Charline Granger
email: charline.granger@yahoo.com

Fabien Amarger

The "Comédie-Française" registers as Linked Open Data: from heterogeneity to quantitative RDF data

Keywords: archives, history of theatre, ontology, Linked Open Data, Digital Humanities

The registers of the Comédie-Française theatre company (Paris, France), kept since the 17th century, record the company's expenses. This data is particularly interesting for historical purposes. There is however currently no other way to study these expenses other than analysing the registers manually. This lack of easy access motivated us to create a database from the expenses registers transcription. The main objective is to address the registers' heterogeneity in order to translate it into a schema. The second challenge is to align these data with other transcription databases in order to obtain a single SPARQL endpoint, which can then be used to obtain quantitative information from initially heterogeneous data.

Introduction

The "Comédie-Française" is a theatre company created by Louis XIV in 1680 to centralize theatre actors: the company's status is ambiguous, since it is pensioned by the king, but run like a private company, with benefits shared among its members. For administration purposes (and to allow control by the royal power), the actors produced daily records from the company creation. Those records contain receipts and expenses for each day. Those accounting archives are, as far as we know, unique regarding their density and their continuity.

The Comédie-Français Registers Project3 [START_REF] Harvey | Le projet des registres journaliers de la comédie-française: Les humanités numériques, dialogue entre les mondes de la recherche et de la documentation[END_REF], a bilingual international research program, has been founded ten years ago to allow wider access to this precious archive [START_REF] Burrows | Digitizing Enlightenment: Digital Humanities and the Transformation of Eighteenth-century Studies[END_REF].

The partners of this project include the library-museum of the Comédie-Française and several universities: Paris Nanterre (France), Sorbonne University (France), University of Rouen Normandie (France), Victoria University (Canada), New York University (U.S.A.) and the Massachusetts Institute of Technology (U.S.A.). The RCF project originated from the need to create a database of evenings' receipts from 1680 to 1793. This period represents 113 seasons and more than 30 000 programmed evenings: the receipt registers contain the evening's programming schedule (typically two representations each evening) and the number of entries sold per seat category. Two relational databases have then been created [START_REF] Guyot | Databases, Revenues, & Repertory: The French Stage Online[END_REF]. The first one is the receipts' database, which represents links between a representation date with a play title and an author name with a theatrical genre. The second one is the "feux" database (distribution by representation), which represents links, for the period 1765-1793, between a representation date with an actor name and a role, which is linked to a play, an author and a genre.

The two main objectives are: creating a third database containing the company expenses between 1680 and 1776, which completes the receipts and the "feux" databases; and deploying a SPARQL endpoint that contains all the data from the three databases, to contribute to the Linked Open Data and use the RDF versatility. The principal challenge is to model partially irregular data as faithfully as possible. The Logilab company, a computer science service provider, is responsible for the technical realization of these two parts of the project.

In this paper we present the registers, in order to observe the irregularities from the data, then the expenses' transcription application, and finally we describe how to merge the data from the three databases into an RDF triple store.

The "Comédie-Française" registers

The challenge when creating these databases is multiple for the researcher community. First, this will give access to a wide audience (students, amateurs and of course academics and specialists) to data that is not easily accessible because it is consigned to the Comédie-Française library-museum funds. The digitized register facsimiles, to which the website give access, are the first step of this dissemination. But above all, the databases allow generating a kind of information that is inaccessible when consulting the registers of the digitized version: thanks to the data model allowed by the databases, a transversal reading is possible which leads to define constants or create long-term surveys. We can then make statistics on the plays that are the most profitable depending on the periods, on the spectator variation depending on the seat categories, on the actor specialization regarding the roles, etc. For the specific situation of the expenses' database, we hope we will be able to know which plays cost the most regarding the decor, the costumes and the additional staff, like musicians or extras (the "assistants" as was said at this time). Thanks to this database, we will understand better the theatre economy and the artist status. Then we will obtain valuable information about the beginnings of the history of staging.

The expenses register transcription application

Register's data are, a priori, easily reconcilable with an automatic data processing since, actually, the daily registers contain systematic data because they are always dependent on a date (the "evening" when the representation took place) that generated the receipt or the expense. Especially for the expenses registers, in addition to this calendar regularity, we can observe that some expense categories are stable during several years, even decades, which allows us to consider these registers as "massive data". For example, the category of "parts d'acteurs" is present from 1680 to 1776. It corresponds to the profits made on the day's performance, which are shared by the actors and actresses forming the company.

However, from another point of view, the data is disparate. First, this is because a lot of expense categories are not stable during the period 1680-1776, which represent 96 seasons, but are stable during shorter periods, decades, years or sometimes only a few months. The RCF policy regarding the data processing is to restore the source in a precise and faithful way, to be as close as possible to the original information as it appears in the registers. This scientific requirement is based on the rejection to deliver biased information, which results from subjective interpretation and choices. These choices would distort the user interpretation. For example, the expense's category "frais ordinaires" (ordinary expenses) (fig. 1) disappears in 1693. In its place appears the category "frais journaliers" (daily expenses). "Frais ordinaires" and "frais journaliers", however, refer to the same types of expenses: : they include the remuneration or "wages" of the theatre's regular staff as well as recurrent operating costs. Nevertheless, we have chosen not to merge them under the same name, but to keep track of these two successive names. That's why we grouped the registers by periods in which the printed categories are stable. The main idea when developing the transcription application was to define several forms depending on the period. We defined 10 different periods. These forms contain expenses fields to be defined. Each expense is associated to an expense value represented with the L (livres), S (sols) and D (deniers) as monetary units.

We defined three different kinds of expense fields:

-Single field A simple field with one single monetary value: for example, in fig. 1, "frais ordinaires" (ordinary expenses). -Multiple field An expense category to add a handwritten expense and the monetary value associated: for example, in fig. 1, "pour les gagistes" (for the staff). -Part field A single field with which we can define the part value and the number of parts. This part field is used to define the actor's payment, as we can observe in fig. 1 ("PART").

Another disparate aspect of the expenses registers is the fact that these registers mix different status data. We need to distinguish between four different expense categories:

-The pre-printed categories: for example in fig. 1, "frais ordinaires" (ordinary expenses).

-The handwritten mentions on the pre-printed categories (that can clarify or even replace them without any mention): for example in fig. 1, "et chandelle des religieux" (candles for religious). -The marginal information (always handwritten, that does not concern any expense but gives some precision about a "relâche" 4 or when someone prestigious is present). For example in fig. 1, we can read "L'on ne joua point hier à cause du départ de Messieurs Poisson et Raisin pour Fontainebleau" (We did not play yesterday because of the departure of Messrs. Poisson and Raisin for Fontainebleau). -The handwritten expenses which are added to the pre-printed categories.

For example in fig. 1, "pour l'affiche noire" (for the black poster).

The transcription application interface has been adapted to deal with this diversity. Thus, the transcriber can record these different mentions and pre-printed categories: each period contains the pre-printed expense categories and some are available to type a free label to name it. These free-label categories are always grouped in a super-category to try to regroup when it is possible. For example, the category "Frais extraordinaires" (extraordinary expenses) is used to group the expenses which concern this specific day, like, in fig. 1, "pour l'affiche noire" (for the black poster) or, in other performances, vin (some wine) or une robe de chambre (a housecoat).

But the problem with these extraordinary expenses is that this accounting category, which defines irregular expenses, as opposed to ordinary expenses, does not have a fixed representation throughout the period. During some seasons, there is a printed category named "Frais extraordinaires" (extraordinary expenses), as we can observe in figure 1. But in other registers, we can observe several handwritten records which are not categorized. In this situation, it is difficult to define a category for these records: is this a real "extraordinary expense" or an uncategorizable expense? The problem here is twofold: how to transcribe these handwritten and uncategorized records and, on the other hand, define how we can, during a post-processing, find a way to categorize these records. This post-transcription categorization is important since it will orient the quantitative analysis afterwards. Depending on the accounting type defined during this step, the results may vary significantly. Since we do make some modelling choices or interpretations, we can diverge from the source and introduce a bias. For now, we have added the handwritten and uncategorized records as "Frais extraordinaires" without any further categorization. Here lies one of the most important challenge that we will discuss at the end of this article.

We can also identify a new problem to be solved: some expense categories partially overlap. In figure 1, we can observe that "frais extraordinaires" and "chandelles des religieux" (candles for religious) are represented as a single category. Then there is no way to define if the recorded 2 livres and 11 sols are about "frais extraordinaires", "chandelles des religieux" or both.

The figure 2 presents an example of what the application looks like. The left part of the web page is dedicated to render the page register to be transcribed.

The right part contains the expense record fields, depending on the period. At the bottom, we can observe some metadata about the transcribe process, such as comments. In this metadata, there is the transcribe process state for this specific page. A page follows a workflow. At first the page is in state to do, then when Fig. 2. Expenses transcribe application the transcribe process is done the page state is changed to to review to let an expert review the data. Once the data is validated, the page goes to the state validated. At this moment the data can be analysed since it has been transcribed and validated. A page can be validated even if some errors appear. For example, one of the most common errors, is that the transcribed total is not equal to the computed total. Most of the time, this error appears in the page itself. We decided to keep the error in the data, but we flag the page as "erroneous". This gives researchers the possibility to study these errors, for example to study if this is a deliberate error or not, if it can be interpreted as money embezzlement. A very important field is the date definition, which appears under the page register image. This date is central information since it will be used to align data with the two other databases.

RDF data to merge the three databases

Once the three databases (dépenses (expenses), recettes (receipts) and feux (roles and actors)) were transcribed and validated, the problem was to merge them all. The alignment process was pretty simple, since each transcribed data contains the representation date. Then we can easily use this date as a pivot concept to link the three bases.Using RDF format and Semantic Web technologies was first the most appropriate idea since the main objective is to give access to as many people as possible. The Semantic Web technologies, and especially the Linked Open Data, are dedicated to that purpose [START_REF] Meroño-Peñuela | Semantic technologies for historical research: A survey[END_REF]. But secondly, the RDF format has become a solution to the problem we described in chapter 2. The data diversity is not easy to handle when dealing with relational databases, but there is no problem to not define some RDF attributes if they are not available during the transcribe process.

First, we needed to define a global ontology. We can observe the unified ontology in figure 3. The pivot concept to create links between all the others is the Journée (day) since all pages are specific to a date. All the upper part of the ontology is dedicated to the Feux (actors and roles); all the down part is dedicated to the Recettes (receipts-at the down left part) and the Dépenses (expenses-at the down right part). All the details (expenses categories, seat categories and so on) are not present in the initial ontology since these details are generated dynamically when exporting the data. To ease the readability of this schema, we do not render the whole ontology in detail. As you can observe, the expenses part do not deal with periods. We do consider only the domain specific and pertinent concepts for the domain.

Once the ontology is defined, we aligned the key concepts with some wellknown ontologies. This alignment is a key feature since the ontology is defined using French words, because the domain and the concerned potential users are mostly French, but there is no reason to not let non-French speaking using this ontology, and the data. The alignment to well-known, and English, ontologies and vocabularies are as well the best way to ease the reusability of the ontology and the data, which is one of the main goals of the RCF project. If an ontology is already aligned with an upper ontology, then the alignment with the RCF ontology will be eased. This alignment can then be used to enrich data from other sources. For example, if a dataset contains actors information, such as birthdate, using an alignment can then be used to obtain all plays which contain actors born in a given date.

The table 1 shows the mapping we made. We can see that we did not perform to use full FRBRoo ontology, even if this is one of the most famous ontology for this domain. This is mainly because of the main design choice for FRBRoo which put the events as the key concept for the whole ontology. We do have some events in the RCF ontology, but not everything is considered as events. For example, we do not have the "birth" event for a person, which is the case in the FRBRoo ontology (event inherited from Cidoc CRM). We want to avoid the complex alignments, then we let these kinds of alignment aside.

Once we had designed the ontology, we exported all the data from the three databases as RDF triples, considering the RCF ontology. All the data are exported in a public SPARQL endpoint5 using Virtuoso Open-Source Edition6 . This export can be used to obtain information such as "what is the cost of plays where a given actor played in and what the extraordinary expenses for these plays?" or compare the receipts for these plays to see if this actor is famous.

To help non-sparql friendly people to access the data, we developed a web application7 using this SPARQL endpoint as backend data store. This application is dedicated to expose the expenses' data for now, but it would be interesting to extend the application to two other types of data. The figure 4 shows what this application looks like. We can see here that we can compare several kinds of expenses regarding a specific period of time. This is especially useful to determine which expense represents a huge amount regarding the other expenses, or during a period. This application let users compare the sum or the average of the selected expenses for some selected plays or authors on the selected period.

Developing this application is necessary to make the data available for a maximum of people. The problem is, the developers must make some choices during the developing process. Even if these choices have been made together with domain experts, these are still choices which consider only one point of view regarding the data interpretation. We can take a simple example: the partitioned expenses (part fields). For now, they are considered as simple expenses, and we compare only total values. If some people want to explore this kind of expenses and study the evolution of the part value, the application will be limited. This is why the SPARQL endpoint is crucial in our implementation, since the raw RDF data is available directly. But the user needs to know how to write some SPARQL queries, and this is why we are considering implementing Sparnatural8 .

Perspectives and future works

We have presented why the Comédie-Française registers are a very interesting and promising source for research in humanities. The transcribe process of those registers is especially interesting since it allows a quantitative analysis of the registers. The expenses registers are particularly disparate because of the irregularities between periods, and handwritten records. We have seen how we have developed an application to transcribe the expenses from the registers, besides this irregularity. Then we have presented how we have merged the three databases into a dedicated RDF triple store to allow as many people as possible to access this data.

The data disparity observed in the RCF registers pushed us to alert the researcher about the quantitative approach limits. There is a bias regarding the observation of the data from visualization applications. Viewing a graph, one may believe in the objectivity of the delivered information. But this is a decoy: the quantitative aspect, which is only one way to read and understand the information, has been built through a long process of choices, discards and subjective renderings. This aspect needs the researcher interpretation, which will give meaning to the quantitative data and make visible all the choices that have led to its building. Contextualized data is therefore essential. We are starting the combined creation of a data dictionary and a documentary and encyclopedic space to enlighten our work and assure the scientific rigour of our approach9 . Main defined terms to understand the registers, like "frais extraordinaires" (extraordinary expenses), "frais ordinaires" (ordinary expenses), "saisons" (seasons), "loges" (lodges), "preciput" (precipitate), etc. will be precisely defined since they are key to understanding and interpreting the numeric values that can be found in the data.

As discussed in chapter 3, some records are uncategorized and handwritten. This is an issue since we create a new expense category, as a specialization of "Frais extraordinaires", for each different handwritten expense type. Since they are handwritten, the labels are, except for a few, different from one another, and we end up with a lot of single categories. This data is meant to help analysis and especially quantitative analysis. But if the records are not categorized at all, then there is no way to study this data efficiently. For this reason, we want to explore the use of the clustering algorithm applied to the "Frais extraordinaires" handwritten categories. The main idea is to use the RCF encyclopedia to detect named entities in the handwritten expense category labels and group them using this detection.

But, beyond this necessary data contextualization, the problem remains to access the raw data for the researchers. Since SPARQL is not easy to learn for humanities researchers, the pedagogic part will be central. Knowledge, even rudimentary, of SPARQL can allow researchers to avoid filters from visualization tools, which are necessarily subjective. How can we bring closer humanities researchers and a data funds as rich as the RCF registers? How can we capitalize on the OWL model to propose the most objective visualization tool? These questions will be central in our works for the next years.

Fig. 1 .

 1 Fig. 1. Expense registry example

Fig. 3 .FRBRoo

 3 Fig. 3. RCF unified ontology

Fig. 4 .

 4 Fig. 4. Web application to visualize the expenses' data from the SPARQL endpoint

Table 1 .

 1 Mapping from RCF ontology with FRBRoo, schema.org, DublinCore and FoaF

"Programme des registres de la Comédie-Française RCF" https://cfregisters. org

"Relace" means "when the theatre must close".

https://rcf-sparql.demo.logilab.fr/

https://vos.openlinksw.com

https://upnd.pages.logilab.fr/rcf-ui-expense/

https://sparnatural.eu/

https://cfregisters.org/#!/encyclopedie/mots


Géraud De Streel
email: geraud.destreel@uclouvain.be

François Lebourgeois
email: francois.lebourgeois@agroparistech.fr

Christian Ammer
email: christian.ammer@forst.uni-goettingen.de

Ignacio Barbeito
email: ignacio.barbeito@slu.se

Kamil Bielak
email: kamil.bielak@wl.sggw.pl

Andres Bravo-Oviedo

Gediminas Brazaitis
email: gediminas.brazaitis@vdu.lt

Lluís Coll
email: lluis.coll@ctfc.es

Catherine Collet
email: catherine.collet@inrae.fr

Miren Del Río
email: delrio@inia.es

Jan Den Ouden
email: jan.denouden@wur.nl

Lars Drössler
email: lars.drossler@iliauni.edu.ge

Michael Heym
email: michael.heym@lrz.tum.de

Václav Hurt
email: vaclav.hurt@slpkrtiny.cz

Viktor Kurylyak

Magnus Löf
email: magnus.lof@slu.se

Fabio Lombardi
email: fabio.lombardi@unirc.it

Bratislav Matovic
email: bratislav.matovic@uns.ac.rs

Renzo Motta
email: renzo.motta@unito.it

Leonid Osadchuk

Maciej Pach
email: rlpach@cyf-kr.edu.pl

M G Pereira
email: gpereira@utad.pt

Hans Pretzsch
email: hans.pretzsch@tum.de

Roman Sitko
email: sitko@tuzvo.sk

Jerzy Skrzyszewski

Vit Sramek
email: sramek@vulhm.cz

Kris Verheyen
email: kris.verheyen@ugent.be

Tzvetan Zlatanov Aa
email: tmzlatanov@gmail.com

Quentin Ponette
email: quentin.ponette@uclouvain.be

Regional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe

Keywords:

Increasing species diversity is considered a promising strategy to mitigate the negative impacts of global change on forests. However, the interactions between regional climate conditions and species-mixing effects on climate-growth relationships and drought resistance remain poorly documented.

In this study, we investigated the patterns of species-mixing effects over a large gradient of environmental conditions throughout Europe for European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.), two species with contrasted ecological traits. We hypothesized that across large geographical scales, the difference of climate-growth relationships between pure and mixed stands would be dependent on regional climate. We used tree ring chronologies derived from 1143 beech and 1164 pine trees sampled in 30 study sites, each composed of one mixed stand of beech and pine and of the two corresponding pure stands located in similar site conditions. For each site and stand, we used Bootstrapped Correlation Coefficients (BCCs) on standardized chronologies and growth reduction during drought years on raw chronologies to analyze the difference in climate-tree growth relationships and resistance to drought between pure and mixed stands.

We found consistent large-scale spatial patterns of climate-growth relationships. Those patterns were similar for both species. With the exception of the driest climates where pure and mixed beech stands tended to display differences in growth correlation with the main climatic drivers, the mixing effects on the BCCs were highly variable, resulting in the lack of a coherent response to mixing. No consistent species-mixing effect on drought resistance was found within and across climate zones. On average, mixing had no significant effect on drought resistance for neither species, yet it increased pine resistance in sites with higher climatic water balance in autumn. Also, beech and pine most often differed in the timing of their drought response within similar sites, irrespective of the regional climate, which might increase the temporal stability of growth in mixed compared to pure stands.

Our results showed that the impact of species mixing on tree response to climate did not strongly differ between groups of sites with distinct climate characteristics and climate-growth relationships, indicating the interacting influences of species identity, stand characteristics, drought events characteristics as well as local site conditions.

Introduction

The effects of global change on forest functioning are a major concern for both forest scientists and forest managers.

With respect to climate, changes in local average precipitation and temperature as well as in the frequency and intensity of extreme events such as drought, are expected to impact the provision of many forest ecosystem services, including wood production [START_REF] Albrich | Trade-offs between temporal stability and level of forest ecosystem services provisioning under climate change[END_REF], in terms of both average performance and temporal stability [START_REF] Boisvenue | Impacts of climate change on natural forest productivity-evidence since the middle of the 20th century[END_REF][START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF]. Indeed, numerous studies reported that the increased frequency and intensity of drought events significantly reduced growth and increased mortality risks [START_REF] Allen | A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[END_REF][START_REF] Lévesque | Growth response of five co-occurring conifers to drought across a wide climatic gradient in Central Europe[END_REF][START_REF] Meir | Drought-related tree mortality: addressing the gaps in understanding and prediction[END_REF]. As a result, great research effort is devoted into finding ways to adapt forests and forest management to cope with those adverse effects of climate change [START_REF] Seynave | GIS Coop: networks of silvicultural trials for supporting forest management under changing environment[END_REF][START_REF] Bowditch | What is Climate-Smart Forestry? A definition from a multinational collaborative process focused on mountain regions of Europe[END_REF]. In addition to selecting tree species or provenances adapted to expected future climate conditions [START_REF] Bolte | Adaptive forest management in central Europe: climate change impacts, strategies and integrative concept[END_REF][START_REF] Frischbier | Climate change adaptation with non-native tree species in Central European forests: early tree survival in a multi-site field trial[END_REF][START_REF] Bert | Which oak provenances for the 22nd century in Western Europe? Dendroclimatology in common gardens[END_REF], increasing stand species diversity has also been proposed to cope with detrimental effects of global change on forests [START_REF] Ammer | Diversity and forest productivity in a changing climate[END_REF][START_REF] Vilà-Cabrera | Forest management for adaptation to climate change in the Mediterranean basin: a synthesis of evidence[END_REF]. Several studies reported a significant influence of species-mixing on climate-growth relationship, including higher resistance to extreme events such as drought; yet those effects were found to be affected by environmental conditions or identity of the species involved [START_REF] Lebourgeois | Mixed stands reduce Abies alba tree-ring sensitivity to summer drought in the Vosges mountains, western Europe[END_REF][START_REF] Pretzsch | Resistance of European tree species to drought stress in mixed versus pure forests: evidence of stress release by inter-specific facilitation[END_REF][START_REF] Gazol | Functional diversity enhances silver fir growth resilience to an extreme drought[END_REF][START_REF] Thurm | Mixture reduces climate sensitivity of Douglas-fir stem growth[END_REF][START_REF] Bosela | Contrasting effects of environmental change on the radial growth of co-occurring beech and fir trees across Europe[END_REF], Pretzsch et al. 2020;[START_REF] Steckel | Implications of Reduced Stand Density on Tree Growth and Drought Susceptibility: A Study of Three Species under Varying Climate[END_REF].

Specifically, the outcome of species interactions (balance between inter-specific competition and complementarity) depends on climate [START_REF] Ammer | Diversity and forest productivity in a changing climate[END_REF][START_REF] Forrester | The spatial and temporal dynamics of species interactions in mixed-species forests: from pattern to process[END_REF], leading to species-mixing effects (differences between pure and mixed stands) on tree-growth relationships to vary depending on environmental conditions (Grossiord et al. 2014;[START_REF] Lebourgeois | Social status-mediated tree-ring responses to climate of Abies alba and Fagus sylvatica shift in importance with increasing stand basal area[END_REF]. Climate is shaping species-mixing effects through different ways. First, individual trees of a given species might be less vulnerable to a similar drought event when growing under dry compared to humid climatic conditions due to long-term adaptation mechanisms [START_REF] Martín-Benito | Response of climate-growth relationships and water use efficiency to thinning in a Pinus nigra afforestation[END_REF]Martinez-Vilalta, 2012;[START_REF] Trouvé | Radial growth resilience of sessile oak after drought is affected by site water status, stand density, and social status[END_REF][START_REF] Stojnić | Variation in xylem vulnerability to embolism in European beech from geographically marginal populations[END_REF]. Intra-specific traits variations associated with adaptation to dry environments include differences in rooting depth, root to shoot ratio, leaf/sapwood area ratio, wood anatomy (thickness of xylem walls, tracheid diameters…) or changes in leaf morphological features such as leaf area [START_REF] Linton | Limits to water transport in Juniperus osteosperma and Pinus edulis: implications for drought tolerance and regulation of transpiration[END_REF][START_REF] Lloret | Seedling survival of Mediterranean shrubland species in relation to root:shoot ratio, seed size and water and nitrogen use[END_REF][START_REF] Grill | Effects of drought on needle anatomy of Pinus canariensis[END_REF]Martinez-Vilalta et al. 2009;[START_REF] Pritzkow | Phenotypic plasticity and genetic adaptation of functional traits influences intra-specific variation in hydraulic efficiency and safety[END_REF]. Under similar drought intensities, the constraint experienced by the trees would thus differ depending on climate. On the other hand, it is also possible that the constraint resulting from a drought event in inherently water limited sites cannot be alleviated by species-mixing as suggested by [START_REF] De Streel | Contrasting patterns of tree species mixture effects on wood δ 13 C along an environmental gradient[END_REF]. Climate characteristics could also affect the balance of species interactions by allowing -or not-compensatory growth [START_REF] Lassoie | Physiological response of large Douglas-fir to natural and induced soil water deficits[END_REF][START_REF] Lévesque | Growth response of five co-occurring conifers to drought across a wide climatic gradient in Central Europe[END_REF][START_REF] Seidel | Compensatory growth of Scots pine seedlings mitigates impacts of multiple droughts within and across years[END_REF] to happen when species with distinct vegetation periods are admixed. For instance, favorable climatic conditions at the end of the growing season could allow for one species to benefit from reduced competition caused by the earlier reduction of physiological activity of another species with shorter vegetation period. As a result, the outcome of species interactions for each species will vary between climatic areas, all other things being equal. Also, average climatic characteristics could influence species-mixing effect on drought reaction by changing the relative competitivity between species. For instance the high sensitivity of beech to late frosts [START_REF] Pretzsch | Growth and yield of mixed versus pure stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) analysed along a productivity gradient through Europe[END_REF][START_REF] Rezaie | Frost and drought: Effects of extreme weather events on stem carbon dynamics in a Mediterranean beech forest[END_REF] could reduce its otherwise high dominance over less competitive tree species in late frosts prone climates. Lastly, spatial and temporal variations of environmental conditions influence species-mixing effect through their impacts on both resources and modulators. According to [START_REF] Forrester | A review of processes behind diversity-productivity relationships in forests[END_REF], the mixing effect is predicted to increase along temporal or spatial gradients of increasing environmental limitation as far as species interactions reduce the constraint.

On the other hand, the drivers of tree growth are expected to change across large geographical areas as a function of regional climate characteristics. In this respect, [START_REF] Babst | Site-and species-specific responses of forest growth to climate across the European continent[END_REF] highlighted that tree growth response to climate across Europe showed consistent biogeographic patterns in relation with distinct climatic constraints, from zones where tree growth was mainly driven by precipitations to others where temperature was the main driver.

Species-mixing effects on climate-growth relationships are therefore likely to differ between such regions. Indeed, Grossiord et al. (2014) have shown that higher species diversity improved resistance to drought events in certain forest types (temperate beech and thermophilous deciduous forests) while no significant effects were found in hemi-boreal, mountainous beech and Mediterranean forests. In addition to those large-scale patterns, local site conditions can also shape the mixing effect, resulting in the lack of any significant relationship between species diversity and drought resistance in selected regions (Grossiord et al. 2014b, Ratcliffe et al. 2017). Species interactions are numerous and complex. Complementarity is considered as a major determinant of species-mixing effects [START_REF] Ammer | Diversity and forest productivity in a changing climate[END_REF], and encompasses several types of interspecific interactions leading to competitive reduction and facilitation [START_REF] Vandermeer | The Ecology of Intercropping[END_REF]. Facilitation refers to mechanisms through which one species improves the resource supply as well as biotic or abiotic conditions for another species [START_REF] Forrester | A review of processes behind diversity-productivity relationships in forests[END_REF]. Competitive reduction is the process by which inter-species traits variation leads to lower inter-specific competition compared to intra-specific competition [START_REF] Scherer-Lorenzen | The Functional Significance of Forest Diversity: A Synthesis[END_REF], and result from either spatial (e.g. difference in rooting depth) or temporal (e.g. difference in growing period) niche complementarity. Because those mechanisms rely largely on inter-specific differences between associated tree species as well as on intra-specific differences resulting from interspecific interactions, trait diversity and trait plasticity are critical determinants for the outcome of mixing [START_REF] Jactel | Tree diversity reduces herbivory by forest insects[END_REF][START_REF] Dawud | Tree species functional group is a more important driver of soil properties than tree species diversity across major European forest types[END_REF].

Numerous traits can influence tree growth response to climate, and thus determine the species-mixing effect. For instance, by their longer vegetation period, coniferous species could have more opportunity for compensatory growth [START_REF] Seidel | Compensatory growth of Scots pine seedlings mitigates impacts of multiple droughts within and across years[END_REF] during climatically favorable periods following a drought event than broadleaves [START_REF] Rezaie | Frost and drought: Effects of extreme weather events on stem carbon dynamics in a Mediterranean beech forest[END_REF]. If conifer compensatory growth happens after the end of the admixed broadleaves vegetation period, conifer could additionally benefit from lower interspecific competition in mixed stands compared to pure stands. Traits associated with tree hydraulic and physiological characteristics are also major determinant of tree growth reaction to climatic variations. Association of species displaying variations in those traits could lead to improved drought response in mixture compared to monocultures [START_REF] Schwendenmann | Tree water uptake in a tropical plantation varying in tree diversity: interspecific differences, seasonal shifts and complementarity[END_REF][START_REF] Grossiord | Having the right neighbors: how tree species diversity modulates drought impacts on forests[END_REF].

Species-mixing effects depend not only on the combination of traits of the species in presence but also on their interaction with environmental conditions. The environmental conditions can indeed influence the expression of the traits for the species in presence. As an example, constraints such as temporary soil waterlogging [START_REF] Kozlowski | Soil aeration and growth of forest trees (review article)[END_REF] or limited depth to bedrock [START_REF] Balneaves | Root patterns of Pinus radiata on five ripping treatments in a Canterbury forest[END_REF]) could prevent any belowground niche partitioning through their negative effect on root development, resulting in a lack of mixing effect on drought response even for species with otherwise potentially different rooting patterns.

As a result, we might expect contrasting impacts of mixing on the response to both climate-growth relationships and reaction to drought events, depending on climate as well as on local site characteristics such as microclimate or soil characteristics (maximal water availability, ...).

In this study, we investigate how growth response (measured by tree-ring width of dominant trees) to climate and drought differs between pure and mixed stands along an unprecedented gradient of environmental conditions across Europe, for European beech and Scots pine. Those two species are of high relevance in this context as they display strong differences in a series of traits potentially involved in growth response to climate variation. More specifically, they differ in the distribution of root biomass within the soil profile (heart-shape rooting pattern for beech vs. peak in most superficial soil layers for pine, higher competitivity of beech root system - [START_REF] Curt | Rooting strategy of naturally regenerated beech in Silver birch and Scots pine woodlands[END_REF][START_REF] Prévosto | Dimensional relationships of naturally established European beech trees beneath Scots pine and Silver birch canopy[END_REF], in the length of the vegetation period (longer growing season of pine compared to beech) and in their hydraulic strategies (isohydric for pine vs. anisohydric for beech [START_REF] Cochard | Vulnerability of several conifers to air embolism[END_REF][START_REF] Martínez-Vilalta | The hydraulic architecture of Pinaceae-a review[END_REF][START_REF] Schäfer | Drought stress reaction of growth and Δ 13 C in tree rings of European beech and Norway spruce in monospecific versus mixed stands along a precipitation gradient[END_REF][START_REF] Pflug | Resilient leaf physiological response of European beech (Fagus sylvatica L.) to summer drought and drought release[END_REF]. Because the ecological traits of the two species are so contrasted, complementarity effects are expected to occur. Using the same dataset from the EuMIXFOR COST action [START_REF] Heym | EuMIXFOR empirical forest mensuration and ring width data from pure and mixed stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) through Europe[END_REF] as the one used in the present study, [START_REF] Pretzsch | Growth and yield of mixed versus pure stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) analysed along a productivity gradient through Europe[END_REF] and del Río et al. (2017) showed that both productivity and temporal stability of productivity, respectively, were on average higher in mixed pine/beech stands than expected from pure stands. They also found a high between-site variability of species-mixing effect on productivity and temporal stability. Although the authors proposed several hypotheses potentially explaining those positive species-mixing effects on productivity, they did not perform any quantitative analyses of the mechanisms at play. As a result, several studies were conducted to highlight the candidate dominant mechanisms. The considered mechanisms include light related processes [START_REF] Barbeito | Terrestrial laser scanning reveals differences in crown structure of Fagus sylvatica in mixed vs. pure European forests[END_REF][START_REF] Forrester | Effects of crown architecture and stand structure on light absorption in mixed and monospecific Fagus sylvatica and Pinus sylvestris forests along a productivity and climate gradient through Europe[END_REF]), water-related processes (de Streel et al. 2019) or nutrient-related processes [START_REF] De Streel | Mixing has limited impacts on the foliar nutrition of European beech and Scots pine trees across Europe[END_REF]. In this paper, we extend the range of mechanisms by considering the climate-related processes. Because species-mixing effect have been found on long-term stand performances (difference in productivity between pure and mixed stands) as well as on reaction to extreme events (stability of productivity, resilience, resistance or recovery to drought...), we explored species-mixing effects on climate-related processes using indicators of both long-term behavior and reaction to drought.

For both species, we hypothesized that the difference in climate-growth relationships including both long-term growth response to climate and short-term reaction to drought event will vary between pure and mixed stands depending on climatic characteristics, resulting in consistent regional patterns across Europe. To test this hypothesis, we conducted dendroecological analyses. First, we analyzed the climate-tree growth relationships of both species in pure and mixed stands, using Bootstrapped Correlation Coefficients. We then tested the difference in resistance to drought event between pure and mixed stands, and investigated its relationship with regional climate.

Materials and methods

Study area and site/stand characteristics

The tree data used in this study came from 30 sites established under the COST Action FP1206 EuMIXFOR (European Network on Mixed Forests). Each site consisted of three stands with similar ecological conditions but varied composition: pure stands of pine and beech and a mixed stand of both species. This triplet-transect approach [START_REF] Pretzsch | Growth and yield of mixed versus pure stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) analysed along a productivity gradient through Europe[END_REF] allowed to cover a large environmental gradient within the overlapping natural ranges of pine and beech, while ensuring homogeneity of site conditions for all three stands related to one triplet; this way, bias due to co-variation between site conditions and stand composition was avoided. Effects of species identity and species mixing could therefore be analysed without influence of confounding factors. Elevation varied between 20 and 1475 m a.s.l; mean annual precipitation sum (P) ranged from 520 to 1175 mm and mean annual temperature (T) from 6 to 10.5 °C. In the mixtures, the percentage of basal area represented by Scots pine ranged from 25 % to 74 %; total basal area ranged from 16 to 80 m 2 ha -1 and stand age from 40 to 130 years. The stands were mostly even-aged and mono-layered. No silvicultural activities had been conducted in the stands during the preceding decades. A standard protocol for tree data collection (diameters, heights of trees and crown bases) and tree coring was applied. The full measurement protocol was described in details by [START_REF] Heym | EuMIXFOR empirical forest mensuration and ring width data from pure and mixed stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) through Europe[END_REF]. Selected site and stand characteristics for each site are presented in Table A1.

Climate data

We used the 0.25°-gridded E-OBS dataset from the EU-FP6 ENSEMBLES project. From this dataset, we obtained series of daily minimum and maximum temperatures and cumulative daily precipitation for the 1979-2013 period (i.e. the length of the dendrochronological series). Climate variables were used to analyze the influence of climate on tree growth. Mean monthly temperature and monthly Standardized Precipitations Evapotranspiration Index (SPEI) were used. SPEI is a (monthly) multi-scalar index that can be used to determine the onset, duration and magnitude of drought conditions with respect to normal conditions [START_REF] Vicente-Serrano | A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index[END_REF]. Positive values indicate above-normal wet conditions, whereas negative values identify dry periods. SPEI values between -0.67 and 0.67 are considered normal, values between -0.67 and -1.28 indicate moderate drought, and values <-1.28 indicate severe drought [START_REF] Isbell | Biodiversity increases the resistance of ecosystem productivity to climate extremes[END_REF]. The potential evapotranspiration (PET) necessary to calculate SPEI was determined using the modified Hargreaves equation [START_REF] Choisnel | uniformisee du calcul de l'evapotranspiration potentielle pour l'ensemble des pays de la communaute europeenne[END_REF][START_REF] Droogers | Estimating Reference Evapotranspiration Under Inaccurate Data Conditions[END_REF], which provides estimations that are close to those obtained from the Penman-Monteith equation [START_REF] Beguería | Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring[END_REF]. Calculations were made using the SPEI-package in R software [START_REF] Beguería | Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring[END_REF][START_REF] Core | R: A language and environment for statistical computing[END_REF].

Climatic parameters were considered from June of the previous year to October of the current year. Climate variables were standardized to remove long-term variability using a smoothing spline with 50% frequency cut-off at a wavelength of two-third of the length of each series [START_REF] Cook | The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies[END_REF]. Standardized climate variables were calculated by taking the difference between climatic values and the spline [START_REF] Bert | Which oak provenances for the 22nd century in Western Europe? Dendroclimatology in common gardens[END_REF].

Dendrochronological methods

Sampled trees and master chronologies

At each site and stand, a minimum of 10 dominant or co-dominant trees per species (i.e. beech or pine in the pure stands, beech and pine in the mixed stand) were cored at breast height in 2013 or 2014 (two cores per tree; northern and eastern directions; last complete tree-ring in 2013). Individual tree-ring series were crossdated and series with unresolvable crossdating problems were dropped [START_REF] Heym | EuMIXFOR empirical forest mensuration and ring width data from pure and mixed stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) through Europe[END_REF]. For each tree, measurements from the two cores were averaged to obtain one individual series. In a limited number of cases (15% of total number of trees), however, only one core per tree could be used for the analyses. In total, 1143 beech trees (592 from pure stands; 551 from mixed stands) and 1164 pine trees (597 from pure stands; 567 from mixed stands) were used for dendrochronological analyses. To remove long-and medium-term growth trends and to focus on high-frequency growth variation, individual tree-ring series were detrended using a smoothing spline with 50% frequency cut-off at a wavelength of two-third of the length of each series [START_REF] Cook | The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies[END_REF]. Master chronologies were calculated using biweighted robust mean (Cook and Kairiukstis, 2013) on the maximum period common to all sites .

A master chronology was established for each stratum (Fig. 1, upper panel. A stratum is defined by a combination of site (30 in total) x species (pine or beech) x stand composition (pure or mixed -in mixed stands, a master chronology was established for each species independently); total number of strata = 120). Detrending was conducted using the DENDRO script [START_REF] Mérian | POINTER and DENDRO -Two applications under R software for analyzing tree response to climate using dendroecological approach[END_REF] within the R software (R Core Team, 2019). Expressed Population Signal (EPS) was calculated to assess the suitability of the dataset for dendroecological analyses. The high EPS values indicated that the chronologies can be used for the analyses (mean and standard deviation = 0.90 ± 0.07 and 0.90 ± 0.05 for pure and mixed beech respectively; 0.92 ± 0.07 and 0.90 ± 0.05 for pure and mixed pine respectively).

Identification of drought events

To analyze tree growth response to drought events, we selected site specific drought events that had a negative effect on tree growth. First, we identified negative pointer years for each species using the "Relative Event Year" definition of [START_REF] Schweingruber | Identification, presentation and interpretation of event years and pointer years in dendrochronology[END_REF], i.e. at least 75% of the trees displayed a reduction of raw growth superior to 15% compared to the average raw growth of the previous 4-year period (Relative Growth Change (RGC) method - [START_REF] Becker | The role of climate on present and past vitality of silver fir forests in the Vosges mountains of northeastern France[END_REF][START_REF] Schweingruber | Identification, presentation and interpretation of event years and pointer years in dendrochronology[END_REF][START_REF] Jetschke | Towards the extremes: A critical analysis of pointer year detection methods[END_REF]. Due to this 4-year window, dry years were selected within the period 1983-2013. In order to ensure that growth limitation was related to a drought event, we then only kept the negative pointer years associated with monthly SPEI values lower than -1.28 [START_REF] Isbell | Biodiversity increases the resistance of ecosystem productivity to climate extremes[END_REF] for at least one month during the growing period (March -August) of either the current year or the previous year. Taking into account the previous-year drought allowed to account for possible carry-over effects. As a result of the selection process, several drought years could be selected for each combination of site and species, and years could vary between species within a site (see Table A2).

Statistical analyses

Climate-growth relationship

Bootstrapped Correlation Coefficients (BCC; [START_REF] Blasing | Response functions revisited[END_REF][START_REF] Guiot | The bootstrapped response function[END_REF] We first used Principal Component Analysis (PCA) on BCCs followed by hierarchical ascending clustering according to Ward D2 method to identify the different patterns in climate-growth relationships over the strata [START_REF] Richman | Rotation of principal components[END_REF][START_REF] Lebourgeois | Social status-mediated tree-ring responses to climate of Abies alba and Fagus sylvatica shift in importance with increasing stand basal area[END_REF]. Using this approach, we were able to properly account for the potentially different climate drivers affecting tree growth across such a large geographical area, and to identify groups of sites displaying similar climate-growth relationships. The optimal number of groups defined by the clustering algorithm was based on the elbow method and the Calinski-Harabasz index. Clustering allowed us to identify groups of sites displaying similar climate-growth relationships. Clustering dendrograms are presented in Figs. A1.1 to A1.4. Stand and climatic characteristics for the groups of sites resulting from the clustering are presented in Table 1 and Figure 3. Additional variables (such as mixing percentage, basal area or stand age) were also considered in the multivariate analyses but were not correlated to any PC.

Thereafter, to better highlight the difference of climate-tree growth relationship between pure and mixed stands for each species independently, we calculated the distance between the pure and the mixed stands of a given site along the principal components, using the following equation:

𝐷 𝑖𝑗 = 𝑃 𝑖𝑗 | | -𝑀 𝑖𝑗 | |
Where D ij corresponds to the displacement from the projection of each species in pure stand to the projection of each species in mixed stand for site i along PC j. P ij is the association of the pure stand from site i with principal component j; similarly, M ij is the association of the mixed stand from site i with principal component j. As a result, inward displacement (i.e. displacement in the direction of the origin of the principal component) is positive while negative values are associated with outward displacement. Inward displacement can, for instance, indicates a decrease in correlation with all variables associated with the corresponding PC. To characterize the patterns of change in climate-growth relationships between pure and mixed stands in different regions, we used the shift between pure and mixed stands positions on the compositional planes as indicator of the intensity of change in sensitivity to environment [START_REF] Thimonier | Simultaneous eutrophication and acidification of a forest ecosystem in North-East France[END_REF][START_REF] Mérian | Size-mediated climate-growth relationships in temperate forests: a multi-species analysis[END_REF]. One-sample t-tests were then used to assess whether the average displacements within groups along a PC were significantly different from zero. In some cases, the low number of sites within a specific group prevented us to properly assess the displacement direction (inward or outward displacement) and amplitude (importance of the displacement between pure and mixed stand of a site along one PC).

Response to extreme drought

In addition to looking at the difference of correlation between growth and climate variables in pure and mixed stands, we aimed at testing the difference in trees response to punctual drought events between the two stand types (Fig. 1, lower panel -right-hand side). To assess how the growth response to punctual drought events differed between pure and mixed chronologies, we calculated the relative growth change per stand type × site × drought event for each species separately, using the corresponding species-related drought years. In a first analysis, a PCA was performed on RGC values to explore to which extent growth response to drought differed between stand types and sites. The displacements of projection from pure to mixed stands were also calculated, and average displacements within groups were tested as described above. In a second step, we used mixed effect models for each species separately, considering year nested within site as random factor, to test for the significance of stand composition (pure vs mixed), average site climatic parameters (average temperature and difference between precipitation and potential evapotranspiration for the following periods: Mar-May, Jun-Aug, Sep-Nov, Dec-Feb in each site), site geographic characteristics (altitude and latitude) and stand characteristics (age and basal area) on growth reduction (RGC) during drought events, according to the original equation:

𝑅𝐺𝐶 𝑠𝑦 = β × 𝐸 𝑠 + 𝑎 𝑠 0, σ 𝑠 2 ( ) + 𝑎 𝑦|𝑠 0, σ 𝑦 2 ( ) + ε(0, σ ε 2 )
Where RGC sy represents the stand average relative growth change for site s, and year y, β is the vector of fixed effect parameters, E is the matrix of the predictors of the fixed effects, a s is the random factor characterized by the inter-site variance σ 2 s , a y|s is the random factor characterized by the interannual variance within a site σ 2 y and ε is the error term of variance σ 2 ε . A series of climatic variables (Tmean spring , Tmean summer , Tmean fall , Tmean winter , SPEI spring , SPEI summer , SPEI fall , SPEI winter ), a dummy variable with two levels (pure/mixed) representing stand composition, other potential confounding factors (stand basal area and stand age) and their interactions were used as candidate variables for fixed effects. Starting from the model with the full set of parameters, the variables with the lower predictive power were then progressively removed based on the Akaike Information Criterion (AIC).

All calculations were made using the R software (R Core Team, 2019).

Results

Major climatic drivers of radial growth

The first principal component accounted for 24% of the total variance for both species (Fig. 4, A and C). Both species presented similar patterns of climate-growth relationships with a major role of summer and fall temperatures. For beech, PC1 revealed an opposition between the influence of current average temperature in September (Tmean SEP )

and the influence of average temperature of previous September and July (Tmean Sep /Tmean Jul ) on growth while for pine Tmean Sep /Tmean Jul vs. Tmean SEP /SPEI Jun were determinant. The second principal component accounted for 17%

of the total variance. As for PC1, both species showed a rather similar response yet with a more important role of summer drought: PC2 was positively associated with SPEI AUG (for beech) and SPEI Aug (for pine) and negatively associated with Tmean JUL (beech) and Tmean AUG (beech and pine). PC3, which held 11% of total variance for both species, was determined by SPEI APR and SPEI Sep . Lastly, PC4 (10% of total variance) was negatively correlated with June temperatures and April temperature of the current year.

Species-mixing effect on climate-growth relationship

The amplitude and direction of change ("displacement along the PCs") of loadings values from pure to mixed chronologies on each PC were highly dependent on site identity for both species, and no global pattern could be found (Fig. 4). For pine, none of the groups displayed significant displacement in any direction. For beech, sites from clusters 1.5 and 1.6 seemed to display coherent displacement along the first and third PCs (Fig. 4) which could indicate a lowering correlation with variables associated with those PCs. However, the low number of sites in those clusters did not allow us to properly test this potential effect. For climatic variables, months in full caps refer to the current year (previous year otherwise). Colors indicate sites grouped together by the clustering algorithm, based on the first (clusters 1.1 to 1.6) and second (clusters 2.1 to 2.3) compositional planes (Fig. A1). Clusters 2.1 and 2.2 are mostly composed of sites belonging to cluster 1.1.

Response to extreme drought

The average number of drought years across the period 1983-2013 (Table A2) was similar for beech and pine (mean ± standard deviation = 2.8 ± 1.3 and 2.7 ± 1.5 for beech and pine respectively). However, the number of drought years strongly differed between specific combinations of site and species, ranging from 0 to 6 for beech and from 0 to 7 for pine. The number of drought years that cleared the "Relative Event Year" criteria was similar between pure and mixed stands of a same species (3.4 ± 1.6 vs. 3.0 ± 2.2 in pure and mixed beech, respectively; 3.5 ± 2.1 vs. 3.6 ± 2.0 in pure and mixed pine, respectively).

In most cases (63% of cases for beech, 60% for pine), strata-level drought years corresponded to "Relative event years" for both pure and mixed stands. When only one of the two plots displayed a relative event year, growth reduction was found equally frequently in pure and mixed stands for beech (56% and 44% of cases, respectively); for pine, pure plots responded more frequently (73% of cases). Beech and pine also tended to react to different years as, out of the total of 93 drought events identified for at least one of the two species at a same site, only 15 were common to both species; out of the 78 remaining events, 50 were associated with pine. Beech displayed high apparent variability in species-mixing effect on growth reduction during drought years. Indeed, out of the 19 sites displaying at least one drought year, 11 sites displayed situations of positive (lower growth reduction in mixed stands) and negative species mixing effects, depending on the year (Table A2); the 8 remaining sites displayed systematically negative (4 sites) or positive (4 sites) species-mixing effects. Species-mixing effect on drought response appeared to be more consistent over the study period for pine as out of the 26 sites displaying at least one drought year, on 18 sites, all drought years were associated with either systematically negative (5 sites) or systematically positive (13 sites) species mixing effects. On the remaining sites, positive and negative species-mixing effects can be found. For both species, sites displaying such consistent positive or negative species-mixing effects across years were found in all groups of sites showing comparable climate-growth relationships. A2).

For beech, the first four axes of the PCA conducted on the drought events accounted for 16, 13, 12 and 10% of total variance respectively. For pine, they accounted for 13, 11, 10 and 9% (Fig. 5). Clusters of sites showing similar climate-growth relationships and certain common climate characteristics did not show any coherent pattern of relative growth variation during drought (Fig. 5). No coherent drought response could be found within the groups of BCC similar sites, indicating those sites had no or only limited drought years in common. In addition, non-responsive sites (11 for beech, 4 for pine; Table A2) were found in all groups.

There was no globally coherent difference in the response to drought between pure and mixed chronologies (Fig. 5).

Despite large differences in the position of pure and mixed stands along the PCA planes for some sites, the displacement of chronologies projections from pure to mixed stands did not show any consistent pattern for beech (Fig. 5). For pine, the displacements along the first and fourth PC were significantly positive when all sites were considered together (p-values of 0.03 in both cases); in addition, all sites from cluster 2.2. showed a significantly positive displacement along the third PC (p-value = 0.04).

According to the linear mixed effect model fitted on beech RGC, pure and mixed stands did not display any significant difference in growth reduction during drought years. For pine, there was no main effect of stand composition, but a significant interaction between stand composition and P -PET during fall as illustrated in Fig. 6. The growth reduction was less pronounced in mixed than in pure stands only for sites with high P -PET fall .

Discussion

Geographical pattern of climate-growth relationship

The response of tree growth to climate at the European scale is primarily driven by the regional climate conditions, for both tree species (pine/beech) and all stand composition (pure/mixed). Summer/fall temperatures (July/September -Figs. 4 andA2) of current and previous years are the main climatic drivers of tree growth which is consistent with previous studies also led at the European scale [START_REF] Briffa | Tree-ring width and density data around the Northern Hemisphere: Part 1, local and regional climate signals[END_REF][START_REF] Wettstein | Coherent region-, species-, and frequency-dependent local climate signals in Northern Hemisphere tree-ring widths[END_REF]. Our clusters (Fig. 4) also agree with biogeographical patterns of climate-tree growth highlighted by [START_REF] Babst | Site-and species-specific responses of forest growth to climate across the European continent[END_REF] over Europe. On the other hand, in contrast to previous studies conducted at local scales (e.g. González de Andrés et al., 2018), both species showed similar climate-growth relationships suggesting that at European scale, the site geographical and climatic characteristics appeared to be stronger determinants of response to climate than species identity.

Species-mixing effect on climate-growth relationships

An apparent coherent displacement from pure to mixed stands was observed for beech only under warm (low latitude) and dry (low summer P-PET) conditions, while no such effect was detected for pine whatever the ecological conditions.

The low number of sites corresponding to those specific conditions did not allow us to accurately test the significance of those displacements for beech. However, we can point out that these displacement from pure to mixed stands correspond to strong ecological constraint suggesting a positive interaction between species on stand functioning (complementarity). Under the less restrictive ecological conditions (Fig. 3), the strong heterogeneity in term of displacement according to mixing confirms that there is a need to have some overwhelming common constraint to express the complementary between species in mixed stands. This pattern clearly fits within the general framework proposed by previous authors [START_REF] Bertness | Positive interactions in communities[END_REF][START_REF] Forrester | A review of processes behind diversity-productivity relationships in forests[END_REF] stating that complementarity increases when environmental conditions become harsher, provided that species-mixing has an impact on the limiting factor. Similar patterns have been highlighted by [START_REF] Lebourgeois | Mixed stands reduce Abies alba tree-ring sensitivity to summer drought in the Vosges mountains, western Europe[END_REF] who found that the shift in the response of silver fir (Abies alba Mill) to climate induced by mixing was restricted to the driest sites. In our study, complementarity could lead to a reduction of the summer constraint and therefore to a lower dependency to previous summer and current September climatic conditions. Nevertheless, future research effort should be dedicated to high constrained conditions to assess the significance of species-mixing effects and the mechanisms at play. In the absence of any major climatic constraint, the effects of species interactions on climate-growth relationships would be mainly driven by local site conditions, whose diversity within a region would explain the lack of any coherent response (see next section).

While admixing pine in beech stands was shown to reduce the constraint on beech trees in water-limited conditions (González de [START_REF] González De Andrés | Increased complementarity in water-limited environments in Scots pine and European beech mixtures under climate change[END_REF], other climatic variables might also impose limitations which could be more difficult to alleviate through mixing such as low temperatures throughout the year. Change in microclimate conditions resulting from difference in canopy structure, phenology or modifications in the length of the growing period, have been reported for some mixtures with respect to their monocultures (e.g. [START_REF] Lebourgeois | Mixed stands reduce Abies alba tree-ring sensitivity to summer drought in the Vosges mountains, western Europe[END_REF][START_REF] Ehbrecht | Quantifying stand structural complexity and its relationship with forest management, tree species diversity and microclimate[END_REF]), yet the extent to which this affects growth still remains to be documented.

Response to a drought event

We found limited evidence that species-mixing effects on drought resistance were shaped by the climate variables controlling tree growth. The fact that drought reaction was different between sites from groups showing similar climate-growth relationships and common climatic characteristics suggests a strong influence of local characteristics such as soil condition. Only the low-elevation and non water-limited sites from the Northwestern part of Europe (Fig.

2), showed a coherent species-mixing effect on drought resistance, yet limited to pine (Fig. 5D). As shown in Figs. 3 and6, for pine many sites displaying high P -PET fall values showed lower growth reduction during drought events in mixed stands than in pure ones. Such favorable climatic conditions at the end of the vegetation period would allow pine to take advantage of its longer vegetation period to compensate for any drought limitation experienced during spring and/or summer; during this late growing period, pine growth could be further favored by the earlier reduction of beech photosynthetic activity.

Such effect may however be offset by factors varying at the site level, such as difference in the characteristics of triggering years (e.g. differences in droughts timing -D' Orangeville et al. 2018) or in local conditions of soil water availability [START_REF] Carrière | Impact of local soil and subsoil conditions on inter-individual variations in tree responses to drought: insights from Electrical Resistivity Tomography[END_REF]. Although we were not able to consistently assess the impact of potential available soil water on drought response due to a lack of soil data in many sites, the information available at some sites shows it ranged between 28 and 715 mm [START_REF] De Streel | Contrasting patterns of tree species mixture effects on wood δ 13 C along an environmental gradient[END_REF]; such a large range is expected to greatly impact tree response to drought [START_REF] Lévesque | Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch[END_REF].

Last, the interannual variation of drought response and of its modulation by mixing within a given site suggests that the timing, duration and intensity of the drought events is important. For instance, [START_REF] Bhuyan | Different responses of multispecies tree ring growth to various drought indices across Europe[END_REF] showed that beech was sensitive to both short-and long-term drought although this sensitivity to drought length was dependent on site climate. Several studies have also shown that drought timing and intensity were major determinants of tree growth response. Tree growth proved to be more severely affected by more intense drought (D'Orangeville et al.

2018

) and, depending on its timing, droughts have specific effects on different species depending on their characteristics such as phenology [START_REF] Vanhellemont | Distinct growth responses to drought for oak and beech in temperate mixed forests[END_REF].

Our results also showed contrasting patterns between beech and pine, in terms of both response to drought and mixing effects. Regarding the reaction to drought, the number of non-responsive sites was much higher for beech compared to pine. In addition, pine and beech most often differed in the timing of their drought response within similar sites, reflecting contrasting sensitivity to drought and suggesting a large decoupling in possible mixing effects between those two species. As previously stated, while no main effect of mixing could be detected for neither species, pine showed a positive mixing effect in sites with high (P -PET) in the late growing period.

Those differences in drought response and mixing effects between the two species remain difficult to clearly explain and could be linked to their respective stomatal adjustment capacity to avoid water stress (isohydric vs. anisohydric behavior) [START_REF] Bello | How do mixing tree species and stand density affect seasonal radial growth during drought events?[END_REF] or to their carbon storage and carbon mobilization strategies during drought [START_REF] Michelot | Comparing the intra-annual wood formation of three European species (Fagus sylvatica, Quercus petraea and Pinus sylvestris) as related to leaf phenology and non-structural carbohydrate dynamics[END_REF][START_REF] Lassoie | Physiological response of large Douglas-fir to natural and induced soil water deficits[END_REF][START_REF] Lévesque | Growth response of five co-occurring conifers to drought across a wide climatic gradient in Central Europe[END_REF][START_REF] Seidel | Compensatory growth of Scots pine seedlings mitigates impacts of multiple droughts within and across years[END_REF].

Because beech and pine were found to respond to distinct drought events, we might expect temporal stability to be higher in mixed compared to pure stands. This is coherent with the results from del Río et al. ( 2017) using the same triplet network who found an increased in temporal stability of productivity at the community level in mixed stands.

Conclusion

We explored the large-scale spatial patterns of species-mixing influence on climate-growth relationships and resistance to drought events for European beech and Scots pine across Europe. We found limited support to our initial hypothesis that the impact of mixing on the response to climate and drought event would be mostly driven by the regional climate, resulting in a coherent response of both climate-tree growth relationships and drought resistance to mixing within climatic groups.

Our results show that there seldom is a coherent species mixing effect, even when considering sites that display similar climate-growth relationships and that share similar climatic characteristics. In absence of strong limiting climate conditions, forest managers should therefore not expect species-mixing to have a coherent effect on climate-growth relationships. Species-mixing could have beneficial or detrimental effects on growth sensitivity to climate depending on local conditions. If there is no major climatic constraint or if species-mixing does not alleviate it, the relative sensitivity of tree climate-growth relationship to climatic and edaphic conditions will lead to positive, neutral or negative species-mixing effects, depending on local factors.

Regarding the resistance to drought, we were unable to detect any significant mixing effect for neither of the species, and no clear geographical pattern emerged. Interestingly however, regional climate was found to potentially impact pine resistance, through a positive effect of higher climatic water balance in autumn, suggesting compensatory growth.

Our results showed that the regional climate only partly determined the impact of mixing on the tree-growth relationships, and highlight the interacting influences of species identity, stand characteristics, drought events characteristics as well as local site conditions. Integrating all those factors into mechanistic models of tree growth such as HETEROFOR [START_REF] Jonard | HETEROFOR 1.0: a spatially explicit model for exploring the response of structurally complex forests to uncertain future conditions-Part 1: Carbon fluxes and tree dimensional growth[END_REF] is the next step to quantify the relative contribution of each, and to be able to make reliable context-dependent predictions. Coefficients for beech between standardized tree-ring width of each strata (site x species x stand) and climatic variables. Numbers are the site id (Table S1); P and Coefficients for pine between standardized tree-ring width of each strata (site x species x stand) and climatic variables. Numbers are the site id (Table S1); P and M, refers to pure and mixed stands, respectively.

  were used to analyze the climate-growth relationship (Fig. 1, lower panel -left-hand side). Master chronologies per strata were used as dependent variable. Correlation functions were calculated using 34 climate regressors: 17 T mean and 17 monthly SPEI values from June of the previous growing season to October of the current growing year. Climatic regressors were written in the form: Variable Month ; months written in full caps indicate variables from the current year while months written in lower case letters indicate climatic variables from the previous year. Sample size-related bias between strata was corrected by dividing BCC values by the square root of the Expressed Population Signal (EPS) as proposed by Mérian et al. (2013).

Figure 1

 1 Figure 1 Flowchart showing the experimental design (upper panel) and the different steps involved in the analysis (lower panel) of the average climate-growth relationships (left-hand side) and of the response to drought (right-hand side).

Figure 2

 2 Figure 2 Distribution of the 30 study sites across Europe. Numbers are the site-ID (Table A1). The size of the points is proportional to the site altitude (20 -1475 m). The colors refer to clusters of sites showing similar climate-growth relationships. These clusters are based on a clustering analysis performed on the first (main map common for the two species, clusters 1.1 to 1.6) and second (smaller maps, clusters 2.1 to 2.3; beech: above, pine: below) compositional planes of a PCA performed on Bootstrapped Correlation Coefficients.

Figure 3

 3 Figure 3 Climate characteristics (P-PET, an indicator of climatic water balance [mm] and Tmean [°C]) of the clusters of sites defined according to their similar climate-tree growth relationship. Lower and upper limit of the boxes represent 25 and 75% quantiles, respectively while the whiskers indicate the lowest or highest value not further than 1.5 x the inter-quartile range from the hinges. Clusters resulting from clustering performed on the second compositional plane (clusters 2.1, 2.2 and 2.3) noted "B" and "P" refer to beech and pine, respectively.

Fig. 4

 4 Fig. 4 Principal Component Analysis of the Bootstrapped Correlation Coefficients for beech (A and B) and pine (C and D) between standardized tree-ring width of each strata (site x species x stand) and climatic variables (monthly Tmean and SPEI values from June of the previous year to November of the current year). Strata corresponding to pure and mixed chronologies are depicted by big filled and small open symbols, respectively. Only the best represented variables are drawn. Arrows indicate the displacement from the pure to the mixed chronologies within each

Fig. 5

 5 Fig. 5 Principal Component Analysis performed on the Relative Growth Variation during drought years for beech (A and B) and pine (C and D). Pure and mixed chronologies are depicted by filled and open symbols, respectively. Arrows indicate the displacement from the pure to the mixed chronologies within each site. Colors represent the clusters delimited based on the main climate-growth relationship patterns found in the PCA conducted on Boostrapped Correlation Coefficients (see Fig.4). Red numbers indicate drought years. A total of 11 sites for beech and 4 sites for pine were found to be non responsive to drought (TableA2).

Fig. 6

 6 Fig. 6 Predicted (lines) and observed (dots) RGC values for pine as a function of P -PET fall in pure and mixed stands. Parameter estimates, p values and R-squared for the linear mixed models adjusted on pine RGC values are presented in the enclosed table. Marginal R-squared (R 2 m ) represents the variance explained by fixed factors; conditional R-squared (R 2 c ) represents the variance explained by both fixed and random factors (full model). Reference level for stand composition is "pure". Open symbols and dashed line represent mixed stands; filled symbols and continuous line represent pure stands. The colors refer to groups of sites showing similar climate-growth relationships; those groups are based on a clustering analysis performed on the first (main figure, clusters 1.1 to 1.6) and second (smaller figure, clusters 2.1 to 2.3) compositional planes of a PCA performed on Bootstrapped Correlation Coefficients.

Fig. A1. 1 .

 1 Fig. A1.1. Dendrograms of the ward D2 clustering performed on the first two principal components of the PCA of the Bootstrapped Correlation Coefficients for

Figure A1. 3 .

 3 Figure A1.3. Dendrograms of the ward D2 clustering performed on the first two principal components of the PCA of the Bootstrapped Correlation Coefficients for

Fig

  Fig. A2 SPEI (plain lines) and Tmean (dashed lines) loadings values on each of the 4 first principal components of the Principal Component Analysis performed on the Bootstrapped Correlation Coefficients between standardized tree-ring width of each stratum for beech (A) and pine (B). Months -6to -12 correspond to June -December of the previous year.

Table 1

 1 Range (minimum/maximum) of selected site and stand characteristics for groups of sites showing comparable climate-growth relationships. The groups are based on a clustering analysis performed on the first (clusters 1.1 to 1.6) and second (clusters 2.1 to 2.3) compositional planes of a PCA performed on Bootstrapped Correlation Coefficients. Because clustering analysis performed on the second compositional planes yielded slightly different groups between the two species, characteristics are given separately for beech and pine for clusters 2.1, mean air temperature (°C); WB, climatic water balance (mm): precipitation minus potential evapotranspiration; Stand type, with BE for beech, PI for pine; BA, basal area (m 2 ha-1 ). Climate data are averages over1979-2013. Age in mixed stands is indicated as follows: beech age range (min/max);pine age range.

		Latitude	Altitude
				Tmean spring	Tmean summer	Tmean fall	Tmean winter	WB spring	WB summer	WB fall	WB winter	Stand type	BA range	Age range
	Cluster	range	range
				[°C]	[°C]	[°C]	[°C]	[mm]	[mm]	[mm]	[mm]	Composition	[m 2 .ha -1 ]	[years]
		[°]	[m]
													BE	23/54	46/150
												Pure
	1.1	48.2/56.2 27/547	6.2/10.1	15.5/18.1	7.3/11.2	-1.4/3.7	-106.5/40.9 -192.1/-10.4	1.3/165.2	58.4/244.2		PI	11/59	45/150
												Mixed	BE/PI	30/52	45/115; 45/130
													BE	21/45	45/102
								-119.6/-54.				Pure
	1.2	48.6/55.5 20/533	6.6/9.0	16.7/17.9	7.5 /9.3	-2.6/0.6		-221.9/-38.9	0.4/185.7	53.3/159.3		PI	24/45	45/102
								4
												Mixed	BE/PI	16/63	45/102; 45/102
													BE	18/53	55/105
												Pure
	1.3	50.0/51.0 209/383	7.6/8.6	17.1/17.9	7.8/8.7	-2.3/-1.3	-91.7/-80.5	-151.8/-136.3	11.1/23.6	59.7/82.3		PI	30/64	55/105
												Mixed	BE/PI	33/47	55/105; 55/105
													BE	23/41	40/55
												Pure
	1.4	44.9/46.1 1038/1475	4.7/5.2	14.2/14.7	6.9/6.9	-2.6/-1.2	-10.0/166.9 -109.9/-103.8	101.1/232.7	35.2/240.6		PI	47/52	40/55
												Mixed	BE/PI	22/41	40/55; 40/55
													BE	33/52	40/50
												Pure
	1.5	42.1/42.2 1099/1292	6.7/10.2	16.5/19.6	9.3/12.8	1.7/5.1	-54.4/-30.8	-387.8/-318.1	15.56/56.7	94.7/136.3		PI	40/55	40/50
												Mixed	BE/PI	32/53	40/50; 40/50
													BE	20/41	65/75
												Pure
	1.6	41.9/43.6 1080/1188	5.7/8.4	16.0/17.8	7.3/9.4	-2.6/-0.7	-53.4/-7.7	-275.5/-168.0	-51.6/90.4	80.0/157.0		PI	33/54	65/75

Table A2 .

 A2 1. (continued). Radial Growth Variation during dry negative pointer years of cluster1.1. Values in red indicate negative pointer years for which the drought happened the previous year. For brevity, only years with at least one dry pointer year are listed. Cells highlighted in yellow indicates year that are characteristic at the plot level.

	1070	pure mixed	pine pine	-55 -30	-35 -24					-24	-32			-42			-29		-45						-33
	1053 1070	mixed pure	beech pine	-32	-28						-40						-39		-54						-28
	1053 1071	pure mixed	beech beech															-32					-43
	1053 1071	mixed pure	pine beech			-24								-31				-38					-39
	1053 1071	pure mixed	pine pine			-30								-40					-37					-32	-28
	1071	pure	pine																-37					-46	-40
	1031	mixed	beech														-38	-51					-33
	1031	pure	beech														-22	-61					-31
	1031	mixed	pine											-30					-39						-25
	1031	pure	pine											-16					-27						-26
	1032 Triplet	mixed plot	beech species	-11 1983	1984	1985	1986	1987	1988	1991	1992	1993	1994	1996	2000	2001	2003	2004	2006	2007	2008	2010	-62 2011	2012	2013
	1032 1054	pure mixed	beech beech	-31							-35	-31											-59
	1032 1054	mixed pure	pine beech	-37							-40	-19					-40
	1032 1054	pure mixed	pine pine	-35							-23						-35		-14
	1033 1054	mixed pure	beech pine								-33								-38
	1033 1057	pure mixed	beech beech											-34				-15					-49	-41
	1033 1057	mixed pure	pine beech											-22 -36				-54			-70		-55	-25
	1033 1057	pure mixed	pine pine											-30 -42		-34					-43
	1034 1057	mixed pure	beech pine											-48	-28	-27	-36
	1034 1058	pure mixed	beech beech												-25		-18			-36
	1034 1058	mixed pure	pine beech	-25												-30	-35			-17
	1034 1058	pure mixed	pine pine	-57												-34	-35		-19
	1040 1058	mixed pure	beech pine																-30
	1040 1063	pure mixed	beech beech
	1040 1063	mixed pure	pine beech
	1040 1063	pure mixed	pine pine											-52					-38
	1043 1063	mixed pure	beech pine	-28										-46					-32			-50	-53
	1043 1070	pure mixed	beech beech	-34													-44		-14			-31	-37
	1043 1070	mixed pure	pine beech	-16	-14					-30				-33			-25		-31
																									57

Table A2 .

 A2 2. Radial Growth Variation during dry negative pointer years for sites of cluster 1.2. Values in red indicate negative pointer years for which the drought happened the previous year. For brevity, only years with at least one dry pointer year are listed. Cells highlighted in yellow indicates year that are characteristic at the plot level.

		pure	beech		-36		-22	-45
	1049	mixed	pine	-31	-42
	1049	pure	pine	-25	-39
	1051	mixed	beech
	1051	pure	beech
	1051	mixed	pine		-26	-38	-34
	1051	pure	pine		-45	-47	-47
	1052	mixed	beech
	1052	pure	beech
	1052	mixed	pine
	1052	pure	pine
	1061	mixed	beech
	1061	pure	beech
	1061	mixed	pine		-17		-10
	1061	pure	pine		-34		-31
	1035	mixed	beech		-24		-32
	1035	pure	beech		-44		-26
	1035	mixed	pine	-40			-34
	1035	pure	pine	-55			-26
	1036	mixed	beech		-46
	1036	pure	beech		-51
	1036	mixed	pine				-24
	1036	pure	pine				-30
	1046	mixed	beech				-40	-27
	1046	pure	beech				-50	-31
	1046	mixed	pine	-22			-33
	1046	pure	pine	-29			-37
	1049	mixed	beech		-40		-29	-34
							59

Table A2 .

 A2 3 Radial Growth Variation during dry negative pointer years for sites of cluster 1.3. Values in red indicate negative pointer years for which the drought happened the previous year. For brevity, only years with at least one dry pointer year are listed. Cells highlighted in yellow indicates year that are characteristic at the plot level.

	1045	mixed beech														-29							-35
	1045	pure	beech														-20							-23
	1045	mixed pine											-22			-41							-38	-54
	1045	pure	pine											-31			-18							-37	-38
	1055 1041	mixed mixed	beech beech
	1060	mixed	beech											-12		-31						-33
	1055 1041	pure pure	beech beech
	1060	pure	beech											-32		-48						-32
	1055 1041	mixed mixed	pine pine								-21								-31	-23					-34
	1060	mixed	pine
	1055 1041	pure pure	pine pine								-35								-31	-42					-42
	1060	pure	pine
	1062 1042	mixed mixed	beech beech		-43			-19								-30						-35
	1062 1042	pure pure	beech beech		-30			-45								-33						-42
	1062 1042	mixed mixed	pine pine		-31		-43																		-16
	1062 1042	pure pure	pine pine		-18		-30																		-39
	Triplet	plot	species	1983	1984	1985	1986	1987	1988	1991	1992	1993	1994	1996	2000	2001	2003	2004	2006	2007	2008	2010	2011	2012	2013
	1037	mixed	beech
	1037	pure	beech
	1037	mixed	pine		-40														-51
	1037	pure	pine		-45														-27
	1044	mixed	beech									-16													-26
	1044	pure	beech									-34													-44
	1044	mixed	pine														-37					-25			-44
	1044	pure	pine														-36					-28			-51

Table A2 .

 A2 6. Radial Growth Variation during dry negative pointer years for sites of cluster 1.6. Values in red indicate negative pointer years for which the drought happened the previous year. For brevity, only years with at least one dry pointer year are listed. Cells highlighted in yellow indicates year that are characteristic at the plot level.

	1047	mixed	beech
	1047	pure	beech
	1047	mixed	pine
	1047	pure	pine
	1056	mixed	beech			-32	-51	-73
	1056	pure	beech			-31	-37	-55
	1056	mixed	pine	-27	-11	-20	-40	-31
	1056	pure	pine	-33	-35	-27	-44	-40
							63

Acknowledgements

The networking in this study has been supported by the COST Action FP1206 EUMIXFOR. All authors thank the numerous persons who contributed to the fieldwork in the different sites across Europe. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu).

The main author obtained a PhD grant from the 'Fonds National de la Recherche Scientifique' (FNRS-FRIA) and additional funding support from the Walloon forest service (Service Public de Wallonie-Département de la Nature et des Forêts) through the 5-year research programme "Accord-cadre de recherches et de vulgarisation forestières". Bratislav Matovic was supported by the Ministry of Education, Science and Technological Advancement of the Republic of Serbia (877 451-03-68/2020-14/ 200197). M.G. Pereira was supported by the National Funds from FCT -Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020. All other contributors thank their national funding agencies.

Geographic pattern of major climatic drivers of radial growth

Clustering made on PC1/2 (clusters 1.1. to 1.6.) and PC3/4 (clusters 2.1. to 2.3.) (Fig. A1.1. to A1.4.) revealed several groups of sites displaying similar climate-growth relationships (Fig. 4), and those patterns were similar for both species.

On the first compositional plane, the Spanish sites (1041, 1042; cluster 1.5) displayed distinct climate sensitivity, due to a strong positive correlation with Tmean SEP (and SPEI Jun for pine) or a negative correlation with Tmean Sep or Tmean Jul . The sites from the southeastern part of the network (1056 and 1047; cluster 1.6) were also strongly associated with PC1 but through a negative correlation. Cluster 1.4 was composed of the two Italian sites (1062 and 1055) and was positively associated with PC1 and, to a lower extent, with PC2. Cluster 1.3 was equally associated with PC1 and PC2 as was cluster 1.2, although the correlation with climatic variables was much lower for the latter.

Lastly, sites from cluster 1.1 displayed low correlation with either of the first two PCs.

On the second compositional plane, clustering defined three groups. Two of them displayed strong and coherent correlation with PC3 and PC4: clusters 2.1 and 2.2, respectively. The majority of sites associated with clusters 2.1 and 2.2 on the second compositional plane belonged to cluster 1.1 on the first plane. Cluster 2.1 corresponded to sites from Central Europe (South of Germany, Czech Republic, Slovakia), while cluster 2.2 brought together sites from the Northwestern part of Europe (Figs. 2 and4).

Clusters differed both in geographic (altitude, latitude) and climatic characteristics (Table 1 and Fig. 3). Indeed, clusters 1.4, 1.5 and 1.6 were all low latitude/high altitude groups of sites (Fig. 2). Cluster 1.5 (and, to a lesser extent 1.6) was characterized by very dry summer, while cluster1.4 showed cold and humid conditions throughout the year (Fig. 3). Clusters 1.1, 1.2 and 1.3 were all mid-range latitude and low altitude groups. Cluster 1.3 displayed low P -PET values during the non-growing period (fall, winter); there was a pattern of decreasing P -PET level during winter when moving from cluster 1.1 to cluster 1.3. In the following, we therefore referred to the climate conditions associated with each cluster as regional climates.