nutritional quality of forage crops[END_REF], or to formulate forage mixtures taking into account the different phenology of plant species [START_REF] Sanderson | Productivity, botanical composition, and nutritive value of commercial pasture mixtures[END_REF].

In addition, changes in the botanical composition of grasslands during winter need to be characterised because, especially at high latitudes and in mountainous areas, they may have important effects on productivity and nutritional quality in spring and summer [START_REF] Rapacz | Overwintering of herbaceous plants in a changing climate. Still more questions than answers[END_REF]. In Mediterranean climates, on the other hand, the predominance of annual species in the vegetation (even when mixed with perennials) requires an adaptive plant-regeneration strategy that responds to the unpredictability of wheather conditions [START_REF] Rhazi | Vegetation of Mediterranean temporary pools: A fading jewel?[END_REF].

Simulation of plant diversity is also essential for scenario analyses in ecological studies, in order to assess the ability of plant species to persist or not in a given location due to low/high rates of disappearance and high/low rates of successful invasion by other taxa [START_REF] Mooney | Global change and invasive species: where do we go from here?[END_REF].

While model developments should be accompanied by a continuous increase in the quantity and diversity of data, they should often be tested against field observations, covering multiple different ecosystem services and not just productivity. Baseline datasets should be collected for as many of the production scenarios experienced by grasslands as possible, including potential growth, water limitation, nutrient limitation, pest reduction, grazing, mowing. Data should cover extreme events (abiotic, biotic) as well as chronic stress conditions. Biodiversity can act to stabilise biogeochemical fluxes under extreme conditions, and simulation models need data to test their ability to account for this. Continuous data collection in both biodiversity manipulation experiments and monitoring studies where biodiversity is not imposed will provide, on the one hand, data that elucidate causal pathways and can be used in the design of model structures and, on the other hand, data from grasslands for which models need to be calibrated to provide reliable estimates.

Table S1 Data source, location (country and Köppen-Geiger climate classification) and survey methodologies for the 43 articles present in the meta-analysis on the effect of mowing on species richness (with greyed areas highlighting the subset of 16 articles present in the meta-analysis on the effect of mowing on harvested biomass). NA (not available) indicates that sufficient information could not be extracted or derived from the original articles. For studies in the United States of America (USA), states are indicated: IA: Iowa; KS: Kansas; OK: Oklahoma. For climate classification: Bsk: cold semi-arid; Cfa: humid subtropical; Cfb: temperate-oceanic; Csa: hot-summer Mediterranean; Cwa: dry-winter humid subtropical; Dfa: hot-summer humid continental; Dfb: warm-summer continental; Dfc: subarctic continental; ET: tundra. For multi-treatment experiments, the information on mowing time is given here for one-cut-per-year treatment only. On botanical method, the mentioned works provide evidence of a vast amount of visually determined plant-cover data that were classified by different methods. A common way to measure plant cover in herbal plant communities is to make a visual assessment of the relative area covered by the different species in a small circle or quadrate, and the visual estimates of cover percentages are categorized using different ordinal classification scheme. For vegetation analyses of permanent quadrats, Londo (1976) used a modified scale with smaller intervals derived from difference-and change quotients based on coverage percentages. In its development, the initially 12-scale grades by Hult (1881) and Sernander (1912) were merged to five grades, describing the cover abundance of species: 1: ≤6.25%, 2: 6.25-12.5%, 3: 12.5-25%, 4: 25-50%, 5: 50-100% cover. Braun-Blanquet (1946) developed a coverabundance/dominance scale with six categories (+: ≤1%, 1: 1-5%, 2: 5-25%, 3: 25-50%, 4: =50-75%, 5: 75-100% cover), and a seventh one (r) representing single occurrence of plants of particular species. The scale that van der Maarel (1979) derived from the previous one includes a differentiation into subclasses (e.g., of the category 2). De Vries (1948) developed a combined frequency and rank determination method. Zobel and Liira (1997) developed a humped pattern between species richness and biomass within quadrats. Data were acquired from 42 experiments (34 articles) varying in duration and number of sampling instances per year (up to 14 in the study period in Shi et al. 2015), conducted between 1998 (one article) and 2019 (two articles). They include the study by Xu et al. (2015), also used in the metaanalysis for the effect of mowing (with no warming) on SR and HB (Table S1). Here, we assess the effect of warming on the abandoned plot and discuss the results obtained by Xu et al. (2015) with the combined treatment of the effects of mowing and warming without including this treatment in the meta-analyses. Broad altitudinal ranges up to above 4700 m a.s.l. in the Tibetan Plateau (Zhang et al. 2015;Jingxue et al. 2019) imply broad ranges of mean air temperatures (from -5.9 °C in the Tibetan Plateau, Xu et al. 2015, to 20 °C in USA -Florida, Gornish andMiller 2015) and annual precipitation (> 1400 mm in the southern Pacific Northwest, Pfeifer-Meister et al. 2016, down to < 90 mm in Norway, [START_REF] Klanderud | Simulated climate change altered dominance hierarchies and diversity of an alpine biodiversity hotspot[END_REF][START_REF] Olsen | Exclusion of herbivores slows down recovery after experimental warming and nutrient addition in an alpine plant community[END_REF]. Where this was clearly established, alternative experimental treatments and conditions were analysed separately. This is the case in the study by Wang et al. (2017), who examined warming impacts in experiments with open-top chambers having different bottom diameters (i.e., 0.85 m, 1.45 m and 2.05 m). Hoeppner and Dukes (2012) described the response of a grassland community by comparing three levels of warming (up to +4 °C) to the control case. [START_REF] Jónsdóttir | Variable sensitivity of plant communities in Iceland to experimental warming[END_REF] studied two grassland communities (i.e., species-poor moss heath and species-rich dwarf shrub heath) at two sites in Iceland. Likewise, Pfeifer-Meister et al. ( 2016) quantified how warming affects the relative dominance of plant functional groups and the diversity of Mediterranean prairies at three sites across a 520-km latitudinal gradient in the Pacific Northwest (USA).

Table S3 Mixed-effects model analysis of moderators for species richness (SR) and harvested biomass (HB): standardized coefficients (slopes), standard errors (se), z-values (z), lower (LCI) and upper (UCI) 0.95 confidence intervals, significance probability of slope estimates (p), omnibus test of heterogeneity (QM), its significance probability (P), amount of residual heterogeneity accounted for by the whole model (R 2 ). Moderators are: Y: year of publication; D (years): duration of the experimental study; S (m 2 ): plot size; T (°C): temperature difference between control and warming treatments; T (°C): mean annual temperature of the site; R (mm): mean annual precipitation of the site; E (m a.s.l.): site elevation; M: warming method (not enough data were available to examine the effects of cutting height). The probabilities of significant slope estimates are marked in red (p<0.05) and bold red (p<0.01)

ModVege model

CoSMo suitability functions

2.1. Grazing (animal preferences) and mowing (days for restarting growth)

𝑆𝑓𝐿𝑖𝑘𝑖𝑛𝑔 𝑖 (𝑡) = {0 𝐿 𝑖 • (1 + [(2 • 𝑆𝐶𝑃 𝑖 (𝑡) -3) • 𝑆𝐶𝑃 𝑖 (𝑡) 2 ) 1 𝑖𝑓 𝑐𝑢𝑡 𝑜𝑟 𝑑𝑢𝑟𝑖𝑛𝑔 𝑆𝐿 𝑖 𝑖𝑓 𝑔𝑟𝑎𝑧𝑖𝑛𝑔 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Li is the animal liking coefficient, ranging from 0 (highest liking) to 1 (lowest liking). A shock length (SL) corresponds to the ability in terms of number of days to restart after cutting or grazing, computed by:

𝑆𝐿 𝑖 = ⌊48 • (𝑓 𝑟𝑒𝑠𝑡𝑎𝑟𝑡,𝑖 -1) 2 + 2⌋
where frestart,i is the restart capacity of group/species i, 1 indicating the highest restart capacity and 0 the lowest. LAImax,i is the maximum leaf area index for group/species i (m 2 m -2 ), LAIcommunity(t) is the current leaf area index for the community (m 2 m -2 ), Hmax,i is the maximum height for group/species i (m) and Hcommunity(t) is the current height for the community (m).

Phenology

The current leaf area index for the community is calculated as follows:

𝐿𝐴𝐼 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 (𝑡) = 𝑆𝐿𝐴 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 * 𝐵𝑀 𝐺𝑉 (𝑡) 10 * 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝐿𝐴𝑀 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦
where SLAi is the specific leaf area of group/species i (m² g -1 ), PercentLAMcommunity is the percentage of laminae in the green vegetative compartment of group/species i (0-1), BM(t)

is the biomass at time t of all compartments (kg DM ha -1 ), BDi is the mean bulk density of all compartments for group/species i (g DM m -3 ) and BMGV is the biomass at time t of the green vegetative compartment (kg DM ha -1 ), calculated as follows:

𝐻 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 (𝑡) = 𝐵𝑀(𝑡) 𝐵𝐷 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 • 10

Water availability

𝑆𝑓𝑊𝑎𝑡𝑒𝑟 𝑖 (𝑡) = 𝑆𝑓𝑃𝐴𝑊 𝑖 (𝑡) + 𝑆𝑓𝑅𝑜𝑜𝑡𝑠 𝑖 (𝑡) 2
SfPAWi(t) is the factor representing the effect of water availability for group/species i (0-1, unitless), calculated as follows:

𝑆𝑓𝑃𝐴𝑊 𝑖 (𝑡) = 𝑚𝑎𝑥 (0; 𝐷𝑇 𝑖 -1 𝐷𝑇 𝑖 + 𝑆𝑊𝐶(𝑡) 𝐷𝑇 𝑖 )

where DTi is the drought tolerance of group/species i (0-1, unitless, the higher the value the higher the tolerance).

SWC(t)

is the current soil water content in the rooted zone simulated for the community at time step t and expressed as percentage of plant available water (PAW = volumetric SWC at field capacity [FC] -volumetric SWC at permanent wilting point [PWP]).

SfRootsi(t) is the root deepening potential for group/species i (0-1, unitless), calculated as follows:

𝑆𝑓𝑅𝑜𝑜𝑡𝑠 𝑖 (𝑡) = 𝑚𝑎𝑥 (0; 𝑅𝐷 𝑚𝑎𝑥,𝑖 -𝑅𝐷 𝑖 (𝑡) 𝑅𝐷 𝑚𝑎𝑥,𝑖 )

where RDmax,i is the maximum rooting depth for group/species i (cm) and RDi(t) is the current rooting depth for group/species i (cm). The latter is derived by the shoot-root ratio of each species i (shootRootRatioi) and the current height (cm) for group/species i (Hi(t)):

𝑅𝐷 𝑖 (𝑡) = 𝑠ℎ𝑜𝑜𝑡𝑅𝑜𝑜𝑡𝑅𝑎𝑡𝑖𝑜 𝑖 • 𝐻 𝑖 (𝑡) where RGRLAIi,max is the maximum relative growth rate of green leaf area index for group/species i (m 2 m -2 d -1 ), LNCi,opt is the optimal leaf N concentration of group/species i (g N m -2 ) and RGRLAIcommunity(t) is the relative growth rate of green leaf area index for the community at time t (m 2 m -2 d -1 ), equal to LAIcommunity(t) -LAIcommunity(t-1).

Nitrogen availability

Study-site

. Map of the study-site. Located in the French commune of Saint-Genès-Champanelle, the site of Theix (45° 43' N, 03° 01' E, 880 m a.s.l.) belongs to the SOERE-ACBB platform (http://www.soere-acbb.com) and includes block 1 (BLOC Blatière) and block 2 (BLOC Moine). Each treatment is repeated twice in each block. Data from the NPK (fully fertilised and mown), NULL (partially fertilised and mown) and Ab (abandoned) plots were used as a block mean, while data from the PK (partly fertilised and mown) plots and the cattle (Bo-, Bo+) and sheep (Ov-) grazed plots were not used in this study. Table A. Total number of plant species detected in the study period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) in three treatments NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks.

Model parameters

Table E. ModVege parameters common to all functional types or species (Jouven et al., 2006). Table G. ModVege parameters specific to each plant species (forbs and legume), either from the calibration performed in this study (Cal), based on published ranges, or from published literature (Lit). Sources: Bourdôt (1980), Gulías et al. (2003), Jouven et al. (2006), Ianovici et al. (2015), Theau et al. (2017). (1965), Bellocchi et al. (1999), Fernandez-Quinantilla et al. (1990), López-Castañeda et al. (1996), Steadman et al. (2003), Monks et al. (2009), Monks and Moot (2010), Rajala and Peltonen-Sainio (2011), Izquierdo et al. (2013), Nori et al. (2014), Movedi et al. (2019).

Parameter

  in cui le specie erbacee sono caratterizzate in base alle loro particolarità morfo-fisiologiche che includono strategie di crescita (cattura e conservazione delle risorse) e la fenologia (precocità della fioritura e della maturazione). La composizione funzionale, basata sull'identificazione delle specie graminacee dominanti, permette di creare una classificazione basata quattro tipi principali: A, B, C e D. I tipi A e B sono a crescita rapida, la loro strategia di assorbimento dei nutrienti dipende dalla fenologia e dominano nei prati fertili, mentre si osserva il contrario per i tipi C e D, che sono a crescita lenta, accumulano nutrienti e fioriscono più tardi. Altri tipi sono stati introdotti daCruz et al. (2010) per caratterizzare le specie alte e a fioritura tardiva in terreni fertili (tipo b) o poveri (tipo d).Coerentemente con il livello di complessità di ModVege, lo studio ha innanzitutto modellato dinamicamente l'abbondanza relativa dei tipi funzionali in una comunità vegetale. In secondo luogo, è stato aggiunto un livello di complessità maggiore per rappresentare le dinamiche delle singole specie vegetali dominanti nella comunità (presenti in media per almeno il 4% nel periodo di simulazione). La valutazione di tre soluzioni modellistiche -il modello ModVege per la stima della biomassa e la sua versione accoppiata a CoSMo (ModVege-CoSMo) per i tipi funzionali e le singole specie -è stata effettuata per la produzione di biomassa e l'abbondanza relativa di tipi di specie e delle specie singole utilizzando i dati pluriennali raccolti dal 2006 al 2018 (eccetto il 2009 per l'abbondanza relativa) di prati multispecie sia sfalciati (fertilizzati o meno) sia indisturbati (abbandonati) nel Massiccio Centrale francese. In particolare, si è fatto riferimento al dispositivo di osservazione a lungo termine di Theix (45° 43' N, 03° 01' E, 880 m s.l.m.), ideato e realizzato nel 2005 su un'area di ~3 ha strutturata in due blocchi, con l'obiettivo di analizzare la risposta di prati e pascoli permanenti a cambiamenti nelle pratiche di gestione. Ogni trattamento è stato ripetuto due volte in ogni blocco, ottenendo 28 parcelle. Per la modellazione, sono stati utilizzati i dati medi per blocco corrispondenti ai trattamenti NPK (fertilizzato e sfalciato tre volte l'anno), Null (non fertilizzato e sfalciato tre volte l'anno) e Ab (non fertilizzato e non sfalciato). Le simulazioni sono state effettuate per determinare (i) la capacità delle soluzioni ModVege basate su CoSMo di simulare l'abbondanza relativa dei tipi funzionali e delle specie vegetali e (ii) le differenze nella produzione di biomassa ottenute con ModVege e ModVege-CoSMo.

  multispecie in ambiente mediterraneo. Le parametrizzazioni ottenute possono quindi essere considerate il primo adattamento di CropSyst e CropSyst-CoSMo a queste condizioni ambientali. Le simulazioni della produzione di biomassa e delle abbondanze relative delle specie vegetali considerate si sono ragionevolmente adattate alle osservazioni condotte durante una sola stagione di crescita. La questione principale ha riguardato il potenziale del modello (e i suoi limiti) nel rappresentare i traits della comunità. Per quanto riguarda lo SLA, ad esempio, le simulazioni di CropSyst-CoSMo a livello di comunità suggeriscono una limitata sensibilità del modello ai fattori ambientali ed ecologici che determinano le fluttuazioni di questo carattere morfologico. Nel complesso, tuttavia, le prestazioni di CropSyst-CoSMo osservate durante la stagione di crescita studiata sono state incoraggianti e i risultati ottenuti forniscono una base per sviluppare ulteriori esperimenti e simulazioni a lungo termine per esplorare meglio l'effetto della variabilità climatica stagionale e le conseguenze della gestione sull'evoluzione delle comunità erbacee.

L

  'objectif de ce travail doctoral était d'étudier la dynamique de la diversité végétale des prairies et pâturages en relation avec des facteurs environnementaux (le pédo-climat dans lequel ils s'insèrent) et anthropiques (la gestion agronomique). Bien que les pâturages au sens strict n'aient pas été traités explicitement, les questions abordées dans la thèse concernent des aspects communs aux deux systèmes. La recherche a porté sur l'identification des hypothèses dans lesquelles les deux catégories de facteurs, environnementaux et anthropiques, interviennent dans les communautés herbacées pour déterminer l'évolution de leur composition floristique. En particulier, le sujet a été abordé sous l'angle de la modélisation explicite et de la dynamique de la diversité végétale, afin d'interpréter l'action exercée au fil du temps par les facteurs environnementaux et la gestion au sein de communautés herbacées complexes. La thèse est divisée en une partie introductive (Ch. 1), dans laquelle est illustrée l'importance de la modélisation dans l'interprétation des réponses des communautés herbacées aux facteurs environnementaux et anthropiques, et un chapitre (Ch. 2) dans lequel l'hypothèse est expliquée et le contexte du travail scientifique réalisé est présenté. Les trois chapitres principaux (chapitres 3, 4 et 5) sont suivis d'une discussion générale avec des remarques finales (chapitre 6). Dans le premier chapitre d'introduction, on a tenté d'illustrer l'importance des prairies et des pâturages en tant qu'écosystèmes qui fournissent des services à la société (au-delà de la production de fourrage pour l'alimentation animale), appelés services écosystémiques, qui dépendent à leur tour de la diversité végétale, laquelle varie dans le temps en fonction de facteurs anthropiques et environnementaux. La modélisation de cette diversité et de son évolution est présentée comme un outil essentiel pour gouverner ces écosystèmes et assurer la continuité de la fourniture de leurs services. La diversité végétale est souvent interprétée dans les modèles selon des schémas simplifiés qui ne sont pas représentatifs de la dynamique réelle, ou trop complexes pour être utilisés dans des applications pratiques à l'appui d'études agronomiques et écologiques. Afin d'équilibrer les exigences de complexité et de opérabilité pratique des modèles, une approche de modélisation qui incorpore un ensemble de règles de fonctionnement d'écosystèmes végétaux multi-espèces dans des modèles de simulation génériques de communautés herbacées a été adoptée dans cette thèse. Le chapitre 2, qui explique le contexte de la thèse, souligne également la complémentarité entre la modélisation, qui permet une représentation détaillée des études de cas, et la recherche de synthèse (méta-analyse), qui apporte des réponses globales. Les deux méthodologies peuvent se compléter sur la base d'hypothèses communes aux deux sur le rôle joué par les facteurs environnementaux et anthropiques dans la dynamique de la diversité végétale. Parmi les trois principaux chapitres, le chapitre 3 est consacré à une double méta-analyse, publiée en 2021 dans la revue Agronomy for Sustainable Development, qui comprend 999 et 1793 études, respectivement, sur les effets de la fauche de l'herbe (facteur anthropique) et de l'augmentation de la température mondiale (facteur environnemental) sur la richesse spécifique (en tant qu'indicateur de biodiversité) des communautés d'herbe. Les deux facteurs étudiés et les mesures de la biodiversité identifiées correspondent à ce que la littérature scientifique internationale propose comme recherche de synthèse. Les chapitres 4 et 5 concernent la modélisation des fluctuations de l'abondance relative des espèces végétales (ou des types d'espèces) sur deux cas d'étude : un site d'observation à long terme dans le Massif central français (facteurs opérant dans un contexte réel) et une expérience menée pendant la thèse en Sardaigne (facteurs manipulés). Les deux chapitres ont été développés sous la forme d'articles scientifiques, dont l'un (dérivé du chapitre 4) est actuellement en cours d'évaluation pour être publié dans la collection Agro-ecology for grassland-based farming systems de la revue Agronomy. Un deuxième article (dérivé du chapitre 5) est en préparation. Les résultats de la méta-analyse ont montré l'effet positif d'une perturbation modérée (une à deux fauches par an) sur l'établissement et le maintien de prairies à flore diversifiée d'intérêt agronomique et écologique. L'augmentation des températures, en revanche, a montré un effet négatif global sur la diversité végétale, contre une augmentation potentielle de la productivité. Ces résultats montrent une tendance générale conforme au principe d'exclusion compétitive, qui prédit une corrélation négative entre les deux variables de réponse considérées, une diversité taxonomique élevée tendant à être associée à une faible production de biomasse et vice versa. Dans cette thèse, une étude approfondie a été menée à l'aide de modèles de simulation qui combinent les paramètres de la communauté (traits) avec les propriétés du sol, le climat et la gestion. Ces modèles sont largement utilisés pour estimer la productivité des prairies et des pâturages ainsi que les cycles biophysiques et biogéochimiques de l'écosystème. Les cycles biogéochimiques n'ont pas été directement abordés dans la thèse, ce qui pourrait faire l'objet de futures études approfondies. En effet, grâce à la possibilité de simuler l'évolution quotidienne de l'abondance relative des espèces d'une communauté herbacée en fonction d'une série de facteurs environnementaux et de gestion, les modèles de simulation utilisés offrent d'amples possibilités d'application à grande échelle, en reconnectant les processus biophysiques et biogéochimiques de l'écosystème à la diversité végétale. La modélisation a permis de simuler la dynamique de l'abondance relative des espèces végétales et les caractéristiques de la communauté qui en dépendent (traits), avec un focus spécifique sur les prairies permanentes du Massif Central français, où des bases de données pluriannuelles détaillées de mesures de production et de diversité végétale étaient disponibles dans des situations pédoclimatiques contrastées non prises en compte dans les études précédentes (ex : abandon). L'expérimentation menée en Sardaigne, limitée à une seule année, a permis de caractériser et de simuler la dynamique de mélanges d'espèces fourragères annuelles typiques des pâturages de la région biogéographique méditerranéenne. Le Community Simulation Model (CoSMo), développé en 2014 au laboratoire Cassandra de l'université de Milan (Italie), s'est révélé être un outil flexible pour intégrer des règles de dynamique végétale dans la structure des modèles de simulation existants. Les résultats des premières applications de CoSMo, obtenus avec l'intégration des modèles de culture CropSyst et WOFOST, en collaboration avec l'Université de Florence (Italie) et l'Unité Mixte de Recherche sur l'Écosystème Prairial (UREP) de l'INRAE, ont montré sa capacité à représenter l'abondance relative des espèces dans des prairies d'Italie centrale fertilisées et fauchées une fois par an. Dans cette thèse, une nouvelle avancée de cet outil a donc été conçue pour répondre à la question de recherche de modèles techniquement avancés capables de concilier la biophysique et la biogéochimie d'une part et la biodiversité d'autre part, facilement applicables aux prés et pâturages. La réponse à cette question a été déclinée en une proposition de feuille de route pour le développement et l'utilisation de modèles permettant de simuler la dynamique de la biodiversité végétale et les impacts de la biodiversité sur les services écosystémiques (van Oijen et al 2020), grâce au projet MODIPRAS (Modelling relationships between species diversity, the functioning of grassland systems and their ability to deliver ecosystem services) du méta-programme ECOSERV (ex-INRA), coordonné par l'UREP. Dans cette thèse, le potentiel de CoSMo a été étendu en implémentant pour la première fois ses règles de fonctionnement d'une communauté végétale multi-espèces dans un modèle de simulation appliqué aux prairies, le modèle ModVege développé à l'UREP du centre INRAE Clermont-Auvergne-Rhône-Alpes, et en évaluant cette solution de modélisation (ModVege-CoSMo) dans des situations de végétation caractérisées par de nombreuses espèces non seulement dans des conditions optimales (prairies fertilisées), mais aussi dans des conditions de stress nutritionnel (absence de fertilisation) et en présence de perturbations anthropiques (fauches multiples au cours de l'année) ou en son absence (abandon

  Poa pratensis et Elymus repens, des graminées de type b dominantes dans les parcelles fertilisées.Dans ce traitement, la diminution simulée de la SLA de la communauté est largement expliquée par la diminution modélisée (et observée) de Taraxacum officinale, une dicotylédone avec une SLA élevée (48 m2 kg -1 ). Dans le traitement fertilisé du bloc 2, la SLA simulée de la communauté diminue au fil du temps en raison du déclin simulé (et observé) de Taraxacum officinale. En ce qui concerne la hauteur maximale du couvert, son augmentation simulée lors de l'abandon du bloc 1 reflète l'abondance accrue (simulée et observée) de deux espèces d'herbes hautes, Alopecurus pratensis (sétaire) et Arrhenatherum elatius (avoine très haute), dont les hauteurs maximales ont été calibrées à environ 1,1-1,2 m. En conclusion, les résultats des simulations de dynamique végétale obtenus avec ModVege-CoSMo sont encourageants car, bien que la qualité des simulations de biomasse n'ait pas été améliorée par rapport à ModVege, elle n'a pas non plus été dégradée par la complexité introduite pour simuler la diversité végétale. Un aspect important est qu'avec CoSMo, il devient possible pour les modèles de croissance de la végétation (y compris le modèle de prairie ModVege) d'obtenir des estimations de la production de biomasse en représentant dynamiquement l'abondance relative des espèces ou des types d'espèces dans des communautés mixtes même complexes, un résultat utile pour déduire la variété des services écosystémiques fournis par ces communautés et médiés par leur diversité floristique.4. Simulation de mélanges de plantes herbacées annuelles : potentiel et perspectives d'utilisation du modèle CropSyst couplé à CoSMoDans cette étude, les espèces fourragères annuelles ont été simulées à l'aide du modèle de croissance des cultures CropSyst(Stöckle et al., 2003) et de sa version couplée CoSMo. CropSyst estime la croissance des plantes sur la base de la valeur minimale entre la biomasse obtenue par la conversion de l'eau transpirée et celle obtenue par l'énergie radiante interceptée, valeurs modulées par un facteur de réduction de la température. Le modèle simule une prairie comme une culture, sans tenir compte de la diversité végétale. Par conséquent, il a été couplé à CoSMo pour simuler les communautés végétales par le biais d'un paramétrage moyen qui ne reste pas constant mais varie en fonction de l'abondance relative de chaque espèce. Dans cette thèse, CropSyst-CoSMo a été évalué sur des prairies méditerranéennes dominées par des espèces annuelles. Le modèle CropSyst et sa version basée sur CoSMo ont été évalués pour leur capacité à simuler un certain nombre de variables : l'indice de surface foliaire, la hauteur de la communauté végétale, la biomasse récoltée (les deux solutions de modélisation) et l'abondance relative des espèces végétales (CropSyst-CoSMo). À cette fin, les données d'un essai mené sur des espèces semées pures et des mélanges d'espèces selon deux régimes de fauche (basse et haute fréquence) ont été utilisées. L'essai a débuté en 2020-2021 à la station expérimentale de Santa Lucia (39° 56' N, 08° 41' E, 15 m d'altitude) de l'Université de Sassari, en Sardaigne (Italie). En novembre 2020, quatre espèces pures (Lolium rigidum var. Nurra, Avena sativa var. Prevision, Trifolium subterraneum var. Antas et Trifolium michelianum var. Paradana) et trois mélanges dans différentes proportions des mêmes espèces (25, 50 et 75 % de graminées) ont été semés selon un plan expérimental en parcelles divisées en blocs aléatoires de quatre répétitions. Les parcelles principales comprenaient sept types de couverture végétale : (i) A. sativa pureté, (ii) L. rigidum pureté, (iii) T. michelinaum pureté, (iv) T. subterraneum pureté, (v) 50% de graminées et 50% de légumineuses, (vi) 25% de graminées et 75% de légumineuses et (vii) 75% de graminées et 25% de légumineuses. Les sous-parcelles ont été caractérisées par les deux options de fauchage : un seul fauchage au début de la floraison (basse fréquence) et plusieurs fauchages à intervalles mensuels (haute fréquence ou simulation de pâturage). Le choix des espèces a été basé sur leurs différents cycles biologiques, leur importance écologique et économique pour les systèmes pastoraux et sylvo-pastoraux de la Sardaigne et la disponibilité des semences sur le marché local. Des collections de variétés et d'écotypes commerciaux sont disponibles pour Avena sativa (avoine commune) et Lolium rigidum (raygrass rigide), deux espèces largement présentes dans la zone méditerranéenne, contrairement à d'autres graminées annuelles comme, par exemple, Avena barbata (avoine barbare), commune à l'état sauvage dans les pâturages méditerranéens. Ces deux trèfles annuels -Trifolium michelianum (trèfle michelian) et T. subterraneum (trèfle souterrain) -ont des capacités d'auto-ensemencement et de fixation de l'azote, contribuent à une haute qualité du fourrage et sont souvent utilisés en mélange avec l'avoine, le ray-grass et d'autres graminées. En plus de la production de biomasse, l'indice foliaire (LAI), la surface foliaire spécifique (SLA) et la hauteur des espèces individuelles et des communautés ont été échantillonnés, ce qui a permis la caractérisation expérimentale des espèces annuelles d'intérêt agronomique et écologique avec des paramètres de végétation spécifiques pour la modélisation. Pour simuler la dynamique de l'abondance relative au sein des mélanges, le paramétrage de CropSyst et CoSMo a été effectué pour chaque espèce, en extrayant les valeurs de la littérature (y compris le manuel du modèle) ou, pour la surface foliaire spécifique et la hauteur maximale (paramètres morphologiques), en utilisant des déterminations expérimentales. Le calibrage a été effectué en comparant les estimations du modèle avec les observations et en ajustant les valeurs des paramètres dans des fourchettes plausibles, garantissant ainsi des paramètres de végétation biologiquement interprétables. La simulation de la production de biomasse a été satisfaisante, avec des différences minimes entre CropSyst et CoSMo-CropSyst. Les valeurs du LAI et de la hauteur ont également été simulées dans le cadre de tendances générales satisfaisantes. La précision de CropSyst-CoSMo dans la simulation des abondances relatives des quatre espèces dans les mélanges était globalement satisfaisante pour une gestion à fréquence de fauche élevée. Bien que dans G25L75, Avena sativa ait atteint une valeur RRMSE de ~101%, la différence entre les moyennes observées et simulées était de ~6%, avec un coefficient de corrélation élevé entre les estimations et les observations (r=0.92). Lolium rigidum, Trifolium michelianum et Trifolium subterraneum ont montré des erreurs quadratiques moyennes relatives (RRMSE) plus faibles, comprises entre ~40% et ~18%, avec des valeurs r atteignant 0,99 pour Trifolium michelianum. En relation avec les abondances relatives des espèces, CropSyst-CoSMo a permis la simulation de certains traits de la communauté. Par exemple, dans les mélanges, la SLA de la communauté simulée reflète la proportion croissante de graminées (valeurs SLA >20 m² kg -1 ) par rapport aux légumineuses (valeurs SLA <20 m² kg -1 ) dans la transition de G25L75 à G75L25. Dans ce cas, les fluctuations simulées montrent une amplitude réduite par rapport aux observations et la performance du modèle tend à diminuer avec l'augmentation du couvert herbacé. Des résultats similaires ont été obtenus avec une faible fréquence de fauche. Il s'agit de la première étude menée avec l'objectif de modéliser les traits agronomiques d'espèces de graminées annuelles à auto-semis et leurs abondances relatives au sein de communautés multi-espèces en milieu méditerranéen. Les paramétrages obtenus peuvent donc être considérés comme la première adaptation de CropSyst et CropSyst-CoSMo à ces conditions environnementales. Les simulations de la production de biomasse et des abondances relatives des espèces végétales considérées ont été raisonnablement adaptées aux observations réalisées au cours d'une seule saison de croissance. La question principale concernait le potentiel du modèle (et ses limites) dans la représentation des traits de la communauté. En ce qui concerne la SLA, par exemple, les simulations de CropSyst-CoSMo au niveau de la communauté suggèrent une sensibilité limitée du modèle aux facteurs environnementaux et écologiques qui déterminent les fluctuations de ce caractère morphologique. Dans l'ensemble, cependant, les performances de CropSyst-CoSMo observées au cours de la saison de croissance étudiée étaient encourageantes et les résultats obtenus fournissent une base pour développer d'autres expériences et des simulations à long terme afin de mieux explorer l'effet de la variabilité climatique saisonnière et les conséquences de la gestion sur l'évolution des communautés herbacées.5. Remarques finalesLe doctorat s'est principalement concentré sur les prairies gérées et non gérées dans des conditions pédologiques et climatiques contrastées, à l'échelle du terrain, avec l'abondance relative des espèces (ou types d'espèces) comme mesure de base de la diversité végétale. Le développement d'une approche de modélisation basée sur la liaison d'un module spécifique à la diversité végétale à des modèles de simulation existants s'est avéré prometteur pour améliorer la capacité de modélisation des communautés multi-espèces. La disponibilité de modèles biophysiques a représenté une opportunité de suivre la dynamique de la diversité végétale d'un point de vue de la modélisation, car elle a permis de faire émerger les effets des facteurs environnementaux et de gestion sur la dynamique végétale dans les systèmes continentaux et méditerranéens, sans explorer, à ce stade, les interactions complexes avec les processus biogéochimiques des cycles du carbone et de l'azote.Suivant la feuille de route MODIPRAS, le doctorat (i) a fait progresser la compréhension des facteurs environnementaux et de gestion qui déterminent la diversité des communautés herbacées et son impact sur les services écosystémiques fournis ; (ii) a examiné les études en cours sur la simulation de la dynamique de la diversité végétale dans les prairies et les pâturages ; et (iii) a abordé la mise en oeuvre de modèles de simulation pour soutenir les études agronomiques et écologiques.En particulier, le doctorat a exploré comment les modèles biophysiques génériques peuvent être étendus pour assurer la cohérence entre la dynamique de croissance de chaque espèce végétale ou type fonctionnel et les flux biophysiques dans la communauté. Les travaux réalisés ont considéré la productivité des communautés herbacées comme un service écosystémique unique, car il s'agit du principal service d'approvisionnement des prairies et des pâturages. Cela signifie que le potentiel de CoSMo ou d'approches similaires pour simuler un ensemble plus large de services écosystémiques devra être évalué plus avant dans un cadre de simulation des processus biogéochimiques, car il est important d'améliorer la représentation par modélisation des effets de la diversité végétale sur les cycles du carbone et de l'azote, ainsi que sur le cycle de l'eau et le bilan énergétique. En intégrant la recherche de synthèse (c'est-à-dire la méta-analyse), la modélisation basée sur les processus (soutenue par des observations à long terme et des preuves expérimentales) et les essais sur le terrain, il a été montré dans cette thèse comment le potentiel de l'application de modèles de simulation génériques peut être étendu pour assurer la cohérence entre la dynamique de croissance des espèces végétales représentées dans une communauté et les flux biophysiques dans cette même communauté. En effet, la diversité végétale affecte les services écosystémiques, ce qui implique la nécessité d'introduire cette diversité -sa dynamique et ses impacts -dans les modèles de simulation afin de prévoir l'impact des changements environnementaux et de gestion sur la fourniture de ces services. Bien que ce travail de thèse ait abordé deux des principaux défis de modélisation identifiés par la communauté internationale, à savoir la modélisation des prairies et pâturages multi-espèces et la modélisation de la fourniture de services écosystémiques, il existe un vaste champ de recherche pour faire progresser les connaissances et la modélisation des services écosystémiques de régulation et de soutien (par exemple, la filtration de l'ammoniac et des nitrates, la régulation de l'eau, la prévention de l'érosion, la réduction des émissions, la séquestration du carbone), qui sont plus difficiles à surveiller que la production.Cela signifie que le développement de modèles doit s'accompagner d'une production continue de données de qualité et de la comparaison des résultats de la modélisation avec de riches ensembles de données de référence pour différents services écosystémiques dans le cadre de scénarios de production alternatifs (y compris la croissance potentielle, la limitation de l'eau, la limitation des nutriments, la réduction des parasites, le pâturage et la fauche), dans le cadre d'événements extrêmes (abiotiques et biotiques) et dans des conditions de stress chronique. La diversité végétale peut agir pour stabiliser les flux biogéochimiques dans des conditions extrêmes, et les modèles de simulation ont besoin de données pour vérifier leur capacité à en tenir compte.Les résultats obtenus dans le cadre du doctorat ouvrent donc de nouvelles perspectives pour la recherche sur les prairies et les pâturages dans différents contextes environnementaux. En ce sens, grâce aux progrès réalisés dans la mise en évidence du rôle des facteurs environnementaux et de gestion sur l'évolution de la diversité végétale dans ces systèmes, les résultats de la thèse représentent un point de départ pour des études de modélisation plus avancées.

  and are in the roadmap of promising developments in grassland modelling (van Oijen et al., 2020). Thivierge MN, Jégo G, Bélanger G, Bertrand A, Tremblay GF, Rotz CA, Qian B (2016) Predicted yield and nutritive value of an alfalfa-timothy mixture under climate change and elevated atmospheric carbon dioxide. Agronomy Journal 108, 585-603. Thornley JH (1998) Grassland dynamics: an ecosystem simulation model. Cab International. Tóth E, Deák B, Valkó O, Kelemen A, Miglécz T, Tóthmérész B, Török P (2018) Livestock type is more crucial than grazing intensity: traditional cattle and sheep grazing in shortgrass steppes. Land Degradation and Development 29, 231-239. Tilman D (1980) Resources: a graphical-mechanistic approach to competition and predation. The American Naturalist 116, 362-393. Tilman D (2020. Plant Strategies and the Dynamics and Structure of Plant Communities. Volume 26, Princeton University Press. Tiscornia G, Jaurena M, Baethgen W (2019) Drivers, process, and consequences of native grassland degradation: Insights from a literature review and a survey in Río de la Plata grasslands. Agronomy 9, 239. van Oijen M, Bellocchi G, Höglind M (2018) Effects of climate change on grassland biodiversity and productivity: The need for a diversity of models. Agronomy 8, 14. van Oijen M, Barcza Z, Confalonieri R, Korhonen P, Kröel-Dulay G, Lellei-Kovács E, Louarn G, Louault F, Martin R, Moulin T, Movedi E, Picon-Cochard C, Rolinski S, Viovy N, Wirth SB, Bellocchi G (2020) Incorporating biodiversity into biogeochemistry models to improve prediction of ecosystem services in temperate grasslands: review and roadmap. Agronomy 10, 259. van Sundert K, Khan MAA, Bharath S, Buckley YM, Caldeira MC, Donohue I, Dubbert, M, Ebeling A, Eisenhauer N, Eskelinen A, Finn A, Gebauer T, Haider S, Hansart A, Jentsch A, Kübert A, Nijs I, Nock CA, Nogueira C, Porath-Krause AJ, Radujković D, Raynaud X, Risch AC, Roscher C, Scherer-Lorenzen M, Schuchardt MA, Schütz M, Siebert J, Sitters J, Spohn M, Virtanen R, Werner C, Wilfahrt P, Vicca S (2021) Fertilized graminoids intensify negative drought effects on grassland productivity. Global Change Biology 27, 2441-2457. Weiss L, Jeltsch F (2015) The response of simulated grassland communities to the cessation of grazing. Ecological Modelling 303, 1-11. White RP, Murray S, Rohweder M (2000) Pilot analysis of global ecosystems: Grassland Ecosystems. World Resources Institute, Washington D.C. Wilson JB, Peet RK, Dengler J, Pärtel M (2012) Plant species richness: The world records. Journal of Vegetation Science 23, 796-802.Given the complexity of grassland dynamics, grassland ecosystems present unique challenges for understanding plant compositional changes and providing context for the evolution of managed grassland communities, e.g.: (1) improving our understanding of the feedbacks between the natural (environmental) and human (management) components of the integrated soil-vegetation-atmosphere continuum, and (2) up-scaling of local understanding to achieve global process understanding of responses to both components. The integration between two approaches in particular -synthesis research (e.g. meta-analysis) and processbased modelling -has the potential to address both of the above challenges(Magliocca et al., 2015). While meta-analyses are suited to the second challenge (i.e. achieving a global understanding of responses), process-based grassland models are deductive in nature but parameterised on case studies, which are, by definition, location-specific. This is partly due to the tendency of case studies to examine local contextual conditions that may not be easily generalised and valid at larger scales or at coarser resolutions of analysis. Meta-analytical studies systematically compare patterns of context-specific versus general causes of grassland response across locations and scales. Such synthetic knowledge can contribute to the development and implementation of widely applicable process-based grassland models,

Figure 2

 2 Figure 2.1. Map of the biogeographical regions of Europe, provided by the European Environment Agency (https://www.eea.europa.eu). The red triangles locate the two modelling study-sites. The figure is an output from a free image available at https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2. Copyright holder: European Environment Agency (EEA).

  previous reports fromTӓlle et al. (2018) on the effects of different mowing frequency on the conservation value of semi-natural grasslands in Europe. It also completes the assessment with a meta-analysis on the effect of warming on the biodiversity of different ecosystems including plant terrestrial ecosystems(Gruner et al., 2017).

Figure 3

 3 Figure 3.1. Wildflowers and grasses in a species-rich grassland. Photo credit: Célia Pouget (PhD student at Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand, France in 2018-2021).

Figure 3

 3 Figure 3.2. Conceptual framework of this study. Direct (blue arrows) and indirect (red arrows) effects of climate change (i.e., warming) and management (i.e., mowing) jointly determine (degraded cross-hatched line) the functioning (expressed by harvested biomass) and stability (expressed by species diversity) of grassland ecosystem, as mediated by plant growth and community properties.

(

  Title)TI=(grassland OR meadow OR pasture OR pampa OR steppe OR prairie OR savanna OR tundra)). AND (Topic)TS=(diversity OR diverse OR richness OR evenness OR cover OR abundance AND plant OR "functional type*")). AND (Title)TI=(cut OR mow OR clip OR treatment OR management)). NOT (Title)TI=(forest OR tree OR shrub*)) TI=(temperature* OR warm* OR air OR heat* OR stress* OR "extreme temperature") AND TI=(grassland* OR meadow* OR pasture* OR pampa* OR steppe* OR prairie* OR savanna* OR tundra*) NOT TI=(forest* OR tree* OR shrub*) AND TS=(diversity* OR diverse* OR richness OR evenness OR cover OR abundance* AND plant* OR "functional type*") Searches were also undertaken with Scopus (http://www.scopus.com) in order to pick up publications that were not indexed in the WoS database: TITLE (grassland OR meadow OR pasture OR pampa OR steppe OR prairie OR savanna OR tundra) AND TITLE-ABS-KEY (diversity OR diverse OR richness OR evenness OR cover OR abundance AND plant OR "functional type*") AND TITLE (cut OR mow OR clip OR treatment OR management) AND NOT TITLE (forest OR tree OR shrub*) AND LANGUAGE (English) AND DOCTYPE (ar). (TITLE (temperature* OR warm* OR air OR heat* OR stress* OR "extreme temperature") AND TITLE (grassland* OR meadow* OR pasture* OR pampa* OR steppe* OR prairie* OR savanna* OR tundra*) AND NOT TITLE (forest* OR tree* OR shrub*) AND TITLE-ABS-KEY (diversity* OR diverse* OR richness OR evenness OR cover OR abundance* AND plant* OR "functional type*") AND LANGUAGE (english)) AND DOCTYPE (ar OR re) AND PUBYEAR > 1984 AND PUBYEAR <2021 AND (LIMIT-TO (SUBJAREA, "AGRI") OR LIMIT-TO (SUBJAREA, "ENVI") OR LIMIT-TO (LIMIT-TO (SUBJAREA, "MATE") OR LIMIT-TO (SUBJAREA, "EART") OR LIMIT-TO (SUBJAREA, "BIOC") OR LIMIT-TO (SUBJAREA, "MULT")) This review covers articles published from 1985 to 2020. The cut-off date for data collection was 31 December 2019, which ensured including 2020 articles web published in 2019.

  sd and N are the standard deviation and sample size of 𝑋 𝑇 and 𝑋 𝐶 , respectively (e.g., Lajeunesse, 2011). The percent change (%) in the level of the outcome from baseline to the treatment is 100•[exp(LRR)-1].

  . As well, the heating technique (M, 0: open-top chambers; 1: heaters) used in vegetation warming experiments (a categorical moderator) may lead to potential differences in plant responses for: (i) the different control on temperatures of passive (e.g., open-top chambers) and active (e.g., infrared heating) warming methods (e.g., De Boeck and Nijs, 2011), and (ii) the size of the device, open-top chambers used in field experiments being

  outcome, where individual experiments were plotted sequentially on the y-axis. The x-axis shows outcome measures (log-ratio and 0.95 confidence interval for each study). Point estimates are represented by square boxes, where the weight of a study is reflected by the size of the square. The point estimates are accompanied by a line, which represents their associated 0.95 confidence interval. A vertical midline (line-of-no-effect) divides the diagram into two parts. A confidence interval that crosses the line-of-no-effect indicates a statistically non-significant difference, whereas a confidence interval that does not cross the midline indicates a significant difference for either the treatment or control, depending on whether it is located at the left side or the right side of the midline. That is, right-sided (left-sided) result estimates (LRR > 0) for our two outcomes of interest, SR and HB, are higher (lower) in the treatment than in the control (and vice versa).

  The heuristic search of the state-of-the art literature in the WoS and Scopus bibliometric databases yielded 999 articles for the effects of mowing (Figure3.3a) and 1793 articles for the effect of warming (Figure3.3b), after removing 467 and 411 duplicates from the original set of 1466 and 2204 records (with pairwise observations in the control and treatments), respectively. The two bulks of articles were reviewed, and initially screened, for their relevance to the study topic. After applying the criteria to the original set of articles and adding 31 articles from other sources, 43 and 34 articles(46 and 42 experiments, respectively) met the criteria and were selected to quantify the effects on SR of mowing or warming, respectively (Supplementary material). In 16 articles for mowing (18 experiments) and 17 articles (22 experiments) for warming the same analysis was performed to assess the effect of the same factors on HB.

  : 15 in China, 10 in the USA, and nine in central and northern Europe. Most of them (41%) are from regions with ice cap and tundra climates, showing that manipulation studies focusing on the effects of warming on grassland systems are not gaining interest in the Mediterranean and developing regions of the world. They are all unfertilised treatments and include two main devices to simulate the experimental climate warming and to study plant responses, i.e., open-top chambers and infrared heaters. As with articles on the effects of mowing, the types and designs varied considerably also within the same study.

Figure 3

 3 Figure 3.3. PRISMA-flow diagram of studies' selection process on the effect of mowing and warming on species richness (n, number of articles) Some articles included more than one experiment and, in this case, these experiments (j) were considered as separate experiments (j = 46 with mowing, j = 42 with warming) Subsets of the identified records also included the effect of mowing (16 articles, 18 experiments) or warming (17 articles, 22 experiments) on harvested biomass.

Figure 3 . 4 .

 34 Figure 3.4. Global map of study sites that provided data for meta-analysis of the effects of mowing (red triangles) or warming (blue dots) on species richness only (empty markers) or on species richness and harvested biomass (solid markers).

  , top graphs) are located on both sides of the funnel, hence indicating no clear-cut direction in the bias. For SR, FigureS2(left) shows that the majority of the data are clustered in one-point cloud (same order of magnitude), with the exception of the study ofLanta et al. (2009), whose high variability is found in the forest plot (Figure3.5a). For warming, significant results with both SR (I 2 = 92%; Q = 815, p < 0.01) and HB (I 2 = 55%; Q = 60, p < 0.01) are taken as evidence of heterogeneity. The overall funnel plots are however relatively symmetric (FigureS2, bottom graphs) and consistent with low likelihood of publication bias (SR: z = 1.40, p > 0.10; HB: z = -0.94, p > 0.10).

  in Belgium (LRR = 0.92), andJacquemyn et al. (2011) in United Kingdom (LRR = 0.71), noticed that several small herbs and rosette plants were quickly lost in abandoned plots, with mowing reducing the proportion of tall-growing plants and increasing light penetration to the ground surface.AsTruus and Puusild (2009), with LRR = 1.53 (wet experiment),Metsoja et al. (2014) -LRR = 1.17 (tall forb meadow) -andNeuenkamp et al. (2013) -LRR = 1.14 (tall forb meadow)observed that mowing had a distinct role in activating the soil seed bank in Estonian flooded, well drained meadows dominated by tall forb meadow communities. These are highly productive communities (e.g., ~ 1000 g m -2 inNeuenkamp et al. 2013), where plant SR is determined primarily by light and litter rather than nutrient availability.Opposite to SR, over the 16 independent studies (18 experiments) for the effect of mowing on HB (Figure3.5b), the pooled LRR value equal to -0.23, or c. -21% (0.95 confidence interval from -0.31 to -0.14, p < 0.01) suggests an overall negative influence of disturbance.In the included studies, mowing (which had a positive effect on SR) distinctly had a negative effect on HB. Although this is undoubtedly a trade-off between a provisioning service (forage production) and biodiversity-mediated ecosystem services (e.g., pollination, pest control, soil fertility and yield stability), there are studies which indicate that vegetation density and biomass production may be reduced in unmanaged treatments because litter accumulated on the sward surface prevents plants sprouting (as observed, for instance, in Czech Republic byPavlů et al., 2016). A stimulating effect of cutting on grassland productivity was also observed bySasaki et al. (2011) in temperate Japan, which was attributed to the over-compensatory growth because of changes in floristic composition owing to the mowing treatment. There is indeed a body of literature (as reviewed, for instance, bySonkoly et al., 2019) that shows that HB increases when SR increases, mainly from experiments with grasslands sown along gradients of a limited number of plant species compared to monocultures.

Figure 3 . 5 .

 35 Figure 3.5. Forest plots of the meta-analysis (log-response ratios and 0.95 confidence limits) comparing species richness, SR (a) and harvested biomass, HB, g DM m -2 (b) in unmown (0, control) and once-a-year mown (1, treatment) grasslands, with the relative standard deviations (sd). RE model stands for random-effects model.

Figure 3

 3 Figure 3.6. Log-response ratios (LRR) and 0.95 confidence bars comparing species richness for different mowing regimes (number of cuts per year). The number of studies behind these data is given in brackets (to the left). For LRR, the values of the mean and standard deviation

  This could be due to the widespread use of small, low-cost open-top chambers (passive warming) in climate change experiments, especially on short-statured vegetation like grassland steppe and temperate grasslands[START_REF] Frei | Assessing the effectiveness of in-situ active warming combined with open top chambers to study plant responses to climate change[END_REF]. According to[START_REF] Leuzinger | Do global change experiments overestimate impacts on terrestrial ecosystems?[END_REF], a diminishing effect size is expected with a longer duration and a larger spatial scope of experiments. In light of this, we would have expected an influence of the experimental methodology on SR/HB responses since infrared heaters (active heating) can be applied to larger plots than open-top chambers. The three experiments ofWang et al. (2017) do indeed indicate that a smaller open-top chamber of different sizes could have an impact on the response to warming on both SR (which tends to became even more negative, LRR = -0.87, with a smaller chamber) and HB (which, conversely, tends to became more positive, LRR = 0.40, with a smaller chamber). The duration of experiments could also have had an influence

(

  annual forbs) to quickly colonize the plant community. InEskelinen et al. (2017), warmer climate increased SR (LRR = 0.11) via recruitment in conditions where competition with the residents was relaxed (e.g., in disturbed sites), where herbivores kept vegetation open and in habitats with relatively low nutrient availability.Over the 17 recorded articles (22 experiments) for the effect of warming on HB (Figure3.7b), the pooled LRR value equal to 0.10, or c. 11% increase (0.95 confidence interval from 0.04 to 0.17), suggests an overall positive influence of increasing temperatures (p < 0.05). This is in accordance with[START_REF] Song | A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change[END_REF] for terrestrial ecosystems and[START_REF] Liang | Global patterns of the responses of leaf-level photosynthesis and respiration in terrestrial plants to experimental warming[END_REF]

Figure 3 . 7 .

 37 Figure 3.7. Forest plots of the meta-analysis (log-response ratios and 0.95 confidence limits) comparing species richness, SR (a) and harvested biomass, HB, g DM m -2 (b) in ambient (C, control) and warmed (W, treatment) grasslands, with the relative standard deviations (sd). RE stands for random-effects model.

  forage production). For that,Cruz et al. (2002) andTheau et al. (2017b) proposed a classification of perennial forage grasses based on six functional characteristics to discriminate between biomass production and fodder quality of species mixtures. This functional typology was based on 38 grass species commonly found in the upland areas of central and southern France, which were characterised under similar and controlled pedoclimatic conditions in order to obtain differences only due to their physiological/morphological peculiarities. These peculiarities include the growth strategies (capture or conservation of resources) and phenology (early-or late-growth and flowering) of the plants, which are rendered from six morphological and phenological traits(Violle et al., 2007): leaf dry matter content, specific leaf area, leaf lifespan, leaf resistance to breakage and, for the whole plant, flowering date and maximum plant height. This functional composition, based on the identification of dominant grass species and four main types (A, B, C, D) makes it possible to create a classification according to the dates of growth peaks, and the digestibility of leaves and stems. Types A and B (fast growing, phenology-dependent nutrient-capture strategy) dominate in fertile grasslands, while the opposite is observed for types C and D (slow growing, nutrient conservation strategy, late flowering). A-type and Btype grasslands are characterised by a high nutritive value in terms of digestibility for livestock feed at the beginning of the first vegetation cycle (and a rapid decline in nutritive value with early-maturity plants) and a high biomass accumulation[START_REF] Michaud | Seasonal dynamics of biomass production and herbage quality of three grasslands with contrasting functional compositions[END_REF]. Conversely, types C and D species are generally characterised by low digestibility values at the beginning of the cycle (followed by a slower decline during the rest of the vegetation cycle), due to later plant maturity and lower biomass accumulation[START_REF] Duru | Relevance of plant functional types based on leaf dry matter content for assessing digestibility of native grass species and species-rich grassland communities in spring[END_REF].Cruz et al. (2010) defined additional types to characterise tall, late-flowering species in fertile (type "b") or poor (type "d") soils. From this knowledge-based classification, it is possible to parameterise alternative

  differentiation needs to be improved to simulate the coexistence of several species(Wirth et al., 2021). DynaGraM(Moulin et al., 2018) is a modelling solution addressing theoretical questions about the response of a grassland species model to climatic, edaphic and management forcing agents as a function of the state of the plant community. Conceived as a mechanistic model of resource competition(Tilman, 1980), it is a stand-alone solution representing the regulation of green biomass and plant competition from the standpoint of resource dynamics(Moulin et al., 2021), integrating eco-physiological and biophysical details inherited from ModVege(Jouven et al., 2006a, b), originally developed to predict vegetation growth from permanent grasslands in central France. Another approach, the Community Simulation Model (CoSMo), provides means for incorporating plant diversity in grassland models(Confalonieri, 2014). With a set of parameter values for each plant type in a community (individual species or groups of species such as functional types), CoSMo updates the relative abundance of different plant categories (at the level of individual species or taxonomic or functional groups of species) at each time step. The characterisation of the different plant types is performed using different sets of values for the same parameters (common set of parameters for all plant categories). As the biological parameters of plant models are generally sensitive to environmental changes and farming practices, CoSMo translates these changes into a numerical framework through suitability functions (on a concept of[START_REF] Confalonieri | A multi-approach software library for estimating crop suitability to environment[END_REF]. Competition and changes in the relative abundance of plant categories are simulated in response to hierarchical environmental drivers (biophysical and management factors, triggered or continuous), which represent the suitability of different plant categories to the conditions explored at each time step. On this basis, CoSMo derives (daily) vegetation parameter values for the plant community from the relative abundance of the different plant species or groups and the parameter values initially set to characterise the same plant species or group. These dynamically derived vegetation parameters are then used at each time step by the growth simulator (ModVege in our case). In doing so, CoSMo goes beyond the conventional assumption that the values assigned to the model parameters (by calibration, experimental measurement or from the literature) are time invariant and remain constant throughout the simulation period. The simplification of time-invariance of parameter values may be too limiting to meet the challenges facing agricultural systems,

  where Py (mm) is total annual precipitation, Ty (°C) is the mean annual temperature, pa (mm) is the total precipitation of the driest month, Ta (°C) is the mean monthly temperature of the driest month. The index b offers the possibility of discriminating different thermo-pluviometric conditions during the study period (Figure1), based on the ranges of values published by[START_REF] Diodato | Multivariate indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands[END_REF]: b<5: extreme aridity; 5≤b<14: aridity; 15≤b<19: semi-aridity; 20≤b<29: sub-humidity; 30≤b<59: humidity; b≥59: high humidity. The highest value of the aridity index (the lowest aridity), b=46, was observed in 2010, a year with the highest rainfall (885 mm). Exceptionally, 2015 was a semi-arid year with below average rainfall (585 mm) and the aridity index b=18.

Figure 4

 4 Figure 4.1. Thermo-pluviometric regimes of the study-site according to the De Martonne-Gottmann aridity (yearly values).

  Galium aparine and Trifolium repens, 0.048 m 2 g -1 and 0.018 m 2 g -1 , are within the ranges of values provided by[START_REF] Poorter | A comparison of specific leaf area, chemical composition and leaf construction costs of field plants from 15 habitats differing in productivity[END_REF] and[START_REF] Nölke | Seasonal plasticity is more important than population variability in effects on white clover architecture and productivity[END_REF]), respectively. Theau et al. (2017b) also provided ranges of values for maximum plant heights (e.g. from <0.4 for Taraxacum officinale, Stellaria media and Trifolium repens to >0.9 m for Galium aparine, Achillea millefolium and Urtica dioica) to which we have referred for the calibrated values attributed to this CoSMo parameter (TableJ). For each type/species, the first determined (and recalculated) relative abundance was used to initialise CoSMo at the start of the simulation (2006, 1 January).The agreement between simulated and observed values of AGB and relative abundance of plant types/species (the latter only for CoSMo-based modelling solutions) was evaluated according to five commonly used metrics (RRMSE, RMAE, R 2 , R, CRM) of model performance[START_REF] Bellocchi | Validation of biophysical models: Issues and methodologies. A review[END_REF]. The relative root mean square error (optimum, 0≤RRMSE (%)<∝) and the relative mean absolute error (optimum, 0≤RMAE (%)<∝) indicate how far the estimates are from the actual data: both explain this concept in relative terms (percentage over the mean), the former in square terms and the second in absolute terms. Squaring emphasises larger differences, a feature that results in giving more weight to large deviations. Algebraically, RMAE≤RRMSE (due to the influence of squaring larger values), with these metrics being approximately equal if the absolute differences are of similar magnitude. This suggests that the absolute differences are more robust (less sensitive) to large biases than squared differences. The coefficient of determination (0≤R2≤1, optimum)is the squared measure of the linear correlation coefficient (-1≤R≤1, optimum) between the estimates and the observations: it assesses the goodness-of-fit of the model, i.e. the proportion of the observed variance explained by the estimates. The coefficient of residual mass (CRM) is a measure of the tendency of the model to overestimate (CRM<0) or underestimate (CRM>0) the observations.

  and E in Supplementary material). Overall, all three modelling solutions accounted for the decreasing trend in biomass production (annual anomalies) over the simulation period (Figure4.2). In the NPK and Null treatments, R-values ranged from 0.60 (with ModVege-CoSMo for functional types under NPK 1) to 0.85 (with ModVege-CoSMo for the functional types under Null 2), while the quality of the simulations of the abandoned plots was deteriorated towards the end of the simulation period. Some discrepancies between simulations and observations were also observed in the years 2008 to 2011, in the transition between mostly positive and mostly negative observed anomalies.

Figure

  Figure4.2. Annual anomalies of aboveground biomass (AGB), observed and simulated with alternative modelling solutions in three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2). Annual mean values of AGB: NPK 1: 2895 kg DM ha -1 ; NPK 2: 2477 kg DM ha -1 ; Null 1: 1417 kg DM ha -1 ; Null 2: 1197 kg DM ha -1 ; Ab 1: 5245 kg DM ha -1 ; Ab 2: 4683 kg DM ha -1 .

  types B and b in block 2 abandonment with 35.4% and 36.1%, respectively; Figure J), and <10% in fertilised treatments (i.e. type A and type b in block 2, with 9.0% and 9.2%, respectively; Figure F). The performance metrics improved at the community level (R>0.70 and RRMSE<25%).

Figure 4

 4 Figure 4.3. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant (grass) functional types in three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2). Type A: grass species of fertile environments, small, with an early phenology and a short life span of the leaves; Type B: grass species of fertile environments, larger, with a moderately early phenology and a longer leaf life than type A; Type b: grass species preferring relatively fertile environments but

  Figure P). A dominant forb (absent in abandoned plots), Taraxacum officinale, shows RRMSE values <25% (RMAE<20%) in block 1 NPK (16.2% and 12.6%; Figure K) and Null (12.4% and 9.8%; Figure M) and in block 2 Null (22.8% and 18%; Figure N), with RRMSE=31.1% (RMAE=24.1). Overall, the dynamics of Trifolium repens in the unfertilised mown treatments were also reproduced (Figure M and Figure N), although some discrepant values in the most recent years (RRMSE~62-77%; RMAE~49-62%). It was then shown that changes in ModVege-CoSMo accuracy are not related with the number of plant species in the community, with community RRMSE values ranging from ~30% to ~46% with six species (block 1 NPK and block 2 Ab) and from ~28% (block 2 Null) to ~52% (block 1 Null) with eight species.

Figure 4

 4 Figure 4.4. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant species in three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2). Graphs of the individual plant species are in Supplementary material (Figures K-P).

Figure 4 . 5 .

 45 Figure 4.5. Daily changes in the specific leaf area (SLA) of the community during the simulation period compared to the fixed values set for the plant species (coloured lines as in Figure 4.4) present in the community (horizontal lines, overlapping for the same values) for three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2).

Figure 4 . 6 .

 46 Figure 4.6. Daily changes in the maximum height of the community during the simulation period compared to the fixed values set for the plant species (coloured lines as in Figure 4.4) present in the community (horizontal lines, overlapping for the same values) for three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2).

  et al. (2019) evaluated the performance of CropSyst-CoSMo in grassland mixtures in continental Italy, dominated by perennial plant species. This study tested the potentials associated to the implementation of the CropSyst-CoSMo modelling framework to Mediterranean annual grasslands. The objectives were (i) to characterize annual species with specific vegetation parameters for modelling (specific leaf area, canopy height), and (ii) to evaluate the stand-alone CropSyst model and its CoSMobased version in terms of their ability to simulate a range of outputs: leaf area index, canopy height, harvested dry matter biomass (both modelling solutions) and relative plant species abundance (CoSMo-based solution).

  photosynthesis without an explicit simulation of biomass partitioning and accumulation

  5 m x 1.0 m random sampling area in each subplot. All plots were cut once at the end of winter, i.e. March 11 th . The low frequency mowing plots were then cut only once at flowering, i.e. May 13 th . The high-frequency mowing plots were cut other two times, on April 14 th and May 6 th .The relative abundance of the four target species was assessed by sorting fresh samples by species (the four sown and the other unsown species). The harvested samples were then stored in plastic bags and dried in a ventilated oven at 65 °C until reaching a constant weight.The Leaf area index (LAI) and specific leaf area (SLA) were measured at each sampling date by cutting the biomass at ground level in a sampling area of 0.25 m x 0.50 m. The fresh biomass was sub-sampled according to the species. The sub-samples leaf area was www.licor.com/env/products/leaf_area/LI-3100C). The measured sub-samples were then oven-dried at 65 °C until reaching constant weight. The SLA was determined by dividing the total area of each subsample by the biomass dry matter.

2. 4

 4 Simulation design and model evaluation Standalone (CropSyst) and coupled (CropSyst-CoSMo) modelling solutions were configured using daily weather, soil properties and management data as measured at the experimental site. The initial proportion of each plant species in the mixed stands was used to initialize CoSMo at the start of the simulation. To simulate the dynamics of relative abundance within mixed stands, the parameterization of CropSyst and CoSMo was performed for each species taking values from the literature (including default values from the model manual) or from the field measurements. The calibration was carried out through a trial-and-error process comparing the model estimates with observational data while ensuring biologically interpretable plant parameters by modifying their values within plausible ranges. The range of parameter values provided by Movedi et al. (

  management and treatment. For AGB, we compared standalone CropSyst and its CoSMobased solution.. Estimates of relative plant abundances obtained with the CoSMo-based modelling solution were evaluated for individual plant species or aggregated into taxonomic groups.3.1 Evaluation of modelling solutions for AGB, LAI and sward height3.1.1 Aboveground biomassRegarding the simulation of AGB in high mowing frequency management of both pure and mixed stands (Figure5.1), both modelling solutions performed well, with only a little difference between CoSMo-CropSyst and CropSyst stand-alone. For instance, for the simulation of the AGB of Lolium rigidum, the RRMSE was <40%, with some underestimation at the second and third mowing. The AGB of both clovers was somewhat overestimated, especially at the second mowing, with RRMSE values of ~32% (balansa clover) and ~47% (subterranean clover). In mixed stands, the differences between the two modelling solutions can be appreciated for G50L50 at second mowing, where the CropSyst-CoSMo version overestimated the AGB by ~1 Mg ha -1 .

Figure 5

 5 Figure 5.1. Observed (dots) and simulated dynamics (lines) of the aboveground biomass (AGB) for the high mowing frequency management in Santa Lucia experimental site. Bars represent 0.95 confidence intervals.

Figure 5

 5 Figure 5.2. Observed (dots) and simulated fluctuations (lines) of the aboveground biomass (AGB) for the low mowing frequency management in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure 5

 5 Figure 5.3. Observed (dots) and simulated fluctuations (lines) of the leaf area index (LAI) for the high mowing frequency management in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure 5

 5 Figure 5.4. Observed (dots) and simulated fluctuations (lines) of the canopy height for the high mowing frequency management in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure 5

 5 Figure 5.5. Observed (dots) and simulated fluctuations (lines) of the canopy height for the low mowing frequency management in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

  Figure H, Figure I, Figure J).

Figure 5

 5 Figure 5.6. Relative abundances of individual species in the three mixed stands for the high mowing frequency management in Santa Lucia experimental station.

Figure 5 . 8 .

 58 Figure 5.8. Daily changes in the maximum height of the community compared to the fixed values set for the plant species present in the mixed stands (coloured horizontal lines) for the high mowing frequency management in Santa Lucia experimental station.

Figure 5

 5 Figure 5.9. Daily changes in the maximum leaf area index (LAI) of the community compared to the fixed values set for the plant species present in the mixed stands (coloured horizontal lines) for the high mowing frequency management in Santa Lucia experimental station.

  scheme was developed to support research synthesis (i.e. meta-analysis) on the ability of selected environmental (i.e. warming) and management (i.e. mowing) drivers to modify species richness and harvested biomass (Chapter 3). The results show the positive effect of moderate disturbance (one to two mowing events per year) on the creation and maintenance of diverse grasslands of ecological and agricultural interest. At the same time, harvested biomass decreased with mowing and increased with warming. The opposite response of species richness and harvested biomass is consistent with the competitive exclusion hypothesis (after[START_REF] Hardin | The competitive exclusion principle[END_REF], according to which environmental conditions and management types that favour high taxonomic diversity lead to low biomass production, and vice versa.Chapter 4 and Chapter 5 illustrate the modelling work that extends this simplified view of a grassland ecosystem to examine in detail local conditions (e.g. daily change in weather

  Figure6.2. Link between biophysical processes and ecosystem services in a grassland system from a modelling perspective, without (top) and with (bottom) the mediation of plant diversity (adapted from[START_REF] Vereecken | Modeling soil processes: review, key challenges, and new perspectives[END_REF]. The grey arrows indicate the controls exerted by the biological, physical and chemical processes on the supporting and degrading processes. The light yellow arrows show the control of supporting and degrading processes on inherent plant growth processes (either or not mediated by plant diversity dynamics), which in turn affect (directly or indirectly) basic processes and supporting, provisioning, regulating and cultural ecosystem services. The green arrows indicate the impact of the plant diversity dynamics on basic processes and ecosystem services.

  Based on the suitability function rules of the Community Simulation Model (CoSMo), this study is one of the few that considers explicit and dynamic modelling of relative plant abundance in grassland communities. Although specific to given simulation models (ModVege and CropSyst) and contexts (continental and Mediterranean), it provides a framework for implementing and assessing the response of biophysically modelled grassland systems beyond the meta-analyses conducted on experimental evidence and the mean parameterisation-based modelling paradigm. The question now is whether CoSMo-based modelling solutions can effectively support agronomic and ecological objectives.

Figure A .

 A Figure A. Overview of the ModVege model(Jouven et al., 2006).

Figure G .

 G Figure G. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional types in the treatment Null (unfertilised) of block 1.

Figure H .

 H Figure H. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional types in the treatment Null (unfertilised) of block 2.

Figure

  Figure I. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional types in the treatment Ab (abandoned) of block 1.

Figure J .

 J Figure J. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional types in the treatment Ab (abandoned) of block 2.

Figure

  Figure M. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional species in the treatment Null (unfertilised) of block 1.

Figure N .

 N Figure N. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional species in the treatment Null (unfertilised) of block 2.

Figure

  Figure O. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional species in the treatment Ab (abandoned) of block 1.

Figure P .

 P Figure P. Observed (dots) and ModVege-CoSMo simulated (lines) fluctuations in the relative abundance of plant functional species in the treatment Ab of block 2.

Figure R .

 R Figure R. Daily changes in the height of the community during the simulation period compared to the fixed values set for the plant functional types (A: red line; B: green line; b: blue line) present in the community (horizontal lines, overlapping for the same values) for three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2).

Figure B .

 B Figure B. Images of the study-site, located in the municipality of Zeddiani, at Santa Lucia (39° 56' N, 08° 41' E, 15 m a.s.l.) in Sardinia (Italy). The photos were taken on 8 February (top) and 15 April (bottom) 2021.

Figure

  Figure C. Observed (dots) and simulated fluctuations (lines) of the leaf area index (LAI) for the low mowing frequency management in Santa Lucia experimental station. Bars represent 0.95 confidence interval.

Figure E .

 E Figure E. Relative abundance of species in the mixed stand G50L50 (50% grass species-50% legume species) with high mowing frequency in Santa Lucia experimental station. Bars represent 0.95 confidence interval.

Figure F .

 F Figure F. Relative abundance of species in the mixed stand G75L25 (75% grass species-25% legume species) with high mowing frequency in Santa Lucia experimental station. Bars represent 0.95 confidence interval.

Figure G .

 G Figure G. Relative abundances of individual species in the three mixed stands for the low mowing frequency management in Santa Lucia experimental station.

Figure H .

 H Figure H. Relative abundance of species in the mixed stand G25L75 (25% grass species-75% legume species) with low mowing frequency in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure I .

 I Figure I. Relative abundance of species in the mixed stand G50L50 (50% grass species-50% legume species) with low mowing frequency in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure J .

 J Figure J. Relative abundance of species in the mixed stand G75L25 (75% grass species-25% legume species) with low mowing frequency in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure K .

 K Figure K. Observed (dots) and daily changes in the specific leaf area (SLA) of the community compared to the fixed values set for the plant species present in the mixed stands (coloured horizontal lines) for the low mowing frequency management in Santa Lucia experimental station. Bars represent 0.95 confidence intervals.

Figure L .

 L Figure L. Daily changes in the maximum height of the community compared to the fixed values set for the plant species present in the mixed stands (coloured horizontal lines) for the low mowing frequency management in Santa Lucia experimental station.

Figure M .

 M Figure M. Daily changes in the maximum leaf area index (LAI) of the community compared to the fixed values set for the plant species present in the mixed stands (coloured horizontal lines) for the low mowing frequency management in Santa Lucia experimental station.

. Importance des prairies et des pâturages

  Festuca arundinacea, Lolium perenne et Poa pratensis, ce qui rend le modèle prometteur pour des études en faveur d'objectifs agronomiques et écologiques. La qualité des simulations s'est

	herbacés améliorés se trouvent dans les zones résultant du travail du sol et de la plantation
	de variétés d'espèces fourragères sélectionnées, y compris des espèces non indigènes,
	caractérisées par un potentiel de production élevé. Outre la production de fourrage, les
	prairies et les pâturages sont également importants car ils fournissent une série de services
	également avérée satisfaisante dans le cas de l'évaluation d'une solution de modélisation écosystémiques, qui peuvent être classés en quatre catégories : services de soutien (par
	existante basée sur CoSMo (CropSyst-CoSMo) pour la simulation des fluctuations intra-exemple, production primaire, cycles de l'eau et des nutriments dans le sol), services
	annuelles des graminées et légumineuses annuelles auto-ensemencées dans divers mélanges. d'approvisionnement (par exemple, production de fourrage), services de régulation (par
	Cependant, l'expérience d'évaluation de la solution de modélisation, qui a été initiée au cours exemple, régulation du climat, contrôle de l'érosion, pollinisation) et services culturels (par
	de la saison 2020-2021 et qui fait l'objet de cette thèse, a révélé le potentiel de CoSMo dans exemple, esthétique, loisirs, patrimoine environnemental).
	le contexte des prairies méditerranéennes et a ainsi ouvert la voie à des investigations Les espèces végétales présentes dans une prairie varient considérablement dans leurs
	supplémentaires. caractéristiques biologiques et morpho-physiologiques. La composition des communautés
	Le reste de cette section de résumé illustre les aspects saillants de la problématique, les végétales varie aussi considérablement, non seulement d'une région à l'autre mais aussi au fil
	méthodes et les résultats les plus significatifs de cette thèse. La littérature citée est limitée aux du temps, ce qui influence la production et la qualité du fourrage, ainsi que la fourniture
	contributions directement liées à ce travail de doctorat, tandis que la liste complète de la d'autres services écosystémiques. Les prairies et les pâturages comptent en effet parmi les
	littérature scientifique consultée a été donnée dans les différents chapitres de la thèse. écosystèmes les plus riches en espèces au monde, mais près de la moitié de ces écosystèmes
	sont dégradés et près de 5 % présentent des niveaux de dégradation élevés ou extrêmes. La
	continuité de ces communautés végétales et de leur fonctionnement est donc essentielle pour 1Les prairies et les pâturages sont des systèmes de production herbacés qui couvrent environ assurer la conservation de leur biodiversité et la fourniture de services écosystémiques,
	40 % de la surface de la terre (52,5 millions de km 2 ) dans toutes les régions du monde, à sachant que la dynamique spatio-temporelle des espèces végétales est fortement influencée
	l'exception de l'Antarctique et du Groenland. Plus précisément, il s'agit de formations par leur gestion. Les pratiques agronomiques telles que l'intensité du pâturage ou la fréquence
	végétales à prédominance herbacée qui peuvent être utilisées directement par les animaux. de fauche, ainsi que les apports d'engrais, sont des facteurs importants de la dynamique de la
	Un pâturage est, plus particulièrement, une surface de terrain sur laquelle poussent végétation et de la répartition des espèces, contribuant à la diversité des paysages et à la
	spontanément diverses espèces de plantes herbacées, utilisées pour l'alimentation directe du quantité et la qualité du fourrage produit. Si les pratiques agronomiques (par exemple, le
	bétail (pâturage). Une prairie, en revanche, est une zone de terre sur laquelle on peut cultiver pâturage, le fauchage, la fertilisation, l'ensemencement de mélanges d'espèces, le contrôle des
	intentionnellement ou laisser pousser à l'état sauvage diverses espèces de plantes herbacées, arbustes) sont les principaux déterminants de la dynamique de la végétation, de la répartition
	qui sont ensuite fauchées et utilisées comme fourrage frais ou conservé (par exemple, foin et de la diversité des espèces, les effets combinés des pratiques de gestion et des changements
	ou ensilage) pour les animaux. Une même surface peut être utilisée comme pâturage et environnementaux restent un champ de recherche ouvert pour garantir le maintien de la
	comme prairie à différents moments de son cycle de production (prairie-pâturage). La valeur des biens et services que ces écosystèmes peuvent fournir dans diverses conditions.
	production herbacée de ces systèmes est la principale source de fourrage pour de nombreuses
	espèces d'herbivores (principalement des ovins, des caprins, des bovins et des chevaux) 1.1
	élevés de manière extensive ou semi-extensive. Étant donné que, dans presque tous les cas,
	les pâturages et les prairies sont des communautés végétales secondaires, c'est-à-dire liées à
	la manipulation par l'homme de la végétation primaire, leur maintien et leur fonctionnement
	nécessitent des pratiques agronomiques efficaces et continues. Les systèmes de production

). La précision de ModVege-CoSMo dans la simulation de l'évolution des abondances relatives dans le temps a ainsi été vérifiée, notamment pour la simulation d'espèces très présentes telles que Elymus repens et Alopecurus pratensis, mais aussi

. Réponses aux perturbations anthropiques et aux facteurs environnementaux

	fertilisation, les techniques de pâturage ou la fauche. La gestion agronomique de la végétation changement climatique menace certaines niches écologiques, avec la disparition d'espèces qui
	des prairies et des pâturages a une influence cruciale sur la diversité des espèces végétales, ne peuvent s'adapter à la hausse des températures aussi rapidement que les autres. Les
	qui a elle-même un impact sur la productivité des prairies et des pâturages et sur la fourniture épisodes de réchauffement et de sécheresse agissent souvent de concert pour modifier la
	de services écosystémiques. L'azote étant l'un des éléments les plus limitatifs de la productivité et les réactions de la biodiversité des prairies. En général, la sécheresse exerce
	productivité des prairies et des pâturages, la fertilisation azotée augmente la production de des effets négatifs sur la production de biomasse, qui peuvent être atténués lorsque la
	biomasse et a un effet négatif sur la diversité des espèces, mais cette réponse varie en fonction diversité végétale est élevée. Cependant, différents groupes fonctionnels d'espèces montrent
	du contexte environnemental et du type de communauté végétale. En général, ce sont les des réponses différentes à la sécheresse et la disponibilité des nutriments peut être un facteur
	graminées qui bénéficient le plus de l'ajout d'azote, augmentant ainsi leur compétitivité face critique pour définir la réponse de la végétation. Par exemple, les graminées, lorsqu'elles sont
	aux légumineuses et autres dicotylédones qui sont moins dépendantes de la disponibilité de fertilisées à l'azote, sont plus sensibles à la sécheresse en termes de biomasse produite que
	l'azote minéral dans le sol. La réduction de l'apport de N, par exemple à la suite d'une les dicotylédones.
	réduction ou d'une absence de fertilisation et d'un apport météorique réduit, peut entraîner
	une augmentation de la diversité végétale. L'ajout de phosphore (P) peut également entraîner 1.2
	une réduction de la richesse spécifique, mais avec un effet moindre que celui de l'azote et
	avec un avantage relatif pour les légumineuses par rapport aux graminées. L'élimination de
	la biomasse aérienne par la fauche ou le pâturage peut avoir un impact significatif sur la
	composition floristique des communautés végétales des prairies et des pâturages, ainsi que
	sur la qualité et le rendement. Cet impact dépend principalement de l'intensité et de la
	fréquence des pratiques de gestion. Par exemple, par rapport à l'abandon, un seul fauchage
	annuel peut avoir un effet positif sur la richesse spécifique et un effet négatif sur la production
	de biomasse. Les effets du pâturage sont généralement similaires à ceux de la fauche, mais le
	pâturage entraîne une plus grande hétérogénéité spatiale. Un effet positif du pâturage sur la
	richesse floristique est généralement obtenu avec des chargements animaux faibles ou
	intermédiaires, alors qu'avec des chargements élevés, la diversité végétale peut être réduite à
	quelques espèces tolérantes ou infestantes.
	Étroitement liées aux conditions climatiques, les prairies et les pâturages sont fortement
	exposés aux risques liés au changement climatique, en particulier à la modification du régime
	des précipitations, avec une fréquence accrue des événements météorologiques extrêmes
	(sécheresses et inondations) et une plus grande variabilité de la production. La hausse des
	températures, en particulier dans les régions au climat atlantique, peut favoriser
	l'augmentation de la production de fourrage en raison d'une saison de croissance plus longue
	et d'une utilisation plus efficace de l'eau et des nutriments, ainsi que de l'effet fertilisant de
	La biomasse végétale des prairies et des pâturages est une composante majeure du régime l'augmentation de la concentration de CO2 dans l'atmosphère. Cependant, l'augmentation
	alimentaire des herbivores domestiques dans les systèmes agricoles extensifs et semi-des températures de l'air a également un impact sur la diversité végétale, favorisant l'invasion
	extensifs, et sa production est influencée par des pratiques agronomiques telles que la d'espèces provenant d'habitats plus chauds et la disparition d'espèces indigènes. Le

. Importance des modèles de simulation

	dire des modèles qui simulent les relations entre les traits fonctionnels des plantes et leur les solutions basées sur CoSMo simplifient les processus individuels en faveur de
	plasticité en réponse aux changements environnementaux. Ces modèles sont complexes et l'applicabilité du modèle (Movedi et al., 2019) et font partie de la feuille de route décrite par
	difficiles à initialiser et à paramétrer pour chaque espèce de la communauté. En revanche, les van Oijen et al. (2020) pour le développement de modèles simulant dynamique des espèces
	modèles qui reproduisent les caractéristiques morphologiques et physiologiques moyennes végétales dans les prairies et les pâturages.
	d'une communauté végétale dans son ensemble ont tendance à ignorer les abondances
	relatives des différentes espèces végétales ou des différents groupes fonctionnels au sein des 1.3
	communautés. Ces modèles sont basés sur la simulation de la prairie ou du pâturage comme
	une culture, avec des paramètres décrivant les caractéristiques morphologiques et
	physiologiques destinées à représenter les traits moyens de la communauté. Cette approche
	est aujourd'hui considérée comme trop simplifiée, car (i) il n'est pas toujours possible de
	mesurer les paramètres décrivant les traits de la communauté végétale, (ii) il n'est pas possible
	de reproduire la dynamique des abondances des différentes espèces pendant la saison de
	La recherche scientifique sur des systèmes complexes tels que les prairies et les pâturages, croissance et (iii) elle ne permet pas de simuler l'effet de la gestion sur les différentes espèces
	dont les communautés végétales abritent une grande biodiversité, nécessite des approches (par exemple, la préférence des animaux et la résilience des plantes après la fauche ou le
	capables d'intégrer différentes compétences écologiques, agronomiques, biologiques et pâturage). La diversité fonctionnelle étant essentielle pour la stabilité et la productivité des
	zootechniques afin d'harmoniser les objectifs de gestion, les outils de suivi et le écosystèmes, des approches basées sur la variabilité temporelle des traits de végétation
	développement socio-économique. En particulier, la compréhension et la projection de la (paramètres) ont été introduites dans les modèles dynamiques de végétation, dépassant ainsi
	dynamique des prairies dans le contexte du changement global nécessitent des modèles de le paradigme qui condense la diversité des types fonctionnels avec des paramètres constants,
	simulation de la végétation capables de représenter explicitement les processus et les facteurs mais sans une représentation explicite et dynamique des abondances relatives. D'autres
	qui régulent le fonctionnement des écosystèmes. Plus précisément, les modèles basés sur les approches réduisent la complexité du système en représentant une espèce principale et,
	processus, en tant qu'alternative aux modèles purement statistiques, permettent l'intégration implicitement, toutes les autres comme une seule espèce concurrente. L'approche adoptée
	d'observations de terrain, fournissant des informations supplémentaires sur les processus et dans cette thèse pour la simulation d'une communauté végétale est celle proposée par
	mécanismes des écosystèmes qui sont difficiles à mesurer ou à déduire des campagnes de Confalonieri (2014), ou CoSMo (Community Simulation Model), car il s'agit d'une approche
	surveillance. Les modèles de simulation, étayés par des observations à long terme et des générique (à une seule instance) permettant de décrire (virtuellement) toutes les espèces
	preuves expérimentales, sont des outils essentiels pour évaluer l'impact combiné des facteurs présentes dans les communautés végétales. En outre, il peut être facilement mis en oeuvre
	environnementaux (par exemple, les événements climatiques extrêmes) et de la gestion sur pour être couplé à n'importe quel modèle de simulation générique d'une couverture végétale
	la structure des communautés végétales locales et leur potentiel productif. L'intérêt croissant afin de calculer les abondances relatives des espèces à un pas de temps donné (par exemple,
	pour les effets du changement climatique sur les agroécosystèmes rend l'analyse de la quotidiennement). CoSMo requiert des informations sur certaines caractéristiques des
	dynamique des espèces végétales encore plus pertinente et urgente (Kipling et al., 2016 ; van plantes, qui doivent être spécifiées par espèce ou groupe fonctionnel de la communauté, mais
	Oijen et al., 2018 ; Ma et al., 2019). il est basé sur l'hypothèse que ces caractéristiques, auxquelles correspondent les paramètres
	Les modèles simulant la dynamique des espèces dans une communauté végétale constituent du modèle, dans un mélange, peuvent être traitées comme additives, sans tenir compte
	un outil puissant pour étudier l'évolution de la diversité végétale dans diverses conditions et explicitement des impacts non linéaires de la variation des caractéristiques, ce qui limite la
	pour étayer les choix de gestion. En termes de modélisation, la complexité des dynamiques complexité et les demandes de calcul. Alors que d'autres modèles simulent la croissance de
	qui caractérisent ces systèmes suggère l'adoption de modèles centrés sur l'individu, c'est-à-plantes individuelles interagissant les unes avec les autres dans une proximité spatiale étroite,

. Contexte du travail de recherche

	2) les modèles formalisent ces réponses par un ensemble de règles de fonctionnement
	des communautés végétales (approche à l'échelle locale).
	2
	Compte tenu de leur complexité, les prairies sont des écosystèmes uniques pour l'étude de la
	dynamique de la végétation et fournissent un cadre conceptuel pour comprendre l'évolution
	de ces communautés végétales en termes de : (1) les rétroactions entre les composantes
	naturelles (environnementales) et humaines (gestion) dans le continuum sol-végétation-
	atmosphère et (2) la réponse à ces deux composantes à grande échelle.
	Pour répondre à ces deux questions, une première hypothèse de la thèse est que la dynamique
	des espèces végétales dans une communauté peut être régie par deux grands types de facteurs
	: environnementaux et de gestion. Les facteurs environnementaux déterminent les conditions
	d'aptitude à la croissance et au développement des différentes espèces végétales d'une
	communauté en fonction des contraintes biophysiques dues aux propriétés du sol et aux
	facteurs climatiques, conditions qui sont à leur tour modifiées par les pratiques
	agronomiques. Une deuxième hypothèse de la thèse est que les relations entre ces facteurs et
	la dynamique de la végétation peuvent être explorées par deux approches : la méta-analyse
	de la littérature et la modélisation basée sur les processus. D'une part, un examen
	systématique et une méta-analyse de la littérature actuelle permettent de comparer les
	résultats de différents tests expérimentaux ou observations, tandis qu'avec la modélisation,
	les processus de fonctionnement pertinents sont inclus dans un modèle biophysique de la
	communauté végétale.
	L'intégration des deux approches a l'avantage de pouvoir aborder les deux questions
	(rétroactions entre les composantes naturelles et anthropiques et réponses à grande échelle),
	car si les méta-analyses permettent d'extraire des réponses générales, les modèles de
	simulation basés sur les processus, bien que de nature déductive, sont paramétrés localement
	et donc spécifiques aux conditions propres à des études de cas spécifiques (Magliocca et al.,
	2015).
	En résumé :
	1) les études méta-analytiques aident à identifier les profils de réponse généraux de
	l'évolution de la diversité végétale (approche globale) ;

. Effets de la fauche et du chauffage sur la richesse spécifique et la production de biomasse des prairies : méta-analyses

	dans 17 articles (22 expériences) sur l'effet du réchauffement, l'effet des mêmes facteurs sur stress environnementaux et aux perturbations anthropiques pour prédire la dynamique des
	le HB a également été évalué. communautés végétales dans le cadre de scénarios environnementaux et de gestion
	A l'exception de trois études, nous avons obtenu un effet significativement positif de la alternatifs. Des analyses quantitatives plus poussées de ces relations pourraient favoriser le
	fauche annuelle sur le RS par rapport à l'abandon (environ 32% d'augmentation moyenne). développement de modèles de simulation de la dynamique de la diversité végétale dans les
	En revanche, le fauchage annuel a un effet négatif sur la production de biomasse (environ communautés herbacées à la flore complexe.
	Une recherche par mots-clés dans deux bases de données bibliographiques internationales, 21% de réduction au total). Ces résultats indiquent un compromis clair entre un service
	ISI Web of Science et Scopus, nous a permis de couvrir les articles publiés de 1985 à 2020, d'approvisionnement (production de fourrage) et les services écosystémiques médiés par la 3.
	auxquels nous avons ajouté d'autres articles pertinents issus de revues à comité de lecture biodiversité végétale (par exemple, la pollinisation, la lutte contre les parasites, la fertilité des
	dont nous avions connaissance (Gruner et al., 2017). Dans les articles sélectionnés, les sols et la stabilité des rendements). Seule l'année de publication s'est avérée être un
	expériences de fauchage et de chauffage comprenaient la température ambiante (sans modérateur significatif de l'effet de la fauche sur le HB, l'impact négatif de la fauche s'étant
	chauffage) et l'abandon (sans fauchage) comme traitements témoins, respectivement, et avéré plus important dans les études plus anciennes (1993-2009).
	rapportaient les effets quantitatifs (ainsi qu'une mesure de la variabilité) du fauchage ou du Le modèle à effets aléatoires a indiqué un effet négatif significatif du réchauffement sur le
	chauffage sur la richesse spécifique (RS, métrique de conservation). Lorsqu'elles étaient SR, avec une réduction d'environ 13 % pour une augmentation moyenne de la température
	disponibles, les déterminations de la biomasse récoltée (HB, indicateur du service de 1,8°C (dans la fourchette 0,15-4,10°C), reflétant des réponses similaires observées dans
	d'approvisionnement) ont également été prises en compte dans les analyses. Une seule fauche les écosystèmes terrestres (Gruner et al., 2017). L'année de publication est apparue comme
	annuelle est la fréquence de fauche la plus couramment utilisée dans les prairies multi-espèces un modérateur important, avec seulement huit études publiées avant 2010, mais
	et a été le régime de fauche de référence pour la méta-analyse (indépendamment de son principalement l'altitude du site et les précipitations annuelles sont apparues comme des
	moment dans l'année). Les résultats des expériences menées avec des régimes de fauche plus variables modératrices significatives, expliquant environ 36% de l'hétérogénéité. Les effets
	complexes ont été utilisés comme élément complémentaire dans la discussion. les plus faibles du réchauffement sur les SR (perte moindre de la diversité végétale) tendent
	Les effets expérimentaux de chaque étude ont été évalués en utilisant le logarithme des ratios à être associés aux zones plus sèches (<300 mm de précipitations annuelles), la réponse au
	de réponse, calculés comme le rapport entre les valeurs moyennes d'un traitement et de son réchauffement étant d'autant plus forte que l'aridité est faible (Peñuelas et al., 2007). De
	contrôle. Avec un modèle à effets aléatoires, une réponse globale généralisée a été obtenue, même, nous avons observé un déclin plus limité de la diversité végétale pour les sites situés
	mais comme une hétérogénéité a été constatée entre les études, des modèles à effets mixtes à moins de 1000 m d'altitude, où le SR est généralement plus faible (Dengler et al., 2014).
	ont été utilisés pour expliquer cette hétérogénéité par l'utilisation de variables modératrices En conclusion, la méta-analyse a rassemblé un grand nombre d'observations et a permis une
	lorsqu'au moins la moitié des études en faisaient état. Les modérateurs identifiés sont les interprétation complète de la réponse des communautés herbacées dans de nombreuses
	suivants : la taille de la parcelle, la durée de l'étude, l'année de publication de l'article, l'altitude études dans différentes régions du monde. Tout d'abord, la richesse en espèces végétales plus
	du site, la température atmosphérique annuelle moyenne de l'air et les précipitations totales élevée et la productivité plus faible observées dans les parcelles fauchées suggèrent
	annuelles moyennes. Pour l'effet de la fauche, la hauteur de coupe a également été prise en l'importance d'une perturbation modérée (par exemple, un seul fauchage annuel) pour
	compte, tandis que la différence de température entre le traitement et le témoin et la favoriser la diversité végétale. Deuxièmement (et contrairement au résultat précédent), la
	technique de chauffage ont été utilisées comme modérateurs dans la méta-analyse sur les productivité peut être accrue sous des températures plus élevées, mais le réchauffement tend
	effets du chauffage. La sélection des contributions extraites a donné lieu à 43 et 34 articles à réduire le nombre d'espèces végétales dans la communauté. Ces réponses opposées au
	(46 et 42 expériences) quantifiant les effets de la fauche et du chauffage sur la richesse stress (réchauffement) et à la perturbation (fauche) suggèrent des mécanismes possibles
	spécifique (RS), respectivement. Dans 16 articles sur l'effet de la fauche (18 expériences) et d'exclusion compétitive, qui n'ont pas été analysés dans cette étude. Les résultats de notre
	méta-analyse confirment l'importance de prendre en compte les réponses des espèces aux

Simulation de communautés végétales multi-espèces dans des prairies perturbées et non fertilisées : développement du modèle de croissance ModVege

	dynamique des espèces végétales individuelles dominantes dans la communauté (présentes ModVege-CoSMo a simulé les fluctuations des types fonctionnels avec une précision
	en moyenne pour au moins 4 % de la période de simulation). suffisante dans tous les traitements, avec des erreurs moyennes quadratiques relatives
	L'évaluation de trois solutions de modélisation -le modèle ModVege pour l'estimation de la (RRMSE) rarement >30%. La précision de ModVege-CoSMo dans la simulation de
	biomasse et sa version couplée avec CoSMo (ModVege-CoSMo) pour les types fonctionnels l'abondance relative des espèces végétales était aussi généralement satisfaisante. Les erreurs
	et les espèces individuelles -a été réalisée pour la production de biomasse et l'abondance de simulation les plus faibles (RRMSE = 11,4 %) ont été obtenues avec Elymus repens
	relative des types d'espèces et des espèces individuelles en utilisant des données (chiendent commun), tandis qu'une autre graminée dominante, Poa pratensis (pâturin des
	pluriannuelles collectées de 2006 à 2018 (sauf 2009 pour l'abondance relative) sur des prairies prés), présentait le plus souvent des valeurs RRMSE <30 %. Une dicotylédone dominante
	multi-espèces fauchées (fertilisées ou non) et non perturbées (abandonnées) dans le Massif dans la fauche (mais absente dans les parcelles abandonnées), Taraxacum officinale (pissenlit),
	central français. En particulier, il a été fait référence au dispositif d'observation à long terme a montré des valeurs RRMSE de 25-30%. Parmi les dicotylédones non légumineuses, les
	ModVege est un modèle basé sur les processus qui estime quotidiennement la quantité et la de Theix (45° 43' N, 03° 01' E, 880 m d'altitude), conçu et mis en oeuvre en 2005 sur une inexactitudes les plus importantes (avec des valeurs RRMSE >100%) concernaient deux
	qualité de la biomasse fourragère dans les prés et pâturages multi-espèces (Jouven et al., surface de ~3 ha structurée en deux blocs, dans le but d'analyser la réponse des prairies et espèces mineures, Galium aparine (gaillet gratteron) et Stellaria media (mouron blanc). Dans
	2006a, b). Le modèle développe l'hypothèse que le comportement d'une communauté pâturages permanents aux changements de pratiques de gestion. Chaque traitement a été l'ensemble, la dynamique de la seule plante légumineuse simulée, Trifolium repens (trèfle blanc),
	herbacée peut être expliqué par les paramètres moyens (traits) de l'espèce d'herbe dominante. répété deux fois dans chaque bloc, ce qui a donné lieu à 28 parcelles. Pour la modélisation, présente dans les pâturages fauchés non fertilisés, a également été reproduite, bien qu'avec
	Plus précisément, ModVege est basé sur six types fonctionnels d'espèces de graminées qui les données moyennes par bloc correspondant aux traitements NPK (fertilisé et fauché trois des valeurs discordantes pour les années plus récentes (RRMSE~70%).
	peuvent être combinés en différentes proportions pour simuler un large éventail de fois par an), Null (non fertilisé et fauché trois fois par an) et Ab (non fertilisé et non fauché)
	communautés végétales, mais ne simule pas les fluctuations de ces proportions dans le temps. ont été utilisées.
	Pour cela, ModVege a été couplé avec CoSMo (Community Simulation Model), dont l'approche Des simulations ont été effectuées pour déterminer (i) la capacité des solutions ModVege
	peut être combinée avec n'importe quel simulateur générique de croissance de la végétation basées sur CoSMo à simuler l'abondance relative des types fonctionnels et des espèces
	pour simuler les communautés végétales à travers un paramétrage moyen qui ne reste pas végétales et (ii) les différences dans la production de biomasse obtenues avec ModVege et
	constant mais varie en fonction de l'abondance relative de chaque espèce ou type d'espèce ModVege-CoSMo. Alors que pour certains paramètres de ModVege des valeurs extraites de
	(Confalonieri, 2014). ModVege fait référence à la typologie fonctionnelle élaborée par Cruz la littérature ont été utilisées, pour d'autres le travail de calibration a été effectué en comparant
	et al. (2002) dans laquelle les espèces herbacées sont caractérisées en fonction de leurs les estimations du modèle avec les données observées, en faisant varier les valeurs des
	particularités morpho-physiologiques qui incluent les stratégies de croissance (capture et paramètres dans des fourchettes plausibles afin d'assurer des paramètres de végétation
	conservation des ressources) et la phénologie (précocité de la floraison et de la maturation). biologiquement interprétables. Les simulations avec CoSMo ont également permis d'évaluer
	La composition fonctionnelle, basée sur l'identification des espèces d'herbes dominantes, les valeurs des paramètres (traits) de la communauté, estimés quotidiennement en fonction
	permet une classification en quatre types principaux : A, B, C et D. Les types A et B ont une de l'abondance relative des espèces ou des types. Dans ce cas, en l'absence d'observations,
	croissance rapide, leur stratégie d'absorption des nutriments dépend de la phénologie et ils les valeurs de deux traits de la communauté végétale (hauteur maximale et surface foliaire
	dominent dans les prairies fertiles, tandis que le contraire est observé pour les types C et D, spécifique) ont été évaluées pour leur variation dans le temps, par rapport aux valeurs
	qui ont une croissance lente, accumulent les nutriments et fleurissent plus tard. D'autres types constantes attribuées aux paramètres correspondants des espèces ou des types fonctionnels
	ont été introduits par Cruz et al. (2010) pour caractériser les espèces à floraison tardive et qui composent la communauté.
	haute dans les sols fertiles (type b) ou pauvres (type d). Pour la production de biomasse, les performances des solutions basées sur CoSMo étaient
	Conformément au niveau de complexité de ModVege, l'étude a d'abord modélisé de manière similaires à celles de ModVege, avec une différence moyenne de <1,2 t ha -1 entre les
	dynamique l'abondance relative des types fonctionnels dans une communauté végétale. simulations et les observations.
	Deuxièmement, un niveau de complexité plus élevé a été ajouté pour représenter la

  of the presence of the different species during the season cannot be reproduced, and (iii) it is unmanageable to simulate the effect of grazing on the different species, both in terms of animal preference and the ability of the grassland to recover. As functional diversity is essential for ecosystem dynamics, stability and productivity, approaches based on temporal

variability of vegetation traits (parameters) have been introduced in dynamic global vegetation models (e.g.

LPJmL, Sackschewski et al., 2015; ORCHIDEE, Peaucelle, 2019)

, thus going beyond the paradigm that condenses the diversity of functional types with constant parameters. The approach for the simulation of a plant community proposed by

Confalonieri (

Table 4

 4

.1) were also observed in terms of botanical composition. At the initial stage, the most abundant species with a cumulative frequency of ~80% were, in block 1, Cerastium glomeratum, Elymus repens, Festuca arundinacea, Lolium perenne, Poa pratensis, Taraxacum officinale, i.e. six species, and in block 2, Agrostis capillaris, Alopecurus pratensis, Holcus lanatus, Lolium perenne, Poa pratensis, Taraxacum officinale, Trifolium repens, Trisetum flavescens, i.e. eight species. 131

Table 4 .

 4 1. Soil properties of the grassland study-site.

	Soil properties	Unit		Block 1		Block 2
	Layer thickness	m	0.10-0.20	0.20-0.40	0.10-0.20	0.20-0.40
	Clay	%	19.7	17.0	23.0	25.0
	Silt	%	26.9	27.4	26.1	24.2
	Sand	%	53.4	55.6	51.0	50.8
	Carbon content	g kg -1	40.3	18.5	43.1	15.1
	pH	-	5.9	6.2	6.0	6.5
	Bulk density	g cm -3	0.94	1.23	0.89	1.18

Table 4 .

 4 2. Relative abundances of dominant species (two blocks and three treatments). Mean values observed over the study period (Obs) were recalculated (Rec) with respect to the dominant species.

		NPK			Null			Ab
		Relative		Relative			Relative
	Species	abundances	Species	abundances	Species	abundances
		Obs	Rec		Obs	Rec		Obs	Rec
					Block 1
	Elymus repens	0.26	0.31	Achillea millefolium	0.04	0.06	Alopecurus pratensis	0.13	0.17
	Festuca						Arrhenatherum
	arundinacea	0.09	0.11	Elymus repens	0.05	0.07	elatius	0.04	0.05
	Lolium						Dactylis
	multiflorum	0.07	0.09	Festuca arundinacea	0.09	0.13	glomerata	0.04	0.05
	Poa pratensis	0.15	0.17	Holcus lanatus	0.05	0.07	Elymus repens	0.27	0.35
							Festuca
	Stellaria media	0.04	0.05	Poa pratensis	0.12	0.17	arundinacea	0.06	0.07
	Taraxacum
	officinale	0.21	0.25	Poa trivialis	0.05	0.08	Galium aparine	0.04	0.05
				Taraxacum
				officinale	0.16	0.22	Poa pratensis	0.16	0.20
				Trifolium repens	0.14	0.29	Urtica dioica	0.04	0.05
	Sum	0.82	1.00	Sum	0.70	1.00	Sum	0.78	1.00
	Nb. of species	6		Nb. of species	8		Nb. of species	8
					Block 2
	Achillea millefolium	0.06	0.07	Achillea millefolium	0.04	0.07	Alopecurus pratensis	0.30	0.46
	Alopecurus			Alopecurus			Arrhenatherum
	pratensis	0.18	0.23	pratensis	0.08	0.11	elatius	0.06	0.08
	Holcus lanatus	0.07	0.08	Festuca arundinacea	0.05	0.07	Elymus repens	0.04	0.07
	Lolium perenne	0.06	0.07	Lolium perenne	0.09	0.13	Holcus lanatus	0.04	0.07
	Poa pratensis	0.17	0.20	Poa pratensis	0.12	0.18	Poa pratensis	0.11	0.18
				Taraxacum
	Stellaria media	0.04	0.05	officinale	0.13	0.18	Urtica dioica	0.09	0.14
	Taraxacum
	officinale	0.15	0.19	Trifolium repens	0.11	0.15

Table 4 .

 4 3. Relative abundances of functional groups (grass species in two blocks and three treatments). Mean values observed over the study period (Obs) were recalculated (Rec) with respect to the dominant groups.

			NPK	Null			Ab
	Function al group	Relative abundances Obs Rec	Relative abundances Obs Rec	Obs	Relative abundances Rec
				Block 1
	A	0.07	0.07	0.21	0.21	0.24		0.24
	B	0.45	0.45	0.49	0.49	0.34		0.34
	b	0.48	0.48	0.30	0.30	0.42		0.42
	Sum	1.00	1.00	1.00	1.00	1.00	1.00
				Block 2
	A	0.47	0.47	0.39	0.40	0.59		0.61
	B	0.33	0.33	0.32	0.32	0.27		0.28
	b	0.20	0.20	0.28	0.28	0.11		0.11
	Sum	1.00	1.00	0.99	1.00	0.97		1.00
	Biomass yield and abundance data for plant types and species used for the modelling work
	are summarised in Table B and Tables

  4.2. Annual anomalies of aboveground biomass (AGB), observed and simulated with alternative modelling solutions in three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (1, 2). Annual mean values of AGB: NPK 1: 2895 kg DM ha -1 ; NPK 2: 2477 kg DM ha -1 ; Null 1: 1417 kg DM ha -1 ; Null 2: 1197 kg DM ha -1 ; Ab 1: 5245 kg DM ha -1 ; Ab 2: 4683 kg DM ha -1 .Overall, the accuracy of ModVege-CoSMo in simulating the relative abundance of plant functional types was satisfactory (Table L in Supplementary material, Figure4.3). Type b had the highest correlation coefficient in block 2 NPK (R=0.95), and type A had the lowest in block 2 abandonment (R=0.04). However, the model accurately simulated the fluctuations of functional types with sufficient accuracy in all treatments, with RRMSE rarely >30% (i.e.

	3.2. Evaluation of modelling solutions for grassland plant diversity
	3.2.1. Relative abundance of plant (grass) functional types

  Galium aparine was notably absent in the first years of the study period(from 2006 to 2012), when the model estimated some presence of this species (FigureO). Observed and simulated means for all grasses or forbs tend to converge, with a maximum departure of 6% abundance in block 1 Ab (0.90 versus 0.84 for grasses and 0.10 versus 0.16 for forbs).

	, Figure 4.4). For the grasses, i.e. the dominant taxonomic
	group, the mean RRMSE is <40% (RMAE<30%). Mean RRMSE~50% (RMAE~40%)
	calculated for forbs reflects the inaccuracies associated with two minor species (with three
	RRMSE values >100%): Galium aparine (block 1 abandonment; Figure O) and Stellaria media
	(NPK in both blocks; Figures K and L). While both are present at the 4% limit on average
	(Table 2),

Of grass species, Festuca arundinacea (RRMSE~38-56%) and Lolium perenne (RRMSE~24-34%) had the best R-values, respectively in the abandoned plot of block 1 (R=0.91) and in the fertilised plot of block 2 (R=0.90). The worst R-value, observed for Trisetum flavescens in block 2 Null (R=0.04), reflects only a few data departing from the observations at the end of the simulation period (e.g. 2015, 2016, 2017; Figure N), which are compatible with satisfactory error amounts (RRMSE=36.4%, RMAE=27.4%). The lowest simulation errors (RRMSE=11.4%, RMAE=8.5%) were obtained with Elymus repens in the abandonment of block 1 (Figure O). Another dominant grass, Poa pratensis (Figures K-P), shows mostly RRMSE<30% -in NPK 1 and 2 (Figures K-L

Table F

 F

	), dominant b-type grasses (with 31% and 17%,
	respectively; Table 4.2) with the environmental and management factors at work in the
	fertilised block 1 community. In this treatment, the modelled decline in community SLA is
	largely explained by the modelled (and observed) decline of Taraxacum officinale
	(Supplementary material, Figure K), a forb with high SLA (48 m 2 kg -1 ; Supplementary
	material, Table G). Likewise, in block 2 NPK, dominated by the A-type grass Alopecurus
	pratensis (with 23% and SLA=44 m 2 kg -1 ; Table 4.2 and Supplementary material, Table G),
	community SLA declines below ~35 m 2 kg -1 as a consequence of the decline of Taraxacum

officinale

(Supplementary material, Figure L)

.

With regard to the estimated values of maximum canopy height, their increase in the Ab community of block 1 reflects the (observed and simulated) increased abundance of two tall grass species, Alopecurus pratensis and Arrhenatherum elatius (Supplementary material, Figure

O

), which have calibrated maximum heights of 1.1-1.2 (Supplementary material, Table

J

).

  Plants experienced some dry conditions early in the growing season in subsequent years with, for instance, a total reported precipitation of 161 mm from January to May in 2011 against ∼300 mm in other years (from 211 mm in 2015 to 453 mm in 2016). In particular, in 2011, precipitation data indicate a relatively dry winter and spring (309 mm of total precipitation between November 2010 and June 2011), with a water deficit in April and soil water content remaining below average during May

Table

B

in Supplementary material) because the soil was waterlogged and the harvesting machine could not be put into the field earlier (due to the reduced bearing capacity of the soil). The biomass measurements were thus carried out on a canopy that had accumulated a lot of biomass at a very early stage of reproductive development. The high biomass observed in the first determination (after the clean-up determination) of 2008 (up to ~9 t DM ha -1 in block 1 NPK, Table

B

in Supplementary material), compared to other years, is thus more likely due to a late harvest (which allowed time for biomass accumulation) than to different weather conditions.

Table 4 .

 4 4. Evaluation of alternative modelling solutions for the simulation of aboveground biomass in the summer sampling of the abandoned plots (two blocks). Grey cells indicate the best performance. FG: functional groups.

			Obser	ModVege	ModVege-CoSMo FG	ModVege-CoSMo Species
	Year	doy	ved biomass	Simul ated biomass	Differe nce	Simul ated biomass	Differe nce	Simul ated biomass	Differe nce
						Block 1
		200	6742	6302	-440	5509	-1233	5503	-1239
		213	5781	8465	2684	7581	1800	7678	1897
		218	8116	7955	-161	7049	-1067	7072	-1044
		-	-	-	-	-	-	-	-
		201	7734	6372	-1362	5445	-2289	5592	-2142
		201	5674	4736	-938	3848	-1826	3675	-1999
		201	8560	6934	-1626	5992	-2568	6111	-2449
		198	6655	6495	-160	5587	-1068	5579	-1076

Table 5 .

 5 1. Soil properties of the study-site. Grey cells indicate estimated values (by the SPAW -Soil-Plant-Air-Water model, https://www.nrcs.usda.gov/wps/portal/nrcs/detailfull/national/?&cid=stelprdb1045331).

	Soil properties	U nit		Block 1
	Soil depth	m		0.60
	Layer thickness	m 0.20	0.20	0.20
	Clay	% 33.9	34.9	37.6
	Silt	% 23.0	23.5	25.4
	Sand	% 43.1	41.6	37.0
	pH	-	8.2	8.3	8.3
	Bulk density	g cm -3 1.44	1.44	1.44
	Hydraulic conductivity at saturation	mm d -1	0.46	0.46	0.46
	Field capacity	m 3 m -3 0.33	0.34	0.36
	Wilting point	m 3 m -3 0.20	0.22	0.24

  Table M in Supplementary material). In G25L75 (Figure D), Avena sativa reached an RRMSE value of ~101%, but the difference between the observed and simulated means is ~6%, with a high correlation coefficient (R=0.92). Lolium rigidum, Trifolium

	michelianum and Trifolium subterraneum showed lower RRMSE values, between ~40% and
	~18%, with R-values up to 0.99 for Trifolium michelianum. For this mixture, taking into account
	the performance metrics for taxonomic aggregations (Supplementary material, Table N), we
	observed that legumes were better simulated than grasses (RRMSE ~21% for legumes and
	~55% for grasses; RMAE ~15% for legumes and ~48% for grasses). The performance of
	the G50L50 (mixed stand simulation, Figure E) was comparable to that of G25L75. Even in
	this case, Avena sativa showed a high RRMSE (~110%) associated with a small mean
	difference (~6%) between observations and simulations. In G75L25 (Figure F), the legume
	simulations showed higher RRMSE values than the other two mixed stands (~60% for
	Trifolium michelianum and ~40% for Trifolium subterraneum; Table M), which were still better
	simulated than grasses when aggregated (RRMSE~46% for legumes, RRMSE~52% for
	grasses; Table N).

Reference Country Climate classification Grassland characteristics Mowing technique Mowing time Botanical method Observat ion period Cuttin g height (cm) Plot size (m 2 ) Durati on (years) Fertilisati on / other practices

	Beltman et al. (2003) Benot et al. (2013) Berg et al. (2016) Billeter et al. (2007) Bobbink and Willems (1993) De Cauwer and Reheul (2009) Dee et al. (2016) Dickson and Foster (2008) Doležal et al. (2019) Fenner and Palmer (1998) Lundberg et al. (2017) Ma et al. (2017) Wang et al. (2012) Mašková et al. (2009) Maurer et al. (2006) Metsoja et al. (2014) Moinardea u et al. (2019) Neuenkam p et al. (2013) Opdekamp et al. (2012) Pavlů et al. (2011) Pecháčkov á et al. (2010) Peet et al. (1999) Peintinger and Bergamini (2006) Pruchniewi cz and Eskelinen et al. (2017) Gedan and Bertness (2009) Gornish and Miller (2015) Grant et al. (2017) Grime et al. (2008) Hoeppner and Dukes (2012) Hollister et al. (2005) Hollister et al. (2015) Olsen and Klanderud (2014) Zhang et al. (2017a) Hou et al. (2013) Jingxue et al. (2019) Jónsdóttir et al. (2005) Klanderud and Totland (2005) Klein et al. (2004) Price and Waser (2000) Shi et al. (2015) Su et al. (2019) Zhang et al. (2015) Yang et al. (2016) Yang et al. (2011) Yan et al. (2015) Press et al. (1998) Xu et al. (2015) Pfeifer-Meister et al. (2016) Zhang et al. (2017b) Wang et al. (2017) Zhu et al. (2015)	Ireland France Estonia Switzerlan d Netherlan d Belgium USA -OK USA -KS Czech Republic England Norway China China Czech Republic Switzerlan d Estonia France Estonia Poland Czech Republic Czech Republic Nepal Switzerlan d Poland Finland USA -RI USA -FL Germany United Kingdom USA -MA USA -AK USA -AK Norway China China China I ce la n d Thin gvellir (64° 17′ N, 21° 05′ W) Aud di (65°1 6′ N, 20°15′ W) Norway China USA -CO USA -OK China China China China China kuluhei Sweden China USA-OR USA -WA China USA -OR China China	Cfb ET Dfb Cfb Cfb Cfb Cfa Dfa Cfb Cfb Dfb Bsk ET Cfb Dfb Dfb Csa Dfb Cfb Cfb Cfb Cwa Dfb Cfb Dfc Dfb Cfa Dfa Cfb Dfb ET ET EF ET Bsk ET ET EF ET ET Cfa ET Bsk ET ET Dwa Dfc Bsk Csc ET Bsk Dwb	species-rich limestone subalpine wetland (substrate composed of marine sands covered by saline littoral soil) montane calcareous wet Chalk wetland tallgrass prairie secondary grassland dominated by Bromus inermis and Poa pratensis (introduced perennial C3 grasses) oligotrophic wet meadow neutral grassland community on a sandy soil semi-natural dune meadows (plots seeded with seven common plant Alpine grassland alpine meadow 2: alpine steppe species-rich mountain meadow grassland parcels 1: tall forb meadow 2: sedge meadow artificial dykes 1: tall forb meadow 2: sedge meadow fen meadow mountain hay meadow mountain NA calcareous fen meadows mountain meadows species) species-rich treeless tundra meadow salt marsh (three sites) native grasses and legumes for agricultural use semi-natural grassland unproductive , grazed grassland old-field herbaceous community infrared heaters manual removal manual removal scissors removal manual removal electric hand mowing device NA NA manual removal mowing removal NA motorized infrared radiators infrared heaters (daytime), May-June or August August end July mid-September end August early September June March or June or September June April, June, August, October June and August August 2 1.28 1.78 (night-grass mower manual removal July NA NA NA June NA May scissors removal late July NA July NA mid July scythe removal mid July manual removal early December manual removal late summer NA mid-June, mid-hexagonal open-top chamber 1.58 open-top chamber 1.40 to September Braun-Blanquet (1964) scale cover with point quadrat percent cover values visual cover estimate biomass sorting De Vries (1948) rank method abundance cover values percent cover values biomass sorting into species percent cover values cover abundance scale by Hult (1881) open-top chambers installed NA all the time estimation June or August before the peak of biomass end July, before cut late July or early August August June and July May June April, June, August, October late June August every two biomass sorting visual and Sernander (1912) years Braun-Blanquet scale July Braun-Blanquet scale NA cover percent values June Braun-Blanquet scale NA cover percent values NA point quadrat beginning of July cover percent values every year point quadrat or cover percent values before cut, July cover percent values late November-December record of all species late summer record of all species NA spring and autumn point-transect method taking data from 100 points in each transect every 0.3 m (Daget and Poissonet 1971) from May percent cover hexagonal open-top chamber 2.50 all the time percent cover infrared heaters 0.90-1.30 winter or summer Biomass sorting September ≤ 5 ≤ 5 ≤ 5 ≤ 5 ≤ 5 NA NA ≥ 10 ≤ 5 NA ≤ 5 late August end of August ≤ 5 ≤ 5 NA NA ≤ 5 NA NA ≤ 5 ≥ 10 NA NA end of July June and August August June-soil surface heating 3.00 November-April point-quadrat June-July ceramic 1: 1.00 all the time record of all species summer 2: 2.70 3: 4.00 dry heath/wet meadow hexagonal open-top chamber 1.40 growing season point-framing method mid-July early August tundra hexagonal open-top chamber 1.40 every year shortly after snowmelt and point-framing method NA tundra hexagonal open-top chamber 1.50 growing season record of all species summer time) temperate steppe infrared radiators 1.10 all the time biomass August sorting 1: removed at the 1: 0.85 0.15 alpine end of the growing season desert steppe infrared lamps 4.1 (2011), 1.58 (2012) all the time record of all species late August alpine steppe infrared lamps 2.0 growing season record of all species mid-August ndra com munit ies heath hexagonal open-top chamber 1.50 summer point-framing method end of June species-rich dwarf shrub heath heathland open-top chamber 1.50 all the time record of all species August four sites: meadow and shrubland open-top chamber 1.30 all the time record of all species mid-July subalpine meadow electric heaters 1.20 all the time all species June tallgrass prairie not grazed for ~ 40 years infrared heaters 1.75 all the time point-quadrat method August semiarid steppe hexagonal open-top chamber 0.33 not specified biomass sorting (soil-level mowing at the peak of biomass) 1: alpine meadow conical open-top chamber 1.73 all the time record of all species to August from July semi-arid temperate steppe infrared radiators 0.71-0.78 daytime and night-time all species August record of August temperate steppe infrared radiators 1.14 March to November estimation August visual from homogeneou s grassland dominated by Chloris virgata Sw. hexagonal open-top chamber 0.93 and October specified NA June, August not mid-record of 2: tundra open-top tents 4.00 from May to September point-intercept method first half of August alpine meadow infrared heaters NA all the time cover values NA percentage tu species-123° 38′ W) moss (42°16′ N, m poor ies Creek Center 3: 2.05 1.80 1: M Tenalquot Prairie (46° 53′ N, 122° hexa m meadow (with a history of low or high grazing open-top chamber 1.10 all the time record of all species NA gonal ed 44′ W) intensity) open-ite rr an ea air 3: Deer ) pr 123°10 W) diameter n infrared radiators 2.75 growing season point-intercept method May-June 2: Willow Creek (44° 01′ N, alpine meadow top chamber (bott om all the time biomass sorting August 2: 1.45 m 1.10 meadow steppe infrared radiators 1.70 from March to November record of all species from June to September	1 1 4 0.04 0.12 5 0.01 1 2 0.04 0.25 1 0.07 1.00 1 25 1 100 00 1 4 25 1 1 0.04 NA 0.56 0.25 1.00 0.81 0.18 0.25 and 0.5 1 1 0.36 0.15 1 1 0.5625 0.18 0.56 0.06 2.00 0.25 0.25 1 1 0.04 0.56 0.06 0.56 1 0.25 0.06	11 2 5 18 13 16 1, 2, 3, 4 average of four years 10 10 12 10 1, 13 1, 1, 2, 5, 12, 17 0, 3, 7, 12 5 1, 2 2 3-5 1, 4 4 0, 1, 2, 3 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 1 0.25 2 1, 2, 3, 4, 5 3 5 2 1, 2, 3, 4, 18 0, 1, 2 0, 1, 2, 3, 4, 5, 6, 7 3	No No No No No No No 16 g N m -2 yr -1 No No No Plant biomass removal Estimation using cover and plant Plant biomass sampling No No No No No No No No No No No Plant biomass removal No Plant biomass removal Mowing in June and September Mowing in October 0.015 to 0.100 m-height hand clipping in June and at the end of growing season No No No height Plant biomass sampling No Plant biomass removal No Plant biomass removal No No mowing at the biomass peak of No Soil-level No No No No sampling biomass Plant No No Sampling at the peak of biomass Mid-August plant biomass sampling
				206

  𝑆𝑓𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑖 (𝑡) = 𝑚𝑎𝑥 (0; (𝑆𝑓𝑁𝐴𝑣 𝑖 (𝑡); 𝑆𝑓𝑁𝑅𝑒𝑞 𝑖 (𝑡)) ) is the soil moisture factor at time t, and TF(t) is the temperature response at time t. The term SMF(t) is calculated as: 𝑖𝑓 𝑆𝑊𝐶 𝑆 -0.1 ≤ 𝑆𝑊𝐶 ≤ 𝑆𝑊𝐶 𝑆 where SWCS is the soil water content at saturation (m 3 m -3 ), SWCWP is the soil water content at wilting point (m 3 m -3 ) and SWC(t) is the water content of the topsoil (0-0.3 m depth) at time t (m 3 m -3 ), estimated from the water reserve (WRi(t)) and the water holding capacity for group/species i (WHCi) both in mm:

	0.6 + 4 • (𝑆𝑊𝐶 𝑆
	-𝑆𝑊𝐶(𝑡))
	𝑆𝑊𝐶(𝑡) =	𝑊𝑅 𝑖 (𝑡) • 𝑆𝑊𝐶 𝑠 𝑊𝐻𝐶 𝑖
	under the assumption that
	𝑊𝑅 𝑖 (𝑡) 𝑊𝐻𝐶 𝑖	=	𝑆𝑊𝐶(𝑡) 𝑆𝑊𝐶 𝑠	.
	The term TF(t) is equal to (0-1, unitless):
	𝑇(𝑡)-20 10 SfNAvi(t) is the factor representing the nitrogen availability for group/species i (0-1, 𝑇𝐹(𝑡) = 2.85 • where T(t) is the current temperature (°C).
	unitless), calculated as: The term SfNReqi(t) is calculated as:
	𝑆𝑓𝑁𝐴𝑣 𝑖 (𝑡) = {𝑆𝑀𝐹(𝑡) • 1 0 𝑆𝑊𝐶(𝑡) -𝑆𝑊𝐶 𝑊𝑃 0.08 𝑅𝐺𝑅𝐿𝐴𝐼 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 (𝑡) -𝑅𝐺𝑅𝐿𝐴𝐼 𝑖,𝑚𝑎𝑥 𝑇𝐹(𝑡) 8.1225 1 𝑖𝑓 𝑛𝑜𝑡 𝑙𝑒𝑔𝑢𝑚𝑒 𝑖𝑓 𝑙𝑒𝑔𝑢𝑚𝑒 < 𝑆𝑊𝐶 𝑆 -0.1 𝑖𝑓 𝑆𝑊𝑃 + 0.08 ≤ 𝑆𝑊𝐶 𝑖𝑓 𝑆𝑊𝐶 < 𝑆𝑊𝐶 𝑊𝑃 𝑖𝑓 𝑆𝑊𝐶 𝑊𝑃 ≤ 𝑆𝑊𝐶 < 𝑆𝑊𝐶 𝑊𝑃 + 0.08 𝑆𝑓𝑁𝑅𝑒𝑞 𝑖 (𝑡) = { 𝑅𝐺𝑅𝐿𝐴𝐼 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 (𝑡) + 𝑅𝐺𝑅𝐿𝐴𝐼 𝑖,𝑚𝑎𝑥 where SMF(t) 𝑆𝑀𝐹(𝑡) = { • 𝐿𝑁𝐶 𝑖,𝑜𝑝𝑡 2 𝑖𝑓 𝑣𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑣𝑒 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡 1 𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒

Table C .

 C Relative abundance of functional typologies(A, B, b) in three treatments (NPK: fertilised; Null: unfertilised; Ab: abandoned) and two blocks (doy: day of year).

			A		0.10		0.25		0.25	0.48		0.29	0.62
		2015	B	127	0.44	129	0.40	136	0.33 126 0.24	127	0.31	0.29
							Block 1				Block 2
			b		0.46		0.35		0.42	0.28		0.40	0.09
	2016	Treatme nt Block 1 2 57 146 202 287 57 146 202 292 NPK 0.03 Survey date (doy) Relative abundance 124 0.10 0.40 0.50 124 0.60 0.32 121 0.06 0.44 0.50 120 0.49 0.48 123 0.03 0.48 0.49 120 0.08 0.45 215 Year NPK Null 35 51 40 49 57 129 17 65 3498 57 85 145 6933 3404 148 5500 146 5213 201 444 180 202 4057 202 136 285 1735 735 286 4462 286 1470 57 23 0 57 3020 57 29 145 6077 2239 146 3548 146 4957 201 3898 1798 202 4790 202 3023 291 196 84 292 5620 292 Functional Null Ab NPK A 0.27 0.22 0.43 typology Survey date (doy) Relative abundance Survey date (doy) Relative abundance Survey date (doy) Relative abundance A 123 0.10 124 0.12 0.66 B 0.60 0.33 122 0.24 b 0.30 0.55 0.10 124 123 B 0.58 0.40 122 0.38 b 0.22 0.44 0.11 A 124 0.15 121 0.25 0.60 B 0.52 0.32 119 0.28 b 0.34 0.43 0.12 2016 124 139 B 0.44 0.41 120 0.34 b 0.29 0.37 0.23 2017 A 124 0.23 130 0.28 0.40 B 0.52 0.26 122 0.34 b 0.25 0.46 0.26 2018 A 122 0.24 158 0.35 0.39 B 0.55 0.32 117 0.34 A 0.08 0.20 0.16 0.51 b 0.48 0.21 0.33 0.27	Ab 38 35 0.26 123 57 8 146 2867 202 110 286 542 57 0 146 2026 202 1496 Null Ab 0.28 Relative abundance Survey date (doy) 123 Survey date (doy) 0.57 0.17 124 0.32 123 0.19 121 0.64 0.16 118 0.20 123 0.33 0.39 123 0.31 0.34 0.35 120 0.26 0.49 0.49 0.25	65 148 202 286 57 146 202 0.65 Relative abundance 0.56 0.19 0.25 0.31 0.13 0.67 0.23 0.10 0.27 0.08 0.74 0.17 0.09 0.75 0.15 0.56 0.10	2258 3736 3652 3670 1620 3462 5132
									-
			A		0.04		0.21		0.29	0.52		0.49	0.59
			B	118	0.32	117	0.47	131	0.32 117 0.28	117	0.26 131	0.30
			b		0.64		0.32		0.39	0.20		0.25	0.11
			A		0.07		0.19		0.21	0.46		0.41	0.50
			B	124	0.42	123	0.46	130	0.37 122 0.34	123	0.31 132	0.40
			b		0.51		0.35		0.42	0.20		0.28	0.10
			A		0.07		0.26		0.24	0.44		0.42	0.53
			B	124	0.44	124	0.40	127	0.38 123 0.34	124	0.30 126	0.40
			b		0.49		0.34		0.38	0.22		0.28	0.07
			A		0.07		0.19		0.24	0.40		0.33	0.57
			B	127	0.43	128	0.49	137	0.32 126 0.40	126	0.38 136	0.30
			b		0.50		0.32		0.44	0.20		0.29	0.13
			A		0.05		0.25		0.22	0.39		0.31	0.59
			B	124	0.47	125	0.47 134 0.38 121 0.39	124	0.38 134	0.32
			b		0.48		0.28		0.40	0.22		0.31	0.09
								231

Table F .

 F Functional type-specific parameters of ModVege(Jouven et al., 2006) * .

				Water
			WHC	holding	100	100	100	100	100	100
				capacity (mm) Maximum	Block 1			Block 2
	Functi onal type	Parameter MinSEA	Description value of the winter growth factor Maximum	NPK 0.7	Null 0.7	Ab 0.7	NPK 0.7	Null 0.7	0.7 Ab
	A b B	Parameter WR Alpha_PAR Tbase T0 ST1 MaxSEA Description Water reserve (mm) Light extinction coefficient Base temperature subtracted each day for the calculation of plant development (°C) Temperature Onset of the NPK 60 0.044 0 reproductive value of the summer growth factor phase (°C-d) ST2 Initial End of the reproductive W_GV biomass of 1070.33 Block 1 Null 60 0.044 0 600 1.3 1200 1200 600 1.3 1070.33 0.044 Ab 60 0 1070.33 NPK 60 0.044 0 600 1.3 1200 1070.33 Block 2 Null 60 0.044 0 600 600 1.3 1.3 1200 1200 1070.33 1070.33 Ab 60 0.044 0 1.3 600 1200 GV (kg ha -1 ) phase (°C-d) IN Soil nutritional index 0.85 0.85 0.85 0.85 0.85 SLA Specific leaf 0.025 0.025 0.025 0.025 0.025 0.025 area (m² g -1 ) 0.85 WHC LLS Leaf lifespan 800 800 800 800 800 800 (°C-d) Water holding 200 200 200 200 200 200 W_GR Biomass of 218.08 218.08 218.08 218.08 218.08 218.08 GR (kg ha -1 ) capacity (mm) MinSEA Maximum W_DV Biomass of 493.70 493.70 493.70 493.70 493.70 493.70 DV (kg ha -1 ) value of the winter growth 0.8 0.8 0.8 0.8 0.8 0.8 W_DR Biomass of DR (kg ha -1 ) 80.64 80.64 80.64 80.64 80.64 80.64 factor threshold for photosynthesis activation (°C) 4 4 4 4 4 4 T1 Optimal MaxSEA Maximum value of the summer 1.2 1.2 1.2 1.2 1.2 Ini_AGE_GV Initial value 310.79 310.79 310.79 310.79 310.79 310.79 of age (°C-d) 1.2 Ini_AGE_GR Initial value of age (°C-d) 1936.77 1936.77 1936.77 1936.77 1936.77 1936.77 growth factor temperature for growth 10 10 10 10 10 10 W_GV Initial biomass of 1219.96 1219.96 1219.96 1219.96 1219.96 1219.96 Ini_AGE_DV Initial value 330.92 330.92 330.92 330.92 330.92 330.92 of age °C-d) (°C) T2 Temperature threshold for growth decline (°C) 20 20 20 20 20 GV (kg ha -1 ) SLA Specific leaf area (m² g -1 ) 0.044 0.044 0.044 0.044 0.044 Ini_AGE_DR Initial value 1339.93 1339.93 1339.93 1339.93 1339.93 1339.93 of age (°C-d) 0.044 Onset of the 20 rho_GV LLS Leaf lifespan (°C-d) 500 500 500 500 500 ST1 reproductive 600 600 600 600 600 600 500 phase (°C-d) Bulk density of GV (g m -3 ) 850 850 850 850 850 850 percentLAM Fraction of leaf of laminae in GV 0.68 0.68 0.68 0.68 0.68 0.68 Rho_GR Bulk density of GR (g m -3 ) 300 300 300 300 300 300 K_VS Senescence coefficient DV (°C d -1 ) 0.002 0.002 0.002 0.002 0.002 0.002 Kl_VS Abscission coefficient DV (°C d -1 ) 0.001 0.001 0.001 0.001 0.001 0.001 Rho_VS Bulk density of DV (g m -3 ) 500 500 500 500 500 500 K_RS Senescence coefficient DR (°C d -1 ) 0.001 0.001 0.001 0.001 0.001 0.001 Kl_RS Abscission coefficient of DR (°C d -1 ) 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 Rho_RS Bulk density of DR (g m -3 ) 150 150 150 150 150 150 W_GR Biomass of 151.13 151.13 151.13 151.13 151.13 End of the 151.13 ST2 reproductive 1100 1100 1100 1100 1100 1100 GR (kg ha -1 ) W_DV phase (°C-d) Biomass of 645.6 645.6 645.6 645.6 645.6 645.6 Soil DV (kg ha -1 ) W_DR IN nutritional 0.9 0.9 0.9 0.9 0.9 0.9 Biomass of 62.35 62.35 62.35 62.35 62.35 62.35 index DR (kg ha -1 ) Ini_AGE_GV Water Initial value 267.76 267.76 267.76 267.76 267.76 267.76 WHC holding 100 100 100 100 100 100 of age (°C-d) Ini_AGE_GR capacity (mm) Initial value 2006.55 2006.55 2006.55 2006.55 2006.55 2006.55 Maximum of age (°C-d) Ini_AGE_DV Initial value 288.35 288.35 288.35 288.35 288.35 288.35 MinSEA value of the 0.7 0.7 0.7 0.7 0.7 0.7 winter growth of age °C-d) Ini_AGE_DR factor Initial value 1461.04 1461.04 1461.04 1461.04 1461.04 1461.04 Maximum of age (°C-d) ST1 Onset of the reproductive 700 700 700 700 700 MaxSEA value of the 1.3 1.3 1.3 1.3 1.3 1.3 summer 700 growth factor phase (°C-d) ST2 Initial End of the reproductive 1350 1350 1350 1350 1350 W_GV biomass of 1364.65 1364.65 1364.65 1364.65 1364.65 1364.65 1350 GV (kg ha -1 ) phase (°C-d) IN Soil nutritional index 0.90 0.90 0.90 0.90 0.90 SLA Specific leaf 0.035 0.035 0.035 0.035 0.035 0.035 area (m² g -1 ) 0.90 LLS Leaf lifespan (°C-d) 830 830 830 830 830 830
					240

Table H .

 H CoSMo parameters common to all functional types or species (Cal: calibrated; Def: set to default value).

	Ini_AGE_D V	Initial value of age °C-d)	Cal	376.45	321	376.45	376.45	321	376.45
	Ini_AGE_D R Parameters	Initial value of age (°C-Description Cal d) Optimal leaf	1209.46 Reference	1383 NPK	1209.46 Block 1 Null	1209.46 Ab	1383 Block 2 1209.46 NPK Null Ab
	IncOpt AnimalLiking	Descriptio n nitrogen concentration Refere nce (g m -2 ) Liking of grazing animals for a	Cal Achillea millefoliu m Def	2 Galiu m aparin e 0	Forbs 2 Stellari a media 0	2 Taraxacu m officinale 0	2 0	Urtic a dioic a	2 0	Legume 2 Trifoliu m repens 0
		Onset of the plant type or
	ST1	reproductiv e phase (°C-species Capacity	Cal to	850	800	600	600	800	800
	d) RestartCapacity	restart after	Cal	0.8	0.8	0.8	0.8	0.8	0.8
		End of the mowing
	ST2	reproductiv e phase (°C-	Cal	1550	1200	1100	1100	1200	1200
		d)
		Soil
	IN	nutritional	Cal	0.8	0.9	0.9	0.9	0.9	0.9
		index
		Water
	WHC	holding capacity		Cal	100	100	100	100	100	100
		(mm)
		Maximum
		value of the
	MinSEA	winter		Cal	0.7	0.7	0.7	0.7	0.7	0.7
		growth
		factor
		Maximum
		value of the
	MaxSEA	summer		Cal	1.3	1.3	1.3	1.3	1.3	1.3
		growth
		factor
		Initial
	W_GV	biomass of	Cal	1000	1144	1000	1000	1144	1000
		GV (kg ha -1 )
	SLA	Specific leaf area (m² g -1 )	Cal/Li t	0.017	0.048	0.030	0.048	0.023	0.018
		Leaf
	LLS	lifespan (°C-	Cal	800	800	800	800	800	800
		d)
		Biomass of
	W_GR	GR ha -1 )	(kg	Cal	150	196	150	150	196	150
		Biomass of
	W_DV	DV ha -1 )	(kg	Cal	500	535	500	500	535	500
		Biomass of
	W_DR	DR ha -1 )	(kg	Cal	60	75	60	60	75	60
	Ini_AGE_G V	Initial value d) of age (°C-	Cal	357.28	302	357.28	357.28	302	357.28
	Ini_AGE_G R	Initial value d) of age (°C-	Cal	1783.87	1961	1783.87	1783.87	1961	1783.87
								244

Table I .

 I Functional type specific CoSMo parameters (calibrated).

				Maximum
			MaxRootingDepth	rooting	1.4	1.4	1.4	1.3	1.3	1.3
				depth (m)		Block 1			Block 2
	Functional	type	Parameter Shoot/root ratio Legume Legume	Description Shoot-root ratio Legume species Legume species Maximum	NPK 0.85 False False	Null 0.85 False False	Ab 0.85 False False	NPK 0.79 False False	Null 0.79 False False	Ab 0.79 False False
			rgrLAImax rgrLAImax	LAI increase (m² d -1 ) Maximum LAI increase	0.52 0.55	0.52 0.55	0.52 0.55	0.52 0.55	0.52 0.55	0.52 0.55
			DT DT	(m² d -1 ) Drought tolerance Drought tolerance Maximum	0.64 0.59	0.60 0.64	0.62 0.68	0.58 0.64	0.57 0.58	0.60 0.75
	A b		LAImax LAImax	Maximum leaf index (m² m -2 ) area leaf area index (m² m -	12.04 12.77	12.04 12.77	12.04 12.77	12.04 12.77	12.04 12.77	12.04 12.77
				2 ) Maximum
			MaxHeight MaxHeight	canopy height (m) Maximum canopy	1.5 1.74	1.5 1.74	1.5 1.74	1.5 1.74	1.5 1.74	1.5 1.74
				height (m) Maximum
			MaxRootingDepth MaxRootingDepth	rooting depth (m) Maximum rooting	1 1	1 1	1 1	1 1	1 1	1 1
			Shoot/root ratio Shoot/root ratio	depth (m) ratio Shoot-root Shoot-root ratio	0.67 0.57	0.67 0.57	0.67 0.57	0.67 0.57	0.67 0.57	0.67 0.57
			Legume	Legume species	False	False	False	False	False	False
				Maximum
			rgrLAImax	LAI increase	0.39	0.39	0.39	0.39	0.39	0.39
				(m² d -1 )
	B		DT	Drought tolerance Maximum	0.63	0.63	0.60	0.62	0.61	0.58
			LAImax	leaf index (m² m -area	9.12	9.12	9.12	9.12	9.12	9.12
				2 )
				Maximum
			MaxHeight	canopy	1.64	1.64	1.64	1.64	1.64	1.64
				height (m)
					246

Table A .

 A Sowing rate of pure and mixed stands in Santa Lucia experimental site. G50L50: 50% grass species-50% legume species; G75L25: 75% grass species-25% legume species; G25L75: 25% grass species-75% legume species.

	Plant species	Pure stand		Sowing rate( kg ha -1 ) Mixed stands
			G50L50	G25L75	G75L25
	Avena sativa	130	32.5	16.3	48.8
	Lolium rigidum	40	7.5	3.8	11.3
	Trifolium michelianum	10	2.5	3.8	1.3
	Trifolium subterraneum	30	9.4	14.1	4.7

Table B .

 B Fertilisation rates of pure and mixed stands in Santa Lucia experimental site. G50L50: 50% grass species-50% legume species; G75L25: 75% grass species-25% legume species; G25L75: 25% grass species-75% legume species.

			G25L75	170.2	-	-	202.9
			G75L25	167.5	-	-	Missing value
			Avena sativa	171.8	184.6	167.3	-
			Lolium rigidum	114.6	166.9	171.2	-
		frequency	Trifolium michelianum	219.5	170.7	172.5	-
		mowing	Trifolium subterraneum	162.4	170.6	178.2	-
		High	G50L50 G25L75	177.6 170.2	184.8 153.0	165.1 217.9	--
			G75L25	167.5	188.4	239.1	-
					Fertilisation rate (kg ha -1 )
			Treatments		Before sowing		After sowing
				N	P		N
			Avena sativa	30	70		60
			Lolium rigidum	30	70		60
	Pure stands	Trifolium michelianum Trifolium subterraneum	--	70 70		--
			G50L50	15	70		30
	Mixed	stands	G75L25 G25L75	23 8	70 70		15 45

Table D .

 D Relative abundances of species observed in three mixed stands for two management regimes in Santa Lucia experimental site. G50L50: 50% grass species-50% legume species; G75L25: 75% grass species-25% legume species; G25L75: 25% grass species-75% legume species.

		High	Maximum
		Temperat	temperature	for			Man		32	32	32	32
		ure	vernalisation (°C)
		GLAImin	Minimum initial green leaf area index (m 2 m -2 )			Cal		0.0 45	0.0 50	0.0 45	0.0 45
		NTC	Night temperature critical (°C)			Cal		10	2	10	10
		NTM	Maximum night temperature (°C)			Cal		22	18	20	20
			Factor to convert
			global	solar
		PARf	radiation	to			Cal		0.5	0.5	0.5	0.5
			photosynthetically
			active radiation
		Photo Inhibition	Light time to start flowering (h)			Man		8	8	8	8
		Photo Insensitivi ty	Light time to stop flowering (h)			Man		13. 5	13. 5	13. 5	13. 5
			Maximum
		RUE	radiation	use			Cal		2.5	2.7	2.5	2.5
			efficiency (g MJ -1 )
		ReGrowt
		h	Regrowth
		Developm	development stage			Cal		1	1	3	1
		ent Stage	critical
		Critical
		SLA	Specific leaf area (m² kg -1 )			Exp		25	23	18	17
	Treatment	Management STP	Steam/leaf Date partition coefficient (m² kg -		Avena sativa	Cal	Lolium rigidum	2.5	Trifolium michelianum 2.0 1.5	Trifolium subterraneum 1.5
			1 )	11-03			0.10		0.11			0.50	0.29
	G25L75	High mowing GDDgf frequency GDDe Low mowing frequency GDDeglai	14-04 Thermal time to begin yield 06-05 formation (°C-d) 11-04 Thermal time for emergence (°C-d) 13-05 Thermal time to end green leaf area	0.06 0.03 0.10 0.21	Cal Cal Cal	0.18 0.29 0.11 0.27	1100 300 1000	1000 300 800	0.36 900 0.28 0.50 350 0.25 600	0.40 0.40 0.29 0.28
			index (°C-d)
	G50L50	High mowing frequency GDDf GDDm	11-03 14-04 Thermal time for flowering (°C-d) 06-05 Thermal time for maturity (°C-d)		0.14 0.03 0.01	Cal Cal	0.17 0.34 0.52	1000 1200	800 1150	0.34 0.31 800 0.20 1350	0.35 0.32 0.27
		Low mowing TBC	11-03 Transpiration biomass			0.14	Cal	0.17	3	6	0.34	6	6	0.35
		frequency	13-05 coefficient (kg m -2 )	missing		missing		missing	missing
	G75L25	High mowing frequency WSI	06-05 susceptible) 11-03 Development 14-04 susceptibility water stress (1: not to		0.04 0.24 0.12	Cal	0.64 0.21 0.46	0.8	0.8	0.20 0.28 0.23 0.9	0.9	0.11 0.28 0.19
		Low mowing		11-03			0.24		0.21			0.28	0.28
		frequency		13-05		missing		missing		missing	missing

Table F .

 F CoSMo parameters for each species at Santa Lucia experimental site from the calibration performed in this study (Cal), based on published ranges. Sources: Monteith et al.

Table L .

 L Performance metrics for the evaluation of alternative modelling solutions for the simulation of aboveground biomass in Santa Lucia experimental station. R=linear correlation coefficient; RRMSE: root mean square error; RMAE: root mean absolute error; CRM: coefficient of residual mass. G50L50: 50% grass species-50% legume species; G75L25: 75% grass species-25% legume species; G25L75: 25% grass species-75% legume species. NA: not applicable.

		High Temperature 6 Results	Maximum temperature for		Man			32		32	32
				vernalisation (°C)
				Minimum initial
		GLAImin		green leaf area		Cal			0.045		0.045	0.045
				index (m 2 m -2 )
				Night
		NTC		temperature			Cal			10	Species 10	6
				critical (°C)
	Modelling solution	Parameter NTM PARf Treatment	Description Maximum night References temperature (°C) Factor to convert global solar radiation to R RRMSE (%) photosyntheticall y active radiation Above ground biomass (kg DM ha -1 ) -three mowing events Avena sativa Lolium rigidum Cal 22 Man 0.5 RMAE (%) CRM		Trifolium michelianum Mean of observations 22 0.5	18 0.5	subetrraneum Trifolium	Mean of simulations
		Photo Inhibition Avena sativa	Light time to start flowering (h) ~1.00 35.5		Man 31.2		8 -0.31		8	594	8	780
		WUT Photo Insensitivity Lolium rigidum	Mean temperature for post-Light time to stop flowering (h) 0.95 22.3	Cal	10 21.3 Man	10 0.12 13.5		13.5	10	1723	10 13.5
		RUE Trifolium michelianum	winter wake-up (°C) Drought Maximum radiation efficiency (g MJ -use 0.94 31.7		Cal 25.5		2.7 -0.25		2.7	2152	2.7
		WCC ReGrowth Development Trifolium subterraneum	tolerance (the highest the value, the 1 ) Regrowth development 0.75	47.1	Cal	0.8 33.9 Man	0.9 -0.19 1		1	1.0 1943	1	1.0
		Stage Critical G25L75	highest ~1.00 stage critical the	15.1		13.7		-0.14			2127
		SLA G50L50	tolerance) Root Specific leaf area type (m² kg -1 ) ~1.00 10.9		Cal 7.2			20 -0.06		20	1723	20
		RTN G75L25 STP	number tap root; 0: (1: fibrous root) Steam/leaf ~1.00 partition coefficient (m² 19.1 Above ground biomass (kg DM ha -1 ) -two mowing events Cal 0 0 18.4 -0.18 Cal 2.5 3.0	1	1627	2.5	1
	CropSyst	LCT GDDgf GDDe Avena sativa Lolium rigidum	Critical temperature below which the plant is not able to compete or to kg -1 ) Thermal time to begin yield formation (°C-d) Thermal time for emergence (°C-NA 34.5 NA 45.9	Cal	Cal Cal 27.3 34.9	1	3 -0.27 1000 340 0.30		1000 340	1	2226 4730	1000 340	1
		GDDeglai Trifolium michelianum	growth (°C) Critical temperature d) Thermal time to end green leaf NA 12.7		Cal 12.7		900 -0.01		900	4917	900
		UCT GDDf Trifolium subterraneum	above which the plant is not able to area index (°C-d) Thermal time for flowering (°C-d) NA 8.4	Cal	Cal 7.0	33	32 -0.05 950		950	34	4361	950	35
		LAImax GDDm G25L75 G50L50 TBC G75L25	compete or to growth (°C) Maximum leaf area index (m 2 m -2 ) Thermal time for NA 110.2 Cal maturity (°C-d) missing missing Transpiration Maximum biomass NA 46.7 coefficient (kg m -2 ) Leaf area index (m 2 m -2 ) -three mowing events 5 6 Cal 2000 80.6 -0.81 missing missing Cal 5 36.7 0.29	2000 4	5 missing 2463 5234	2000 5	5	missing
		Hmax WSI Avena sativa	plant height (m) Optimal Development susceptibility to water stress (1: 0.88 80.1	Cal	1.5 75.0 Cal	1.2 -0.75 0.8		0.6	0.6	0.89	0.8	0.6	1.56
		Topt Lolium rigidum	temperature for growth and not susceptible) ~1.00 72.0	Cal	20 71.3	15 -0.71			18	1.48	18	2.53
			development
			(°C)
						283

Table M .

 M Performance metrics for the evaluation of alternative modelling solutions for the simulation of relative abundances of plant species in Santa Lucia experimental station. R=linear correlation coefficient; RRMSE: root mean square error; RMAE: root mean absolute error; CRM: coefficient of residual mass. G50L50: 50% grass species-50% legume species; G75L25: 75% grass species-25% legume species; G25L75: 25% grass species-75% legume species.

			G75L25	0.22	0.28	0.25
		Mean of simulations	G25L75 G50L50 G75L25	0.13 0.37 0.46	0.37 0.12 0.04	0.25 0.25 0.25
	Manage	Performa	Treatme	Avena	Lolium	Trifolium	Trifolium
	ment	nce metrics	nt	sativa	rigidum	michelianum	subterraneum
			G25L75	0.92	0.98	0.99	0.68
		R	G50L50	0.94	~1.00	0.91	0.99
			G75L25	~1.00	~1.00	~1.00	0.99
			G25L75	100.8	40.2	22.7	18.4
		RRMSE	G50L50	110.0	36.0	28.9	15.3
			G75L25	96.2	37.9	60.2	39.6
			G25L75	93.13	33.8	17.9	11.1
	High mowing frequency	RMAE CRM	G50L50 G75L25 G25L75 G50L50	91.23 83.64 0.93 -0.91	32.1 32.0 -0.30 -0.10	24.9 59.6 0.11 0.22	13.8 34.4 -0.11 0.09
			G75L25	-0.84	-0.14	0.60	0.17
		Mean of observations	G25L75 G50L50 G75L25	0.06 0.06 0.13	0.19 0.34 0.44	0.38 0.28 0.24	0.36 0.31 0.19
		Mean of simulations	G25L75 G50L50 G75L25	0.00 0.12 0.24	0.25 0.38 0.50	0.34 0.22 0.10	0.40 0.29 0.16
			G25L75	NA	NA	NA	NA
		R	G50L50	g missin	missing	missing	missing
			G75L25	g missin	missing	missing	missing
			G25L75	103.0	43.1	30.6	42.0
		RRMSE	G50L50	g missin	missing	missing	missing
			G75L25	g missin	missing	missing	missing
			G25L75	95.0	33.8	29.1	42.0
		RMAE	G50L50	g missin	missing	missing	missing
	Low mowing frequency	CRM	G75L25 G25L75 G50L50	missin g 0.95 g missin	missing -0.34 missing	missing 0.09 missing	missing -0.42 missing
			G75L25	g missin	missing	missing	missing
			G25L75	0.16	0.19	0.37	0.28
		Mean of observations	G50L50 G75L25	missin g g missin	missing missing	missing missing	missing missing
			G25L75	0.01	0.25	0.34	0.40
		Mean of simulations	G50L50 G75L25	missin g g missin	missing missing	missing missing	missing missing

0.33

Acknowledgements

This work was undertaken under the auspices of the project MODIPRAS (Modelling relationships between species diversity, the functioning of grassland systems and their ability to deliver ecosystem services; 2018-2019) of the former INRA (French National Institute for Agricultural Research) meta-programme ECOSERV (Ecosystem services). We wish to acknowledge the commitment of Benjamin Muñoz (MS student in 2021 at University of Clermont Auvergne, Clermont-Ferrand, France). Developed within the PhD work of the first author (2019-2022), the present work falls within the thematic area of the French government IDEX-ISITE initiative (reference: 16-IDEX-0001; project CAP 20-25). Developed in international co-tutorship between the University of Clermont Auvergne (UCA) in France and the University of Sassari (UNISS) in Italy, the PhD thesis was supported by the meta-programme ECOSERV (former INRA) and projects of the Desertification Research Centre and the Department of Agricultural Sciences (UNISS). The data used in this study are from the long-term permanent grassland site of Theix (France), as part of the Système d'observation et d'expérimentation sur le long terme pour la recherche en environnement -Agro-écosystème, cycle biogéochimique et biodiversité, labelled as SOERE-ACBB (http://www.soere-acbb.com) and integrated in AnaEE-France (https://www.anaeefrance.fr/service/experimentation-in-natura/agrosysteme/prairie-permanente) funded by ANR-11-INBS-001. The monitoring of weather data by the INRAE CLIMATIK platform (https://agroclim.inrae.fr/climatik), managed by the AgroClim laboratory (Avignon, France), is acknowledged. Acknowledgements This study is part of the PhD thesis of the first author, developed in international cotutorship between the University of Clermont Auvergne (UCA) in France and the University of Sassari (UNISS) in Italy, and supported by the meta-programme ECOSERV (former INRA) and projects of the Desertification Research Centre and the Department of Agricultural Sciences (UNISS). This study was supported by the Atlantide (Advanced Technologies for LANds management and Tools for Innovative Development of an

Availability of data and material

Supplementary files are available online (https://doi.org/10.15454/M9P0TT) as 'database_mowing_meta-analysis_one_cut.tab', 'database_mowing_review_different_cuts.tab' and 'database_warming_meta-analysis.tab' at Data INRAE portal (https://data.inrae.fr). Additional data that support the findings of this study are available upon request from the authors.

Tilman (1980). Resources: a graphical-mechanistic approach to competition and predation.

The American Naturalist 116, 362-393. [START_REF] Confalonieri | A preliminary evaluation of the simulation model CropSyst for alfalfa[END_REF]. The whole grassland community is considered dormant after seven consecutive days with a mean temperature below a given threshold (wake-up temperature calibrated at 10 °C in Santa Lucia; Table F in Supplementary material), in a period starting in autumn when the night length is above 13 hours. In spring, when the night duration is less than 13 hours, full resumption of plant growth occurs when the mean temperature is above a given threshold (10 °C, calibrated) for more than seven consecutive days.

For the parameters of the plant morphology such as specific leaf area (SLA, from 17 m 2 kg - 1 for Trifolium subterraneum to 25 m 2 kg -1 for Avena sativa) and maximum height (Hmax, from 0.6 m for clovers and 1.5 m for Avena sativa) we referred to our field measurements. The values of 5-6 m 2 m -2 that we have estimated for the maximum leaf area index (LAImax) are compatible with upright foliage, according to Monteith (1965), who reported values of LAImax>5 m 2 m -2 . Bellocchi et al. (1999) also reported LAImax values of 5 m 2 m -2 for clovers (Trifolium incarnatum and Trifolium subterraneum). The solar radiation extinction coefficient (k)

was set to values between 0.40 and 1.00, which fall within the ranges for perennial ryegrasses (k=0.40-0.70) and clovers (k=0.90-1.00) published by a number of sources reviewed by different authors, e.g. [START_REF] Brown | Leaf area index in pasture growth[END_REF], [START_REF] Trenbath | Leaf inclination and crop production[END_REF][START_REF] Trenbath | Leaf inclination and crop production[END_REF][START_REF] Monteith | Vegetation and the atmosphere[END_REF].

Optimum temperature (Topt) and base temperature (Tb), parameters of CropSyst common to CoSMo, have the same meaning in both models, and their values were matched (Table E, Table F).

The water stress parameters, WSI in CropSyst (Table E) and WCC in CoSMo (Table F), are similar, and the high values assigned (>0.8) reflect the relatively high water stress tolerance of the four studied species (e.g. Bellocchi et al., 1999).

The CropSyst parameter values obtained for the four individual species (Supplementary material, Table E) were used as a basis for deriving the parameter values for the simulation

Canopy traits

Linked to the relative abundances of species, CropSyst-CoSMo allowed the simulation of some canopy traits. The seasonal dynamics of these traits depend on the variation of the relative abundances of the species. For instance, with different stands, the simulated SLA of the community (Figure 5.7, dashed line) reflects the increasing proportion of grasses (SLA values >20 m² kg -1 ; Table E) over legumes (SLA values <20 m² kg -1 ; Table E) as it happened moving from G25L75 to G75L25. If the simulated fluctuations show a narrow amplitude, the model performance declined with increasing grass cover, with a mean difference of ~0.9 m² kg -1 in G25L75 (mean simulated SLA of 18.9 m² kg -1 versus 18.0 m² kg -1 observed), while the mean difference increased to 3.8 m² kg -1 with G75L25 (simulated vs. observed SLA mean of 23.6 m² kg -1 vs. 19.8 m² kg -1 respectively). Similar results were obtained with low mowing frequency management (Supplementary material, Figure K). The seasonal dynamics of the maximum canopy height showed a pattern similar to that of SLA across mixed stands, being limited in amplitude under with both high-frequency (Figure ). This is also relevant because not only accurate estimates of relative plant abundances were observed in unstressed and perturbed (fertilised and mown) plots (as already documented in Movedi et al., 2019), but also in stressed (unfertilised) and undisturbed (abandoned) plots where CoSMo-based solutions have not been evaluated before.

In Chapter 5, the relevance of an existing CoSMo-based solution (CropSyst-CoSMo) for the simulation of intra-annual seasonal dynamics of species abundance of self-reseeding grass and legume species (various mixtures) was studied. The modelling framework was thus extended to Mediterranean grasslands grown in a dedicated field experiment run in Sardinia.

Again, the accuracy of estimated plant abundances was satisfactory and holds potential for inferring the range of ecosystem services provided by Mediterranean grasslands, of which feed provision and biodiversity conservation are among the most important in pastoral farms.

A prominent achievement of both studies is the feedback between plant species/type parameter values and modelled relative abundances, which allowed simulating fluctuations in community traits, e.g. specific leaf area and maximum canopy height, whose trends reflected experimental determinations. In fact, CoSMo-based solutions drew a distinction between fixed trait values for individual species/types and the dynamics of aggregated values at the community level. Thus, these dynamically estimated functional traits can support predictions of the community composition along environmental gradients and provide further insight into changing ecosystem functioning. This is important because species ) and were analysed separately. The meta-analysis integrated several ecological zones, from the cold and arid Tibetan Plateau (> 4600 m a.s.l.), with -5.9 °C mean air temperature and 277 mm annual precipitation (Xu et al. 2015) to the humid Swiss pre-Alps (with ~ 2000 mm annual precipitation, [START_REF] Peintinger | Community structure and diversity of bryophytes and vascular plants in abandoned fen meadows[END_REF] and the mild slopes (14 °C on average) of the lower course of the Rhone River, France [START_REF] Moinardeau | Short-term effects on diversity and biomass on grasslands from artificial dykes under grazing and mowing treatments[END_REF].

Table S2 Data source, location (country and Köppen-Geiger climate classification) and survey methodologies for the 34 articles present in the meta-analysis on the effect of warming on species richness (with greyed areas highlighting the subset of 17 articles present in the meta-analysis on the effect of warming on harvested biomass). ΔT is the temperature difference (°C) between control and warming treatments. NA (not available) indicates that sufficient information could not be extracted or derived from the original articles. For studies in the United States of America (USA), states are indicated: AK: Arkansas; CO: Colorado; FL: Florida; MA: Massachusetts; NM: New Mexico; OR: Oregon; OK: Oklahoma; RI: Rhode Island; TN: Tennessee; WA: Washington. For climate classification: Bsk: cold semi-arid; Cfa: humid subtropical; Cfb: temperate-oceanic; Csa: hotsummer Mediterranean; Csc: cold-summer Mediterranean; Dfa: humid continental; Dfb: warm-summer continental; Dfc: subarctic continental; Dwa: humid continental; Dwb: warm-summer humid continental; EF: ice cap; ET: tundra. On survey sampling, year 0 measurements were taken at the beginning of the study (before the experimental treatment began)