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France displays a latitudinal range for the expansion of Aedes albopictus invasive populations that is not yet completely colonized providing a critical opportunity to address key invasion processes. We propose a spatio-temporal model (DISTIGRI) to describe and predict current and future expansion at both intra- and inter-annual scales of A. albopictus. This process-based model integrates mechanistic descriptions of the developmental cycle and the dispersal process of A. albopictus within a reaction-diffusion framework, depending on climatic suitability and photoperiod with a high spatio-temporal resolution. Using this model coupled with a climatic database, we propose several maps describing the current intra-annual distribution of A. albopictus, including the date of first emergence and the length of the period with significant adult presence. We also compute its future distribution over the next 10 years under several climatic scenarios, which shows a range expansion with a strong dependence on the climatic scenario. The outputs of the model may constitute a valuable asset for designing control and avoidance strategies, and to anticipate the biting nuisance with a high spatio-temporal resolution. These outputs also emphasize the importance of taking both dispersal and life cycle into account to obtain accurate descriptions of out-of-equilibrium processes such as ongoing invasions.

SummaryResistant cultivars are of value for protecting crops from disease, but can be rapidly overcome by pathogens. Several strategies have been proposed to delay pathogen adaptation (evolutionary control), while maintaining effective protection (epidemiological control). Resistance genes can bei) combined in the same cultivar (pyramiding),ii) deployed in different cultivars sown in the same field (mixtures) or in different fields (mosaics), oriii) alternated over time (rotations). The outcomes of these strategies have been investigated principally in pathogens displaying pure clonal reproduction, but sexual reproduction may promote the emergence of superpathogens adapted to all the resistance genes deployed.We improved the spatially explicit stochastic modellandsepito include pathogen sexual reproduction, and then investigate the effect of sexual reproduction on evolutionary and epidemiological outcomes across deployment strategies for two major resistance genes.Sexual reproduction only favours the establishment of a superpathogen when single mutant pathogens are present together at a sufficiently high frequency, as in mosaic and mixture strategies.We concluded that, although sexual reproduction may promote the establishment of a superpathogen, it did not affect the optimal strategy recommendations for a wide range of mutation probabilities, associated fitness costs, and landscape organisations (notably the cropping ratio of resistant fields).

Comprendre où et quand une espèce est présente est central en écologie. Pour aller au-delà des approches corrélatives et expliciter les processus écologiques sous-jacents aux distributions observées nous montrons ici comment expliciter les mécanismes dans les modèles de distribution d’espèces. Nous verrons comment définir mathématiquement ces modèles et mener leur inférence dans une approche mécanistico-statistique. Ce chapitre ce terminera sur trois exemples.

Les biologistes n’ont souvent qu’une vision partielle des processus éco-évolutifs à l’œuvre en population naturelle. Tout ce qu’il observe n’est qu’une mesure bruitée ou incomplète des mécanismes sous-jacents. Nous montrons que les processus d’intérêt tels que les compromis évolutifs peuvent être représentés par des variables cachées qui ne sont pas mesurées directement mais sont partiellement renseignés par des variables observées.

Individuals who invest more in the development of their dispersal-related traits often reduce their investment in reproduction. Thus, there are two possible eco-evolutionary strategies: grow faster or disperse faster (R—Darbitrage). Here we explore, through a reaction-diffusion model, how spatial heterogeneity can shape theR—Dtrade-off by studying the spreading dynamics of a consumer species exploiting a resource in a spatially fragmented environment. Based on numerical simulations and analytical solutions derived from simpler models, we show that the classical mathematical symmetry between the effects of growth and dispersal on the spatial spreading speed is broken in the presence of competition between phenotypes. At the back of the forefront, the dynamics is almost always driven by theRspecialists. On the forefront, R-strategies are favored in spatially homogeneous environments, but the introduction of heterogeneity leads to a shift towards D-strategies. This effect is even stronger when spatial heterogeneity affects the diffusion term and when spatial fragmentation is lower. Introducing mutations between phenotypes produces an advantage towards the R-strategy and homogenizes the distribution of phenotypes, also leading to more polymorphism on the forefront.

Pest regulation by natural enemies has a strong potential to reduce the use of synthetic pesticides in agroecosystems. However, the effective role of predation as an ecosystem service remains largely speculative, especially with minute organisms such as mites.Predatory mites are natural enemies for ectoparasites in livestock farms. We tested for an ecosystem level control of the poultry pest Dermanyssus gallinae by other mites naturally present in manure in poultry farms and investigated differences among farming practices (conventional, free‐range, and organic).We used a multiscale approach involving (a) in vitro behavioral predation experiments, (b) arthropod inventories in henhouses with airborne DNA, and (c) a statistical model of covariations in mite abundances comparing farming practices.Behavioral experiments revealed that three mites are prone to feed on D. gallinae. Accordingly, we observed covariations between the pest and these three taxa only, in airborne DNA at the henhouse level, and in mites sampled from manure. In most situations, covariations in abundances were high in magnitude and their sign was positive.Predation on a pest happens naturally in livestock farms due to predatory mites. However, the complex dynamics of mite trophic network prevents the emergence of a consistent assemblage‐level signal of predation. Based on these results, we suggest perspectives for mite‐based pest control and warn against any possible disruption of ignored services through the application of veterinary drugs or pesticides., In vitro tests have been confronted with mite inventories and airborne DNA analyses. Three out of seven taxa of native predatory mites are able to feed on Dermanyssus gallinae. The same three predators are associated with the parasite in poultry houses. We've detected no obvious signals from any pest control service. This may be due to the ubiquity of predators or to prey–predator dynamics.

International audience; Despite growing evidence of spatial dispersal and gene flow between salmonid populations, the implications of connectivity for adaptation, conservation, and management are still poorly appreciated. Here, we explore the influence of a gradient of dispersal rates on portfolio strength and eco-evolutionary dynamics in a simulated population network of Atlantic salmon (Salmo salar) by extending a demo-genetic agent-based model to a spatially explicit framework. Our model results highlight a non-linear relationship between dispersal rates and the stability of the metapopulation, resulting in an optimal portfolio effect for dispersal rates around 20%. At local population scale, we also demonstrate phenotypic changes induced by density-dependent effects modulated by dispersal, and a dispersal-induced increase in genetic diversity. We conclude that it is critical to account for complex interactions between dispersal and ecoevolutionary processes and discuss future avenues of research that could be addressed by such modeling approaches to more fully appreciate responses of Atlantic salmon to environmental changes and investigate management actions accordingly.; Malgré des preuves de plus en plus abondantes de dispersion spatiale et de flux génétique entre populations de salmonidés, les conséquences de la connectivité en ce qui concerne l’adaptation, la conservation et la gestion demeurent mal comprises. Nous explorons l’influence d’un gradient de taux de dispersion sur la force du portefeuille et les dynamiques éco-évolutives d’un réseau de populations simulé de saumon atlantique (Salmo salar) en élargissant un modèle démo-génétique basé sur les agents à un cadre spatialement explicite. Les résultats du modèle font ressortir une relation non linéaire entre les taux de dispersion et la stabilité de la métapopulation, qui se traduit par un effet de portefeuille optimal pour des taux de dispersion d’environ 20 %. Nous démontrons aussi la présence de changements phénotypiques à l’échelle de la population locale induits par des effets dépendants de la densité modulés par la dispersion, ainsi qu’une augmentation de la diversité génétique induite par la dispersion. Nous concluons qu’il est d’importance capitale de tenir compte des interactions complexes entre la dispersion et les processus éco-évolutifs et abordons des avenues de recherche future pouvant être explorées à l’aide de telles approches de modélisation pour mieux comprendre les réponses du saumon atlantique aux changements environnementaux et examiner différentes mesures de gestion.

In agricultural landscapes, the amount and organization of crops and semi-natural habitats (SNH) have the potential to promote a bundle of ecosystem services due to their influence on ecological community at multiple spatio-temporal scales. SNH are relatively undisturbed and are often source of complementary resources and refuges, therefore supporting more diverse and abundant natural pest enemies. However, the nexus of SNH proportion and organization with pest suppression is not trivial. It is thus crucial to understand how the behavior of pest and natural enemy species, the underlying landscape structure, and their interaction, may influence conservation biological control (CBC). Here, we develop a generative stochastic landscape model to simulate realistic agricultural landscape compositions and configurations of fields and linear elements. Generated landscapes are used as spatial support over which we simulate a spatially explicit predator-prey dynamic model. We find that increased SNH presence boosts predator populations by sustaining high predator density that regulates and keeps pest density below the pesticide application threshold. However, predator presence over all the landscape helps to stabilize the pest population by keeping it under this threshold, which tends to increase pest density at the landscape scale. In addition, the joint effect of SNH presence and predator dispersal ability among hedge and field interface results in a stronger pest regulation, which also limits pest growth. Considering properties of both fields and linear elements, such as local structure and geometric features, provides deeper insights for pest regulation; for example, hedge presence at crop field boundaries clearly strengthens CBC. Our results highlight that the integration of species behaviors and traits with landscape structure at multiple scales is necessary to provide useful insights for CBC., Author summary In the agricultural context, the loss of semi-natural surfaces often results in high pest abundance requiring elevated pesticide loads. Habitat heterogeneity resulting from the agricultural intermixing of arable fields and semi-natural areas is key to allow organism fluxes across agro-ecological interfaces by influencing ecological processes. Semi-natural habitats (SNH) are often restricted to linear structures, such as hedgerows, but they play an important role by hosting a large number of species. However, the effect of hedgerows is controversial, as it could result in a positive, ineffective or negative effect for CBC. Usually, the impacts of landscape structure on pest population dynamics and resulting CBC are assessed through field experiments with a specific focus, which cannot be generalized, lack flexibility and are limited by the need to manipulate relatively large landscapes. Here, we tackle the challenge to investigate the controversial role of semi-natural habitats for CBC by presenting a simulation-based approach, which allows us to characterize the joint influence of landscape structure and species traits on CBC service. Our study corroborates that spatial heterogeneity, species traits and their interactions are fundamental for CBC. We show that hedge presence alone is not sufficient to lead to strong pest reduction, but hedge-based predators help to maintain the pest density under the pesticide threshold. Instead, SNH presence coupled with appropriate predator traits leads to stronger decrease of pest population. Moreover, we highlight an important scaling effect of SNH, which at the local scale has an even more important impact on CBC as local properties are considered.

National audience

International audience; Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

In unicellular organisms such as bacteria and in most viruses, mutations mainly occur during reproduction. Thus, genotypes with a high birth rate should have a higher mutation rate. However, standard models of asexual adaptation such as the ‘replicator-mutator equation’ often neglect this generation-time effect. In this study, we investigate the emergence of a positive dependence between the birth rate and the mutation rate in models of asexual adaptation and the consequences of this dependence. We show that it emerges naturally at the population scale, based on a large population limit of a stochastic time-continuous individual-based model with elementary assumptions. We derive a reaction-diffusion framework that describes the evolutionary trajectories and steady states in the presence of this dependence. When this model is coupled with a phenotype to fitness landscape with two optima, one for birth, the other one for survival, a new trade-off arises in the population. Compared to the standard approach with a constant mutation rate, the symmetry between birth and survival is broken. Our analytical results and numerical simulations show that the trajectories of mean phenotype, mean fitness and the stationary phenotype distribution are in sharp contrast with those displayed for the standard model. The reason for this is that the usual weak selection limit does not hold in a complex landscape with several optima associated with different values of the birth rate. Here, we obtain trajectories of adaptation where the mean phenotype of the population is initially attracted by the birth optimum, but eventually converges to the survival optimum, following a hook-shaped curve which illustrates the antagonistic effects of mutation on adaptation.

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories (i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where the organisation of cultivated fields and semi-natural elements are known to play a crucial role for pest regulation. It aids to formulate guidelines for biological control strategies at global and local scale.

International audience; Tidal habitats sustain fragile ecosystems, undergoing pressures from coastal artificialization and rising sea levels. Saltpans are a substitution habitat for birds that breed, winter or stop-over along coastlands where most pristine tidal habitats have been removed. Balancing the economical, patrimonial and biodiversity values of former saltpans is thus needed to mitigate the threats posed by global changes on waterbirds. In this study, we scrutinized the influence of management practices on waterbirds on two isolated saltpans located on the French Mediterranean shore, several tens of kilometres apart from other suitable habitats. We analysed three years of bird counts for nine protected species that breed, forage and roost on these saltpans. We used a multispecies hierarchical model to relate variations in bird counts to water levels, oxygenation and salinity, the three parameters targeted by the saltpans management plan to promote bird settlement. We showed that the hypersaline conditions that dominate in these saltpans are suboptimum to most species, suggesting that waterbird concentrations are dictated by the lack of alternatives in the surrounding landscape rather than by habitat suitability. Intraspecific variations in species' responses to these variables should orient towards the creation of a habitat mosaic within the saltpans. Eventually, between-site differences in bird responses to water conditions pointed the effects of disturbance, predation and other landscape-level features. Our results reveal that high waterbird numbers on isolated saltpans may be a misleading measure of their ecological suitability, and that management on these sites needs to incorporate conflicts and complementarity in species' habitat use.

In agricultural landscapes, the amount and organization of crops and semi-natural habitats (SNH) have the potential to promote a bundle of ecosystem services due to their influence on ecological community at multiple spatio-temporal scales. SNH are relatively undisturbed and are often source of complementary resources and refuges, supporting more diverse and abundant natural pest enemies. However, the nexus of SNH proportion and organization with pest suppression is not trivial. It is thus crucial to understand how the behavior of pest and auxiliary species, the underlying landscape structure, and their interaction may influence conservation biological control (CBC). Here, we develop a generative stochastic landscape model to simulate realistic agricultural landscape compositions and configurations of fields and linear elements. Generated landscapes are used as spatial support over which we simulate a spatially explicit predator-prey dynamic model. We find that SNH boost predator population, but predator movement from hedges to fields is fundamental for an efficient pest regulation by auxiliaries and to decrease pesticide treatments. Moreover landscape elements may lead to different effects on pest reduction depending on the considered scale. Integration of species behaviors and traits with landscape structure at multiple scales are needed to provide useful insights for CBC.

In symbiotic interactions, spatiotemporal variation in the distribution or population dynamics of one species represents spatial and temporal heterogeneity of the landscape for the other. Such interdependent demographic dynamics result in situations where the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Using a detailed survey of three metapopulations of the succulent plant

International audience; In symbiotic interactions, spatiotemporal variation in the distribution or population dynamics of one species represents spatial and temporal heterogeneity of the landscape for the other. Such interdependent demographic dynamics result in situations where the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Using a detailed survey of three metapopulations of the succulent plant Cakile maritima and the necrotrophic fungus Alternaria brassicicola located along the southeastern Australian coast, we developed a series of statistical analyses-namely, synchrony analysis, patch occupancy dynamics, and a spatially explicit metapopulation model-to understand how habitat quality, weather conditions, dispersal, and spatial structure determine metapopulation dynamics. Climatic conditions are important drivers, likely explaining the high synchrony among populations. Host availability, landscape features facilitating dispersal, and habitat conditions also impact the occurrence and spread of disease. Overall, we show that the collection of extensive data on host and pathogen population dynamics, in combination with spatially explicit epidemiological modeling, makes it possible to accurately predict disease dynamics-even when there is extreme variability in host population dynamics. Finally, we discuss the importance of genetic information for predicting demographic dynamics in this pathosystem.

1AbstractA trade-off between growth and fecundity, reflecting the inability of simultaneously investing in both functions when resources are limited, is a fundamental feature of life history theory. This particular trade-off is the result of evolutionary and environmental constrains shaping reproductive and growth traits, but it remains difficult to pinpoint in natural populations of long-lived plants. We developed a hierarchical Bayesian model to estimate the inter-individual correlation among growth and reproduction, using observations at individual level over several years combined with resource simulations from an ecophysiological-based model (CASTANEA). In the Bayesian model, the resource, simulated by CASTANEA and incorporated as a latent variable, is allocated to tree growth, reproductive buds initiation and fruit maturation. Then, we used individual random effects correlated among energetic sinks to investigate potential trade-offs. We applied this original approach to a Mediterranean coniferous tree, Atlas Cedar (Cedrus atlantica), at two contrasted levels of competition, high versus low density population. We found that trees initializing many reproductive buds had a higher growth. Moreover, a negative correlation was detected between growth and fruit survival during maturation. Finally, trees investing more resource to maturate fruits initiated less reproductive buds. The level of competition did not impact the sign of these three correlations, but changed the level of resource allocation: low density population favored growth whereas high density favored reproduction. The level of resource have an impact on individual strategies. This new modeling framework allowed us to detect various individual strategies of resource allocation to growth versus late-stage reproduction on the one hand, and to early-versus late-stage reproduction on the other hand. Moreover, the sign of the correlation between growth and reproductive traits depends on the stage of reproduction considered. Hence, we suggest that the investigation of potential trade-offs between growth and reproduction requires to integrate the dynamics of resource and sink’s phenology, from initiation to maturation of reproductive organs.

International audience

Modern agriculture faces the dual challenges of feeding a growing human population, while preserving natural resources and slowing current trends in climate change and its impacts. A deep understanding of the functioning of agricultural landscapes appears crucial if we are to move towards sustainable, complex and resilient agroecosystems. Modelling is a powerful tool to address these issues since it can inform these transformations by simulating the multiscale ecological flows and myriad interactions agroecosystems host, and the multilevel stakeholder actions and their feedbacks in landscapes. This chapter shows that models can provide guidance on the transition towards future multifunctional agricultural landscapes. We have focused on process-based models, which allow for a more thorough understanding of the underlying mechanisms and how these may be manipulated. We first examine how models can simulate the structure and the dynamics of agricultural landscapes, emphasizing the complex mosaic of urban, peri-urban, rural and seminatural habitats. Then, we consider the simulation of biotic and abiotic flows and their complex interactions in the mosaic habitats. Using formalisms from the social sciences, we integrate human decision-making and actions into the landscape models, thereby encompassing a major component in the landscape transformation process. Finally, we outline some avenues for future research. We have focused on improvements to landscape representation, and have suggested ways to bridge the gap between future landscape conception and manipulation, thus providing operational guidance for the transition towards future agricultural landscapes that achieve our objectives.

International audience; The COVID-19 epidemic was reported in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginning of March 2020. Since then, several countries went into lockdown. Using a mechanistic-statistical formalism, we estimate the effect of the lockdown in France on the contact rate and the effective reproduction numberR(e)of the COVID-19. We obtain a reduction by a factor 7 (R-e= 0.47, 95%-CI: 0.45-0.50), compared to the estimates carried out in France at the early stage of the epidemic. We also estimate the fraction of the population that would be infected by the beginning of May, at the official date at which the lockdown should be relaxed. We find a fraction of 3.7% (95%-CI: 3.0-4.8%) of the total French population, without taking into account the number of recovered individuals before April 1st, which is not known. This proportion is seemingly too low to reach herd immunity. Thus, even if the lockdown strongly mitigated the first epidemic wave, keeping a low value ofR(e)is crucial to avoid an uncontrolled second wave (initiated with much more infectious cases than the first wave) and to hence avoid the saturation of hospital facilities.

The COVID-19 epidemic started in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginning of March 2020. Since then, several countries went into lockdown. We estimate the effect of the lockdown in France on the contact rate and the effective reproduction number Re of the COVID-19. We obtain a reduction by a factor 7 (Re = 0.47, 95%-CI: 0.45-0.50), compared to the estimates carried out in France at the early stage of the epidemic. We also estimate the fraction of the population that would be infected by the beginning of May, at the official date at which the lockdown should be relaxed. We find a fraction of 3.7% (95%-CI: 3.0-4.8%) of the total French population, without taking into account the number of recovered individuals before April 1st, which is not known. This proportion is seemingly too low to reach herd immunity. Thus, even if the lockdown strongly mitigated the first epidemic wave, keeping a low value of Re is crucial to avoid an uncontrolled second wave (initiated with much more infectious cases than the first wave) and to hence avoid the saturation of hospital facilities. Our approach is based on the mechanistic-statistical formalism, which uses a probabilistic model to connect the data collection process and the latent epidemiological process, which is described by a SIR-type differential equation model.

Where and when alien organisms are successfully introduced are central questions to elucidate biotic and abiotic conditions favorable to the introduction, establishment and spread of invasive species. We propose a modelling framework to analyze multiple introductions by several invasive genotypes or genetic variants, in competition with a resident population, when observations provide knowledge on the relative proportions of each variant at some dates and places. This framework is based on a mechanistic-statistical model coupling a reaction-diffusion model with a probabilistic observation model. We apply it to a spatio-temporal dataset reporting the relative proportions of five genetic variants of watermelon mosaic virus (WMV, genus Potyvirus, family Potyviridae) in infections of commercial cucurbit fields. Despite the parsimonious nature of the model, it succeeds in fitting the data well and provides an estimation of the dates and places of successful introduction of each emerging variant as well as a reconstruction of the dynamics of each variant since its introduction.

International audience; Mixed fruit tree–vegetable systems simultaneously combine the production of vegetables at the ground level with that of fruits in tree canopies. We tested whether such crop diversification may reduce herbivores on the vegetable crop in the case of an apple fruit–cabbage association. For this purpose, we monitored arthropod pests and natural enemies during six sampling sessions over one growing season with direct observations and pitfall traps. Unexpectedly, we found that four out of seven pest stages [the aphid Myzus persicae Sulzer (Aphididae), lepidoptera eggs and caterpillars, and pupae of whiteflies] were more abundant or more frequent in the mixed fruit tree–vegetable plots than in control plots. In contrast, we observed more of three out of the six main predator taxa (chilopods, earwigs and ants) in the control plots. The mechanisms possibly explaining our results include better microclimatic conditions, the dilution of predators and increased bird abundance in the agroforestry plots as well as the longer generation time of predators versus pests.

International audience; Mixed fruit tree-vegetable systems, a particular type of agroforestry, simultaneously combine the production of vegetables at the ground level with that of fruits in tree canopies. Crop diversification generally promotes pest natural enemies but its impact on pest abundance is more ambivalent, possibly because the presence of trees also directly impacts pest reproduction, survival and movements. The purpose of this study was thus to estimate population dynamics parameters of vegetable pests (predation, immigration and survival) and compare them between mixed fruit tree-vegetable plots and vegetable control plots. For this purpose, seven pest stages were monitored in an experiment with exclusion cages set up in mixed fruit tree-vegetable plots, combining apple and cabbage, and two control plots grown with cabbage only, over one growing season. The data were then fed into a mechanistic-statistical model to estimate these parameters. Most significant results concerned the aphid Myzus persicae that had higher intrinsic growth rate, less immigration and suffered less predation in mixed fruit tree-vegetable plots. Egg laying also appeared larger for Lepidoptera species in these plots but egg survival was lower. Predation of caterpillars was also lower. Lastly predation of the aphid Brevicoryne brassicae, in contrast to M. persicae, was higher in mixed fruit tree-vegetable plots. Either no difference was noted between plot types for other parameters or the pest biology made it difficult to relate the model parameters to population dynamics process. We thus provide evidence that reproduction or survival of some pests may increase in mixed fruit tree-vegetable plots and their predation level may be similar or lower than in control plots which could explain the diversity of results concerning the effects of agroforestry on pest control. We also found no strong evidence that trees were a barrier to immigration in our simple mixed fruit tree-vegetable plots.

National audience; Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution. Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate spatio-temporal processes in ecological models. As a case study, we considered the wolf (Canis lupus) that has been recolonizing Eastern France naturally through dispersal from the Apennines since the early 90's. Using partial differential equations for modeling species diffusion and growth in a fragmented landscape, we develop a mechanistic-statistical spatio-temporal model accounting for ecological diffusion, logistic growth and imperfect species detection. We conduct a simulation study and show the ability of our model to i) estimate ecological parameters in various situations with contrasted species detection probability and number of surveyed sites and ii) forecast the distribution into the future. We found that the growth rate of the wolf population in France was explained by the proportion of forest cover, that diffusion was influenced by human density and that species detectability increased with increasing survey effort. Using the parameters estimated from the 2007-2015 period, we then forecasted wolf distribution in 2016 and found good agreement with the actual detections made that year. Our approach may be useful for managing species that interact with human activities to anticipate potential conflicts.

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (https://doi.org/10.24072/pci.evolbiol.100072); The successful establishment of a population into a new empty habitat outside of its initial niche is a phenomenon akin to evolutionary rescue in the presence of immigration. It underlies a wide range of processes, such as biological invasions by alien organisms, host shifts in pathogens or the emergence of resistance to pesticides or antibiotics from untreated areas. In this study, we derive an analytically tractable framework to describe the coupled evolutionary and demographic dynamics of asexual populations in a source-sink system. In particular, we analyze the influence of several factors — immigration rate, mutational parameters, and harshness of the stress induced by the change of environment — on the establishment success in the sink (i.e. the formation of a self-sufficient population in the sink), and on the time until establishment. To this aim, we use a classic phenotype-fitness landscape (Fisher’s geometrical model in n dimensions) where source and sink habitats determine distinct phenotypic optima. The harshness of stress, in the sink, is determined by the distance between the fitness optimum in the sink and that of the source. The dynamics of the full distribution of fitness and of population size in the sink are analytically predicted under a strong mutation strong immigration limit where the population is always polymorphic. The resulting eco-evolutionary dynamics depend on mutation and immigration rates in a non straightforward way. Below some mutation rate threshold, establishment always occurs in the sink, following a typical four-phases trajectory of the mean fitness. The waiting time to this establishment is independent of the immigration rate and decreases with the mutation rate. Beyond the mutation rate threshold, lethal mutagenesis impedes establishment and the sink population remains so, albeit with an equilibrium state that depends on the details of the fitness landscape. We use these results to get some insight into possible effects of several management strategies.

Agroecosystems produce food and many other services that are crucial for human well-being. Given the scales at which the processes underlying these services take place, agricultural landscapes appear as appropriate spatial units for their evaluation and management. The design of sustainable agricultural landscapes that value these services has thus become a pressing issue but faces major challenges stemming from the diversity of processes, their interactions and the number of scales at stake. Agricultural landscape modelling can provide a key contribution to this design but must still overcome several difficulties to offer reliable tools for decision makers. Our study aimed at shedding light on the main scientific and technical difficulties that make the building of landscape models that may efficiently inform decision-makers a complex task, as well as translating them in terms of challenges that can be further investigated and discussed. We examine current issues and challenges and indicate future research needs to overcome the scientific and technical obstacles in the development of useful agricultural landscape models. We highlight research perspectives to better couple landscape patterns and process models and account for feedbacks, integrate the decisions of multiple stakeholders, consider the spatial and temporal heterogeneity of data and processes, explore alternative landscape organisations and assess multiobjective performance. Coping with the issues and challenges discussed in this paper should improve our understanding of agroecosystems and give rise to new hypotheses, thereby informing future research.

International audience; Once deployed uniformly in the field, genetically controlled plant resistance is often quickly overcome by pathogens, resulting in dramatic losses. Several strategies have been proposed to constrain the evolutionary potential of pathogens and thus increase resistance durability. These strategies can be classified into four categories, depending on whether resistance sources are varied across time (rotations) or combined in space in the same cultivar (pyramiding), in different cultivars within a field (cultivar mixtures) or among fields (mosaics). Despite their potential to differentially affect both pathogen epidemiology and evolution, to date the four categories of deployment strategies have never been directly compared together within a single theoretical or experimental framework, with regard to efficiency (ability to reduce disease impact) and durability (ability to limit pathogen evolution and delay resistance breakdown). Here, we used a spatially explicit stochastic demogenetic model, implemented in the R package landsepi, to assess the epidemiological and evolutionary outcomes of these deployment strategies when two major resistance genes are present. We varied parameters related to pathogen evolutionary potential (mutation probability and associated fitness costs) and landscape organization (mostly the relative proportion of each cultivar in the landscape and levels of spatial or temporal aggregation). Our results, broadly focused on qualitative resistance to rust fungi of cereal crops, show that evolutionary and epidemiological control are not necessarily correlated and that no deployment strategy is universally optimal. Pyramiding two major genes offered the highest durability, but at high mutation probabilities, mosaics, mixtures and rotations can perform better in delaying the establishment of a universally infective superpathogen. All strategies offered the same short‐term epidemiological control, whereas rotations provided the best long‐term option, after all sources of resistance had broken down. This study also highlights the significant impact of landscape organization and pathogen evolutionary ability in considering the optimal design of a deployment strategy.

International audience; A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape-based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life-history traits on three measures of disease control. Our results show that short-term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well-mixed landscapes (balanced proportions with low aggregation) are optimal for long-term evolutionary equilibrium (defined here as the level of long-term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history.

Hedgerows are agro-ecological infrastructures that are assumed to enhance biodiversity in an agro-ecosystem and the control of crop pests. However, local movements of arthropod predators from hedgerows to crops remain poorly understood. In this research, these movements were analysed in eleven commercial apple orchards over two weeks in the spring of 2014. Predators were indirectly marked by spraying ovalbumin on the hedgerows. Canopy and ground predators were captured using beating and pitfall traps, respectively, in both the orchards and the hedgerows, and individuals marked by ovalbumin were detected using ELISAs. Approximately 20% of the 1272 captured predators were identified as marked. Movements between the orchards and the hedgerows of the most abundant predator taxa ( Forficula auricularia , Chrysoperla sp., Philodromus spp., Cheiracanthium mildei , and Nebria brevicollis ) and of four guilds (ground spiders, ground beetles, canopy spiders and canopy insects) captured across the eleven locations were estimated using a Bayesian model. On one hand, canopy insects and ground spiders were less likely to stay in the hedgerows than were the canopy spiders and ground beetles. On the other hand, the canopy spiders and ground beetles were less likely to stay in the orchard than were the canopy insects and ground spiders. However, there were exceptions within these groups: F. auricularia and N. brevicollis exhibited a high probability of staying in the hedgerow and in the orchard, respectively. Overall, these results demonstrate the frequent movements that occurred between the orchard and the adjacent hedgerow in a diverse range of predator taxa. The probabilities of movement were further affected by the characteristics of the orchard for most taxa. Therefore, agro-environmental measures that focus on hedgerow management require consideration of the local arthropod predator communities and their characteristics to enhance pest control in apple orchards.

The role of environmental heterogeneity in the evolution of biological diversity has been studied only for simple types of heterogeneities and dispersals. This article broadens previous results by considering heterogeneities and dispersals that are structured by several environmental factors. It studies the evolution of a metapopulation, living in a network of patches connected by dispersal, under the effects of mutation, selection and migration. First, it is assumed that patches are equally connected and that they carry habitats characterized by several factors exerting selection pressures on several individual traits. Habitat factors may vary in the environment independently or they may be correlated. It is shown that correlations between habitat factors promote adaptive diversification and that this effect may be modified by trait interactions on survival. Then, it is assumed that patches are structured by two crossed factors, called the row and column factors, such that patches are more connected when they occur in the same row or in the same column. Environmental patterns in which each habitat appears in each row the same number of times and appears in each column the same number of times are found to hinder adaptive diversification.

1.Visualisation of spatial networks based on pairwise metrics such as (dis)similarity coefficients provides direct information on spatial organization of biological systems. However, for large networks, graphical representations are often unreadable as nodes (samples), and edges (links between samples) strongly overlap. We present a new method, MAPI (Mapping Averaged Pairwise Information), allowing translation from spatial networks to variation surfaces.[br/] 2.MAPI relies on i) a spatial network in which samples are linked by ellipses and, ii) a grid of hexagonal cells encompassing the study area. Pairwise metric values are attributed to ellipses and averaged within the cells they intersect. The resulting surface of variation can be displayed as a colour map in Geographical Information System (GIS), along with other relevant layers, such as land cover. The method also allows the identification of significant discontinuity in grid cell values through a nonparametric randomisation procedure.[br/] 3.The interest of MAPI is here demonstrated in the field of spatial and landscape genetics. Using simulated test datasets, as well as observed data from three biological models, we show that MAPI is i) relatively insensitive to confounding effects resulting from isolation-by-distance (i.e. over-structuring), ii) efficient in detecting barriers when they are not too permeable to gene flow and, iii) useful to explore relationships between spatial genetic patterns and landscape features.[br/] 4.MAPI is freely provided as a PostgreSQL/PostGIS database extension allowing easy interaction with GIS or the R software and other programming languages. Although developed for spatial and landscape genetics, the method can also be useful to visualise spatial organisation from other kinds of data from which pairwise metrics can be computed.

International audience; Advances in genomic and molecular technologies coupled with an increasing understanding of the fine structure of many resistance and infectivity genes, have opened up a new era of hope in controlling the many plant pathogens that continue to be a major source of loss in arable crops. Some new approaches are under consideration including the use of nonhost resistance and the targeting of critical developmental constraints. However, the major thrust of these genomic and molecular approaches is to enhance the identification of resistance genes, to increase their ease of manipulation through marker and gene editing technologies and to lock a range of resistance genes together in simply manipulable resistance gene cassettes. All these approaches essentially continue a strategy that assumes the ability to construct genetic-based resistance barriers that are insurmountable to target pathogens. Here we show how the recent advances in knowledge and marker technologies can be used to generate more durable disease resistance strategies that are based on broad olutionary principles aimed at presenting pathogens with a shifting, landscape of fluctuating directional selection..