Stella G Muthuri

Sudhir Venkatesan

Puja R Myles

Jo O Leonardi-Bee

Tarig S A Al Khuwaitir

Adbullah Al Mamun

Ashish P Anovadiya

Eduardo Azziz-Baumgartner

Clarisa Báez

Matteo Bassetti

PUJA R Myles

ADBULLAH A L Khuwaitir

A L Mamun

Bojana Beovic

Barbara Bertisch

Isabelle Bonmarin

Robert Booy

Victor H Borja-Aburto

Heinz Burgmann

Bin Cao

Jordi Carratala

Justin T Denholm

PERICLES Samuel R Dominguez

A D Duarte

Gal Dubnov-Raz

Marcela Echavarria

Sergio Fanella

Zhancheng Gao

Patrick Gérardin

Maddalena Giannella

Sophie Gubbels

ANJARATH L HIGUERA IGLESIAS Jethro Herberg

Peter H Hoger

QUAZI T ISLAM Xiaoyun Hu

MIRELA F JIMÉNEZ Amr Kandeel

Gerben Keijzers

Hossein Khalili

Marian Knight

Koichiro Kudo

Gabriela Kusznierz

Ilija Kuzman

IDRISS C Kwan

Lahlou Amine

Eduard Langenegger

Kamran B Lankarani

Yee-Sin Leo

Rita Linko

FARIS MADANAT Pei Liu

Elga Mayo-Montero

Allison Mcgeer

Ziad Memish

Gokhan Metan

Auksė Mickiene

Dragan Mikić

Kristin G I Mohn

Ahmadreza Moradi

Pagbajabyn Nymadawa

Maria E Oliva

Mehpare Ozkan

Dhruv Parekh

Mical Paul

Fernando P Polack

Barbara A Rath

Alejandro H Rodríguez

Elena B Sarrouf

Anna C Seale

MARILDA Bunyamin Sertogullarindan

M Siqueira

Joanna Skręt-Magierło

Frank Stephan

Ewa Talarek

Julian W Tang

Kelvin K W To

SELDA H TÖRÜN Antoni Torres

Dat Tran

Timothy M Uyeki

Annelies Van Zwol

Wendy Vaudry

Tjasa Vidmar

Renata T C Yokota

PRIDE CONSORTIUM INVESTIGATORS † Paul Zarogoulidis

Jonathan S Nguyen-Van-

DM Jonathan Van-Tam

F Hoffmann

La Roche

Effectiveness of neuraminidase inhibitors in reducing mortality in patients admitted to hospital with influenza A H1N1pdm09 virus infection: an individual participant data meta-analysis

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

FINDINGS

We included data for 29 234 patients from 78 studies of patients admitted to hospital between Jan 2, 2009, and March 14, 2011. Compared with no treatment, neuraminidase inhibitor treatment (irrespective of timing) was associated with a reduction in mortality risk (adjusted odds ratio [OR] 0•81; 95% CI 0•70-0•93; p=0•0024). Compared with later treatment, early treatment (within 2 days of symptom onset) was associated with a reduction in mortality risk (adjusted OR 0•48; 95% CI 0•41-0•56; p<0•0001). Early treatment versus no treatment was also associated with a reduction in mortality (adjusted OR 0•50; 95% CI 0•37-0•67; p<0•0001). These associations with reduced mortality risk were less pronounced and not significant in children. There was an increase in the mortality hazard rate with each day's delay in initiation of treatment up to day 5 as compared with treatment initiated within 2 days of symptom onset (adjusted HR 1•23 [95% CI 1•18-1•28]; p<0•0001 for the increasing HR with each day's delay).

INTERPRETATION INTRODUCTION

The neuraminidase inhibitors, oral oseltamivir and inhaled zanamivir, were the predominant medical countermeasure available from emergence of the influenza A H1N1pdm09 virus in early 2009, until the first release of monovalent H1N1 vaccines in October, 2009. Prescribing data from seven countries (Australia, Canada, France, Germany, Japan, UK, USA) suggest at least 18•3 million individuals received oseltamivir between May 1, 2009, and Dec 31, 2009. [START_REF] Donner | Safety profile of oseltamivir during the 2009 influenza pandemic[END_REF] Country-specific policies for use of neuraminidase inhibitors during the 2009-10 pandemic varied from no use, to targeted use in at-risk patients (most countries), to treatment of all patients with clinical illness (UK). Most use of neuraminidase inhibitors worldwide was in the form of oseltamivir-eg, 97•5% of neuraminidase inhibitors used in the USA. 2 There is little prepandemic evidence pertaining to the effectiveness of neuraminidase inhibitors in reducing mortality in patients admitted to hospital with influenza; most evidence comes from observational studies of treatment of seasonal influenza, often in highly specific groups of patients. [3][START_REF] Chemaly | Neuraminidase inhibitors improve outcome of patients with leukemia and influenza: an observational study[END_REF][START_REF] Hanshaoworakul | Severe human influenza infections in Thailand: Oseltamivir treatment and risk factors for fatal outcome[END_REF][START_REF] Hassan | Antiviral therapy improves outcomes of influenza infections in patients requiring admission to intensive care[END_REF][START_REF] Lee | Outcomes of adults hospitalised with severe influenza[END_REF][START_REF] Mcgeer | Antiviral therapy and outcomes of influenza requiring hospitalization in Ontario, Canada[END_REF][START_REF] Nichols | Influenza infections after hematopoietic stem cell transplantation: risk factors, mortality, and the effect of antiviral therapy[END_REF] Thus, in 2009-10, neuraminidase inhibitors were used on the basis of rational deduction that they would reduce mortality due to influenza A H1N1pdm09 virus infection rather than on strong preexisting evidence, although data from treatment of human influenza A H5N1 cases suggested this reduction in mortality might be possible. [START_REF] Adisasmito | Effectiveness of Antiviral Treatment in Human Influenza A(H5N1) Infections: Analysis of a Global Patient Registry[END_REF][START_REF] Chan | Determinants of Antiviral Effectiveness in Influenza Virus A Subtype H5N1[END_REF] Japanese clinicians used neuraminidase inhibitors widely to treat all people presenting with clinical influenza in 2009-10 and recorded the lowest pandemic mortality rate of any developed country. [START_REF] Nakai | Characteristics of pregnant Japanese women who required hospitalization for treatment of pandemic (H1N1) 2009--low mortality rate may be due to early antiviral use[END_REF][START_REF] Sugaya | Widespread use of neuraminidase inhibitors in Japan[END_REF][START_REF] Sugaya | Very low pandemic influenza A (H1N1) 2009 mortality associated with early neuraminidase inhibitor treatment in Japan: Analysis of 1000 hospitalized children[END_REF] Although a similar treat-all policy existed in the UK in 2009, uptake of neuraminidase inhibitors in patients admitted to hospital with influenza A H1N1pdm09 was low. [START_REF] Myles | Predictors of clinical outcome in a national hospitalised cohort across both waves of the influenza A/H1N1 pandemic 2009-2010 in the UK[END_REF] Two systematic reviews and meta-analyses have examined the effectiveness of neuraminidase inhibitors in reducing mortality due to influenza. Both suggest substantial reductions in mortality by two-thirds to three-quarters compared with no treatment. [START_REF] Hsu | Antivirals for treatment of influenza: a systematic review and meta-analysis of observational studies[END_REF][START_REF] Muthuri | Impact of neuraminidase inhibitor treatment on outcomes of public health importance during the 2009-2010 influenza A(H1N1) pandemic: a systematic review and metaanalysis in hospitalized patients[END_REF] However, limitations are apparent, such as the heterogeneity of studies included and inadequate adjustment for potential confounding.

Importantly, neither was able to adjust for the likelihood of a patient receiving antiviral treatment (propensity)-a crucial consideration when antiviral drugs might have been prioritised towards the sickest patients-and neither was able to use a pooled analysis approach with individual participant data. [START_REF] Riley | Meta-analysis of individual participant data: rationale, conduct, and reporting[END_REF]

METHODS

Study design and identification of datasets

The assessing the association between neuraminidase inhibitor treatment and clinical outcomes (mortality, influenza-related pneumonia, admission to critical care, length of stay in hospital and admission to hospital). We searched Ovid Medline (reports from 1996 onwards) and Embase (1980 onwards) using a comprehensive search strategy. We also searched CINAHL, CAB Abstracts, ISI Web of Science, PubMed, UK PubMed Central, Scopus, WHO regional indexes, LILAC, and J-STAGE databases using Boolean logic and core search terms relating to pandemic influenza (including influenza A virus OR H1N1 subtype OR swine origin influenza AH1N1 virus) AND exposure of interest-ie, antiviral drugs (including neuraminidase inhibitors OR oseltamivir OR zanamivir OR peramivir) AND clinical outcome measures (including pneumonia, or critical care/intensive care, or mortality). We identified further studies from reference lists of relevant articles and through contact with subject area experts (via JSN-V-T). All search results were limited to human beings with no language restrictions. Our detailed search strategy is reported elsewhere. [START_REF] Muthuri | Impact of neuraminidase inhibitor treatment on outcomes of public health importance during the 2009-2010 influenza A(H1N1) pandemic: a systematic review and metaanalysis in hospitalized patients[END_REF] On the basis of this search, we contacted 401 potential data contributors, identified during the conduct of our previously reported systematic review; 17 these potential contributors included several corresponding authors from different papers but potentially related to the same source dataset, as an allinclusive approach. We recruited additional centres through our network of global collaborators, publicity at conferences attended, and by word-of-mouth. Centres fulfilling the minimum dataset requirement (appendix pp 7-8) were eligible for inclusion. We requested data for both laboratory confirmed and clinically diagnosed pandemic influenza A H1N1pdm09 cases, but allowed centres to provide individual patient data extending to March 14, 2011 (third pandemic wave cases). Clinically diagnosed cases that could not be confirmed by virology were diagnosed on the basis of clinical signs and symptoms that, in the opinion of the attending physician, were judged to be representative of influenza-like illness, in the absence of any other more likely diagnosis. We deliberately accepted diagnoses made on clinical judgment rather than specifying a set of clinical criteria, since case definitions of influenza-like illness vary within and between countries. This study was granted exemption from full ethical review by the University of Nottingham Medical School Research Ethics Committee, provided that each contributing centre held its own institutional review board approval for data collection and sharing.

Data standardisation, exposures, outcomes, and covariates

A common data dictionary was developed and individual datasets standardised according to these definitions (appendix pp 9-15) before pooling for analysis. The primary outcome variable was mortality, defined as death occurring during admission to hospital or individual study follow-up period for the generalised linear mixed regression models and as death occurring within 30 days of illness onset in the Cox regression models. Use of neuraminidase inhibitors (exposure) was defined and compared as follows: neuraminidase inhibitor treatment (irrespective of timing) versus none; early neuraminidase inhibitor treatment (starting treatment ≤2 days after symptom onset) versus later (initiation >2 days after symptom onset); early neuraminidase inhibitor treatment versus none; and later neuraminidase inhibitor treatment versus none. Additionally, we created a continuous exposure variable, representing time (in days) between symptom onset and treatment initiation (0 meaning treatment commenced on day of symptom onset). Covariates in the final multivariable models were "inpatient treatment with oral or intravenous antibiotics" and "inpatient treatment with systemic corticosteroids" prescribed during the admission to hospital for influenza along with treatment propensity scores. We were unable to adjust for dose or duration of such treatments because of the scarce availability of these data across the individual datasets.

Propensity scoring

We calculated propensity scores for the likelihood of neuraminidase inhibitor treatment for each patient within individual datasets using multivariable logistic regression for binary treatment variables and generalised propensity score estimation for the continuous time to treatment variable as described by Hirano and Imbens. [START_REF] Hirano | The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data[END_REF] For each separate study dataset we calculated propensity scores (likelihood of treatment) for each of the four main exposure measures: neuraminidase inhibitor at any time (yes or no), early versus late neuraminidase inhibitor, early versus no neuraminidase inhibitor, and later (>2 days) versus no neuraminidase inhibitor. Covariates were then included as follows, irrespective of significance: age, sex, comorbidity (yes or no), a proxy indicator of severe disease (yes or no), which were, in order of preference, severe respiratory distress; shortness of breath; unweighted symptom score; or, if none of these indicators were available, we used one of the following measures of severity: AVPU (alert, voice, pain, unresponsive) mental status examination score, Glasgow Coma Scale score, Sequential Organ Failure Assessment score, or CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥65 years) pneumonia severity scores, if these were available, entered as a continuous variable. We added the following variables when available to create an extended model, using a parsimonious approach that retained only significant covariates in the final model: obesity, smoking, pregnancy, asthma, chronic obstructive pulmonary disease, lung disease, heart disease, immunosuppression, neurological disease, renal disease, and diabetes. We rejected variables with more than 25% missing data. Some variables used for the propensity score calculation, such as comorbidity (binary) and illness severity at presentation (binary), were derived at individual study level only and were not appropriate for inclusion in the pooled dataset analysis because of the heterogeneity in definition of these variables between studies.

The appropriateness of the propensity derivation models was assessed graphically by comparing the distribution of estimated propensity scores across treatment groups for each individual dataset. [START_REF] Harder | Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research[END_REF] Propensity scores were then categorised into quintiles for each individual dataset.

Statistical analysis

We used a generalised linear mixed model to account for clustering of effects by study using the xtmelogit command in Stata (version 12). We included "study" as a random intercept to account for differences in baseline crude mortality rate at each site. We adjusted the model for treatment propensity, inpatient antibiotics, and systemic corticosteroids. We included missing data in covariates as a separate dummy category. The overall analysis included patients of all ages with laboratory or clinically diagnosed influenza A H1N1pdm09. We did prespecified stratified analyses for adults and children (<16 years), pregnant women (irrespective of age), laboratory confirmed influenza A H1N1pdm09 cases, and patients admitted to critical care units. Additionally, for a subset of our sample for whom exact onset and treatment initiation times were available, we investigated the association between time to initiation of antiviral treatment and mortality within 30 days of illness onset using a time-dependent Cox regression shared frailty model (to account for clustering by study) adjusted for propensity score and inpatient treatment with antibiotics or systemic corticosteroids. Antiviral treatment was modelled as a time-dependent covariate to overcome immortal time bias (ie, survivor bias). Results from the generalised linear mixed model are expressed as relative risks of mortality using odds ratios (ORs) and hazard ratios (HRs) for the Cox regression analysis with 95% CIs. We used Stata (version 12) for all analyses.

The protocol [START_REF] Myles | A systematic review of the impact of neuraminidase inhibitor antiviral use on outcomes of public health importance during the 2009/10 (swine) influenza A/H1N1v pandemic[END_REF] for this study was registered with the PROSPERO register of systematic reviews, number CRD42011001273.

Role of the funding source

The funder of the study had no role in the study design, data collection, data analysis, data interpretation, writing of the report. The funder has not and will never have access to the data. Each collaborator had access to the raw data from his or her centre. SGM, SV, PRM, JL-B, and JSN-V-T had access to the pooled dataset. The corresponding author (JSN-V-T) had full access to all the data in the study and the final responsibility for the decision to submit for publication.

RESULTS

We received replies from 128 (32%) of 401 centres contacted; of these 77 (60%) confirmed willingness to participate and the remainder declined (36 [28%] had no data; three [2%] agreed initially but later withdrew because of lack of capacity for data extraction, institutional review board restrictions preventing sharing of individual participant data, or failure to obtain government approval for data sharing; 12 [9%] had agreed in principle, but were unable to share data within project timescales). No data were requested from nor provided by pharmaceutical companies. After exclusion of duplicate responses (same source dataset), and addition of three further datasets provided through informal contact with domain experts, 80 research groups from 38 countries in six WHO regions contributed anonymised data for 168 117 patients, of whom 24 416 had laboratory results indicative of noninfluenza A H1N1 disease. Among the remaining 143 701 laboratory confirmed or clinically diagnosed (without standard study-wide case definition) influenza A H1N1pdm09 cases, 106 138 were outpatients and 2593 had missing information for hospital admission. The remaining 34 970 inpatients were eligible for inclusion (figure 1). Patients without neuraminidase inhibitor treatment data and therefore excluded from analysis were more likely to be older, to have presented to hospital later, less likely to have a laboratory confirmed diagnosis, and more likely to be treated with antibiotics than were patients included in the analysis (appendix pp 26-27). However, they were less likely to be smokers, obese, or to have an underlying comorbidity. Additionally, their hospital stay was shorter, and they were less likely to have severe outcomes (admission to critical care unit or death), or influenza-related pneumonia (appendix pp 26-

27).

After adjustment for propensity score and corticosteroid and antibiotic treatment, the likelihood of mortality in patients treated with a neuraminidase inhibitor was 0•81 (95% CI 0•70-0•93), compared with no treatment (table 2). The OR did not change substantially when only laboratory confirmed cases were included (adjusted OR 0•82 [95% CI 0•70-0•95]). Similarly, we identified significant associations with a reduced mortality risk in adults, pregnant women, and critically ill adult patients (table 2). However, there was no significant association between neuraminidase inhibitor treatment and mortality in children aged 0-15 years (table 2). Post-hoc analyses restricted to children up to 1 year of age and up to 5 years of age did not change this finding (appendix p 27).

Early neuraminidase inhibitor treatment compared with later treatment initiation was associated with an overall significant reduction in mortality risk (adjusted OR 0•48 [95% CI 0•41-0•56]; table 3). The ORs remained essentially unchanged when only laboratory confirmed cases were considered, but risk reduction was higher in pregnant women (table 3). Notably, there was again no significant association between early treatment and mortality in children after adjustment (table 3). 3), with significant risk reductions also noted among laboratory confirmed cases, adults, pregnant women, and adult patients admitted to critical care (table 3). However, there was no significant association with a lower mortality risk in children aged 0-15 years (table 3).

With regard to neuraminidase inhibitor treatment started more than 2 days after symptom onset compared with none, we identified no significant association with mortality in all patients (adjusted OR ). [START_REF] Muthuri | Impact of neuraminidase inhibitor treatment on outcomes of public health importance during the 2009-2010 influenza A(H1N1) pandemic: a systematic review and metaanalysis in hospitalized patients[END_REF] We applied search terms relating to pandemic influenza (including "Influenza A Virus" OR "H1N1Subtype" OR "swine origin influenza AH1N1 virus"), AND exposure of interest-ie, antiviral drugs (including "neuraminidase inhibitors" OR "oseltamivir" OR "zanamivir" OR "peramivir") AND clinical outcome measures (including "pneumonia", "critical or intensive care", "mortality") to 11 databases (search range from Jan 1, 2009, to Aug 10, 2010; last search on April 19, 2012) without imposing language restrictions. Importantly, both studies acknowledged limitations such as the heterogeneity of studies included and inadequate adjustment for potential confounding.

Moreover, neither was able to adjust for the likelihood of a patient receiving antiviral treatment (propensity)-a crucial consideration when antiviral drugs might have been prioritised towards the sickest patients.

Interpretation

By using a meta-analysis of individual patient data, which permits a uniform approach to potential confounding and adjustment for treatment propensity, and through the assembly of a very large international dataset, our study adds substantially to the evidence that neuraminidase inhibitors administered to adults admitted to hospital with influenza A H1N1pdm09 reduced mortality, especially when started promptly. Since placebo-controlled randomised controlled trials of neuraminidase inhibitors are not ethically feasible during a pandemic, the evidence we have assembled is likely to be the best that will be available. The US Centers for Disease Control and Prevention recommend neuraminidase inhibitor treatment as early as possible for any patient with confirmed or suspected influenza who is hospitalised; has severe, complicated or progressive illness; or is at higher risk for influenza complications. [START_REF]Influenza antiviral medications: summary for clinicians[END_REF] Neuraminidase inhibitors are also widely prescribed in Japan, but elsewhere their use is far less common. Although a similar treat-all policy existed in the UK in 2009, uptake of neuraminidase inhibitors inpatients admitted to hospital with influenza A H1N1pdm09 virus was low. [START_REF] Myles | Predictors of clinical outcome in a national hospitalised cohort across both waves of the influenza A/H1N1 pandemic 2009-2010 in the UK[END_REF] We advocate early instigation of neuraminidase inhibitor treatment in adults admitted to hospital with suspected or proven influenza infection.

DISCUSSION

Our results show that neuraminidase inhibitor treatment was associated with reduced mortality in adult patients admitted to hospital with influenza A H1N1pdm09 virus infection. Neuraminidase inhibitor treatment of influenza A H1N1pdm09 at any stage of illness compared with none revealed an associated reduction in the likelihood of mortality (table 2). We identified an associated likelihood of lower mortality when comparing early versus later initiation of treatment and when comparing early treatment with none (table 3, panel). Although we included 4233 patients (14%) without laboratory confirmed influenza A H1N1pdm09, restriction to laboratory-confirmed cases produced near identical estimates, suggesting that the data are not confounded by misclassification bias attributable to other causes (tables 2, 3). Additionally, we noted much the same findings in adults, pregnant women, and adult patients needing admission to critical care. The finding regarding critical care suggests that neuraminidase inhibitors were associated with mortality reduction across the spectrum of severity in adult patients admitted to hospital with influenza A H1N1pdm09. These findings accord closely with previous studies [START_REF] Hsu | Antivirals for treatment of influenza: a systematic review and meta-analysis of observational studies[END_REF][START_REF] Muthuri | Impact of neuraminidase inhibitor treatment on outcomes of public health importance during the 2009-2010 influenza A(H1N1) pandemic: a systematic review and metaanalysis in hospitalized patients[END_REF] but have increased precision and reduced estimates of effectiveness consistent with more complete adjustment for confounders and treatment propensity. They are also consistent with ecological data. [START_REF]Transmission dynamics and impact of pandemic influenza A (H1N1) 2009 virus[END_REF][START_REF] Chowell | Impact of antiviral treatment and hospital admission delay on risk of death associated with 2009 A/H1N1 pandemic influenza in Mexico[END_REF][START_REF] Miller | Supply of Neuraminidase Inhibitors Related to Reduced Influenza A (H1N1) Mortality during the 2009-2010 H1N1 Pandemic: An Ecological Study[END_REF] We were consistently unable to show any association of neuraminidase inhibitor treatment with mortality reduction in children. Possible explanations include lower case fatality proportion in paediatric patients (thus reduced statistical power), [START_REF] Van Kerkhove | Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis[END_REF][START_REF] Jessica | Case fatality risk of influenza A(H1N1pdm09): a systematic review[END_REF] higher influenza A H1N1pdm09 viral load in children [START_REF] Li | Correlation of pandemic (H1N1) 2009 viral load with disease severity and prolonged viral shedding in children[END_REF] than adults leading to reduced drug effectiveness, suboptimum dosing in very young children, [START_REF] Kimberlin | Oseltamivir pharmacokinetics, dosing, and resistance among children aged <2 years with influenza[END_REF] secondary bacterial infections (eg, methicillin-resistant Staphylococcus aureus), confounding by indication [START_REF] Signorello | Confounding by indication in epidemiologic studies of commonly used analgesics[END_REF] (children might have been more likely to have had antivirals prescribed if they had more severe disease or if they failed to respond to other treatments), or a combination of these factors. Since it has been suggested that younger children might be admitted with milder disease compared with older children and adults (precautionary physician behaviour), that the pharmacokinetics and pharmacology of oseltamivir might be different in very young children, [START_REF] Kimberlin | Oseltamivir pharmacokinetics, dosing, and resistance among children aged <2 years with influenza[END_REF] and that influenza pathogenesis might differ by age, [START_REF] Sun | Age-related sensitivity and pathological differences in infections by 2009 pandemic influenza A (H1N1) virus[END_REF] we did post-hoc sensitivity analyses separately in children up to 1 year of age and up to 5 years of age, but our findings did not change (appendix p 27).

However, we note that these results contrast with those of Louie and colleagues, [START_REF] Louie | Neuraminidase Inhibitors for Critically Ill Children With Influenza[END_REF] who recently showed Our analyses examining the effect of later treatment versus none are especially relevant to the continued clinical debate about the value of delayed therapy. Combining all subgroups of patients, we did not identify any protective association with treatment delayed more than 2 days after symptom onset (table 4). This finding could be explained by confounding by indication. However, we noted that in adult patients admitted to critical care, delayed treatment was associated with reduced likelihood of mortality compared with no treatment (table 4), suggesting that delayed therapy might still be worthwhile in severely ill patients; this finding is plausible since, within this subgroup, treated and untreated patients (who all needed admission to critical care) are likely to have been more balanced in terms of illness severity thereby overcoming confounding by this factor to some extent. Additionally, some patients admitted to critical care might have had prolonged influenza A H1N1pdm09 virus replication in the lower respiratory tract, which might benefit from later initiation of neuraminidase inhibitor treatment. To gain further understanding about overall timing-dependent benefit, we modelled time to start of antiviral treatment using a time-dependent Cox regression model, which showed a significant detrimental survival benefit associated with delay in treatment beyond 2 days after symptom onset (p<0•0001), albeit with overlapping 95% CIs when time to treatment was modelled as a categorical variable; the latter finding suggests that potential differences in treatment benefit between starting on day 3 after symptom onset through to more than 5 days after symptom onset cannot be further clarified through our data. This finding could seem to conflict with the findings in table 4 comparing later neuraminidase inhibitor treatment to no neuraminidase inhibitor treatment but is not surprising, because by comparing only treated patients in figure 2, we possibly eliminated some of the confounding due to indication, which allowed us to identify the potential survival benefits conferred by later treatment, albeit detrimental in proportion to treatment delay.

One of the strengths of this study is the very large number of patients from geographically diverse clinical centres and source populations. We made exhaustive efforts to identify suitable datasets from around the world, but nevertheless cannot comment on the extent to which bias might have been introduced by failing to include centres that did not respond (we cannot say if they had suitable data or not), or that declined to share data; in a worst case scenario, it is possible that less than 20% of potential sites contributed to this analysis. Furthermore, comparatively few cases were from the WHO African (0•1%) and South-East Asia (0•7%) regions, which might limit the extent to which our findings can be generalised.

A clear limitation of our study is that we were unable to adjust specifically for disease severity in our multilevel models because of the heterogeneity of severity measures used across individual datasets.

However, we made every effort to include relevant data including severity measures, within each propensity score, but there is still likely to be some residual confounding, particularly due to illness severity at presentation. Likewise, we attempted to control for study-level biases, such as treatment policies, and healthcare seeking behaviour, using multilevel models but there might be residual confounding. A further limitation of our dataset is that 10% of the patients had missing data for exposure to neuraminidase inhibitors and were excluded from the analysis; the characteristics of these patients are compared with those with data for neuraminidase inhibitor exposure in appendix pp 26-27;

these patients were more likely to be older, to have presented to hospital later, less likely to have a laboratory confirmed diagnosis, and more likely to be treated with antibiotics.

The decision to adjust for treatment with antibiotics and corticosteroids was taken after consultation with clinical colleagues within the PRIDE study collaboration. This decision results from widespread clinical practice to treat patients admitted to hospital with respiratory illness with corticosteroids and antibiotics. There is particular uncertainty about the possible effect of corticosteroids on the course of severe influenza infection. [START_REF] Hui | Adjunctive therapies and immunomodulating agents for severe influenza[END_REF][START_REF] Myles | Differences between asthmatics and nonasthmatics hospitalised with influenza A infection[END_REF] Therefore, it was necessary to separate out the possible effects of antivirals from these other commonly used treatments. We did not do specific analyses to establish the potential effect of antibiotic or corticosteroid use on mortality, but recognise that these factors both warrant further research. Although we were able to adjust for inpatient antibiotics and systemic corticosteroid use, we were unable to adjust for pandemic H1N1 vaccination since 35% (8284 of 23633) of our case series were admitted to hospital before the first availability of vaccine in October, 2009, and 71% (10 967 of 15 349) of data for vaccination status were missing among those admitted after that juncture; however, the available data suggest uptake was no higher than 8% during the study period.

This meta-analysis of individual patient data offers the most rigorous assessment of mortality benefits of neuraminidase inhibitor treatment during the 2009-10 pandemic that is likely to be possible using retrospective observational data. The greatest likelihood of reduced mortality seems to be attributable All other named authors declare no conflicts of interest.

  Post-pandemic Review of anti-Influenza Drug Effectiveness (PRIDE) research consortium was set up in October, 2011, and is coordinated by the Health Protection and Influenza Research Group at the University of Nottingham, Nottingham, UK. The aim of the collaboration is to do individual participant data meta-analyses of the effectiveness of antiviral use on outcomes of public health importance during the 2009-10 influenza pandemic.Members of the PRIDE research consortium are listed in appendix pp 1-6. The initial identification of potential data contributors was done on the basis of a systematic search of 11 databases (date of last search April 19, 2012) for observational studies (case series, case-control, and cohort studies) and randomised controlled trials done between March 1, 2009 (Mexico), or April 1, 2009 (rest of the world), until the WHO declaration of the end of the pandemic (Aug 10, 2010),

Figure 1 :

 1 Figure 1: Study flow diagram

  †adjusted for treatment propensity (by quintile), corticosteroid use and antibiotic use Neuraminidase inhibitor treatment within 2 days of symptom onset compared with none was also associated with a significant reduction in mortality in all patients (adjusted OR 0•50 [95% CI 0•37-0•67]; table

  †adjusted for treatment propensity (by quintile), corticosteroid use and antibiotic use Information about exact timing of neuraminidase inhibitor treatment from symptom onset was available for 65% (12284 of 18803) of those receiving such treatment. After taking into account clustering by study, propensity score quintiles, and inhospital treatment with antibiotics or systemic corticosteroids, when antiviral use was modelled as a time-dependent covariate to overcome potential immortal time bias (ie, survivor bias), neuraminidase inhibitor treatment was significantly associated with decreased hazard rate of mortality over a 30-day follow-up period (adjusted HR 0•51 [95% CI 0•45-0•58], p<0•0001) as compared with no antiviral treatment. When only treated cases were included, there was an increase in the hazard with each day's delay in initiation of treatment up to day 5 as compared with treatment initiated within 2 days of symptom onset (adjusted HR 1•23 [95% CI 1•18-1•28], p<0•0001 for the increasing HR with each day's delay). The unadjusted and adjusted survival curves comparing survival by time to treatment initiation are shown in figure 2 and appendix pp 28-29.

  †adjusted for treatment propensity quintiles, corticosteroid use and antibiotic use

Figure 2 :

 2 Figure 2: Survival by time to treatment

  a two-thirds reduction in mortality among children treated with neuraminidase inhibitors admitted to hospital with influenza (OR 0•36 [95% CI 0•16-0•83]).The finding that no treatment was better than late treatment is probably explained by confounding due to illness severity at the point of treatment initiation (ie, confounding by indication). Untreated patients probably had milder disease and patients treated later in the course of their illness might have had delays in hospital admission, delays in diagnosis after admission, or delays in being considered for neuraminidase inhibitor treatment (treatment only started once their condition deteriorated), or combinations of these factors. We advocate early consideration of a diagnosis of influenza in patients admitted to hospital with respiratory infection during periods of known influenza activity, and early instigation of neuraminidase inhibitor treatment based on rapid laboratory confirmation or clinical suspicion.

  to treatment started within 2 days of symptom onset. These data offer evidence of the effectiveness of neuraminidase inhibitors during the 2009-10 pandemic and are superior to extrapolations from earlier data on seasonal influenza; they could retrospectively vindicate prepandemic neuraminidase inhibitor antiviral stockpiling decisions made by governments worldwide. Treatment guidance policies should increase emphasis on early empirical neuraminidase inhibitor treatment of adult patients admitted to hospital after presenting with proven or clinically suspected influenza A H1N1pdm09 virus infection. However, most adult patients with suspected or confirmed influenza are not admitted to hospital within 48 h of illness onset. Therefore, the implications of these findings, although based on patients admitted to hospital with influenza A H1N1pdm09, encourage early initiation of neuraminidase inhibitor treatment in outpatients who are appreciably unwell with suspected or confirmed influenza, or at increased risk of complications, including those with influenza A H3N2 or influenza B. Further studies are needed in children to confirm the adequacy of present dose regimens and duration of therapy in terms of clinical efficacy. attend clinical seminars to present this work. Professor Robert Booy (RB) has received financial support from CSL, Sanofi, GlaxoSmithKline, Novartis, Roche and Wyeth to conduct research and present at scientific meetings. Any funding received is directed to an NCIRS research account at The Children's Hospital at Westmead, New South Wales, Australia, and is not personally accepted by RB.

Mortality data missing, n=2,095

	Of the 34 970 inpatients eligible for inclusion, 2095 (6%) had missing information for mortality status,
	and 3584 (10%) for exposure to neuraminidase inhibitors; 57 (<1%) were unsuitable for inclusion for
	401 centres contacted
	other reasons (figure 1). Ultimately, we included 29 234 records from 78 studies (two studies provided
	only outpatient data and were excluded from analysis) of patients admitted to hospital between Jan 2,
	324 centres excluded 273 centres did not respond 2009, and March 14, 2011: 25 001 (86%) laboratory confirmed; 9218 (32%) children; and 1600 (5%)
	51 declined to participate aged 65 years or older. Appendix p 16 show the incidence of cases by month. Full characteristics of the
	3 centres identified by contact with experts pooled dataset are listed in table 1with absolute risks of mortality for various exposure categories and
	subgroups summarised in appendix p 16. Baseline characteristics of each constituent dataset are
	168 117 potentially eligible patients presented in appendix pp 17-21.
	disclosed by 80 centres
	24,416 patients without influenza A
	H1N1pdm09 virus infection
	143,701 patients with laboratory
	confirmed or clinically diagnosed
	influenza A H1N1pdm09 virus
	infection
	108,731 excluded
	106,138 outpatients (two
	studies of outpatients only
	excluded)
	2593 unknown admission status
	34 970 inpatients from 78 centres
	5736 patients excluded
	3584 missing data for exposure
	to neuraminidase inhibitors
	2095 missing mortality status
	57 other reasons*
	29234 patients from 78 centres
	included in analysis

Table 1 : Characteristics of pooled dataset of 29,234 patients hospitalised with A(H1N1)pdm09 virus infection included in mortality analysis

 1 Data are n (%) or median (IQR). COPD=chronic obstructive pulmonary disease. NAI=neuraminidase inhibitor. *All percentages have been calculated using these denominators unless otherwise specified. †Missing data; n shows number of cases with data. ‡Reported as clinically obese or using WHO definition for obesity (BMI ≥30 kg/m² in adults aged ≥20 years). §Proportions were calculated as a percentage of pregnant patients among female patients of reproductive age (13-54 years); the broader age range was selected in preference to the WHO definition (15-44 years) after consultation with data contributors to reflect the actual fertility experience of the sample. ¶For definition of comorbidity, see appendix pp 9-11. ||Denominators for pandemic vaccine based on patients admitted after Oct 1, 2009 (when vaccine potentially available). **Percentages calculated as a proportion of the sample receiving NAI therapy. † †Clinically or radiologically diagnosed pneumonia.

		All hospitalised	Deceased	Survived
	Characteristic (denominator)	patients	n (%)	n (%)
		n (%)

Table 2 : NAI TREATMENT (AT ANY TIME) VS. NONE

 2

		Crude analysis		Adjusted † analysis
	Subgroups	OR (95% CI)	P	OR (95% CI)	P
			value		value
	Laboratory confirmed or clinically
	diagnosed (all ages); n=29,234

Table 3 : EARLY NAI TREATMENT (≤2 DAYS AFTER ONSET) VS. LATER (>2 DAYS) OR NONE Early treatment vs. Later treatment:

 3

		Crude analysis		Adjusted † analysis
	Subgroups	OR (95% CI)	P value	OR (95% CI)	P value
	Laboratory confirmed or clinically	0•36 (0•31 to 0•41)	<0•0001	0•48 (0•41 to 0•56)	<0•0001
	diagnosed (all ages); n=13,254
	Laboratory confirmed cases (all ages) ;	0•36 (0•31 to 0•41)	<0•0001	0•48 (0•41 to 0•56)	<0•0001
	n=12,992
	Adults (16 years and above); n=9,270	0•37 (0•32 to 0•44)	<0•0001	0•45 (0•38 to 0•54)	<0•0001
	Children (below 16 years); n=3,899	0•53 (0•35 to 0•80)	0•0026	0•67 (0•44 to 1•03)	0•07
	Pregnant women ; n= 917	0•20 (0•09 to 0•46)	0•0002	0•27 (0•11 to 0•63)	0•0026
	ICU patients
	Adults (≥16 years); n=3,385	0•64 (0•51 -0•79)	<0•0001	0•62 (0•49 to 0•77)	<0•0001
	Children (<16 years); n=683	1•12 (0•63 to 1•99)	0•69	1•15 (0•64 to 2•06)	0•64
	Early treatment vs. none:
	Laboratory confirmed or clinically	0•54 (0•40 to 0•72)	<0•0001	0•50 (0•37 to 0•67)	<0•0001
	diagnosed (all ages); n=16,425
	Laboratory confirmed cases (all ages);	0•53 (0•39 to 0•71)	<0•0001	0•48 (0•36 to 0•66)	<0•0001
	n= 13,200
	Adults (16 years and above) ; n=10,607	0•39 (0•28 to 0•55)	<0•0001	0•38 (0•27 to 0•54)	<0•0001
	Children (below 16 years); n=5,696	1•08 (0•61 to 1•93)	0•79	0•85 (0•47 to 1•53)	0•59
	Pregnant women, n=1,303	0•16

Table 4 : LATER NAI TREATMENT (>2 DAYS) VS. NONE

 4

		Crude analysis		Adjusted † analysis
	Population Subgroups	OR (95% CI)	P value	OR (95% CI)	P value
	Laboratory confirmed or clinically	1•27 (1•00 to 1•61)	0•0497	1•20 (0•93 to 1•54)	0•15
	diagnosed (all ages); n=17,670
	Laboratory confirmed cases (all ages);	1•25 (0•98 to 1•59)	0•07	1•17 (0•92 to 1•51)	0•21
	n=14,409
	Adults (16 years and above); n=12,269	1•01 (0•77 to 1•32)	0•94	1•01 (0•76 to 1•33)	0•96
	Children (below 16 years); n=5,282	1•34 (0•78 to 2•31)	0•29	1•29 (0•75 to 2•21)	0•36

Section for Infectious Diseases, Medical Department, and Department of Research and Development, Haukeland University Hospital

The Influenza Centre, Department of Clinical Science, University of Bergen, Norway

Acknowledgements

Preliminary findings from this work were presented at the International Infectious Diseases Conference, Kaunas, Lithuania, Nov 9, 2012; XV International Symposium on Respiratory Viral Infections, Rotterdam, March 15, 2013; and at two closed meetings: ESWI FluQuest workshop, Brussels, May 3, 2013, and WHO/ECDC meeting on influenza, Istanbul, May 31, 2013.

We thank the many medical personnel and other individuals who helped in collating and maintaining the clinical data used in our analyses. We thank all participating hospitals, institutions, and research networks (listed appendix pp 1-6) that supplied anonymized patient data. We thank Maroeska Rovers, Radboud University Nijmegen Medical Center and co-convenor of the Cochrane individual patient data meta-analysis methods group for advice on methodological aspects of the study, Benjamin J Cowling, Hong Kong University, for advice on statistical approaches, and Malcolm G (Calum) Semple, University of Liverpool, for helpful comments on the report.

Contributors

JSN-V-T, PRM, SGM, SV, and JL-B conceived and designed the study. All authors, apart from SGM, SV, and JL-B, contributed to acquisition and local preparation of constituent datasets. SGM, SV, PRM, and JL-B contributed to dataset amalgamation and standardisation, design of statistical analyses, and data analysis. JSN-V-T, PRM, SGM, SV, and JL-B interpreted the data and wrote the paper. All authors contributed to critical examination of the paper for important intellectual content and approval

Front page author affiliations:

Kristin GI Mohn

First name Surname Affiliation

Ahmadreza Moradi


Cendrine Mony
email: cendrine.mony@univ-rennes1.fr

Juliet Abadie

Assu Gil-Tena

Francoise Burel

Aude Ernoult

Dr Monika Wulf

Effects of connectivity on animal-dispersed forest plant communities in agriculture-dominated landscapes

Keywords: plant assemblages, functional ecology, dispersal traits, plant-animal interactions, zoochory, LTSER "ZA Armorique" (NW France)

published or not. The documents may come

Introduction

Habitat loss is considered as a major driver of plant community dynamics, often resulting in species extinction and decrease in biodiversity [START_REF] Brooks | Habitat loss and extinction in the hotpsots of biodiversity[END_REF][START_REF] Fahrig | Effects of habitat fragmentation on biodiversity[END_REF][START_REF] Helm | Slow response of plant species richness to habitat loss and fragmentation[END_REF]

Accepted Article

2006). Because of the reduced patch size, the populations have a higher probability of going extinct in small habitat surfaces [START_REF] Levins | Extinctions. In: Some mathematical questions in biology[END_REF]) while big inter-patch distances promote their isolation and, hence reduce the likelihood of recolonization [START_REF] Young | The population genetic consequences of habitat fragmentation for plants[END_REF]. Forests in Europe have a long history of fragmentation, with extensive forest destruction [START_REF] Jacquemyn | Influence of environmental and spatial variables on regional distribution of forest plant species in a fragmented and changing landscape[END_REF]. Nevertheless, in many places of Western Europe, land abandonment and subsequent shrub encroachment and afforestation have been observed since the 20 th century [START_REF] Verburg | Response of forest plant species to land-use change: a life-history trait-based approach[END_REF][START_REF] Navarro | Rewilding abandoned landscapes in Europe[END_REF]. The greening measures promoted within the European Common Agricultural Policy (Regulation EEC Nº 2080/92) tend also to increase plantations of forest habitats conducting to numerous but isolated woodlots embedded within the agricultural matrix. Empirical studies have demonstrated that habitat isolation and/or reduced size negatively impact the composition of local forest plant communities by decreasing species richness, especially for the forest specialists [START_REF] Ehrlen | Dispersal limitation and patch occupancy in forest herbs[END_REF][START_REF] Graae | The impact of forest continuity and management on forest floor vegetation evaluated by species traits[END_REF]Dupré & Ehrlen 2002;[START_REF] Jacquemyn | Influence of environmental and spatial variables on regional distribution of forest plant species in a fragmented and changing landscape[END_REF][START_REF] Kolb | Effects of Life-History Traits on responses of Plant Speices to Forest Fragmentation[END_REF][START_REF] Honnay | Forest fragmentation effects on patch occupancy and population viability of herbaceous plant species[END_REF]. In agriculture dominated landscapes, woodlots offer though the last remnant habitats for the forest species provided they have the capacity to (re)colonize and establish in these isolated ecosystems.

The capacity for long-distance dispersal by plant species at the landscape scale may contribute to their persistence in the landscape, because this strategy compensates for the negative effects of isolation [START_REF] Cain | Long-distance seed dispersal in plant populations[END_REF]. Most plant species in forests are dispersallimited, being unable to colonize newly established or far away woodlots [START_REF] Honnay | Permeability of ancient forest edges for weedy plant species invasion[END_REF][START_REF] Whigham | Ecology of woodland herbs in temperate deciduous forests[END_REF]). Such species tend to exhibit low seed production, and often lack specific adaptations for long-distance seed dispersal [START_REF] Bierzychudek | Life histories and demography of shade-tolerant temperate forest herbs: a review[END_REF]Dupré & Ehrlen 2002;Verheyen et al. 2003;Kolb & Diekmann 2004;[START_REF] Whigham | Ecology of woodland herbs in temperate deciduous forests[END_REF]). Consequently, long-distance dispersal events are rare, and are mostly promoted by transport through zoochory [START_REF] Howe | Ecology of seed dispersal[END_REF][START_REF] Vellend | Dispersal of Trillium seeds by deer: Implications for long-distance migration of forest herbs[END_REF]. In fact, animals may serve as important vectors for the

Accepted Article

dispersal of forest plants between isolated patches. However, lack of connectivity (i.e., the degree to which a given landscape facilitates or impedes the movement of organisms among habitat resources; [START_REF] Taylor | Connectivity is a vital element of landscape structure[END_REF]) may also negatively affect the abundance and diversity of animals, potentially reducing the probability of plant seed dispersal. [START_REF] Grashof-Bokdam | Forest species in an agricultural landscape in the Netherlands: Effects of habitat fragmentation[END_REF] demonstrated that the richness of zoochorous species, especially core forest species, decreases when forest surface around patches decreases. However, this study considered connectivity as a combination of the amount of habitat cover and Euclidian distance to the source patch, which might not completely reflect seed dispersal patterns in zoochorous species. In fact, these patterns are not a simple function of distance from the parent population, but may also be affected by animal behaviour [START_REF] Nathan | Spatial patterns of seed dispersal, their determinants and consequences for recruitment[END_REF][START_REF] Haddad | Corridor use by diverse taxa[END_REF][START_REF] Will | A mechanistic simulation model of seed dispersal by animals[END_REF]. This type of dispersal behaviour might be impacted by the permeability of landscape elements to animal movement [START_REF] Bélisle | Measuring landscape connectivity: the challenge of behavioral landscape ecology[END_REF].

Thus, more representative functional connectivity indices must be used. In this sense, we would expect the effect of connectivity on zoochorous assemblages to be rather explained by the potential functional connectivity of woodlots, taking into account behavioural responses of organisms to landscape structure and elements [START_REF] Tischendorf | On the usage and measurement of landscape connectivity[END_REF][START_REF] Kindlmann | Connectivity measures: a review[END_REF]La Point et al. 2015). [START_REF] Vittoz | Seed dispersal distances: A typology based on dispersal modes and plant traits[END_REF] reported four different categories of zoochorous species based on the type of dispersal: (i) epizoochory, when seeds are transported passively by animals in fur; (ii) endozoochory, when seeds are eaten and pass undamaged through the digestive system of the animal; (iii) dyszoochory, when seeds are foraged by animals that store them for winter or accidentally lose them during transport; and (iv) myrmecochory, which is a particular case of dyszoochory, in which ants generally eat seed elaisome but leave the rest of seed untouched. Myrmecochory usually contributes to short distance dispersal, because ants have small home ranges that are limited when the configuration of forest habitat is fragmented Accepted Article [START_REF] Müller-Schneider | Verbreitungsbiologie (Diasporologie) der Blütenpflanzen. 3. Aufl[END_REF][START_REF] De Sanctis | Effects of habitat configuration and quality on species richness and distribution in fragmented forest patches near Rome[END_REF]. The three other zoochorous categories may contribute to long-distance dispersal (>100 m sensu [START_REF] Cain | Long-distance seed dispersal in plant populations[END_REF]. However, few studies have empirically measured the actual dispersal distances that are achieved through these different modes. Such actual dispersal distances are calculated using marked seeds or seed trapping (e.g., [START_REF] Tewksbury | Corridors affect plants, animals, and their interactions in fragmented landscapes[END_REF][START_REF] Levey | Effects of Landscape Corridors on Seed Dispersal by Birds[END_REF]. Data are then scarce owing to the difficulty to perform such records. The existing literature measuring actual dispersal has focused on large mammals, including wild herbivores and cattle [START_REF] Pakeman | Plant migration rates and seed dispersal mechanisms[END_REF][START_REF] Couvreur | An experimental assessment of seed adhesivity on animal furs[END_REF][START_REF] Cosyns | Endozoochorous seed dispersal by cattle and horse in a spatially heterogeneous landscape[END_REF]. These studies suggest these dispersers to contribute to longer distance of propagation through endozoochory compared to epizoochory. However, dispersal distances depend on a variety of attributes, including fur type, seed ingestion, and gut retention rates [START_REF] Vittoz | Seed dispersal distances: A typology based on dispersal modes and plant traits[END_REF][START_REF] Will | A mechanistic simulation model of seed dispersal by animals[END_REF]. In contrast, fewer studies have investigated small-sized animals as vectors, such as insects, rodents, and birds. In fact, gut retention time in birds may be very short (Fukui 1996;[START_REF] Traveset | Effect of seed passage through vertebrate frugivores' guts on germination: a review[END_REF], suggesting that epizoochory contributes more to long-distance dispersal than endozoochory in small-sized dispersal vectors. Even fewer studies have investigated dyszoochory. An alternative to measuring actual dispersal distances is to assess the potential response of these plant dispersal groups to connectivity loss at different distances of functional connectivity. This may help to detect distance thresholds and infer potential dispersal distances to each dispersal type. This indirect measurement is cost-effective [START_REF] Calabrese | A comparison-shopper's guide to connectivity metrics[END_REF] and could start determining the scale at which the different modes of dispersal are sensitive.

The present study aimed to analyse how connectivity influences the structure of zoochorous plant assemblages in small post-agricultural woodlots of agriculture-dominated landscapes. Connectivity was assessed through habitat reachability metrics based on graph theory calculated as a function of landscape permeability to animal movement [START_REF] Saura | A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study[END_REF]. We only focused on long-distance dispersal (>100 m sensu Cain et al.

Accepted Article

2000), and did not take into account myrmecochory. We specifically tested the following hypotheses:

(i)

The abundance and richness of zoochorous species increase with increasing potential functional connectivity of woodlots;

(ii) Connectivity affects the different types of zoochorous mode in a distancedependent manner. Specifically, if dispersal is achieved through large mammals, epizoochorous species may be more dependent on short-distance disruption in connectivity compared to endozoochorous species. Alternatively, if smaller size vectors are involved, endozoochorous species may be more sensitive to connectivity loss at shorter distances compared to epizoochorous species. Because dyszoochory is based on a foraging behaviour, we expect a response similar to endozoochorous species.

Material and Methods

Study site

The present study was carried out in the Long-Term Socio-Ecological Research (LTSER) site of "Zone Atelier Armorique" (Bocage -countryside-research site), which is located in Brittany, Western France (48° 36′ N, 1° 32′ W), and covers ca. 150 km². In order to remove edge effects in the connectivity assessment (Pascual-Hortal & Saura 2007), we enlarged 3 km the extent around the LTSER site using the same aerial photography and satellite imagery (see [START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF]). The landscape is characterised by an agriculture-dominated area, with a well-developed hedgerow network. This hedgerow network increases in density from north to south, partly driven by soil characteristics and types of agricultural practices. In the south, soil bedrock is granite whereas in the north, soil is on sandstone with more fertile soils than in the

Accepted Article

south. Soil fertility therefore conditions the type of land-use and the parcel size and hence the hedgerow occurrence (i.e., more dense hedgerow network in the south) and may act on connectivity measures (see section "Connectivity assessment of woodlots") which may then covary at coarse scale with these environmental variables.

The study area stands in a context of colonization of post-agricultural woodlots (Fig. 1). Most of the woodlands in the area are composed of originally planted woodlots with further spontaneous colonization by other plants and natural succession. The area comprises a large ancient forest in the south of the site (Fig. 1). Strong modifications of agricultural matrix configuration have been occurring since the 1960s due to the European Common Agricultural

Policy and the resulting change in agricultural practices (e.g., increase in parcel size, hedgerows and isolated trees cutting due to reparcelling, abandonment of less productive parcels). These landscape modifications increased the already existing isolation of the woodlots and matrix friction for forest species. In the same time, woodland cover seems to have slightly increased through mostly plantations of post-agricultural woodlots, enhancing the potential habitat area of forest species.

The land-use map was obtained from a photointerpretation of aerial photography (French National Institute of Geographic and Forest Information) in combination with object based and remote sensing aerial classification by RapidEye satellite. Eight land-use categories were identified at 5 m resolution: crops, semi-natural grasslands, managed grasslands, woodlands, urban areas, water bodies, hedgerows and roads. The hedgerow network and roads were identified from the vector geographic database BDTopo® (2003BDTopo® ( -2006)), which was produced by the French National Institute of Geographic and Forest Information.

Woodlots closer than 25 m were considered as belonging to the same entity. This aggregation distance threshold was chosen because fairly corresponds to small-distance dispersal of plants [START_REF] Cain | Long-distance seed dispersal in plant populations[END_REF] whereas allowing to have a significant subset of

Accepted Article

candidate woodlands to sample (>1ha) within the study area according to the spatial resolution of the aerial photography. Following this criteria, we detected 143 entities within the LTSER site according to the land-use map. We then calculated woodlot size and shape (edge:area ratio) and selected 26 woodlots with similar size (close to 2 ha which is the average size of the woodlots in the area) and with high compactness (edge:area ratio below 0.04 which is the median of the woodlots in the area) (Fig. 1; Appendix A). The sampled woodlots were composed of broadleaved trees, dominated by Fagus sylvatica, Quercus robur and Castanea sativa. These woodlots were established on former agricultural lands (arable lands or meadows) mainly during the 20th century and had a similar extensive management.

Connectivity assessment of woodlots

We computed woodlot connectivity through a graph-theory based reachability metric [START_REF] Saura | A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study[END_REF]. specifically considering in this study as dispersal flux the movement of seed plant dispersal vectors (i.e., animals). We considered as habitat patches the sampled woodlots and the unsampled forests and woodlots larger than 1 ha (Fig. 1). The Proportion of Total Potential Dispersal Flux (dF* k ) is based on patch removal experiments and was computed to assess for a certain sampled woodlot (patch k) the percentage of total potential dispersal flux among all woodlots and forests in the landscape [LTSER site (+3 km buffer area)] through the connections of patch k with all other patches in the landscape (i.e., by accounting for the proximity of that woodlot regarding the remaining habitat patches). For a given woodlot (patch) k, dF* k is therefore given by:

Accepted Article           n i n j i j ij n k i i ik k p p dF 1 , 1 * 1 , 1 * *
where p ij * is the maximum product probability of all possible paths between two patches, i and j, in the landscape, including direct and non-direct (facilitated by other intermediate patches functioning as stepping stones) dispersal between the two patches [START_REF] Saura | A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study[END_REF]. The probability of direct dispersal among woodlots i and j (p ij ) is here modelled as a negative exponential function of the dispersal distance among woodlots i and j (see references therein [START_REF] Saura | A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study[END_REF].

In this study we considered inter-patch dispersal distances as a function of landscape permeability and the matrix resistance to the animal movement (i.e., plant seed dispersal vectors) rather than based on euclidian distances. We used a friction map (i.e., impedance/cost for movement cartography) obtained from the land-use categories through classifying them according to their permeability for the movement of forest woodland species [START_REF] Watts | Targeting and evaluating biodiversity conservation action within fragmented landscapes: an approach based on generic focal species and least-cost networks[END_REF]. Friction value distribution corresponded to a mathematical exponential function with minimum friction value of one for forests woodlots larger than 1 ha and a maximum friction threshold of 50 for urban areas (Appendix B); that is to say, for the seed plant dispersal vectors (i.e., animals) moving one meter distance in urban areas is fifty times more difficult than moving 1 m in woodlands and forests ≥ 1 ha. The friction values were previously parameterized for the study area by [START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF] and were based on those attributed by [START_REF] Watts | Targeting and evaluating biodiversity conservation action within fragmented landscapes: an approach based on generic focal species and least-cost networks[END_REF] as a function of the vertical structure of the habitat. In [START_REF] Watts | Targeting and evaluating biodiversity conservation action within fragmented landscapes: an approach based on generic focal species and least-cost networks[END_REF], friction values were established based on expert criteria and focusing on generic woodland species in fragmented countryside landscapes in UK. In our study, the distance between each woodlot pair was calculated from the friction map as the accumulated cost (i.e., resistance to movement) along the least cost path by the Graphab 1.0 software (Foltête et al.

Accepted Article

2012; [START_REF] Adriaensen | The application of 'least-cost' modelling as a functional landscape model[END_REF]. We selected a minimum planar graph to compute these least cost paths among each patch pair (Appendix A) as [START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF] demonstrated that connectivity indices calculated with minimum planar graph or complete graph are correlated.

We calculated the percentage of dispersal flux for each woodlot (dF k *) using fifteen potential dispersal distances (from 100 m to 1500 m at 100 m intervals) to determine the most relevant distance to characterise functional connectivity depending on zoochorous types. This multiscale approach based on a large range of distances to assess connectivity may enable to detect for fine-grain functional resolution thresholds of community assemblage responses.

Each potential dispersal distance was multiplied by the statistical median value of friction in the LTSER site + 3 km buffer area [START_REF] Gurrutxaga | Key connectors in protected forest area networks and the impact of highways: A transnational case study from the Cantabrian Range to the Western Alps (SW Europe)[END_REF][START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF]. Each "modified" potential dispersal distance by the median friction value was used to set the decay rate of the negative exponential function at p ij = 0.5. These calculations were made using Conefor 2.6 [START_REF] Saura | Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity[END_REF].

Floristic surveys

We sampled each woodlot in the core and margin areas. The core area was defined as the area located close to the centre of the woodlot and at least at 25 m from the border [START_REF] Matlack | Microenvironment variation within and among forest edge sites in the eastern United States[END_REF].

Because of the large range of possible values for edge effect reported in the literature depending on the set of environmental or organisms considered, we fixed this 25m-value arbitrarily based on the size range of sites considered. We cannot therefore exclude that assemblages at the centre of the woodlot are affected by edge effects because of the woodlot small sizes. For each woodlot, we selected six 14 × 5 m plots using a systematic sampling design: three plots were located in the core area and three plots were located in the margin area. We performed floristic surveys by assessing plant species composition and abundance (in percentage cover) for two vegetation layers: shrubs and herbaceaous plant species. Ferns

Accepted Article

and mosses were not taken into account because their dispersal mode differs from angiosperms and does not rely on zoochory. We did not analyse the composition of the tree layer, due to its dependence on the initial planting and management of the woodlot. From the 140 species pool recorded, 11 species (nine herbaceous and two shrub species) were discarded because they were not considered as forest species (i.e., listed in the French Forest flora, [START_REF] Rameau | Flore forestière française. 1[END_REF].

Assessment of dispersal traits in forest fragment communities

Dispersal traits were assessed using the Baseflor database [START_REF] Julve | Baseflor. Index botanique, écologique et chorologique de la flore de France[END_REF]. We selected the primary dispersal vector of each species using the following typology: endozoochorous, dyszoochorous, epizoochorous, or other. For a few cases (<5 species, ~4 % of the analysed species pool) where data were not available at the species level, the dispersal mode of close relatives growing in the same habitat was used (e.g., Rumex or Epilobium species). For each woodlot and each type of dispersal vector, we calculated the total number of species and the percentage of cover over the sampling plots (i.e., mean percentage cover over the plots of the woodlots), either considering only plots located at the centre of the woodlot ("core assemblage") or plots located both at the centre and the margin of the woodlot ("total assemblage"). We analysed the effect of connectivity on (i) zoochorous species richness and cover, and their proportions over the core and total plant assemblage (referred to as "zoochorous relative richness" and "zoochorous relative cover", respectively); (ii) the proportion (in species richness and cover) of endo-, dys-and epi-zoochorous species over zoochorous species (referred to as "endo-, dys-, epizoochorous relative richness" and "endo-, dys-, epizoochorous relative cover", respectively).

Accepted Article

Statistical analyses

Before conducting our analyses, we checked for independence between the ecological variables studied through pair-wise correlations (Pearson correlations). Inside each plant assemblage (total and core), all plant-dispersal assemblage indexes were independent. We also analysed the degree to which these indexes were correlated between core and total assemblages. All indexes were significantly correlated between core and total assemblages but correlations were lower than 0.9 for all variables except for species number and species cover of zoochorous species. Similarity between core and edge assemblages was additionally measured using Sorensen index.

To test for the effect of connectivity on the plant-dispersal assemblage indexes, we used multiple linear regressions with connectivity metrics and woodlot area as explanatory variables. In addition, we checked whether the community response of forest plants to woodland connectivity was mediated by patch size through considering their interaction.

When necessary, the index data were log-transformed to improve the normality of the distribution of model residuals. One model was developed for each distance (100 m to 1500 m, at 100 m intervals), producing 15 models per response variable, with each one being optimised using a backward stepwise selection procedure for explanatory variable selection based on Akaike's information criterion (AIC). We compared these optimized models for each variable using second-order AIC corrected for small sample sizes (AICc). In our analyses, we selected the most supported models as those with Δi[AICc] within ≤2 units of the model with the lowest AICc [START_REF] Arnold | Uninformative Parameters and Model Selection Using Akaike's Information Criterion[END_REF]. We calculated the regression coefficients and the proportion of trait variation that was accounted for by the coefficient of determination (R²) in these models. The significance of each explanatory variable in the model was assessed by ANOVAs. All statistical tests were performed using R 2.15.3. software (R Foundation for

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Statistical Computing, Vienna, Austria) through the packages Effect, HH, MASS and AICmodavg.

Results

Characteristics of the woodlot assemblages

Forest plant assemblages at the woodlot level contained from 10 to 43 species, with between 47 and 187% percentage cover (Table 1). Sorensen index calculated for assessing similarity between edge and core assemblages varied from 0.31 to 0.84 for total species and from 0.38 to 0.89 for zoochorous species [mean (SD): 0.59 (0.10) for total; 0.67 (0.10) for zoochorous assemblages] (Table 1). Zoochorous species represent the majority of species within the assemblages, either in richness (44.19 to 90.91% of forest species) or cover (48.96 to 99.75% of forest species), with a large range of variation among woodlots (Table 1). Within zoochorous species, endozoochorous type dispersal was the dominant dispersal mode, followed by epizoochorous and dyszoochorous types.

Effect of connectivity and woodlot area on zoochorous species

Species richness, cover and relative cover were not dependent on connectivity or woodlot area regardless total or core assemblages (Table 2). The relative richness of species was dependent on woodlot area: the proportion of zoochorous species in the assemblage increased with woodlot size (significant effect for total assemblage, marginal effect for core assemblage, Fig.

2).

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Effect of connectivity on the relative proportion of the dispersal modes of zoochorous species

We found that woodlot area and/or connectivity did not affect the relative richness of endozoochorous species in total and core assemblages. However, the relative cover of endozoochorous species at the woodlot total assemblage scale was dependent on an interactive effect of both factors (Table 3). The proportion of endozoochorous species in the plant cover decreased with connectivity in small woodlots, but increased in big woodlots (Fig. 3). This effect was only detected for connectivity calculated at a short distance of 100 m, with a relatively high R² of 0.26. This effect was not detected in core assemblages.

We detected no effect on the relative cover of epizoochorous species but an interactive effect between connectivity and area on their relative richness both for the woodlot total and core assemblages. The relative richness of epizoochorous species in zoochorous assemblages decreased with connectivity in small woodlots, but increased in big woodlots. This phenomenon was detected for a connectivity distance of 100 to 300 m for total assemblages and from 100 to 200 m for core assemblages. The highest R² (0.21-0.23) was obtained for 200 m in both types of assemblage.

We observed a significant positive effect of woodlot area on the relative richness of dyszoochorous species, both for woodlot total and core assemblages. In addition, connectivity had a marginally positive effect on species relative richness for total assemblage at the smallest connectivity distance tested (100 m). Relative cover was independent on both factors, regardless the assemblage being considered.

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Discussion

Most of the species were dispersed by zoochory (excluding myrmecochorous mode), demonstrating the key role of animals in dispersing forest propagules between local populations. Edge and core assemblages were partially similar indicating a potential smoothing gradient from the border to the centre of the woodlot. Despite this change in species composition, our results showed that connectivity and/or woodlot area had mostly similar effects on the variables measured for both the total and core assemblages suggesting the independence of the pattern from the specific assemblage composition.

Zoochorous plant assemblages depend more on woodlot area than on their isolation

In contrast to our first hypothesis, plant zoochorous assemblages only depended on woodlot area in the range of connectivity tested. The number of these species in proportion within the community increased with woodlot area, whereas neither richness nor cover (proportional or not) of zoochorous species were dependent on the tested predictors. This suggests that this size effect is not directly linked to a carrying capacity of the woodlot to harbour a large number of plant species, or to act on the local dynamics of plant species, as previously reported by other studies [START_REF] Honnay | Impact of habitat quality on forest plant species colonization[END_REF]. Woodlot size may act on dispersal mode composition by slightly increasing zoochory and/or disfavouring the other dispersal modes.

Indeed large woodlots may be less permeable to wind and then disfavour anemochory, which would then indirectly promote zoochorous species proportion in the assemblages. Large woodlots may also favour a large abundance of dispersers, then indirectly affecting plant recruitment [START_REF] Helliwell | The effect of size and isolation on the conservation value of wooded sites in Britain[END_REF][START_REF] Van Dorp | Effects of patch size, isolation and regional abundance on forest bird communities[END_REF]. Large woodlots may harbour a higher number and diversity of animals involved in plant dispersal because they provide a large home range and diversified microhabitats [START_REF] Honnay | Forest fragmentation effects on patch occupancy and population viability of herbaceous plant species[END_REF], which, in turn, promote

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zoochory. Because the shape of the woodlot (perimeter:area ratio) was standardised in this study, larger woodlots also exhibited larger edge length. When considering animal behaviour, increased edge effects may promote their ability to enter the woodlot and feed on or disperse seeds through their displacement [START_REF] Murcia | Edge effects in fragmented forests: implications for conservation[END_REF].

Connectivity affects the relative proportion of zoochorous dispersal modes

Connectivity was not related to zoochorous assemblages, but rather acted on the relative proportion of different zoochorous modes (except for the dyszoochorous mode). The dyszoochorous mode was affected by woodlot size, with connectivity having a marginal positive effect. This result might be explained by the confounding effect of species composition. Dyszoochorous species generally correspond to tree plantlets, and are composed of a very small number of species. Their dependence on woodlot size may be the result of higher diversity in microhabitats providing a larger range for species expression. Some dyszoochorous species, surveyed in the herbaceous or shrub layers, such as Fagus sylvatica, Quercus robur and Castanea sativa, are linked to the recruitment of the planted species, which may explain their lack of sensitivity to connectivity.

As expected in our second hypothesis, both epizoochorous and endozoochorous species were affected by connectivity but only in interaction with woodlot area and at relatively short distances (from 100 to 300 m). Connectivity at short distances increased the proportion of epizoochorous species richness and the proportion of endozoochorous cover within the assemblage for large woodlot size but the reverse was found for small woodlot size. The influence of connectivity was therefore dependent on a certain carrying capacity of woodlots to harbour animals (i.e., dispersers). Intermediate and big woodlot sizes enable to harbour a large variety of dispersers in contrast to small-size woodlots. In the latter case,

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dispersers may remain less time in small woodlots when connectivity is high and then reduce the possibility to dispersal events of epi-and endozoochorus plant species. The effect on the proportion of species richness in epizoochorous plants vs. cover (i.e., abundance) in endozoochorous plants may be linked with differences in the behaviour of animals involved in endozoochory and epizoochory. We can speculate that when foraging, animals may focus on particular vegetation patches; thus, disseminating greater numbers of endozoochorous seeds of these particular species contributing hence to their large cover. This may be reinforced by the clonal character of these endozoochorous plant species (e.g., Hedera helix, Lonicera periclymenum, Prunus species, Rubus fruticosus). Passive dispersal through epizoochory may involve foraging or non-foraging behaviour (e.g., reproduction, response to predators or territory defence), increasing the likelihood of having a large range of plant species encountering the animal during its movement. These results have however some limitations, as the ability to be dispersed by multiple dispersal vectors is a common phenomenon in most plant species with an average of 2.15 vectors per species [START_REF] Ozinga | Dispersal potential in plant communities depends on environmental conditions[END_REF]). Thus, the ability of a given species to be epizoochorous, endozoochorous or dyszoochorous may also depend on the animals that inhabit or move through the habitat, which may call for complementary analyses based on seed fluxes to better refine how these processes affect plant species richness and abundance. Overall our study demonstrated that endo-and epizoochorous species are favoured only in woodlots with a sufficient carrying capacity for dispersers.

Connectivity affects endo-and epizoochorous dispersal at short distances

Beyond the effect of connectivity on the respective zoochorous types, plant assemblages were affected by relatively short distances of isolation. Both epizoochorous and endozoochorous modes operated at similar distances of a few hundred meters, which is lower than the Accepted Article expected mean distance (i.e., median value of 400, Vittoz & Engler 1997) reported in the literature for large mammals [START_REF] Vellend | Dispersal of Trillium seeds by deer: Implications for long-distance migration of forest herbs[END_REF][START_REF] Mouissie | Estimating adhesive seed-dispersal distances: field experiments and correlated random walks[END_REF]. This may be due to the extinction or rareness of plant species that would depend on long distance zoochory. These extinctions could be the effect of habitat loss selecting out plants that depend on large areas to keep their populations. Our results may alternatively possibly reflect the decrease of consumers that perform long distance movement. Consumers involved in endozoochory may indeed include a large range of animals, from small invertebrates, such as carabid species or snails, to big vertebrates, such as frugivorous birds and mammals. Thus, dispersal distance is highly dependent on the size and mobility of animals (Vittoz & Engler 1997). Our results suggest that, in such landscapes with small woodlots embedded in a dominant agricultural matrix, feeders may be small animals, because of the limited carrying capacity of such landscapes for hosting viable populations of larger animals (forest specialists). This phenomenon may also apply to epizoochory. These short distances reflect the routine movements of animal species around their habitat patch rather than dispersal movements.

The absence of long-distance dispersal effect on plant zoochorous assemblages in the present study may be also due to the intrinsic characteristics of these long movements which concern only few individuals. The effects of these rare events on plant assemblage structure take a long time to appear and are difficult to detect by diversity indexes. Long-distance movements may also not be well predicted through the connectivity metrics used. First, there can be a discrepancy between the spatial extent of the study area and the actual dispersal distances for the species group under consideration [START_REF] Fahrig | Rethinking patch size and isolation effects: the habitat amount hypothesis[END_REF]. In this case, relationships may go undetected. We assumed that the spatial extent of the study area was related to the movement range of the studied species but predicting these ranges a priori is difficult, because of the absence of data especially for long-distance dispersal events. Second, there might be also a delay in population response to this type of change in patch isolation, which is

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gradual in time. Populations may indeed not appear to be affected by habitat loss due to behavioural adaptation (With 2015) or to a lagged response to landscape change [START_REF] Schrott | On the importance of landscape history for assessing extinction risk[END_REF]. Landscape dynamic and especially change over time of interpatch connectivity is not often taken into account in connectivity models based on network theory (but see [START_REF] Martensen | Spatio-temporal connectivity: assessing the amount of reachable habitat in dynamic landscapes[END_REF]. These models represent hence dynamic systems by a static network contrary to dynamic network models [START_REF] Ferrari | Retention Time of Seeds in Bird Guts: Costs and Benefits for Fruiting Plants and Frugivorous Birds[END_REF]. They may then underestimate long-distance dispersal event effects because those may depend also on past connectivity [START_REF] Saura | Stepping stones are crucial for species'long distance dispersal and range expansion through habitat networks[END_REF].

Overall, agricultural-landscape matrix, by decreasing the functional connectivity for animal movement, interacts with the biological dispersal distance of plants to constrain their actual dispersal, thus claiming for the development of functional measurements of landscape parameters.

Using a functional approach to assess the effect of habitat isolation on plant assemblages:

Implications for forest species conservation in small woodlots

Through this study, we assessed whether habitat isolation affects plant functional structure by selecting species based on their dispersal mode. Yet, this effect was not detected using an index based on specific community structure, underlying the key interest of functional approaches for understanding the processes involved in the recruitment of forest species.

More specifically, we demonstrated that both habitat isolation and habitat size are involved in shaping the functional structure of the community, but they act at different dispersal mode resolutions. The reduction in habitat size decreased the proportion of zoochorous species in the community, favouring other dispersal modes. In contrast, habitat isolation through the reduction of connectivity (in interaction with size) affected the type of zoochory (endo-vs. epizoochory) selected. This functional approach could be further refined by taking into

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account traits determining the degree to which plant species have access to particular groups of dispersers out of the dispersers involved in epizoochory, endozoochory and dyszoochory.

For instance, certain traits (such as seed size) may be involved in the selection of particular consumers based on their size. In contrast, seed retention time in the guts could determine the type of feeding behaviour of animals. The effect of isolation on dispersal strategy of plant species may also take into account a potential effect of phylogenetic dependency among species. Unmeasured establishment-traits may be additional causes of interspecific variation in colonization capacity and dispersal may be simply correlated to these traits via shared ancestry [START_REF] Baeten | Disentangling dispersal from phylogeny in the colonization capacity of forest understorey plants[END_REF]. Unfortunately, many of these traits linked to establishment or dispersal are not available for a large number of species, calling for increased research efforts on this topic. Characterising the plant traits involved in the response to habitat isolation is essential for predicting changes in biodiversity and for proposing conservation actions.

Overall, this study suggests that, in agriculture-dominated landscapes, long-distance zoochoric dispersal mechanisms, have been lost either due to the selection of dispersers of smaller size and lower mobility or to the rareness or extinction of plant species that need larger habitat size to maintain. Maintaining connectivity in the surroundings of woodlots is therefore important for maintaining present day dispersal patterns and plant community characteristics in woodlots but management actions at the larger landscape scale are necessary to restore a more complete set of original community characteristics and their dispersal mechanisms.

  For each target woodlot we computed the Proportion of Total Potential Dispersal Flux in the landscape (dF* k ; Saura & Rubio 2010; Gil-Tena et al. 2014),

Figure 1 :Figure 2 :

 12 Figure 1: Map of the study site (Armorique Long-Term Socio-Ecological Research -LTSER -enlarged with a 3 km buffer). We show in gray all the woodland habitats with a size over 1 ha and in black those sampled sites

Figure 3 :

 3 Figure 3: Interactive effect of connectivity and woodlot area on endo-and epizoochorous species and of woodlot area on dyszoochorous species for total assemblages. Three levels of woodlot size were chosen to plot the interaction effects on traits values: the lowest value of woodlot size, the average (mean) value of woodlot size, and the highest value of woodlot size.

Table 1 : Plant community characteristics (Total and Core assemblages). Species richness and cover in percentage (%, % may exceed 100% due to vegetation stratification).

 1

		Total assemblage	Core assemblage
		Mean (SD)	Min-Max	Mean (SD)	Min-Max
	Whole plant community
	Richness	27.5 (9.2)	10-43	15.7 (5.4)	6-25
	Cover (%)	112.0 (36.1)	46.8-186.7.8	101.6 (46.0)	31.7-200.3
	Zoochorous species
	Richness	18.6 (5.1)	9-31	11.2 (3.2)	6-19
	Cover (%)	99.4 (32.4)	41.2-167.7	86.4 (40.3)	27.3-166
	Relative richness (%)	71.1 (14.2)	43.6-90.9	74.0 (14.7)	40.9-100
	Relative cover (%)	91.4 (9.2)	65.1-99.8	87.6 (19.0)	29.7-100
	Endozoochorous species
	Relative richness (%)	47.9 (8.4)	30.8-66.7	52.3 (11.1)	28.6-72.7
	Relative cover (%)	69.4 (11.5)	51.0-95.1	67.5 (19.3)	14.8-96.5
	Epizoochorous species
	Relative richness (%)	29.5 (11.0)	11.1-53.9	22.9 (13.6)	0-44.4
	Relative cover (%)	8.4 (7.6)	0.4-26.1	8.6 (8.4)	0-34.7
	Dyszoochorous species
	Relative richness (%)	18.2 (6.3)	9.7-33.3	26.2 (12.5)	10.5-50
	Relative cover (%)	23.3 (15.2)	0.9-50.8	24.4 (21.4)	1-87.0

Table 2 : Most supported models according to the AIC framework for woodlot plant assemblages. Models in bold are significant at p < 0.05; *: p < 0.05 ; t: p < 0.1. We did not show columns corresponding to the Connectivity and interaction term (Area × Connectivity), as they are not included in the optimised models.

 2

		General model		Intercept		Area
		Distance	R²	p		F
	Total assemblage
	Zoochorous richness	-	-0.02	0.48	163.71
	Zoochorous relative richness	-	0.14	0.03	212.66	5.10*	3.23
	Zoochorous cover	-	-0.04	0.83	260.64
	Zoochorous relative cover	-	0.06	0.13	-46.85
	Core assemblage
	Zoochorous richness	-	0.04	0.17	138.98
	Zoochorous relative richness	-	0.1	0.07t	-23.63	3.63*	2.90
	Zoochorous cover	-	-0.04	0.78	271.94
	Zoochorous relative cover	-	-0.01	0.42	-44.87

Table 3 : Most supported models according to the AIC framework for the different zoochorous types. Richness and cover are expressed as relative to zoochorous assemblage. Models in bold are significant. ***: p < 0.001, **: p < 0.01; *: p < 0.05; t: p < 0.1.

 3

			General Model		Intercept		Area	Connectivity	Interaction
		Distance	R²	p		F	Estimate	F	Estimate	F	Estimate
	Total assemblage
	Endozoochorous
	Relative richness	-	0.13	0.11
	Relative cover	100m	0.26	0.02	82.08	2.53	-0.04	0.18	-1.10	8.96**	0.30
	Epizoochorous
	Relative richness	200m	0.23	0.04	57.47	1.29	-8.03	1.26	-137.31	7.71*	32.88
		100m	0.17	0.07	47.30	1.20	-5.47	1.60	-98.43	5.23*	23.49
		300m	0.19	0.06	59.47	1.23	-8.40	0.84	-135.41	6.65*	32.91
	Relative cover	-	0.03	0.20
	Dyszoochorous
	Relative richness	100m	0.22	0.02	13.44	5.41*	0.91	3.68 t	14.60
			0.13	0.04	14.23	4.86*	1.40
	Relative cover	-	0.10	0.12
	Core assemblage
	Endozoochorous
	Relative richness	-	-0.002	0.34
	Relative cover	-	0.0045	0.30
	Epizoochorous
	Relative richness	200m	0.21	0.04	56.81	2.25	-10.7	0.15	-152.04	7.44*	40.02
		100m	0.19	0.06	46.39	2.17	-7.60	0.89	-118.5	5.68*	29.8
		300m	0.16	0.08	56.46	2.11	-10.81	0.0003	-136.3	5.74*	38.2
	Relative cover	-	-0.002	0.34
	Dyszoochorous
	Relative richness	-	0.21	0.01	16.56	7.82*	3.4
	Relative cover	-	-0.04	0.71

Table A .1. Sampled woodlot area and shape and descriptive statistics of interpatch Euclidean distances of the sampled woodlots relative to the other woodlots in the study area. Euclidean distances were calculated based on the planar graph used to compute connectivity analysis (See Figure B.1).

 A

	Sampled	Sampled woodlot characteristics	Sampled woodlot context
	woodlot identificator	Area (ha)	Shape	neighboring # of	Distance (m)
				woodlots	min	max	mean
	1	1.78	0.04	6	151.33	1494.52 773.33
	2	1.54	0.04	5	340.59	1325.63 800.06
	3	1.66	0.04	5	113.14	1082.31 627.88
	4	8.00	0.03	7	199.25	893.20	544.13
	5	1.17	0.04	6	190.00	1067.05 659.56
	6	4.44	0.03	7	330.15	962.60	657.95
	7	6.60	0.02	3	67.08	320.16	157.56
	8	4.97	0.02	4	164.01	593.63	389.89
	9	4.84	0.02	5	546.72	1686.56 932.24
	10	1.13	0.04	2	72.80	160.00	116.40
	11	3.67	0.02	4	220.23	575.85	458.42
	12	1.17	0.04	4	10.00	762.43	442.48
	13	3.35	0.04	5	138.92	844.81	441.97
	14	1.49	0.04	4	214.71	487.54	352.06
	15	1.44	0.04	4	331.06	820.24	577.17
	16	2.99	0.03	6	70.00	980.20	476.23
	17	3.60	0.03	4	60.00	463.25	194.12
	18	4.69	0.03	4	130.00	834.39	504.09
	19	1.37	0.04	5	308.71	852.12	496.56
	20	1.85	0.04	5	782.94	1201.04 958.52
	21	2.34	0.03	5	405.22	892.02	672.90
	22	2.86	0.02	6	560.80	1559.55 998.83
	23	3.43	0.03	7	205.91	2041.30 1126.95
	24	1.06	0.04	6	191.05	1287.63 773.45
	25	1.14	0.04	7	390.51	1644.69 1096.74

Acknowledgements

We thank B. Clément and Q. Landais for assisting with data collection and J. Nabucet for assisting with land-use maps and connectivity parameterization. We are also grateful to three anonymous reviewers for contributing to improve the final manuscript version with their

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valuable comments. RapidEye imagery was provided by GEOSUD. This work was funded by the French Ministry of Agriculture (DIVA 3 -Agriconnect project: Continuités écologiques dans les paysages agricoles) and supported by landscape database collected in the LTSER sites of Pleine-Fougères (CNRS funding). A. Gil-Tena was funded by the Spanish Ministry of Economy and Competitiveness (JCI-2012-12089).

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