Josianne Lachapelle

Elvire Bestion

Eleanor E Jackson

C-Elisa Schaum
email: elisa.schaum@uni-hamburg.de

Presence of a resident species aids invader evolution

Keywords: Extinction, colonisation, invasion, plasticity, global warming, co-culture

Phytoplankton populations are intrinsically large and genetically variable, and interactions between species in these populations shape their physiological and evolutionary responses.

Yet, evolutionary responses of microbial organisms in novel environments are investigated almost exclusively through the lens of species colonising new environments on their own, and invasion studies are often of short duration. Although exceptions exist, neither type of study usually measures ecologically relevant traits beyond growth rates. Here, we experimentally evolved populations of fresh-and seawater phytoplankton as monocultures (the green algae Chlamydomonas moewusii and Ostreococcus tauri, each colonising a novel, unoccupied salinity) and co-cultures (invading a novel salinity occupied by a resident species) for 200 generations. Colonisers and invaders differed in extinction risks, phenotypes (e.g. size, primary production rates) and strength of local adaptation: invaders had systematically lower extinction rates and broader salinity and temperature preferences than colonisers -regardless of the environment that the invader originated from. We emphasise that the presence of a locally adapted species has the potential to alter the invading species' eco-evolutionary trajectories in a replicable way across environments of differing quality, and that the evolution of small cell size and high ROS tolerance may explain high invader fitness.

To predict phytoplankton responses in a changing world, such interspecific relationships need to be accounted for.

Introduction

Ecology and evolution affect invader success and native species' responses, and in a warming, changing world, invasion scenarios are likely to become more frequent [START_REF] Seebens | No saturation in the accumulation of alien species worldwide[END_REF][START_REF] Mellin | Forecasting marine invasions under climate change: Biotic interactions and demographic processes matter[END_REF][START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF]. Ecoevolutionary studies on biological invasions for microbial populations are arduous to carry out -especially when encompassing an element of tracking evolutionary responses in real time for species less cultivable than bacteria ( e.g. [START_REF] Li | Niche and fitness differences determine invasion success and impact in laboratory bacterial communities[END_REF]), and are accordingly rare (but see e.g. [START_REF] Faillace | Evolution alters the consequences of invasions in experimental communities[END_REF][START_REF] Faillace | Evolution alters post-invasion temporal dynamics in experimental communities[END_REF])

When the survival of an organism -including invaders, colonisers, and resident specieshinges largely on physiological short-term responses, they can cope with a changed or changing environment through phenotypic plasticity. There, a given genotype produces a different phenotype in response to changes in the abiotic or biotic environment [START_REF] West-Eberhard | Developmental plasticity and evolution[END_REF]. In asexually dividing microbes, these plastic responses occur within a single or a few generations and unless the new environment is lethal to most individuals in a sufficiently diverse population, it is unlikely that selection acts through sorting on such short time-scales.

In clonal populations -the standard scenario in many laboratory experiments -evolution through de novo mutations is similarly rare on such short time-scales [START_REF] Elena | Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation[END_REF][START_REF] Lenski | Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations[END_REF].

In the long term, the speed at which genetic sorting can affect the mean population trait in a diverse population depends on the amount of standing genetic variation, the strength of selection, and the size of the population [START_REF] Rengefors | Genetic diversity and evolution in eukaryotic phytoplankton: revelations from population genetic studies[END_REF][START_REF] Collins | Evolutionary potential of marine phytoplankton under ocean acidification[END_REF]. Both plasticity and changes in allele frequency contribute to the magnitude and direction of an organism's response to a novel environment. In aquatic microbes, short-and long-term responses to environmental change have been extensively studied in single isolate experiments (e.g. e.g. [START_REF] Listmann | Swift thermal reaction norm evolution in a key marine phytoplankton species[END_REF][START_REF] Schluter | Adaptation of a globally important coccolithophore to ocean warming and acidification[END_REF][START_REF] Schaum | Plasticity predicts evolution in a marine alga[END_REF][START_REF] Lohbeck | Adaptive evolution of a key phytoplankton species to ocean acidification[END_REF]). Far fewer studies consider ecologically complex environments [START_REF] Wolf | Company matters: The presence of other genotypes alters traits and intraspecific selection in an Arctic diatom under climate change[END_REF][START_REF] Wolf | Resilience by diversity: Large intraspecific differences in climate change responses of an Arctic diatom[END_REF][START_REF] Scheinin | Experimental evolution gone wild[END_REF][START_REF] Schaum | Adaptation of phytoplankton to a decade of experimental warming linked to increased photosynthesis[END_REF]. Studies that have tested how species interactions evolve are traditionally carried out in environments that are of low quality for the focal species (traditionally low-nutrient or toxic environments), i.e. in environments that lead to slower growth and a drastic and near-fatal decline in population size of the focal species [START_REF] Elena | Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation[END_REF][START_REF] Rozen | Long-Term Experimental Evolution in Escherichia coli. VIII. Dynamics of a Balanced Polymorphism[END_REF].

Under circumstances where resources are scarce, competition is often the main type of interaction [START_REF] Burson | Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community[END_REF][START_REF] Tilman | Resource Competition between Plankton Algae: An Experimental and Theoretical Approach[END_REF][START_REF] Ji | Competition between cyanobacteria and green algae at low versus elevated CO2: who will win, and why[END_REF]. Whether competition can then help or hinder evolution is highly contextdependent: Competition can explain changes in lineage or species frequencies as a function of the environment (e.g. light, nutrients, temperature) [START_REF] Follows | Emergent Biogeography of Microbial Communities in a Model Ocean[END_REF], but interactions between microbes need not be solely competitive and can range from competition to facilitation to mutualism to interdependence and combinations thereof [START_REF] Hesse | Ecological selection of siderophore-producing microbial taxa in response to heavy metal contamination[END_REF][START_REF] Ponomarova | Metabolic interactions in microbial communities: untangling the Gordian knot[END_REF][START_REF] Morris | The Black Queen Hypothesis: Evolution of Dependencies through Adaptive Gene Loss[END_REF], especially when environmental change is not leading to a decrease, but an increase in growth rate [START_REF] Snell-Rood | Life-history evolution in the anthropocene: effects of increasing nutrients on traits and trade-offs[END_REF]. While the breadth of interactions is well understood in ecology [START_REF] Fussmann | Eco-evolutionary dynamics of communities and ecosystems[END_REF][START_REF] Loeuille | Influence of evolution on the stability of ecological communities[END_REF][START_REF] Barraclough | How Do Species Interactions Affect Evolutionary Dynamics Across Whole Communities?[END_REF], today's models, when considering how microbes behave in a changing world, largely assume that interactions be competitive in nature [START_REF] Collins | Growth rate evolution in improved environments under Prodigal Son dynamics[END_REF]. Finally, these models are increasingly incorporating traits (and changes therein) other than growth rates, such as cell size and carbon fixation. Those traits are not routinely monitored in most selection experiments, where the focus tends to be on growth rates.

Here, we used an experimental evolution approach to conceptually investigate if coloniser or invader status changes an organism's chances of survival in a new salinity and whether these dynamics differ between short-term (a few generations) and long-term (~200 generations) responses under otherwise non-limiting conditions. We define colonisers as a species moving into a novel environment that is unoccupied (an ecologically rather unlikely scenario, but standard in most selection experiments), and invaders as species moving into a novel environment where local species are present. We used two green algae, the freshwater phytoplankton Chlamydomonas moewusii and the marine picoplankton Ostreococcus tauri.

Both genera of green algae are cosmopolitan in natural systems and established model organisms for long-term studies in experimental evolution [START_REF] Brennan | Evolutionary consequences of multidriver environmental change in an aquatic primary producer[END_REF][START_REF] Lachapelle | Experimental adaptation to marine conditions by a freshwater alga[END_REF][START_REF] Heath | Mode of resistance to viral lysis affects host growth across multiple environments in the marine picoeukaryote Ostreococcus tauri[END_REF] . We evolved all populations for ca 200 generations either alone or in co-culture, in freshwater and marine conditions (Figure 1). To test whether differences between colonisers and invaders are general and repeatable in environments favouring faster growth, we superimposed a fullfactorial temperature treatment, where all invaders and all colonisers were grown at 22ºC (the culture 'control' temperature) , 26ºC (mild warming, and constituting a 'better' , favourable environment where growth is faster and stress is lower), 32ºC (extreme warming, constituting a 'worse', unfavourable environment, where growth is reduced and stress is higher), and a fluctuating temperature treatment, where temperature cycles between 22ºC and 32ºC every 3-5 generations (constituting a 'better' , 'favourable' environment). The temperatures were chosen based on pilot studies examining the temperature tolerance curves of each species (see supporting information). We show that across all environments, freshwater and marine invaders fare better than colonisers, that samples selected under the invasion scenario evolve more generalist tendencies than samples selected as colonisers in the same environment, and that organisms in the invasion scenario evolve significantly different phenotypes compared to those in the coloniser scenario.

Results

Extinction risk is lower for invaders than for colonisers

Rapid changes in salinity can reduce the probability of phytoplankton survival and may limit population sizes to a point at which evolutionary adaptation becomes increasingly unlikely [START_REF] Bell | Adaptation and Evolutionary Rescue in Metapopulations Experiencing Environmental Deterioration[END_REF][START_REF] Ketola | Rapid evolutionary adaptation to elevated salt concentrations in pathogenic freshwater bacteria Serratia marcescens[END_REF]. In our case, populations of all surviving cultures remained large enough to supply mutations and avoid drift at ca 10 5 cells mL-1, which ensures a large enough supply of mutations for an evolutionary response, and makes differences between colonisers' and invaders' or different species' responses in the long-term likely to be independent of population size.

Extinction rates in a new salinity were lower in invading than colonising phytoplankton (in a novel salinity, invading Chlamydomonas were two thirds less likely to go extinct than colonising Chlamydomonas, and for invading Ostreococcus, the likelihood of extinction was halved compared to colonising Ostreococcus, survival analysis: z = -2.90, P = 0.0037 Supporting Tables 1 and2). Extinction events occurred early on in the experiments (within the first 70 generations, z = -4.13, P = 3.6 x 10 -5 ; Fig. 2; Supporting Table 1 and2, Supporting Figure 1), with no further extinctions after 100 generations. After the first ~ 70 generations, population sizes in all surviving invader cultures stabilised and were no longer statistically different from colonisers (F 1,2 =1.72 , p = 0.32; more details in Supporting Tables 3 to 4). 70 -100 generations is a time-frame corresponding roughly to a single growing season of fastgrowing green algae, and is comparable to other marine microbial experiments where evolution occurred on the scale of just below 100 to a few 100 generations [START_REF] Schluter | Adaptation of a globally important coccolithophore to ocean warming and acidification[END_REF][START_REF] Lohbeck | Adaptive evolution of a key phytoplankton species to ocean acidification[END_REF][START_REF] Schaum | Enhanced biofilm formation aids adaptation to extreme warming and environmental instability in the diatom Thalassiosira pseudonanaand its associated bacteria[END_REF], though as few as two generations have been reported to suffice for an evolutionary response [START_REF] Lenski | What is adaptation by natural selection? Perspectives of an experimental microbiologist[END_REF].

Theory predicts that evolutionary potential should be high in good quality environments leading to an increased or unchanged fitness, and that extinction risk should be low in environments that fluctuate predictably (e.g. [START_REF] Ashander | Predicting evolutionary rescue via evolving plasticity in stochastic environments[END_REF]). We found that extinctions were indeed overall lowest in the ameliorated environments, i.e. under mild warming at 26ºC, and in the fluctuating environment (survival analysis: z = -1.22, P = 0.043; Supporting Figure 1, Supporting Table 2). Ostreococcus colonisers had a small but significant (see Supporting Table 2) advantage over Chlamydomonas colonisers, with higher survivability over all. While we cannot determine the mechanism by which co-culture favours survival, or whether Ostreococcus are per se better colonisers than Chlamydomonas (Fig. 2 and Supporting Table 2), we can begin to quantify the effects of abiotic environment and species interactions on evolutionary and short-term responses.

Invaders and colonisers differ in salinity and temperature tolerances

Survival is insufficient: Once extinction is no longer one of the main mechanisms driving responses, we need to know the performance of the population across environments, and the phenotypes they might display. To integrate plastic and evolutionary responses into ecosystem [START_REF] Dutkiewicz | Impact of ocean acidification on the structure of future phytoplankton communities[END_REF] and individual-based models [START_REF] Beckmann | Phytoplankton adaptation in ecosystem models[END_REF], and to better understand the dynamics in laboratory experiments [START_REF] Denman | A Model Simulation of the Adaptive Evolution through Mutation of the Coccolithophore Emiliania huxleyi Based on a Published Laboratory Study[END_REF] knowledge of phenotypic traits and organismal biology is needed. Here, colonisers differed from invaders in the magnitude of their evolutionary response, their ability to grow in their ancestral environments, and in the phenotypes they evolved.

When colonisers were transferred back into their ancestral salinity, their growth rates were the same as or lower than they had been in that same salinity before evolution in a novelsalinity environment (e.g. average growth rate per day of the coloniser in the novel salinity after evolution in novel salinity: 1.13 ± 0.02 SEM, and after transfer back into the ancestral salinity: 0.79 ± 0.02 SEM , Fig. 3, Supporting Tables 5 and6 for more details). Growth rates of invaders were overall higher, and did not decrease significantly upon being transplanted back into their ancestral salinity (average growth rate of the invader in the novel salinity after evolution in novel salinity: 1.31 ± 0.01 SEM, and after transfer back into the original salinity:1.29 ± 0.01 SEM). Invaders and colonisers also differed with regards to their responses to warming (supporting Tables 7 and8 for details), where invaders again outperformed colonisers. This pattern was exacerbated under mild warming in 26ºC where invader growth rates were on average 1.3 times higher than coloniser growth rates (tukey post hoc, P<0.001), and under the fluctuating treatment, with an average fold increase of invaders vs colonisers of 1.2 (tukey post hoc, P<0.001). The most pronounced advantage of invaders over colonisers was at the selection temperature (Fig. 4 A, Supporting Tables 7 and8).

Growth rates were diminished under environmental deterioration at 32º C, and this decrease in growth was the least pronounced in invading species (Fig. 4A). We find support that in the unfavourable environments (high temperature, changed salinity), intracellular reactive oxygen species (ROS) production is higher, and ROS tolerance impeded, but that this effect is more pronounced in colonisers than invaders (Supporting Figures 2 and3).

Phenotypic traits of colonisers and invaders

Cell size overall declined with selection temperature regardless of selection regime or species. Ostreococcus was more reactive to temperature than Chlamydomonas overall (Supporting Figure 4, Supporting Tables 9 and10), and whether the species was invading or colonising also had an impact on the focal species' cell size (Supporting Tables 9 and10, Supporting Figure 4), with smaller invaders than colonisers. Cell size of Chlamydomonas was more likely to change in response to a resident species than cell size of Ostreococcus, with Chlamydomonas cells up to 1.43 fold smaller after evolution invading the marine species in saltwater than after colonising saltwater on their own (also Figure 4B).

While we cannot disentangle the relative contributions of the individual species to Net

Photosynthesis rates in the co-cultures (NP, i.e. rates of photosynthesis after respiration has been accounted for), net-photosynthesis per gram carbon of evolved co-cultured samples was on average 13% higher than expected from the NP of the same two species at the same salinity in monoculture in line with over-yielding observed in other species [START_REF] Bestion | Abrupt declines in marine phytoplankton production driven by warming and biodiversity loss in a microcosm experiment[END_REF] (Supporting Figure 5, and Supporting Tables 11121314). The same pattern emerged when we assayed the same species at the same salinity after decomposition of the co-cultures into monocultures (e.g. physically separating a former mixed culture of Chlamydomonas residents and Ostreococcus invaders into monocultures (Supporting Tables 11121314)).

Experimental community decomposition

Experimentally separating ('decomposing') the evolved co-culture samples into monocultures yielded insights into how strongly the invaders had adapted to the presence of the resident species, and what effect the invader had on the growth of the resident species (see methods for details). In samples that had only lived in co-culture for two transfers (<20 generations), growth after decomposition was indistinguishable from growth in monocultures at the same salinity and temperature (Supporting Table 15, Supporting Figure 6). In contrast, in samples that had lived in co-culture for ~200 generations, growth of the invading species when assayed alone in the selection salinity was reduced by up to 30% compared to when assayed in co-culture (Supporting Figure 7), and compared to the same species evolved in mono-culture at the same salinity/temperature regime. Of the resident species, Ostreococcus selected in co-culture with Chlamydomonas showed evidence of a marked decrease in growth when the invading Chlamydomonas was removed. Resident Chlamydomonas grew faster when the invading Ostreococcus population was removed, with no significant effect of temperature on this pattern.

In the decomposed samples, patterns in net primary production of former invaders mirrored the patterns found in growth rates: invaders always photosynthesised less after decomposition than the same species evolved in monoculture in the same selection salinity. High photosynthesis rates in formerly invaded Chlamydomonas were in line with higher growth rates in formerly decomposed Chlamydomonas (Supporting Tables 11-14, Supporting Figure 7). The resident species Ostreococcus photosynthesised more after decomposition than when evolved in monoculture -but grew more slowly. The higher NP rates were, at least for the duration of the assay (two weeks), not directly channelled into growth, indicating that the presence of other species may explain hitherto often observed but poorly explained variations in growth rates in more complex systems (but see [START_REF] Wolf | Company matters: The presence of other genotypes alters traits and intraspecific selection in an Arctic diatom under climate change[END_REF][START_REF] Collins | Diverse strategies link growth rate and competitive ability in phytoplankton responses to changes in CO<sub>2</sub> levels[END_REF]). We found that samples with the highest surplus NP (or least increase in growth) had a tendency to have higher Nile Red fluorescence, indicating higher lipid storage (Supporting Figure 8). Similar responses including high rates of NP but suppressed growth can be achieved by merely spiking Ostreococcus and Chlamydomonas cultures with water conditioned by the other species (Supporting Figure 9).

Discussion and Conclusions

Rapid adaptation to a novel salinity or the evolution of salinity tolerance are major driving forces in determining the distribution and phenotypic characteristics of phytoplankton communities [START_REF] Mousing | Evidence of small-scale spatial structuring of phytoplankton alpha-and beta-diversity in the open ocean[END_REF][START_REF] Bertos-Fortis | Unscrambling Cyanobacteria Community Dynamics Related to Environmental Factors[END_REF][START_REF] Godhe | Physical barriers and environmental gradients cause spatial and temporal genetic differentiation of an extensive algal bloom[END_REF][START_REF] Larson | Salinity and nutrients influence species richness and evenness of phytoplankton communities in microcosm experiments from Great Salt Lake, Utah, USA[END_REF][START_REF] Rengefors | Evidence of concurrent local adaptation and high phenotypic plasticity in a polar microeukaryote[END_REF]. Changes in salinity (IPCC, 2014), particularly in combination with elevated temperatures, have the potential to impact the phenotypic characteristics of phytoplankton species, the communities they populate, and the role of phytoplankton species on aquatic food webs and global nutrient cycles [START_REF] Godhe | Physical barriers and environmental gradients cause spatial and temporal genetic differentiation of an extensive algal bloom[END_REF][START_REF] Rengefors | Evidence of concurrent local adaptation and high phenotypic plasticity in a polar microeukaryote[END_REF][START_REF] Godhe | The role of intraspecific variation in the ecological and evolutionary success of diatoms in changing environments[END_REF]. Here, we found that species colonising a new salinity were prone to extinctions, but that survivors rapidly became locally adapted to their novel salinity. Rapid evolution to a novel salinity has been proven before [START_REF] Lachapelle | Experimental adaptation to marine conditions by a freshwater alga[END_REF][START_REF] Latta | The evolution of salinity tolerance in Daphnia: a functional genomics approach[END_REF], but evolution as a single species might not be a common ecological scenario, as species are likely to not arrive in a new environment and find it unoccupied. Species invading a new salinity were less likely to go extinct and evolved high tolerance to both fresh and saltwater, especially under environmental amelioration, such as mildly elevated or rapidly fluctuating temperatures. Invaders had higher survival and growth rates, and were also characterised by overall smaller cell size, lower reactive oxygen species (ROS) production, higher ROS tolerance, and a tendency to store lipids. ROS are a natural by-product of cellular metabolism, but can damage the cell at high quantities [START_REF] Shuryak | A model of interactions between radiation-induced oxidative stress, protein and DNA damage in Deinococcus radiodurans[END_REF]. Therefore, higher tolerance toward or lower quantities of ROS may infer a fitness benefit [START_REF] Lindberg | Quality-quantity tradeoffs drive functional trait evolution in a model microalgal 'climate change winner[END_REF]. Taking into account growth rates across all temperature treatments, the invader became more of a generalist, with better performance across multiple environments. Successful invading species often have traits associated with generalists (see e.g. [START_REF] Sriswasdi | Generalist species drive microbial dispersion and evolution[END_REF][START_REF] Mellin | Forecasting marine invasions under climate change: Biotic interactions and demographic processes matter[END_REF]), but whether generalist traits enable successful invasions or whether organisms evolve to have more generalist traits as a consequence of invasions remains an open question.

Our results suggest that under warming and increased climate variability, invasions through small, warm-adapted taxa with intrinsically elevated metabolic and growth rates may become more frequent ('tropicalisations', see [START_REF] Verges | The tropicalization of temperate marine ecosystems: climatemediated changes in herbivory and community phase shifts[END_REF][START_REF] Vergés | Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp[END_REF]), with nigh-unpredictable consequences on aquatic ecosystems as a whole. As changes in fitness, cell size and metabolic activity are often linked [START_REF] Marañón | Cell Size as a Key Determinant of Phytoplankton Metabolism and Community Structure[END_REF][START_REF] Brown | Toward A Metabolic Theory Of Ecology[END_REF], it stands to reason that one possible mechanisms for higher invader fitness in our invader samples lies in their ability to rapidly down-regulate cell size [START_REF] Malerba | Cell size, photosynthesis and the package effect: an artificial selection approach[END_REF][START_REF] Malerba | Testing the drivers of the temperature-size covariance using artificial selection[END_REF][START_REF] Key | Cell size trade-offs govern light exploitation strategies in marine phytoplankton[END_REF], which in turn might be what is giving rise to their ability to better handle reactive oxygen species [START_REF] Lindberg | Quality-quantity tradeoffs drive functional trait evolution in a model microalgal 'climate change winner[END_REF][START_REF] Malerba | Testing the drivers of the temperature-size covariance using artificial selection[END_REF] (Figure 4B and Supporting Figure 10 -the smallest cells had highest fitness and were better able to detoxify ROS). The dynamics and mechanisms of increasing fitness under constant directional selection are well understood [START_REF] Wiser | Long-Term Dynamics of Adaptation in Asexual Populations[END_REF][START_REF] Burke | Standing Genetic Variation Drives Repeatable Experimental Evolution in Outcrossing Populations of Saccharomyces cerevisiae[END_REF], and experimental evolution lends itself well to linking environmental cause to evolutionary effect, but it is limited in accurately deciphering the mechanisms that underlie trait evolution. Strategies that increase fitness can vary over time [START_REF] Lenski | Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations[END_REF] and when there are multiple genotypes in a population, evolutionary trajectories, as well as the traits evolved will depend on the environment as well as the genotype [START_REF] Lenski | What is adaptation by natural selection? Perspectives of an experimental microbiologist[END_REF]. Due to the complex nature of fluctuating selection regimes, barring further analyses, for example on the level of the transcriptome, we cannot with certainty elucidate the exact mechanism that allows for the evolution of these strikingly different phenotypes in invasion vs. colonisation scenarios. Still, Ostreococcus selected in co-culture with Chlamydomonas showed evidence of a marked decrease in growth when the invading Chlamydomonas was removed, suggesting that interactions were mutualistic or facilitating in nature. Chlamydomonas, when Ostreococcus were removed, did not show a marked decrease in growth, making it seem likely that the fact of being an invader had direct phenotypic and fitness consequences regardless of the nature of the interaction.

Understanding the impacts of environmental change over evolutionary timescales will require that we experimentally investigate the mechanisms underlying the differences between colonisers and invaders, the direct effects of rising temperatures on species interactions, and the indirect reciprocal feedbacks between ecological and evolutionary dynamics [START_REF] Fussmann | Eco-evolutionary dynamics of communities and ecosystems[END_REF][START_REF] Loeuille | Influence of evolution on the stability of ecological communities[END_REF][START_REF] Fussmann | Ecological stability in response to warming[END_REF][START_REF] Gravel | Experimental niche evolution alters the strength of the diversity-productivity relationship[END_REF][START_REF] Urban | Evolution mediates the effects of apex predation on aquatic food webs[END_REF].

This study was funded by a British Ecological Society 'small grant' to ES (BES 5705 / 6749). Ostreococcus samples were kindly provided by Samuel Barton and Sarah Heath. Chlamydomonas samples were sourced by EB. Pilot studies were carried out by ES and JL at the University of Edinburgh, where Nick Colegrave and Sinéad Collins provided bench space and equipment. Gabriel Yvon-Durocher provided bench and incubator space for the main experiment at the Environment and Sustainability Institute in Penryn, UK. The authors thank Etienne Low-Decarie and Georgina Brennan for comments that greatly improved the manuscript, and Stefanie Schnell for maintaining the cultures in Hamburg. 2 species in 3 biotic treatments and 4 temperatures in full interaction, with n=8 in each unique environment.

Cultures were maintained at these conditions with regular transfers. Figure 2: Survival when invading into a novel salinity from rare is enhanced in the presence of a resident species across all selection regimes. Displayed is the mean survivorship for samples in all temperature regimes over time (where one cycle or transfer corresponds to one week), with 1.0 as 100% of populations surviving, and shaded areas denoting 95% confidence intervals. All treatment combinations started with N = 8. The proportion of populations and replicates surviving decreased rapidly at the beginning of the experiment but levelled off after about 10 transfers as extinctions stopped. The number of surviving biological replicates at the end of the experiment is shown in Table S1. Invader trajectories in red, coloniser trajectories in yellow. Salinity regime 'Mono-culture ancestral' denotes that the species was assayed in its ancestral salinity in mono-culture after evolution in the ancestral salinity in mono-culture. 'Short-term in novel salinity' is for growth rates measured after the sample had spent two transfers in the novel salinity. 'Long-term in novel salinity' is the growth rate of the sample in the salinity that it was evolving in. 'Transplant back to ancestral is for growth rates measured when samples were transferred back into the ancestral environment after evolution in the novel salinity. We express changes in growth as compared to Ostreococcus or Chlamydomonas in its home salinity at 22ºC in mono-culture, i.e. values < 1 (below dotted line) indicate that a sample grew more slowly than the same species in mono-culture, in its home salinity at 22ºC, and Invaders evolve small cells, yielding higher growth. Visualisation of reciprocal temperature assays for colonisers and invaders. A: Tiles indicate whether the response to the assay condition (y axis) of invading and colonising cultures selected at a specific temperature (x axis) was to grow more slowly (purple hues) or faster (green and yellow hues) than the coloniser at 22ºC in the ancestral salinity. B: Within each regime (invasion -red, or colonisation -orange), invaders tend to have smaller cells. Smaller cell size is associated with faster growth (and higher ROS tolerance, as well as lower ROS production, see Supporting Figure 11). Number of replicates varies due to treatment specific differences in extinction probability.

+ + + - - + + + + + + - + - - - - - - - - - - + - - - - - + + + - + + - + + + + + - - - - + + + + + + - - + + + + + + - - + + - - + + + - - + + + - - - + + - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + - + + + - + - - - - - - + + + + + + + + - + - - - - + + + + + + + + - + - - - + + - - - - - - + +

Methods

Should the methods exceed the allowed number of pages, we will provide a methods summary here, and the detailed methods, in the supporting information.

Algae strains

The marine picoplankton Ostreococcus tauri (clone of the original OTH95) and the freshwater alga Chlamydomonas moewusii (CCAP 11/5B) were sourced as non-axenic stock cultures from the Roscoff culture collection and the CCAP (Culture Collection of Algae and Protozoa) respectively. The fact that these two species do not usually co-occur in nature is not problematic here because species invasion is the result of a new species being introduced into a new environment with resident species it has not interacted with before.

Pilot studies revealed that long-term growth was impoverished upon removal of the associated bacterial component or after antibiotic treatment, and thus no further attempts at using axenic cultures were made for the purpose of this study. The total amount of bacterial referred to from now on as saltwater or SW) in f/2 media [START_REF] Keller | Media for the Culture of Oceanic Ultraphytoplankton[END_REF], Chlamydomonas in modified Bold's media (roughly 0.025g NaCl*l -1 ; referred to from now on as freshwater or FW).

Concentrations of major nitrogen and phosphorus sources were the same in the fresh-and saltwater media.

Selection experiment

We set up our experiment using two salinity regimes (saltwater and freshwater, where we refer to the salinity that the species originated from as the 'ancestral' salinity) and three biotic regimes (two monoculture, and one co-culture scenario; Figure 1A). Residents are species evolved in their ancestral salinity (i.e. Chlamydomonas in freshwater, Ostreococcus in saltwater) either in mono-culture or in co-culture with an invading species. Invaders are species evolved in a novel salinity where the resident species is present (i.e. Chlamydomonas invading Ostreococcus in saltwater, Ostreococcus invading Chlamydomonas in freshwater).

Finally, colonisers are species evolved in a novel salinity as a mono-colture (i.e. Ostroeoccus in freshwater, and Chlamydomonas in saltwater).

In pilot studies, we characterised temperature reaction curves for each species at each salinity. Temperature/salinity combinations that lead to a significant decrease in growth rate in the short-term compared to the coloniser in its ancestral salinity at 22ºC were called 'low' quality or 'unfavourable environments. Thermal environments where growth rate increased were called 'high' quality or 'favourable' environments (here, these are the 26ºC and fluctuating environment -this is also reflected in samples' abilities to deal with reactive oxygen species). Based on these pilot studies, the long-term experiment was replicated across four different temperature regimes, for a total of 24 unique treatments (x 8 biological replicates = 192 cultures, Figure 1A). The temperature regimes consisted of a fluctuating temperature treatment and three stable temperatures, encompassing a stable ambient 22ºC treatment (control), a stable 32ºC treatment (severe warming), and a stable 26ºC treatment (mild warming). In the fluctuating temperature treatment, temperature was switched between 22ºC and 32ºC .ca every 3-5 generations.

We expect the first adaptive step to occur more rapidly in genetically diverse starting populations than in clonal populations, and have leveraged this in our study by starting with genetically diverse rather than clonal populations. [START_REF] Elena | Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation[END_REF] All cultures started out as mono-cultures before invading species were added. Cultures were grown on 48-well plates with sterile, breathable membranes (Aeraseal TM , Sigma-Aldrich) to minimise uneven evaporation and air exchange across plates. Monocultures were initially inoculated with 100 cells of Chlamydomonas or 1000 cells of Ostreococcus to account for the difference in cell size. In co-cultures, the resident species were inoculated at 100 fold the biomass of the invading species, for an 'invading from rare' scenario at the beginning of the experiment. The invasion event occurred only once at the beginning of the experiment, after which we tracked the fate of the invaders throughout the experiment. The 48-well plates were positioned randomly in the incubator, and their position was changed every other day to minimise location effects. Cultures were maintained in semi-continuous batch culture, where well-mixed samples of 200µl were serially transferred into 1200µl of new media every 7-10 generations ('transfers'). At each transfer, cell count was determined using an Accuri c6 (BD Scientific) flow cytometer at high flow rate. Cells from the two species grown in co-culture could be distinguished based on the SSC (side scatter for granularity), FSC (forward scatter for cell size), and FL3 (red fluorescence for chlorophyll content) channels (Supporting Figure 11), allowing for species growth curves to be tracked separately. To analyse differences in cell sizes between treatments and species, we calibrated the flow cytometer with beads of known size.

Cell counts at the beginning and end of each transfer cycle were used to calculate the rate of increase in cell numbers and approximate generation times. Rates of increase in cell number were determined assuming logistic growth (based on pilot experiments), using the formula

µ = (ln(N 1 )-ln(N 0 ))/dt (1)
Where N 1 is the cell count at the end, and N 0 at the start of the transfer, and dt is the length of the transfer cycle (seven days).

The experiment was carried out for approximately 200 generations.

Reciprocal assays

After 27 transfers in their respective selection environments, all samples were subjected to a full reciprocal transplant assay in all salinity and temperature regimes to test whether the surviving colonisers and invaders had adapted to the novel salinity in each temperature regime (Figure 1 B and C), and to calculate the magnitude of the short term and evolutionary responses (Figure 1B andC). A well-mixed sample from each surviving population was used to seed the assays. Assays were performed using the same inoculum size and duration of transfer cycle as during the selection experiment. The assays consisted of two transfers,

where the first was used to allow the cultures to acclimate to the environment, and the second was used to measure the rate of increase in cell number as a proxy for fitness. Samples evolved at 22 o C in their ancestral salinity in mono-culture were used as 'evolved controls', which take into account any evolution that may have occurred due to laboratory conditions per se.

We measured three types of responses: the short term response (occurring largely through rapid sorting and physiological acclimation within the same or a few generations, here, less than 10-14 generations), the long term response (likely largely evolutionary, > 100 generations), and the correlated response (growth in environments other than the selection environment, 10-14 generations after termination of the long-term experiment). See e.g. [START_REF] Schaum | Enhanced biofilm formation aids adaptation to extreme warming and environmental instability in the diatom Thalassiosira pseudonanaand its associated bacteria[END_REF] for calculation of the magnitude of short-and long term responses, as well as responses in the reciprocal environments.

Experimental decomposition of populations grown and evolved in co-culture into monocultures

To assess whether invaders evolved in co-culture had adapted to the novel salinity, the presence of the resident species, or both, we passed all co-cultured samples through a 5µm nitrocellulose filter, allowing Ostreococcus cells to pass, while Chlamydomonas cells remained on the filter, from which they could be rinsed off. Samples were then inspected under the microscope and flow cytometric data was again used as above to ensure a good separation of the two species. Samples were grown for two transfers in both their evolved and their ancestral salinity (Figure 1D). We compared the increase in cell number when they were grown on their own after decomposition to when they were grown in co-culture or had been selected for growth in monoculture. For logistic reasons, samples were only assayed at the temperatures that they had evolved in and not across all temperatures. To measure the shortterm acclimation response to encountering another species, we re-created the starting conditions of the invasion experiment using mono-culture evolved samples (colonisers evolved in their ancestral salinity). These samples were inoculated to recreate the invasion from rare scenario as described above. The new co-cultures were maintained for two cycles, and then separated again by filtration afterwards. This tested for whether dependence on the species was established within very few generations (Supporting Figure 6).

Characterisation of net primary production

To characterise phenotypic changes in the different treatments, we gathered data on cell size and chlorophyll through flow cytometry (Accuri B6), and measured rates of oxygen evolution and consumption using a 24-channel PreSens Sensor Dish Reader. For all phenotypic characterisations, samples were harvested during exponential growth. The reader was placed in the incubator at assay temperature in a manner such that the light gradient across the reader plate was minimal ( <5 µE m -2 s -1 ). Glass vials were filled to 1.2 mL with the respective sample, i.e. colonisers, invaders, or decomposed samples, covered with para-film and sealed tight. The samples were then left in the dark for 35 minutes, and gently inverted before measurements of oxygen evolution at the light level in the incubator for 5 minutes, and measurements of oxygen consumption in the dark for another 5 minutes. A vial containing filtered Bold's medium or f/2 medium at the appropriate salinity was used to account for any drift in the oxygen measurements. Cell count was determined using a flow cytometer as described above. The rates of oxygen evolution and consumption were then calculated per unit biomass, assuming spherical cells and carbon conversion factors after [START_REF] Montagnes | Estimating carbon, nitrogen, protein, and chlorophyll a from volume in marine phytoplankton[END_REF] (Supporting Figure 12 and Supporting Tables 16 and17 for effects of selection regimes on biomass).

Nile Red stain

A Nile Red stain was used as a proxy to determine relative quantities of intracellular polar and neutral lipids [START_REF] Guzmán | Estimate by means of flow cytometry of variation in composition of fatty acids from Tetraselmis suecica in response to culture conditions[END_REF]. It works well for Ostreococcus [START_REF] Schaum | Environmental stability affects phenotypic evolution in a globally distributed marine picoplankton[END_REF] and while stains of the BODIPY class are preferred for quantification of lipids in Chlamydomonas, Nile Red can serve well to establish relative differences [START_REF] Kou | Fluorescent measurement of lipid content in the model organism Chlamydomonas reinhardtii[END_REF]. The dye was added to each 200 µL sample on a 9-well plate for a final concentration 15 m and left to incubate in the dark for 30 min, as pilot trials had shown that after this, fluorescence levels were stable long enough for the time taken to measure one 96 well plate. As Nile Red excites in the same wavelength as chlorophyll (FL3) and chlorophyll derivatives (FL2), samples were measured before and after adding the dye, and the chlorophyll fluorescence subtracted from the fluorescence obtained after staining the sample (Supporting Figure 9).

ROS assay

We tested how capable samples were of detoxifying harmful reactive oxygen species (ROS) and also estimated the intra-cellular ROS levels in order to gain an estimate on whether samples under unfavourable conditions experience more stress, and are therefore producing more/ being less able to detoxify ROS. We used the protocols established by [START_REF] Lindberg | Quality-quantity tradeoffs drive functional trait evolution in a model microalgal 'climate change winner[END_REF][START_REF] Malerba | Testing the drivers of the temperature-size covariance using artificial selection[END_REF].

Samples from 'unfavourable environments' had higher intra-cellular ROS content, were less well able to detoxify harmful ROS (Supporting Figures 2,[START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF][START_REF] Collins | Evolutionary potential of marine phytoplankton under ocean acidification[END_REF].

Statistical analyses

All data were analysed in R versions 3.3.1 and 3.3.3 [START_REF]Development Core Team, R: A language and environment for statistical computing[END_REF].

Survival analysis

We first analysed the extinction dynamics by performing a survival analysis using a Cox proportional hazards regression model with the R package 'survival'(Supporting Table 1 and 2). The model included biotic regime, temperature regime, and species as fixed effects.

Biological replicate strains (per species) were treated as random effects. We also included a censor variable for populations that had not gone extinct by the end of the experiment. Note that an extinction event here was defined as cell numbers of a population declining below the detection limit of the flow cytometer. We treat extinction as an event occurring on the replicate level in each individual treatment. rates in the novel salinity after two weeks of culturing in the novel salinity, 'long' for growth rates in the novel salinity after evolution in the novel salinity, 'back' for growth rates in the ancestral salinity after evolution in the novel salinity). The ancestral and selection salinities can be inferred from the species and biotic regime factors, and therefore selection salinity was not added as an explicit factor. Replicates (Supporting Table 1 for number of surviving replicates in each unique treatment) nested within 'unique treatment' were used as random factors. The nesting was necessary as all replicates originally came from the same starting culture, i.e. replicate 1 of any given treatment was not more or less related to replicate 1 in another treatment than it was to, e.g., replicate 5. We ran the model only on samples where the assay temperature was identical to the selection temperature. We started the model with the fixed factors in full interaction, and searched for the model with the lowest AICc scores through the 'dredge' function within the MuMIn package (version 1.40.4). The model with the lowest AICc was consequently used. In all cases of model selection by AICc, we used a delta value of > 2 to confirm the best model (Supporting Tables 5 and6).

Analysis

Analysis of local adaptation to temperature

To specifically test whether samples had locally adapted to their selection temperature without risking over parameterising the mixed model, we built a separate mixed effects model using data where the assay salinity was the same as the selection salinity, thus focusing on the temperature dependence of growth rates in the samples' selection salinity.

We used growth rates relative to the evolved control in mono-culture at 22ºC as the response variable, and species (Chlamydomonas or Ostreococcus), biotic regime (invading or colonising), selection temperature (22ºC,26ºC,32ºC, or fluctuating), and assay temperature (22ºC,26ºC,32ºC, or fluctuating) as fixed factors. The random factors and model fitting were as described above (Supporting Tables 7 and8).

Analysis of growth rates in decomposed samples

To analyse whether invaders and residents developed a dependence on each other in the short-term, we compared growth rates of the decomposed samples (decomposition after two weeks of co-culture ) to growth rates of the same species at the same temperature and salinity in mono-culture via a t-test (Supporting Table 15). For the long term-responses, we analysed the decomposed samples (measured at selection temperature) by fitting a mixed model, using the ratio between growth rates of either species after decomposition and growth rates of the species in co-culture as the response variable. We fitted species (Chlamydomonas or Ostreococcus), biotic interaction during selection(resident or invader) and selection temperature as the fixed effects. Random effects and model fitting were as described above (Supporting Tables 11 and12).

Figure 3 :

 3 Figure 3: Invaders and colonisers differ in their responses to salinity regimes across time scales.Salinity regime 'Mono-culture ancestral' denotes that the species was assayed in its ancestral salinity in mono-culture after evolution in the ancestral salinity in mono-culture. 'Short-term in novel salinity' is for growth rates measured after the sample had spent two transfers in the novel salinity. 'Long-term in novel salinity' is the growth rate of the sample in the salinity that it was evolving in. 'Transplant back to ancestral is for growth rates measured when samples were transferred back into the ancestral environment after evolution in the novel salinity. We express changes in growth as compared to Ostreococcus or Chlamydomonas in its home salinity at 22ºC in mono-culture, i.e. values < 1 (below dotted line) indicate that a sample grew more slowly than the same species in mono-culture, in its home salinity at 22ºC, and

Figure 4 :

 4 Figure4: Invaders fare better than colonisers in deteriorated (e.g. 32ºC degree) and ameliorated (e.g. 26ºC) environments. Invaders evolve small cells, yielding higher growth. Visualisation of reciprocal temperature assays for colonisers and invaders. A: Tiles indicate whether the response to the assay condition (y axis) of invading and colonising cultures selected at a specific temperature (x axis) was to grow more slowly (purple hues) or faster (green and yellow hues) than the coloniser at 22ºC in the ancestral salinity. B: Within each regime (invasion -red, or colonisation -orange), invaders tend to have smaller cells. Smaller cell size is associated with faster growth (and higher ROS tolerance, as well as lower ROS production, see Supporting Figure11). Number of replicates varies due to treatment specific differences in extinction probability.

  co-inhabitants was tracked and did not change throughout the experiment. Samples were maintained in semi-continuous batch culture (i.e. a fixed volume of exponentially growing cells was transferred into fresh medium at regular intervals) at 22ºC, 100µmol quanta*s -1 *m -2 under a 12:12 hour light:dark cycle in INFORS TM multitron incubators with integrated shakers until use. Ostreococcus was grown at a salinity of 32 (PSU, roughly 30g NaCl*l -1 ;

  of short-and long-term responses to changes in salinity, in stable and fluctuating temperatures We analysed the growth of the surviving replicates as assayed at the end of the experiment in the reciprocal transplants using analyses of variance within a mixed effects model (package nlme, version 3.1-131). Growth relative to growth of the evolved control at 22ºC in monoculture was the response variable. This normalisation by growth under standard laboratory conditions allows us to correct for evolution occurring merely due to selection for laboratory conditions and further creates a baseline for easy comparison of the selection temperatures in relation to each other. We fitted the following fixed factors in the global model: species (Chlamydomonas or Ostreococcus), biotic regime (invading or colonising), selection temperature (22ºC,26ºC,32ºC, or fluctuating), and response type ('short' for growth

  (below dotted line) indicate that they grew faster. Each panel is for one selection temperature. Orange boxplots are for colonising species, and red, for invaders. See TableS2for details on n per treatment. Boxplots are displayed as is standard, with the belt indicating the median. Fitted lines are for visualisation.

	1.50 1.75 values > 1 B selected at 22ºC selected at 26ºC A	--+ + + + ---	+ + + + + + + + + + + +	+ + + + + + + + + +	------+		Fold change in growth compared to coloniser at 22ºC in ancestral salinity
	selected at 22/32ºC
	selected at 32ºC
		Short-term in novel	Long-term in novel	Transplant back to	Mono culture	Short-term in novel	Long-term in novel	Transplant back to
	ancestral	salinity	salinity	salinity ancestral	salinity ancestral	salinity	salinity	salinity ancestral

Acknowledgments

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Author contributions: ES and JL conceived and designed the experiment and wrote the manuscript. ES, EB, and EJ carried out experiments, and ES supervised laboratory work. JL and ES analysed data, and all authors contributed to writing the manuscript. The authors declare no conflict of financial or other interest, and all data will be made available on zenodo or data dryad upon acceptance. During the pre-print stage data are available from the authors upon request.

Phenotypic characterisation

In order to estimate the effect of the selection regimes (biotic scenarios, temperature, and salinity) on cell size and total biomass we fitted a mixed model with the full interaction of the parameters species (Chlamydomonas or Ostreococcus), assay salinity ('home' for the assay salinity being equal to the focal species' selection salinity, and 'away' for assay salinity being different from the focal species' selection salinity), biotic regime (invading or colonising) and selection temperature (22ºC, 26ºC, 32ºC, fluctuating). Model fitting and selection proceeded as described above (Supporting Tables 9 and10 for size, Supporting Tables 16 and17 for biomass). For the analysis of rates of net primary production in the evolved and decomposed samples specifically, a mixed model was fitted using species (Chlamydomonas or Ostreococcus), 'previous interaction' (invader or resident), assay salinity (including a unique identifier for each salinity in interaction with whether the sample had been decomposed and at which point in time -after 2 weeks, or at the end of the experiment -it had been decomposed, Fig. 1D for an example), selection temperature (22ºC, 26ºC, 32ºC, fluctuating), 'biotic selection regime' (colonisers or invaders) as the fixed effects. Model fitting and selection then proceeded as described above (Supporting Tables 13 and14).


Sylvain Moulherat
email: sylvain.moulherat@terroiko.fr.

Elvire Bestion

Michel Baguette

Matthieu Moulherat

Stephen C F Palmer

Justin M J Travis

Jean Clobert

MetaConnect, a new platform for population viability modelling to assist decision makers in conservation and urban planning

Keywords: Modelling platform, individual-based model, process-based model, decision-making tool, dispersal, landscape genetics, landscape planning

come   L'archive ouverte pluridisciplinaire

Introduction:

In a context of rapid global change, habitat loss and habitat fragmentation have become the major threats to biodiversity (IUCN 2013), and have been considered for some time as the principal cause of species extinction [START_REF] Dobson | Hopes for the future: Restoration ecology and conservation biology[END_REF]Millenium Ecosystem Assessment 2005). They have consequences from the ecosystem [START_REF] Fahrig | Effects of habitat fragmentation on biodiversity[END_REF][START_REF] Cardinale | Biodiversity loss and its impact on humanity[END_REF][START_REF] De Mazancourt | Predicting ecosystem stability from community composition and biodiversity[END_REF] to the genetic scale [START_REF] Ingvarsson | Restoration of genetic variation lost -The genetic rescue hypothesis[END_REF][START_REF] Baguette | Individual dispersal, landscape connectivity and ecological networks[END_REF]) by modifying landscape patterns in a four-step process: reduction in amount of habitat, increase in number of habitat patches, decrease patch sizes and increase in patch isolation [START_REF] Fahrig | Effects of habitat fragmentation on biodiversity[END_REF]. This alteration of landscape patterns has diverse effects on population dynamics (e.g. [START_REF] Baguette | Individual dispersal, landscape connectivity and ecological networks[END_REF]. As patches become smaller, the size of the population supported decreases; this can increase stochastic risk of extinction from demographic processes [START_REF] Legendre | Demographic Stochasticity and Social Mating System in the Process of Extinction of Small Populations: The Case of Passerines Introduced to New Zealand[END_REF][START_REF] Reed | Emerging issues in population viability analysis[END_REF], but also from genetic stochasticity: small populations are more subjected to risk of inbreeding and consanguinity depression (Brook et al. 2002b), loss of genetic diversity and mutational accumulation [START_REF] Rowe | Population on the verge of a mutational meltdown? Fitness costs of genetic load for an amphibian in the wild[END_REF], leading to the extinction vortex [START_REF] Gilpin | Minimum viable populations: processes of extinction[END_REF][START_REF] Fagan | Quantifying the extinction vortex[END_REF]. Moreover, by increasing inter-patch distances and therefore dispersal cost, habitat loss and fragmentation decrease the probability of individuals moving between sub-populations. This can hinder recolonization or the demographic rescue of patches where sub-populations have become extinct, or declining, potentially leading to stochastic extinction of metapopulations [START_REF] Fahrig | Effects of habitat fragmentation on biodiversity[END_REF]. Furthermore, by reducing gene flow between subpopulations, isolation can lead to genetic differentiation of sub-populations and impede genetic rescue of highly inbred sub-populations [START_REF] Ingvarsson | Restoration of genetic variation lost -The genetic rescue hypothesis[END_REF][START_REF] Keller | Inbreeding effects in wild populations[END_REF][START_REF] Tallmon | The alluring simplicity and complex reality of genetic rescue[END_REF].

Historically, an ecological tool much used by scientists and conservation managers was population viability analysis (PVA), which aims at estimating extinction or quasi-extinction probability of a species [START_REF] Boyce | Population viability analysis[END_REF][START_REF] Legendre | ULM, a software for conservation and evolutionary biologists[END_REF][START_REF] Beissinger | On the use of demographic models of population viability in endangered species management[END_REF] and which was used to inform conservation programs [START_REF] Southgate | Modeling the reintroduction of the greater bilby Macrotis-lagotis using the metapopulation model analysis of the likelihood of extinction (ALEX)[END_REF][START_REF] Ferriere | Matrix population models applied to viability analysis and conservation: Theory and practice using the ULM software[END_REF][START_REF] Letcher | An individual-based, spatially-explicit simulation model of the population dynamics of the endangered redcockaded woodpecker, Picoides borealis[END_REF][START_REF] Schtickzelle | Metapopulation viability analysis of the bog fritillary butterfly using RAMAS/GIS[END_REF][START_REF] Radchuk | Behind the scenes of population viability modeling: Predicting butterfly metapopulation dynamics under climate change[END_REF].

Most of the PVA models focused on species population dynamics [START_REF] Lindenmayer | A review of the generic computer-prgrams ALEX, RAMAS/SPACE and VORTEX for modeling the viability of wildlife metapopulations[END_REF][START_REF] Brook | Comparison of the population viability analysis packages GAPPS, INMAT, RAMAS and VORTEX for the whooping crane (Grus americana)[END_REF][START_REF] Legendre | Demographic Stochasticity and Social Mating System in the Process of Extinction of Small Populations: The Case of Passerines Introduced to New Zealand[END_REF], and offered limited flexibility regarding population genetics (but see [START_REF] Lacy | Vortex: A stochastic simulation of the extinction process[END_REF] or metapopulation functioning [START_REF] Reed | Emerging issues in population viability analysis[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF]. PVA is principally focused on demographic and genetic processes and individual movement behaviour determining species persistence [START_REF] Keller | Inbreeding effects in wild populations[END_REF][START_REF] Fahrig | Effects of habitat fragmentation on biodiversity[END_REF][START_REF] Piou | A demo-genetic individual-based model for Atlantic salmon populations: Model structure, parameterization and sensitivity[END_REF][START_REF] Frank | Simulating brown trout demogenetics in a river/nursery brook system: The individual-based model DemGenTrout[END_REF][START_REF] Noel | Integrating demographic and genetic effects of connections on the viability of an endangered plant in a highly fragmented habitat[END_REF].

Population genetics has made major advances during recent decades, and it is now possible to identify at generation g those individuals that are offspring of individuals immigrating at generation g-1; and assign these immigrants to their original population [START_REF] Beerli | Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach[END_REF]. Besides, the rise of landscape genetics allows the assessment and quantification of how landscape elements affect gene flow in a metapopulation [START_REF] Manel | Landscape genetics: combining landscape ecology and population genetics[END_REF][START_REF] Manel | Ten years of landscape genetics[END_REF]. Integrating both population and landscape genetics approaches in models of metapopulation functioning to support conservation managers' and policy makers' plans should be highly valuable [START_REF] Baguette | Individual dispersal, landscape connectivity and ecological networks[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF].

The growth of a community of ecological modellers using individual-based models (IBMs) rather than the mathematical approaches has led to the production of a huge number of models [START_REF] Deangelis | Individual-based modeling of ecological and evolutionary processes[END_REF] and metrics [START_REF] Moilanen | Simple connectivity measures in spatial ecology[END_REF][START_REF] Calabrese | A comparison-shopper's guide to connectivity metrics[END_REF]). Moreover, most such models were developed to answer specific questions, which renders comparison between outputs difficult, if not impossible [START_REF] Deangelis | Individual-based modeling of ecological and evolutionary processes[END_REF][START_REF] Kindlmann | Connectivity measures: a review[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF], and their application inefficient out of the narrow context for which each was typically developed [START_REF] Grimm | META-X: Generic Software for Metapopulation Viability Analysis[END_REF][START_REF] Kindlmann | Connectivity measures: a review[END_REF]. Generic PVA modelling platforms built from IBM and process-based modelling are lacking (but see [START_REF] Grimm | META-X: Generic Software for Metapopulation Viability Analysis[END_REF][START_REF] Lacy | Vortex: A stochastic simulation of the extinction process[END_REF][START_REF] Bocedi | RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes[END_REF] or do not permit a sufficient level of flexibility (VORTEX is spatially implicit, RangeShifter and MetaX have limited demographic modules) to deal with a large spectrum of conceptual framework and ecological themes [START_REF] Deangelis | Individual-based modeling of ecological and evolutionary processes[END_REF][START_REF] Evans | Predictive ecology: systems approaches Introduction[END_REF][START_REF] Evans | Predictive systems ecology[END_REF][START_REF] Purves | Time to model all life on Earth[END_REF].

Because PVA is a relevant basic decision-making tool [START_REF] Brook | Predictive accuracy of population viability analysis in conservation biology[END_REF]Brook et al. 2002a;[START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF], we developed MetaConnect, a generic PVA-based IBM following the [START_REF] Beissinger | On the use of demographic models of population viability in endangered species management[END_REF] framework, which allows the design of a range of models from very simple to highly detailed and which integrates demography and genetics in a spatiallyexplicit context [START_REF] Baguette | Individual dispersal, landscape connectivity and ecological networks[END_REF]. MetaConnect not only aims at performing traditional PVAs based on demographic data, but also integrates the recent development of population and landscape genetics, which allows the assessment of functional connectivity (sensu Taylor, Fahrig & With 2006). We expect that this integrated modelling platform will be a useful tool for scientists, conservation managers and policy makers. Here we present MetaConnect's core base modelling, its validation (Appendix A) and present a case study demonstrating its use to assess the impact of infrastructure development on the population viability, genetic differentiation and functional connectivity of existing populations of the endangered natterjack toad (Epidalea calamita).

Model

Model design

MetaConnect simulates metapopulation dynamics and genetics using the species life cycle and life history traits, the landscape characteristics and their interactions. The simulations allow inferring of individual dispersal and local and global extinction probabilities, genetic diversity and genetic differentiation (from classical Fst analyses or as input files for the Structure software [START_REF] Pritchard | Inference of population structure using multilocus genotype data[END_REF]).

MetaConnect is an individual-and process-based model which means that:

• all individuals in the model are independent and behave in respect to their phenotype.

• patterns emerging in the different outputs of the model are the products of flexible and adjustable rules implemented in the model.

Model structure

Landscape

The landscape is imported as two shapefiles and then rasterized by MetaConnect.

• Patches: locates suitable habitats for the focal species. The carrying capacity (inidividual/m²) can be assumed to be constant, or an optional shapefile of carrying capacities can be provided.

• Costs: provides a coefficient (of rugosity) representing the ability of a given species to move through each habitat type of the landscape. The higher the cost, the harder to cross.

Demography

Population dynamics is represented by a succession of individual states linked by transitions.

The user builds the species life-cycle by assembling "bubbles" representing the individual state and "arrows" representing transition rules between individual states (Figure 1, 2). The "bubbles", hereafter regarded as classes, correspond to age classes, sex or anything that can be defined as a group of individuals with the same demographic characteristics. Density dependence can be scramble or contest and designed as a part of transitions. The mating system can be chosen from monogamy, polygamy, polyandry and/or polygyny [START_REF] Legendre | Demographic Stochasticity and Social Mating System in the Process of Extinction of Small Populations: The Case of Passerines Introduced to New Zealand[END_REF]. The demographic parameters (Table 1) can be patch-specific. Environmental stochasticity has been included as random processes inducing normal variation around the patch's mean value of demographic parameters truncated to realistic values set by the user. As an example, the fecundity parameter follows a Poisson distribution (demographic stochasticity) with parameter λ equal to the average fecundity (𝐹 ↝ 𝒫(𝐹 ̅ )). The average fecundity can vary from one patch to another and within simulation time steps following a Gaussian distribution (Table 1).

Dispersal probability 𝑝 𝑑 is implemented by setting a proportion of individuals in a given class leaving a patch. The density-dependent recruitment probability 𝑝 𝑟 is determined by equation 1 where 𝑁 𝑇 can be a chosen combination of the number of individuals per class (i.e. 𝑁 𝑇 .could be the total population or the number of individual in a given class) [START_REF] Caswell | Matrix Population Models: construction, analysis and interpretation[END_REF]. Equation 1:

Contest: 𝑝 𝑟 = 1 1 + 𝑘𝑁 𝑇 Scramble: 𝑝 𝑟 = 𝑒 -𝑘𝑁 𝑡
Dispersal is age-and sex-dependent, and the process by which individuals disperse can be chosen from three families of movement rules:

• Dispersal between patches is modelled by a probability for an individual to reach another patch, ignoring rugosity coefficient. The probability of reaching a patch can be equal between patches, depend on the Euclidean distance between patches' centres, or be set manually.

• The interaction between individuals and their environment depends on rugosity coefficients. This family comprises a random-walk (RW) and a correlated random-walk rule (CRW). The CRW assumes a degree of directional persistence, (i.e. movement direction at time t+1 depends on the direction taken at time t) and not solely an environmental based one. Dispersing individuals benefit from a "energy gauge" decreasing at each step of the (C)RW in respect of the cell cost weighted by direction (1 for straight moves, √2 for diagonal moves).

• This family of rules assumes that individuals have knowledge of their environment, and move by one of two methods. From a focal patch, the least cost path (LCP) algorithm usually assumes that a single patch can be reached [START_REF] Botea | Near Optimal Hierarchical Path-Finding[END_REF][START_REF] Adriaensen | The application of 'least-cost' modelling as a functional landscape model[END_REF][START_REF] Pe'er | Incorporating the perceptual range of animals into connectivity models[END_REF][START_REF] Barraquand | Cognitive abilities of a central place forager interact with prey spatial aggregation in their effect on intake rate[END_REF]. Such an assumption is unrealistic, and to relax it we implemented a multiple LCP movement rule, in which we calculated all possible LCPs between the focal patch and all other patches (Urban et al. 2009;[START_REF] Foltete | A software tool dedicated to the modelling of landscape networks[END_REF]. Then, for reachable patches (i.e. LCP length less than the maximum dispersal distance), the probability to reach a patch is inversely weighted by the LCP length (number of map cells crossed) or cumulative cost (total cost of all map cells crossed). We also adapted the Stochastic Movement Simulator (SMS) [START_REF] Palmer | Introducing a 'stochastic movement simulator' for estimating habitat connectivity[END_REF][START_REF] Aben | Simple individual-based models effectively represent Afrotropical forest bird movement in complex landscapes[END_REF][START_REF] Palmer | Inter-individual variability in dispersal behaviours impacts connectivity estimates[END_REF]Coulon et al. Submitted), which relaxes the assumption of omniscience inherent in the LCP approach. With the SMS rule, individuals make movement decisions based on the environment within a limited perceptual range and a tendency to directional persistence similar to that in a CRW. At each movement, the SMS algorithm calculates a movement probability for each cell surrounding the current cell based on the rugosity coefficient of the cells in the perceptual range (see [START_REF] Palmer | Introducing a 'stochastic movement simulator' for estimating habitat connectivity[END_REF] for details).

The dispersal event ends when the individual dies or reaches a patch different from his original patch regardless of the arrival patch quality.

Genetics

Individuals are genetically tagged using neutral polymorphic loci. The number of loci and number of alleles per locus can be specified by the user. A single mutation rate (a probability of creating a new allele without possibility of reverse mutation) implemented in the model allows the production of new alleles at each locus during simulations, and can be specified by the user. Gene transmission is assumed to be Mendelian and siblings are assumed to have the same father (randomly chosen from the female harem for polyandrous cases).

Model outputs

The model provides many forms of outputs based on focal species life history traits and landscape maps, which are adaptable to various theoretical and applied contexts. The outputs report the results at three levels at a frequency specified by the user, allowing dynamic visualization of the simulations: • Genetics: genetic diversity and differentiation (Fst, Fis, Fit, He and Ho) at the local and/or global scale applicable to each statistic.

All these outputs can be directly plotted using the MetaConnect project manager (figure 2) or extracted as text files. In addition R (R Development Core Team 2005) has been incorporated to the project manager, which allows direct analysis of the outputs.

Model sensitivity:

MetaConnect is a highly flexible and modular IBM, which means that dozens of variables can be specified in various modelling contexts, rendering global sensitivity analyses impossible to run [START_REF] Coulon | Using logistic regression to analyze the sensitivity of PVA models: a comparison of methods based on African wild dog models[END_REF][START_REF] Naujokaitis-Lewis | Sensitivity Analyses of Spatial Population Viability Analysis Models for Species at Risk and Habitat Conservation Planning[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF]. A thematic sensitivity analysis will be presented in the MetaConnect user manual, in which sensitivity of extinction probabilities, genetic structure and connectivity metrics will be analyzed in relation to the appropriate model parameters and their relative contribution to the sensitivity estimate [START_REF] Coulon | Using logistic regression to analyze the sensitivity of PVA models: a comparison of methods based on African wild dog models[END_REF].

Case study: Changes in the metapopulation functioning of an existing natterjack toad population under scenarios of economic development.

MetaConnect parameterization

We used a model designed with MetaConnect to determine the potential impact of the development of an industrial area and a terrestrial transport infrastructure (high-speed railway), both alone and together, on the population viability of Epidalea calamita populations in south-western France close to Agen (44°11'36"N, 0°31'14"E) (figure 3).

Habitat patches were determined a priori based on expert assessment (figure 3). Preferences for landscape elements were derived from those experimentally determined by [START_REF] Stevens | Gene flow and functional connectivity in the natterjack toad[END_REF]. E. calamita was assumed to disperse following the stochastic-movement-simulator (SMS) assumptions [START_REF] Palmer | Introducing a 'stochastic movement simulator' for estimating habitat connectivity[END_REF]Coulon et al. Submitted). MetaConnect was parameterized with published values of life history traits [START_REF] Stevens | Importance of Habitat Quality and Landscape Connectivity for the Persistence of Endangered Natterjack Toads[END_REF] (Appendix B).

We analysed MetaConnect genetic outputs for all the patches containing more than 10 individuals at the end of each run of 100 time steps. This analysis was performed using STRUCURE [START_REF] Pritchard | Inference of population structure using multilocus genotype data[END_REF] with an admixture model assuming that the origin population of an individual is known and the allele frequencies are independent. STRUCTURE runs were performed for a variable number of clusters between 1 and 7, and with 10 iterations of 100000 steps (50000 burn-in and 50000 analysis steps to ensure model convergence). This procedure was reproduced for each iteration of the simulation of a given scenario. We determined the best number of genetic clusters following the Evanno method (see [START_REF] Evanno | Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study[END_REF] using STRUCTURE HARVESTER [START_REF] Earl | STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method[END_REF]. We counted the assignation of a patch to a cluster for each MetaConnect run analysed with STRUCTURE and tested the clustering robustness by performing a χ² test per patch.

Current metapopulation functioning

Within the study site, (10 km around the industrial area development), the E. calamita population is not threatened (extinction probability pe = 0) and we observed two main dispersal corridors. The major corridor joins the north (quarry, patch 29 and 32) to the south (motorway, patch 103) and is stopped by the motorway in its southern part and by the Garonne River at its northern part. The minor corridor joins the quarry (east) to a pond (patch 34, west) (figure 4.A). Our focal area covers these two corridors (figure 3). Both corridors incorporate stepping-stone sites (North-south: patches 50 and 67, east-west: patch 36) (figure 4.A). Within the focal area all the patches have a very low extinction probability (see figure 4), and except for patch 103, all patches are able to exchange individuals with all the others. Indeed, only patch 103 is unable to provide individuals to patches 34, 36, 29 and 32 or to receive individuals from patches 34 and 36.

The analysis of the genetic output suggests that currently the study site is divided into three separate clusters (all cluster differentiation p.values<0.05). The first one is situated at the north of the Garonne, the second between the Garonne and the motorway and the third at the south of the motorway (figure 5.A).

Expected consequences of the industrial area and of the high-speed railway building

The industrial area development and the associated destruction of patch 67 leads to a reduction of individual flow between patches to the north and south of the industrial area and to a population size reduction within the southern patches (figure 6.B). Such a modification would not threaten E. calamita persistence in the study site (all 𝑝 𝑒 = 0, figure 6.D). However, dispersal would be more concentrated between patches 29, 32 and 50 due to the repelling effect of the industrial area for E. calamita (figure 6.D). In addition, the reduction of dispersal along the north-south axis should lead to a fourth genetic cluster in the western part of the study sites (Figure 5.B).

The high-speed railway project will fragment the study site landscape. This would lead to the reduction of patches 34 and 103 areas (since none of the newly created patches 45, 46 nor 112 could shelter a population (Respectively 𝑝 𝑒 = 0.71, 0.71, 0.61 , figure 6.E)) inducing the reduction of their sheltered population sizes (figure 6.C). In addition, dispersal along the north-south corridor is reduced to the south of the patch 50 (figure 6.C). Moreover, the connectivity along the north-south corridor is also decreased due to the population size reduction of patches 71 and 103. The reduction of the patch population size and the change in the connectivity along the north-south axis change the clustering outcome of these last simulations. Indeed, if the Evanno method suggests that 2 clusters can be identified, the χ² test shows that the assignation to a second cluster does not differ significantly from a random assignation, suggesting that only a single cluster exists or that the cluster 1 (north of the Garonne) is poorly differentiated from cluster 2 between the Garonne and the high-speed railway (Figure 5.C).

Although the development of the industrial area and the building of the high-speed railway do not directly threaten the E. calamita survival in the study site (all 𝑝 𝑒 = 0) (figure 6.F), it will restrain the species' displacement capabilities and reduce the global population size (respectively, average population size within the focal patches are 2907, 2635 and 2166 individuals). In addition, figure 8.F highlights the central role of patch 50 as a stepping-stone patch, which allows the maintenance of connectivity between the north (patches 29 and 32) and the south (patches 71 and 103). Such a map constitutes a powerful decision-making tool in the new ecological context of compensatory measures which will be taken in the context of large-scale landscape planning [START_REF] Lanius | Aligning Biodiversity Compensation and REDD+: a primer on integrating private sector conservation financing schemes in the tropics and sub-tropics[END_REF][START_REF] Regnery | Offsets and Conservation of the Species of the EU Habitats and Birds Directives[END_REF].

Model limitations:

By its structure, MetaConnect allows the user to take into account most of the requirements necessary for a complete and flexible PVA and decision-making tool (i.e. metapopulation dynamics and genetics) [START_REF] Grimm | META-X: Generic Software for Metapopulation Viability Analysis[END_REF][START_REF] Baguette | Individual dispersal, landscape connectivity and ecological networks[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF].

Currently, its main limitation comes from the landscape representation. In MetaConnect the landscape is represented using the patch-matrix approach, in which a cell is a suitable habitat or not. This approach will not be fully unrealistic for many species [START_REF] Clobert | Dispersal[END_REF]Urban et al. 2009;[START_REF] Pe'er | Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes[END_REF]. Furthermore, reproduction in the suitable habitat is assumed to be panmictic which is usually not true because patch shape and structure isolate or aggregate individuals within a patch and individual behaviour (territoriality, mating system, cooperation,…) may aggregate or isolate individuals within a patch [START_REF] Doebeli | Sex and population-dynamics[END_REF][START_REF] Snyder | Local dispersal can facilitate coexistence in the presence of permanent spatial heterogeneity[END_REF]Ylonen, Pech & Davis 2003;[START_REF] Nonacs | Social heterosis and the maintenance of genetic diversity[END_REF]. Further development of MetaConnect toolboxes would allow tackling this limitation of intra-patch spatial structuring by splitting individual use of space into daily movements and dispersal events [START_REF] Mueller | Search and navigation in dynamic environments -from individual behaviors to population distributions[END_REF][START_REF] Roshier | Animal movement in dynamic landscapes: interaction between behavioural strategies and resource distributions[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF].

A second limitation is the way genetic mutation is modelled. Currently, the mutation model is very simple, just assuming that a new allele can occur at a given constant rate and that no reverse mutations are possible. Further toolbox development would allow various methods for modelling mutations to be incorporated (Willi, Van Buskirk & Hoffmann 2006;[START_REF] Neher | Genetic Draft, Selective Interference, and Population Genetics of Rapid Adaptation[END_REF]Wray 2013) and in addition would permit simulation of the action of the genotypes on the individual phenotype [START_REF] Montalvo | Restoration biology: A population biology perspective[END_REF][START_REF] Mouquet | Ecophylogenetics: advances and perspectives[END_REF]Moulherat et al. submitted).

Conclusion: model application and perspectives

European regulation requires spatial planners to evaluate precisely the impacts of developments on ecological network functioning. [START_REF] Baguette | Individual dispersal, landscape connectivity and ecological networks[END_REF] recommended a robust workflow in that direction. The procedure comprises performing an analysis of metapopulation dynamics and dispersal over a landscape for each affected species to design sound ecological network functioning. MetaConnect provides an important tool that moves in this direction. Indeed, the user can easily build consensus networks for several species within the same study site and under the same framework with standardized and comparable outputs.

However, such an approach does not yet incorporate the inter-specific interactions that could greatly change population dynamics and dispersal [START_REF] Caswell | Matrix Population Models: construction, analysis and interpretation[END_REF][START_REF] Clobert | Dispersal Ecology and Evolution[END_REF].

Further MetaConnect toolbox development will integrate basic inter-specific interactions such as competition, predation and parasitism.

Application of graph-theoretic connectivity is increasing at an exponential rate in ecology and conservation [START_REF] Kindlmann | Connectivity measures: a review[END_REF][START_REF] Kadoya | Assessing functional connectivity using empirical data[END_REF][START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF]. In this framework, graph nodes represent habitat patches and graph edges represent the connectivity between patches (Urban & Keitt 2001). Whilst the mathematical background of graph theory is promising to deal efficiently with the ecological connectivity (Urban & Keitt 2001), the simplification made by modelling a metapopulation with a graph [START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF] has the potential to lead to limited interpretation and operational efficiency [START_REF] Kadoya | Assessing functional connectivity using empirical data[END_REF][START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF]. [START_REF] Kadoya | Assessing functional connectivity using empirical data[END_REF] concluded that graph modelling of metapopulations provides little congruence with connectivity inferred from population genetic structure and [START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF] listed the limits of graph-theoretic connectivity in spatial ecology. With MetaConnect, as shown in our case study (figure 6.A-C), we provide an efficient tool to define habitat patches (nodes of a graph) with predictions of the demographic module and dispersal functionality between patches (graph's edges) with dispersal assessed from dispersal simulation or derived from genetic estimates applicable from local to national scales and grid-based data. However, further development of MetaConnect and the development of a specific toolbox will allow that the graph does not model the metapopulation functioning as such, but to be used as a powerful analytical tool preventing the graph-theoretic connectivity analysis from biases described by [START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF].

We conclude by highlighting the recent call for a new generation of models that begin to provide predictive systems ecology [START_REF] Evans | Predictive ecology: systems approaches Introduction[END_REF][START_REF] Evans | Predictive systems ecology[END_REF]. This call argued that, while in a few sub-disciplines such as dynamic vegetation modelling and climate change modelling we have already developed a capacity for simulating complex systems, typically we lack such a predictive modelling capability elsewhere in ecological and evolutionary disciplines [START_REF] Moorcroft | A method for scaling vegetation dynamics: The ecosystem demography model (ED)[END_REF][START_REF] Evans | Predictive ecology: systems approaches Introduction[END_REF][START_REF] Hoban | Computer simulations: tools for population and evolutionary genetics[END_REF][START_REF] Purves | Time to model all life on Earth[END_REF][START_REF] Bocedi | RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes[END_REF]. The climate modelling community and the dynamic vegetation modelling communities both possess several models with which they can conduct inter-model comparisons, conduct crossvalidations, etc [START_REF] Evans | Predictive ecology: systems approaches Introduction[END_REF][START_REF] Pe'er | A Protocol for Better Design, Application, and Communication of Population Viability Analyses[END_REF]. This is also now true of the population genetics community, which has several software packages available (reviewed in [START_REF] Hoban | Computer simulations: tools for population and evolutionary genetics[END_REF]. We believe that MetaConnect, together with the recently published software RangeShifter [START_REF] Bocedi | RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes[END_REF]) and the integrative statistical inferences procedure proposed by [START_REF] Pagel | Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics[END_REF], begin to address this challenge to develop complex systems models that can be used to inform policy in the sphere of spatial and landscape ecology.

Taylor, P.D., Fahrig, L. & With, K.A. (2006) Landscape connectivity: a return to the basics.

•

  Demography: population size is split into the classes and sexes implemented in the model. The model derives extinction probability, colonization probability and time to extinction and colonization. These indicators are calculated at the local (patch) and global (metapopulation) scales. • Dispersal: the model provides the number of individuals reaching a new patch or dying during dispersal. Maps of cell occupancy are drawn from successful dispersal events (number of individuals visiting each cell during the whole run).

  Connectivity conservation (eds K. Crooks & M.A. Sanjayan), pp. 29-43. University Press, Cambridge, Cambridge. Urban, D. & Keitt, T. (2001) Landscape connectivity: A graph-theoretic perspective. Ecology, 82, 1205-1218. Urban, D.L., Minor, E.S., Treml, E.A. & Schick, R.S. (2009) Graph models of habitat mosaics. Ecology letters, 12, 260-273. Willi, Y., Van Buskirk, J. & Hoffmann, A.A. (2006) Limits to the adaptive potential of small populations. Annual Review of Ecology Evolution and Systematics, pp. 433-458. Wray, G.A. (2013) Genomics and the Evolution of Phenotypic Traits. Annual Review of Ecology, Evolution, and Systematics, Vol 44 (ed. D.J. Futuyma), pp. 51-72. Ylonen, H., Pech, R. & Davis, S. (2003) Heterogeneous landscapes and the role of refuge on the population dynamics of a specialist predator and its prey. Symposium on Costs of Coexistence, pp. 349-369. Jyvaskyla, Finland.

Figure 1 :

 1 Figure 1: Screenshot example of the Legendre et al. (1999) passerine life cycle with two age-

Figure 2 :Figure 3 :Figure 4 :

 234 Figure 2: E. calamita landscape use during dispersal (indiv./year) estimated with MetaConnect. Data to be displayed or analysed are selected in

Figure 5 :

 5 Figure 5: Genetic clustering of the suitable patches for E. calamita. The development of the

Figure 6 :

 6 Figure 6: Graphical representation of the focal area metapopulation functioning derived from

Table 1 :

 1 Nomenclature of MetaConnect's main parameters and variables.

	Parameters and	Description	Distribution
	variables		law of random
			variables
	Demographic characteristics
	𝐾	Carrying capacity*
	𝑘	Total competition coefficient
	𝑠 𝑖	Survival of individual from class i	Bernoulli
	𝐹	Fecundity	Poisson
	𝜎	Primary sex ratio	Bernoulli
	Mating system
	Mating system	Mating system assumptions
	𝐻 𝑚	Male harem size	Poisson
	𝐻 𝑓	Female harem size	Poisson
	Genetics
	𝐿	Number of loci
	𝐴	Number of alleles per locus
	𝜇	Mutation rate
	Dispersal
	𝑝 𝑑	Dispersal probability	Bernoulli
	Dispersal rule	Dispersal algorithm
	Initialization and model parameterization
	𝑁 0	Initial number of individuals
	𝜎 0	Initial sex-ratio
	𝑓 𝑎	Initial class structure
	𝑇	Time steps
	𝑀𝐶 𝑙	Number of landscape random generations
	𝑀𝐶 𝑑	Number of population dynamic simulations per landscape
	* The carrying capacity K is derived from the competition coefficient and the competition
	assumption.

Acknowledgement: SM, EB, JC and MB were supported by the European project SCALES funded by the European Commission as Large-scale Integrating Project within FP7 under grant 226852. JC, JT, SP and MB were supported by EU FP6 Biodiversa ERANET TenLamas. JT and SP were supported by NERC. SM and MM were supported by the TerrOïko's R&D internal program. This work is part of the "Laboratoire d'Excellence" TULIP (ANR-10-LABX-41).

Where to find MetaConnect MetaConnect (IDDN.FR.001.430001.000.S.C.2012.000.20600) is developed in C++ by SM and MM and runs exclusively in cloud-computing under UNIX. A Graphical User Interface (GUI) developed in OpenGL insures the access to MetaConnect from multiple platforms (currently available for Linux Centos7). MetaConnect is available for academic purposes contacting the authors (SM).