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Occupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities—including probabilities conditional to the other species’ presence. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. We further evaluate the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. Most parameters are estimated with low uncertainty (i.e. narrow posterior distributions). More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online.

International audience; Organisms need access to particular habitats for their survival and reproduction. However, even if all necessary habitats are available within the broader environment, they may not all be easily reachable from the position of a single individual. Many species distribution models consider populations in environmental (or niche) space, hence overlooking this fundamental aspect of geographical accessibility. Here, we develop a formal way of thinking about habitat availability in environmental spaces by describing how limitations in accessibility can cause animals to experience a more limited or simply different mixture of habitats than those more broadly available. We develop an analytical framework for characterizing constrained habitat availability based on the statistical properties of movement and environmental autocorrelation. Using simulation experiments, we show that our general statistical representation of constrained availability is a good approximation of habitat availability for particular realizations of landscape-organism interactions. We present two applications of our approach, one to the statistical analysis of habitat preference (using step-selection functions to analyze harbor seal telemetry data) and a second that derives theoretical insights about population viability from knowledge of the underlying environment. Analytical expressions for habitat availability, such as those we develop here, can yield gains in analytical speed, biological realism, and conceptual generality by allowing us to formulate models that are habitat sensitive without needing to be spatially explicit.

Knowledge of survival rates and their potential covariation with environmental drivers, for both adults and juveniles, is paramount to forecast the population dynamics of long-lived animals. Long-lived bird and mammal populations are indeed very sensitive to change in survival rates, especially that of adults. Here we report the first survival estimates for the Icelandic gyrfalcon (Falco rusticolus) obtained by capture-mark-recapture methods. We use a mark-recapture-recovery model combining live and dead encounters into a unified analysis, in a Bayesian framework. Annual survival was estimated at 0.83 for adults and 0.40 for juveniles. Positive effects of main prey density on juvenile survival (5% increase in survival from min to max density) were possible though not likely. Weather effects on juvenile survival were even less likely. The variability in observed lifespan suggests that adult birds could suffer from human-induced alteration of survival rates.

In a recent opinion article, Muff et al. recapitulate well-known objections to the Neyman-Pearson Null-Hypothesis Significance Testing (NHST) framework and call for reforming our practices in statistical reporting. We agree with them on several important points: the significance threshold P 0.1 should be reported as "little or no evidence" and a P-value of 0.001 as "strong evidence" in favor of the alternative hypothesis H1., This is a letter in response to Muff, S., Nilsen, E. B., O'Hara, R. B., & Nater, C. R. (2022). Rewriting results sections in the language of evidence. Trends in Ecology & Evolution

How evolutionary forces interact to maintain quantitative genetic variation within populations has been a matter of extensive theoretical debates. While mutation and migration increase genetic variation, natural selection and genetic drift are expected to deplete it. To date, levels of genetic variation observed in natural populations are hard to predict without accounting for other processes, such as balancing selection in heterogeneous environments. We aimed to empirically test three hypotheses: (i) admixed populations have higher quantitative genetic variation due to introgression from other gene pools, (ii) quantitative genetic variation is lower in populations from harsher environments (i.e. experiencing stronger selection), and (iii) quantitative genetic variation is higher in populations from spatially heterogeneous environments. We used phenotypic measurements of five growth, phenological and functional traits from three clonal common gardens, consisting of 523 clones from 33 populations of maritime pine (Pinus pinaster Aiton). Populations from harsher climates (mainly colder areas) showed lower genetic variation for height in the three common gardens. Surprisingly, we did not find any association between within-population genetic variation and environmental heterogeneity or population admixture for any trait. Our results suggest a predominant role of natural selection in driving within-population genetic variation, and therefore indirectly their adaptive potential.

Explaining coexistence in species-rich communities of primary producers remains a challenge for ecologists because of their likely competition for shared resources. Following Hutchinson's seminal suggestion, many theoreticians have tried to create diversity through a fluctuating environment, which impairs or slows down competitive exclusion. However, fluctuating-environment models often only produce a dozen of coexisting species at best. Here, we investigate how to create richer communities in fluctuating environments, using an empirically parameterized model. Building on the forced Lotka-Volterra model of Scranton and Vasseur (Theor Ecol 9(3):353-363, 2016), inspired by phytoplankton communities, we have investigated the effect of two coexistence mechanisms, namely the storage effect and higher intra- than interspecific competition strengths (i.e., strong self-regulation). We tuned the intra/inter competition ratio based on empirical analyses, in which self-regulation dominates interspecific interactions. Although a strong self-regulation maintained more species (50%) than the storage effect (25%), we show that none of the two coexistence mechanisms considered could ensure the coexistence of all species alone. Realistic seasonal environments only aggravated that picture, as they decreased persistence relative to a random environment. However, strong self-regulation and the storage effect combined superadditively so that all species could persist with both mechanisms at work. Our results suggest that combining different coexistence mechanisms into community models might be more fruitful than trying to find which mechanism best explains diversity. We additionally highlight that while biomass-trait distributions provide some clues regarding coexistence mechanisms, they cannot indicate unequivocally which mechanisms are at play., Comment: 27 pages, 9 figures, Theor Ecol (2019)

Seed formation is part of the reproductive cycle, leading to the accumulation of resistance stages that can withstand harsh environmental conditions for long periods of time. At the community level, multiple species with such long-lasting life stages can be more likely to coexist. While the implications of this process for biodiversity have been studied in terrestrial plants, seed banks are usually neglected in phytoplankton multispecies dynamic models, in spite of widespread empirical evidence for such seed banks. In this study, we build a metacommunity model of interacting phytoplankton species, including a resting stage supplying the seed bank. The model is parameterized with empirically-driven growth rate functions and field-based interaction estimates, which include both facilitative and competitive interactions. Exchanges between compartments (coastal pelagic cells, coastal resting cells on the seabed, and open ocean pelagic cells) are controlled by hydrodynamical parameters to which the sensitivity of the model is assessed. We consider two models, i.e., with and without a saturating effect of the interactions on the growth rates. Our results are consistent between models, and show that a seed bank allows to maintain all species in the community over 30 years. Indeed, a fraction of the species are vulnerable to extinction at specific times within the year, but this process is buffered by their survival in their resting stage. We thus highlight the potential role of the seed bank in the recurrent re-invasion of the coastal community, and of coastal environments in re-seeding oceanic regions. Moreover, the seed bank enables populations to tolerate stronger interactions within the community as well as more severe changes to the environment, such as those predicted in a climate change context. Our study therefore shows how resting stages may help phytoplanktonic diversity maintenance., Comment: 46 pages ; 13 figures

International audience; Most quantitative biologists and applied statisticians interested in identifiability-i.e., whether a unique set of parameter values can be found to maximize the likelihood of a model-currently have to swift through piles of manuscripts that are often only tangentially relevant to their needs. A colleague who fits dynamic models to data recently lamented: 'Is there something that I could recommend to my graduate students and postdocs that would not be a very technical book from the 80s or 90s?' Now there is. Diana Cole's new book, Parameter redundancy and identifiability, explores all facets of model identifiability and its little cousin parameter redundancy (when the model is non-identifiable because it could be re-parameterized with fewer parameters). The book is written with mostly ecological modelling in mind (in the broadest sense-including fisheries science, epidemiology, ...). However, as the author notes in the preface, the methodology is general and applies equally to other fields: for instance, to compartmental models from pharmacokinetics, that are covered to some degree, and many other dynamical systems. In many ways, this book builds on the author's previous work (Cole et al., 2010; Cole & McCrea, 2016), but presents identifiability techniques in a more accessible and comprehensive manner than the original papers, which were more focused on so-called exhaustive summaries.

International audience; Identifying directed interactions between species from time series of their population densities has many uses in ecology. This key statistical task is equivalent to causal time series inference, which connects to the Granger causality (GC) concept: $x$ causes $y$ if $x$ improves the prediction of $y$ in a dynamic model. However, the entangled nature of nonlinear ecological systems has led to question the appropriateness of Granger causality, especially in its classical linear Multivariate AutoRegressive (MAR) model form. Convergent-cross mapping (CCM), developed for deterministic dynamical systems, has been suggested as an alternative, although less grounded in statistical theory. Here, we show that linear GC and CCM are able to uncover interactions with surprisingly similar performance, for predator-prey cycles, 2-species deterministic (chaotic) or stochastic competition, as well 10- and 20-species interaction networks. There is no correspondence between the degree of nonlinearity of the dynamics and which method performs best. Our results therefore imply that Granger causality, even in its linear MAR($p$) formulation, is a valid method for inferring interactions in nonlinear ecological networks; using GC or CCM (or both) can instead be decided based on the aims and specifics of the analysis.

International audience; One target of biological conservation is the protection of biodiversity in agricultural landscapes. However, in their land-use decisions, farmers are often challenged with balancing biodiversity maintenance and profit generation. Under the current agricultural system, this tends to result in intensive farming which destroys suitable habitat for wild species. Thus, the potential for land-use conflicts between agriculture and biological conservation is high. Previous studies that investigated the effects of subsidies and taxes suggest mild trade-offs between conservation and farming, thus favouring land-sharing solutions to biological conservation. However, many ecological-economic models that dealt with this issue neglect possible Allee effects, which have been found by ecologists to be a common phenomenon. The existence of Allee effects markedly alters predictions of ecological-economic models: we show that conservation success is accompanied by substantial losses in agricultural production. More suitable habitat is required to prevent extinction of the species of interest, and conservation measures should start before the population has declined to some critical value. We emphasize the effect of spatial fragmentation on population viability under an Allee effect, as a clumped area of suitable habitat protects a population much better from extinction than a fragmented mosaic of habitat patches.

Dynamic food web models describe how species abundances change over time as a function of trophic and life-history parameters. They are essential to predicting the response of ecosystems to perturbations. However, they are notoriously difficult to parameterize, so that most models rely heavily either on allometric scaling of parameters or inverse estimation of biomass flows. The allometric approach makes species of comparable body mass have near-identical parameters which can generate extinctions within a trophic level. The biomass flow approach is more precise, but is restricted to steady-states, which is not appropriate for time-varying environments. Adequately parameterizing large food webs of temperate and arctic environments requires dealing both with many species of similar sizes and a strongly seasonal environment. Inspired by the rich empirical knowledge on the vertebrate food web of the Bialowieza forest, we parameterize a bipartite food web model comprising 21 predators and 124 prey species. Our model is a non-autonomous coupled ordinary differential equations system that allows for seasonality in life-history and predation parameters. Birth and death rates, seasonal descriptions of diet for each species, food requirements and biomass information are combined into a seasonal parameterization of a dynamic food web model. Food web seasonality is implemented with time-varying intrinsic growth rate and interaction parameters, while predation is modelled with both type I and type II functional responses. All our model variants allow for >80% persistence in spite of massive apparent competition, and a quantitative match to observed (seasonal) biomasses. We also identify trade-offs between maximizing persistence, reproducing observed biomasses, and ensuring model robustness to sampling errors. Although multi-annual cycles are expected with type II functional responses, they are here prevented by a strong predator self-regulation. We discuss these results and possible improvements on the model. We provide a general workflow to parameterize dynamic food web models in seasonal environments, based on a real case study. This may help to better predict how biodiverse food webs respond to changing environments.

International audience; 1. The persistence of phytoplanktonic diversity in spite of competition for basic resources has long been a source of wonder and inspiration to ecologists. To sort out, among the many coexistence mechanisms suggested by theory and experiments , which ones actually maintain diversity in natural ecosystems, long-term field studies are paramount. 2. We analysed a large dataset of phytoplankton abundance time series using dynamic, multivariate autoregressive (MAR) models. Phytoplankton was counted and identified down to the genus level, every 2 weeks over 20 years, at 10 sites along the French coastline. MAR models allowed to estimate biotic interaction networks while also accounting for abiotic variables that may drive part of the phytoplankton fluctuations. We then analysed the ratio of intra to intertaxa interactions (a measure of niche differentiation), the frequency of negative versus positive interactions, and how stability metrics (both at the network and genus level) relate to network complexity and genus self-regulation or abundance. 3. We showed that a strong self-regulation, with competition strength within a taxon (genus) an order of magnitude higher than between taxa, was present in all phy-toplanktonic interaction networks. This much stronger intragenus competition suggests that niche differentiation-rather than neutrality-is commonplace in phytoplankton. Furthermore, interaction networks were dominated by positive net effects between phytoplanktonic taxa (on average, more than 50% of interactions were positive). While network stability (sensu resilience) was unrelated to complexity measures, we unveiled links between self-regulation, intergenera interaction strengths and abundance. The less common taxa tend to be more strongly self-regulated and can therefore maintain in spite of competition with more abundant ones. 4. Synthesis. We demonstrate that strong niche differentiation, widespread facilitation between phytoplanktonic taxa and stabilizing covariances between interaction strengths should be common features of coexisting phytoplankton communities in the field. These are structural properties that we can expect to emerge from plausible mechanistic models of phytoplankton communities. We discuss mechanisms, such as predation or restricted microscale movement, that are consistent with these findings, which paves the way for further research.

The persistence of phytoplanktonic diversity in spite of competition for basic resources has long been a source of wonder and inspiration to ecologists. To sort out, among the many coexistence mechanisms suggested by theory and experiments, which ones actually maintain diversity in natural ecosystems, long-term field studies are paramount.We analysed a large dataset of phytoplankton abundance time series using dynamic, multivariate autoregressive models. Phytoplankton was counted and identified down to the genus level, every two weeks over twenty years, at ten sites along the French coastline. Multivariate autoregressive models allowed to estimate biotic interaction networks, while also accounting for abiotic variables that may drive part of the phytoplankton fluctuations. We then analysed the ratio of intra-to inter-taxa interactions (measuring self-regulation, itself a measure of niche differentiation), the frequency of negative vs positive interactions, and how stability metrics (both at the network and genus level) relate to network complexity and genus self-regulation or abundance.We showed that a strong self-regulation, with competition strength within a taxon (genus) an order of magnitude higher than between taxa, was present in all phytoplanktonic interaction networks. This much stronger intragenus competition suggests that niche differentiation - rather than neutrality - is commonplace in phytoplankton. Furthermore, interaction networks were dominated by positive net effects between phytoplanktonic taxa (on average, more than 50% of interactions were positive). While network stability (sensu resilience) was unrelated to complexity measures, we unveiled links between self-regulation, intergenus interaction strengths and abundance. The less common taxa tend to be more strongly self-regulated and can therefore maintain in spite of competition with more abundant ones.Synthesis: We demonstrate that strong niche differentiation, widespread facilitation between phytoplanktonic taxa and stabilizing covariances between interaction strengths should be common features of coexisting phytoplankton communities in the field. These are structural properties that we can expect to emerge from plausible mechanistic models of phytoplankton communities. We discuss mechanisms, such as predation or restricted microscale movement, that are consistent with these findings, which paves the way for further research.

Inferring interactions between populations of different species is a challenging statistical endeavour, which requires a large amount of data. There is therefore some incentive to combine all available sources of data into a single analysis to do so. In demography and single-population studies, Integrated Population Models combine population counts, capture-recapture and reproduction data to fit matrix population models. Here, we extend this approach to the community level in a stage-structured predator-prey context. We develop Integrated Community Models (ICMs), implemented in a Bayesian framework, to fit multispecies nonlinear matrix models to multiple data sources. We assessed the value of the different sources of data using simulations of ICMs under different scenarios contrasting data availability. We found that combining all data types (capture-recapture, counts, and reproduction) allows the estimation of both demographic and interaction parameters, unlike count-only data which typically generate high bias and low precision in interaction parameter estimates for short time series. Moreover, reproduction surveys informed the estimation of interactions particularly well when compared to capture-recapture programs, and have the advantage of being less costly. Overall, ICMs offer an accurate representation of stage structure in community dynamics, and foster the development of efficient observational study designs to monitor communities in the field.

Most mechanistic predator–prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator–prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data – including in small amounts – can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.

International audience; Coupled dynamical systems in ecology are known to respond to the seasonal forcing of their parameters with multiple dynamical behaviours, ranging from seasonal cycles to chaos. Seasonal forcing is predominantly modelled as a sine wave. However, the transition between seasons is often more sudden as illustrated by the effect of snow cover on predation success. A handful of studies have mentioned the robustness of their results to the shape of the forcing signal but did not report any detailed analyses. Therefore, whether and how the shape of seasonal forcing could affect the dynamics of coupled dynamical systems remains unclear, while abrupt seasonal transitions are widespread in ecological systems. To provide some answers, we conduct a numerical analysis of the dynamical response of predator-prey communities to the shape of the forcing signal by exploring the joint effect of two features of seasonal forcing: the magnitude of the signal, which is classically the only one studied, and the shape of the signal, abrupt or sinusoidal. We consider both linear and saturating functional responses, and focus on seasonal forcing of the predator's discovery rate, which fluctuates with changing environmental conditions and prey's ability to escape predation. Our numerical results highlight that a more abrupt seasonal forcing mostly alters the magnitude of population fluctuations and triggers period-doubling bifurcations, as well as the emergence of chaos, at lower forcing strength than for sine waves. Controlling the variance of the forcing signal mitigates this trend but does not fully suppress it, which suggests that the variance is not the only feature of the shape of seasonal forcing that acts on community dynamics. Although theoretical studies may predict correctly the sequence of bifurcations using sine waves as a representation of seasonality, there is a rationale for applied studies to implement as realistic seasonal forcing as possible to make precise predictions of community dynamics.

Specialist predators with oscillating dynamics are often strongly affected by the population dynamics of their prey, yet they are not always the cause of prey cycling. Only those that exert strong (delayed) regulation of their prey can be. Inferring predator–prey coupling from time series therefore requires contrasting models with top‐down versus bottom‐up predator–prey dynamics. We study here the joint dynamics of population densities of the Icelandic gyrfalcon Falco rusticolus, and its prey, the rock ptarmigan Lagopus muta. The dynamics of both species are likely not only linked to each other but also to stochastic weather variables acting as confounding factors. We infer the degree of coupling between populations, as well as forcing by abiotic variables, using multivariate autoregressive models MAR(p), with p = 1 and 2 time lags. MAR(2) models, allowing for species to cycle independently from each other, further suggest alternative scenarios where a cyclic prey influences its predator but not the other way around (i.e., bottom‐up scenarios). The classical MAR(1) model predicts that the time series exhibit predator–prey feedback (i.e., reciprocal dynamic influence between prey and predator), and that weather effects are weak and only affecting the gyrfalcon population. Bottom‐up MAR(2) models produced a better fit but less realistic cross‐correlation patterns. Simulations of MAR(1) and MAR(2) models further demonstrate that the top‐down MAR(1) models are more likely to be misidentified as bottom‐up dynamics than vice versa. We therefore conclude that predator–prey feedback in the gyrfalcon–ptarmigan system is likely the main cause of observed oscillations, though bottom‐up dynamics cannot yet be excluded with certainty. Overall, we showed how to make more out of ecological time series by using simulations to gauge the quality of model identification, and paved the way for more mechanistic modeling of this system by narrowing the set of important biotic and abiotic drivers.

Binomial N-mixture models are commonly applied to analyze population survey data. By estimating detection probabilities, N-mixture models aim at extracting information about abundances in terms of actual and not just relative numbers. This separation of detection probability and abundance relies on parametric assumptions about the distribution of individuals among sites and of detections of individuals among repeat visits to sites. Current methods for checking assumptions are limited, and their computational complexity have hindered evaluations of their performances. We develop computationally efficient graphical goodness of fit checks and measures of overdispersion for binomial N-mixture models. These checks are illustrated in a case study, and evaluated in simulations under two scenarios. The two scenarios assume overdispersion in the abundance distribution via a negative binomial distribution or in the detection probability via a beta-binomial distribution. We evaluate the ability of the checks to detect lack of fit, and how lack of fit affects estimates of abundances. The simulations show that if the parametric assumptions are incorrect there can be severe biases in estimated abundances: negatively if there is overdispersion in abundance relative to the fitted model and positively if there is overdispersion in detection. Our goodness of fit checks performed well in detecting lack of fit when the abundance distribution is overdispersed, but struggled to detect lack of fit when detections were overdispersed. We show that the inability to detect lack of fit due to overdispersed detection is caused by a fundamental similarity between N-mixture models with beta-binomial detections and N-mixture models with negative binomial abundances. The strong biases in estimated abundances that can occur in the binomial N-mixture model when the distribution of individuals among sites, or the detection model, is mis-specified implies that checking goodness of fit is essential for sound inference in ecological studies that use these methods. To check the assumptions we provide computationally efficient goodness of fit checks that are available in an R-package nmixgof. However, even when a binomial N-mixture model appears to fit the data well, estimates are not robust in the presence of overdispersion unless additional information about detection is collected.

The relative impact of light, depth, hydrology, hydrodynamics, sediment and nutrients on growth of the seagrass Zostera noltei was investigated in Arcachon Bay, where the extent of seagrass beds has been decreasing for 20 years. A comprehensive multi-parameter survey of 9 sites was conducted monthly over one year. A three-parameter logistic growth model was fitted to biomass data to unravel the relative contributions of environmental factors on seagrass growth. The present study highlights the fact that, in addition to the dominant positive effect of light on plant growth, the hydrological and hydrodynamic parameters were important drivers for Z. noltei growth at the scale of the whole Bay. At the scale of individual sites, other factors such as macroalgal mats or micro-topography (such as presence of tidal pools) may also influence seagrass growth. The statistical model also suggested that the different factors contribute differently to Z. noltei growth in different sites. Finally, as the model is not able to predict biomass accurately for sites that received very little light, and seagrasses in these sites having higher chlorophyll content and longer shoots, we suspect that adaptation to low light condition was at play. In a global context of seagrass beds decline, our study provides a framework to disentangle the site-specific effects of physical and biological drivers on seagrass seasonal growth.

International audience; Background – The role of evolution in biological invasion studies is often overlooked. In order to evaluate the evolutionary mechanisms behind invasiveness, both quantitative and population genetics studies are underway on Robinia pseudoacacia L., one of the worst invasive tree species in Europe. Methods – A controlled experiment was set up using 2000 seeds from ten populations in Southern France and ten populations in Belgium. Seedlings were cultivated in two climatic chambers set at 18°C and 22°C. Early development life history traits (e.g.seedling phenology) and functional traits (e.g.growth rates) were monitored. Genotyping using SNP markers was used to evaluate the genetic differentiation among the populations and a QST – FST comparison was done in order to test for the role of selection. Results – Populations exhibited a strong plasticity to temperature for all measured traits, the warmer environment being generally more suitable, irrespective of their origin. No significant departure from neutral evolution was evidenced by the QST – FST comparisons, although we found a slightly significant differentiation at the molecular level. Conclusion – Plasticity for the functional and life history traits was evidenced but no genetic interaction suggesting no possible evolution of plasticity at those traits. Moreover, no support for genetic differentiation and local adaptation was found among studied populations within invasive range, raising two main questions: first, what is the role of selection on functional and life-history traits; and second, is the elapsed time since first introduction sufficient to allow evolution and local adaptation?

A recent paper claims the existence of one of the most large-scale travelling waves ever recorded for any animal population. Here we address why conceptual and methodological pitfalls may have served to exaggerate or even impose the spatial patterns reported. Photo credit: Jane U. Jepsen.

Phytoplanktonic communities maintain a high diversity in a seemingly homogeneous environment, competing for the same set of resources. Many theories have been proposed to explain this coexistence despite likely competition, such as contrasted responses to temporal environmental variation. However, theory has developed at a faster pace than empirical evaluation using field data, which requires inferring drivers of community dynamics from observational time series. Here, we combine autoregressive models with a data set spanning more than 20 years of phytoplankton counts every two weeks, together with nutrients and physical variables. By comparing models dominated by nutrients or physical variables (hydrodynamics and climate), we first explore which abiotic factors contribute more to phytoplankton growth and decline. We find that physical drivers – such as irradiance, wind, and salinity – explain some of the variability in abundances unexplained by biotic interactions. In contrast, responses to nutrients explain less of the phytoplankton variability. Concerning biotic drivers of community dynamics, multivariate autoregressive models reveal that competition between different groups (genera) has a much weaker effect on population growth rates than competition within a group. In fact, the few biotic interactions between genera that are detected are frequently positive. Hence, our system is unlikely to be best represented as a set of competitors whose differing responses to fluctuating environments allow coexistence, as in ‘paradox of the plankton’ models with a storage effect or a relative nonlinearity of competition. Coexistence is more likely to result from stabilizing niche differences, manifested through high intragroup density‐dependence. Competition between phytoplanktonic groups and nutrient limitation are often invoked as drivers of phytoplankton dynamics; our findings suggest instead that more attention should be given to the physical structure of the environment and natural enemies such as grazers and pathogens, that could explain the high intraspecific density dependence found here.

Specialist predators with oscillating dynamics are often strongly affected by the population dynamics of their prey, yet they do not always participate in a predator-prey cycle. Only those that exert strong population regulation of their prey do so. Inferring the strength and direction of the predator-prey coupling from time series therefore requires contrasting models with top-down versus bottom-up predator-prey dynamics. We examine such population-level coupling using multivariate autoregressive models. The models translate several hypotheses for the joint dynamics of population densities of the Icelandic gyrfalcon Falco rusticolus, and its prey, the rock ptarmigan Lagopus muta. The dynamics of both species are likely not only linked to each other but also to stochastic weather variables acting as confounding factors on the joint dynamics. The classical MAR(1) model, used most often in ecology, predicts that the times series exhibit predator-prey feedback (i.e., Granger causality): the predator helps to explain prey dynamics and the prey helps to explain predator dynamics. Weather, in the form of spring temperature, influences gyrfalcon population growth but not ptarmigan population growth, despite individual-level evidence that ptarmigan chicks can be strongly affected by weather. MAR(2) models, allowing for species to cycle independently from each other, further suggests alternative scenarios where a cyclic prey influence its predator but not the other way around; such bottom-up models produce a better fit but less realistic cross-correlation patterns. Simulations of MAR(1) and MAR(2) models further demonstrate that the top-down MAR(1) models are most likely to be misidentified as bottom-up dynamics than vice-versa. We therefore conclude that predator-prey feedback in the gyrfalcon-ptarmigan system is very likely, though bottom-up dynamics cannot be excluded with certainty. We finally discuss what sort of information is needed to advance the characterization of joint predator-prey dynamics in birds and other vertebrates.

Binomial N-mixture models are commonly applied to analyze population survey data. By estimating detection probabilities, N-mixture models aim at extracting information about abundances in terms of actual and not just relative numbers. This separation of detection probability and abundance relies on parametric assumptions about the distribution of individuals among sites and of detections of individuals among repeat visits to sites. Current methods for checking assumptions are limited, and their computational complexity have hindered evaluations of their performances.We develop computationally efficient graphical goodness of fit checks and measures of overdispersion for binomial N-mixture models. These checks are illustrated in a case study, and evaluated in simulations under two scenarios. The two scenarios assume overdispersion in the abundance distribution via a negative binomial distribution or in the detection probability via a beta-binomial distribution. We evaluate the ability of the checks to detect lack of fit, and how lack of fit affects estimates of abundances.The simulations show that if the parametric assumptions are incorrect there can be severe biases in estimated abundances: negatively if there is overdispersion in abundance relative to the fitted model and positively if there is overdispersion in detection. Our goodness of fit checks performed well in detecting lack of fit when the abundance distribution is overdispersed, but struggled to detect lack of fit when detections were overdispersed. We show that the inability to detect lack of fit due to overdispersed detection is caused by a fundamental similarity between N-mixture models with beta-binomial detections and N-mixture models with negative binomial abundances.The strong biases in estimated abundances that can occur in the binomial N-mixture model when the distribution of individuals among sites, or the detection model, is mis-specified implies that checking goodness of fit is essential for sound inference in ecological studies that use these methods. To check the assumptions we provide computationally efficient goodness of fit checks that are available in an R-package nmixgof. However, even when a binomial N-mixture model appears to fit the data well, estimates are not robust in the presence of overdispersion unless additional information about detection is collected.

Phytoplanktonic communities maintain a high diversity in a seemingly homogeneous environment, competing for the same set of resources. Many theories have been proposed to explain this coexistence despite likely competition, such as contrasted responses to temporal environmental variation. However, theory has developed at a faster pace than its empirical evaluation using field data, that requires to infer biotic and abiotic drivers of community dynamics from observational time series. Here, we combine autoregressive models with a data set spanning more than 20 years of biweekly plankton counts and abiotic variables, including nutrients and physical variables. By comparing models dominated by nutrients or physical variables (hydrodynamics and climate), we first explore which abiotic factors contribute more to phytoplankton growth and decline. We find that physical drivers - such as irradiance, wind, and salinity - explain some of the variability in abundances unexplained by biotic interactions. In contrast, responses to nutrients explain less of phytoplankton variability. Concerning biotic drivers of community dynamics, multivariate autoregressive models reveal that competition between different groups (at the genus level for most) has a much weaker effect on population growth rates than competition within a group. In fact, the few biotic interactions between genera that are detected are frequently positive. Hence, our system is unlikely to be best represented as a set of competitors whose differing responses to fluctuating environments allow coexistence, as in “paradox of the plankton” models with a storage effect or a relative nonlinearity of competition. Coexistence is more likely to result from stabilizing niche differences, manifested through high intragroup density-dependence. Competition between planktonic groups and nutrients are often invoked as drivers of phytoplankton dynamics; our findings suggest instead that more attention should be given to the physical structure of the environment and natural enemies, for coastal phytoplankton at least.

Arditi and Ginzburg (2012) propose ordinary differential equations (ODEs) with ratio-dependent functional responses as the new null model for predation, based on their earlier work on ratio-dependent food chains and a number of functional response measurements. Here, I discuss some of their claims, arguing for a flexible and problem-driven approach to predator–prey modeling. Models to understand population cycles and models to predict the effect of basal enrichment on food chains need not be the same. While ratio-dependent functional responses in ODE models might sometimes be useful as limit cases for food chains, they are not intrinsically more useful than prey-dependent models to understand the effect of a given predator on prey population dynamics—and sometimes less useful, given the small temporal scales considered in many models. “Instantism” is showed to be an invalid criticism when ODEs are interpreted as describing average trajectories of stochastic birth–death processes. Moreover, other modeling frameworks with strong ties to time series statistics, such as stochastic difference equations, should be promoted to improve the feedback loop between field and theoretical research. The main problems of current trophic ecology do not lie in a wrong null model, as ecologists have already several at their disposal. The loose connection of ODE models with empirical data and spatial/temporal scaling up of empirical measurements constitute more serious challenges to our understanding of trophic interactions and their consequences on ecosystem functioning.

International audience; Geographical variation in forest insect sensitivity to environmental conditions is often overlooked. Yet considering regional variation in response to climate is necessary to better predict the consequences of climate change on pest population dynamics and associated damage. We investigated the climatic drivers of demographic fluctuations in a forest tree defoliator, the pine pro cessionary moth (PPM), in 8 biogeographical regions in France. We studied the effects of precipitation and temperature for both cold and warm periods, in accordance with PPM biology. We trained second-order log linear models of infestation dynamics based on records spanning over 32 years (1981-2014) and 1239 forest plots, and tested their forecast accuracy on two additional years (2013-2014). PPM population growth rate decreased with higher precipitations in 5 regions and increased with increasing temperatures in the cold period in 4 regions. The magnitude and direction of temperature effects in the warm period varied among biogeographical regions. Our results also revealed that PPM population dynamics were largely triggered by density-dependent factors independent from climatic drivers. Short-term forecasts were generally accurate irrespective of the inclusion of climatic variables into the models. To better predict the consequences of climate change on insect population dynamics, it is advisable to take geographical variability of species response into account and develop regional forecasting models.

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer–resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

There is a large variation in home range size within species, yet few models relate that variation to demographic and life-history traits. We derive an approximate deterministic population dynamics model keeping track of spatial structure, via spatial moment equations, from an individual-based spatial consumer-resource model; where space-use of consumers resembles that of central place foragers. Using invasion analyses, we investigate how the evolutionarily stable home range size of the consumer depends on a number of ecological and behavioral traits of both the resource and the consumer. We show that any trait variation leading to a decreased overall resource production or an increased spatial segregation between consumer and resource acts to increase consumer home range size. In this way, we extend theoretical predictions on optimal territory size to a larger range of ecological scenarios where home ranges overlap and population dynamics feedbacks are possible. Consideration of spatial traits such as dispersal distances also generates new results: (1) consumer home range size decreases with increased resource dispersal distance, and (2) when consumer agonistic behavior is weak, more philopatric consumers have larger home ranges. Finally, our results emphasize the role of the spatial correlation between consumer and resource distributions in determining home range size, and suggest resource dispersion is less important.

International audience; When foraging in a landscape, predators choose travelling directions according to their immediate knowledge of prey distribution within their perceptual fields and, when appropriate, to their past foraging experience. A substantial part of foraging theory is based on patch use in spatially implicit domains, and rarely has it considered foraging paths involving directional choices driven by memory and perception. Using a model mixing directed and random movements of a predator, we investigated how perception range and duration of spatial memory influence the long-term intake rate of a central place forager depending on prey spatial distribution. We found that intake rate increased and eventually saturated with increasing perception range regardless of prey spatial distribution. In contrast, the effect of spatial memory duration was mediated by the level of prey spatial aggregation. Assuming that an increase in memory or perception abilities has a cost, we found that it was not beneficial to possess simultaneously a wide perception range and a long-term spatial memory when prey distribution was aggregated. Moreover, when looking at the functional response of predators with different ‘cognitive strategies', we found that those relying mainly on memory were limited by the prey global densities while those relying mainly on perception were limited by the level of prey aggregation. These results suggest that cognitive strategies might have evolved as a response to the prey spatial distribution and that prey spatial aggregation, not only prey global density, should be considered an important component of the functional response.

International audience; Common voles in western France exhibit threeyearpopulation cycles with winter crashes after largeoutbreaks. During the winter of 2011–2012, we monitoredsurvival, reproduction, recruitment and population growthrate of common voles at different densities (from low tooutbreak densities) in natura to better understand densitydependence of demographic parameters. Between Octoberand April, the number of animals decreased irrespective ofinitial density. However, the decline was more pronouncedwhen October density was higher (loss of &54 % ofindividuals at low density and 95 % at high density). Usingcapture-mark-recapture models with Pradel’s temporalsymmetry approach, we found a negative effect of densityon recruitment and reproduction. In contrast, density had aslightly positive effect on survival indicating that mortalitydid not drive the steeper declines in animal numbers at highdensity. We discuss these results in a population cycleframework, and suggest that crashes after outbreaks couldreflect negative effects of density dependence on reproductionrather than changes in mortality rates.

International audience; We present a case for using Global Community Innovation Platforms (GCIPs), an approach to improve innovation and knowledge exchange in international scientific communities through a common and open online infrastructure. We highlight the value of GCIPs by focusing on recent efforts targeting the ecological sciences, where GCIPs are of high relevance given the urgent need for interdisciplinary, geographical, and cross-sector collaboration to cope with growing challenges to the environment as well as the scientific community itself. Amidst the emergence of new international institutions, organizations, and meetings, GCIPs provide a stable international infrastructure for rapid and long-term coordination that can be accessed by any individual. This accessibility can be especially important for researchers early in their careers. Recent examples of early-career GCIPs complement an array of existing options for early-career scientists to improve skill sets, increase academic and social impact, and broaden career opportunities. We v www.esajournals.org 1 April 2015 v Volume 6(4) v Article 68 provide a number of examples of existing early-career initiatives that incorporate elements from the GCIPs approach, and highlight an in-depth case study from the ecological sciences: the International Network of Next-Generation Ecologists (INNGE), initiated in 2010 with support from the International Association for Ecology and 20 member institutions from six continents.

International audience; : Population cycles in voles are often thought to be generated by one-year delayed density-dependence on the annual population growth rate. In common voles, however, it has been suggested by Turchin (2003) that some populations exhibit first-order cycles, resulting from strong overcompensation (i.e. carrying capacity overshoots in peak years, with only an effect of the current year abundance on annual growth rates). We focus on a common vole (Microtus arvalis) population from western France, that exhibits 3-year cycles. Several overcompensating nonlinear models for populations dynamics are fitted to the data, notably those of Hassell, and Maynard-Smith and Slatkin. Overcompensating direct density-dependence (DD) provides a satisfactory description of winter crashes, and one-year delayed density-dependence is not responsible for the crashes, thus these are not classical second-order cycles. A phase-driven modulation of direct density-dependence maintains a low-phase, explaining why the cycles last three years instead of two. Our analyses suggest that some of this phase-dependence can be expressed as one-year delayed DD, but phase-dependence provides a better description. Hence modelling suggests that cycles in this population are first-order cycles with a low phase after peaks, rather than fully second-order cycles. However, based on the popular log-linear second-order autoregressive model, we would conclude only that negative delayed density-dependence exists. The additive structure of this model cannot show when delayed DD occurs (here, during lows rather than peaks). Our analyses thus call into question the automated use of second-order log-linear models, and suggests that more attention should be given to non-(log)linear models when studying cyclic populations. From a biological viewpoint, the fast crashes through overcompensation that we found suggest they might be caused by parasites or food rather than predators, though predators might have a role in maintaining the low phase and spatial synchrony. This article is protected by copyright. All rights reserved.

We develop a theory of generalist predation showing how alternative prey species are affected by changes in both mean abundance and variability (coefficient of variation) of their predator’s primary prey. The theory is motivated by the indirect effects of cyclic rodent populations on ground-breeding birds, and developed through progressive analytic simplifications of an empirically-based model. It applies nonetheless to many other systems where primary prey have fast life-histories and can become superabundant, thus facilitating impact on alternative prey species and generating highly asymmetric interactions. Our results suggest that predator effects on alternative prey should generally decrease with mean primary prey abundance, and increase with primary prey variability (low to high CV)—unless predators have strong aggregative responses, in which case these results can be reversed. Approximations of models including predator dynamics (general numerical response with possible delays) confirm these results but further suggest that negative temporal correlation between predator and primary prey is harmful to alternative prey. Finally, we find that measurements of predator numerical responses are crucial to predict–even qualitatively–the response of ecosystems to changes in the dynamics of outbreaking prey species.

Summary1. Environmental variability, through interannual variation in food availability or climaticvariables, is usually detrimental to population growth. It can even select for constancy in keylife-history traits, though some exceptions are known. Changes in the level of environmentalvariability are therefore important to predict population growth or life-history evolution.Recently, several cyclic vole and lemming populations have shown large dynamical changesthat might affect the demography or life-histories of rodent predators.2. Skuas constitute an important case study among rodent predators, because of theirstrongly saturating breeding productivity (they lay only two eggs) and high degree of sitefidelity, in which they differ from nomadic predators raising large broods in good rodentyears. This suggests that they cannot capitalize on lemming peaks to the same extent asnomadic predators and might be more vulnerable to collapses of rodent cycles.3. We develop a model for the population dynamics of long-tailed skuas feeding on lemmingsto assess the demographic consequences of such variable and non-stationary prey dynamics,based on data collected in NE Greenland. The model shows that populations of long-tailedskua sustain well changes in lemming dynamics, including temporary collapses (e.g. 10 years).A high floater-to-breeder ratio emerges from rigid territorial behaviour and a long-life expec-tancy, which buffers the impact of adult abundance’s decrease on the population reproductiveoutput.4. The size of the floater compartment is affected by changes in both mean and coefficient ofvariation of lemming densities (but not cycle amplitude and periodicity per se). In Greenland,the average lemming density is below the threshold density required for successful breeding(including during normally cyclic periods). Due to Jensen’s inequality, skuas therefore benefitfrom lemming variability; a positive effect of environmental variation.5. Long-tailed skua populations are strongly adapted to fluctuating lemming populations, aninstance of demographic lability in the reproduction rate. They are also little affected by poorlemming periods, if there are enough floaters, or juveniles disperse to neighbouring popula-tions. The status of Greenland skua populations therefore strongly depends upon floaternumbers and juvenile movements, which are not known. This reveals a need to intensifycolour-ringing efforts on the long-tailed skua at a circumpolar scale.Key-words: demographic buffering, environmental variance, floaters, population cycles,territoriality

Husek et al. (Popul Ecol 55:363–375, 2013) showed that the numerical response of storks to vole prey was stronger in regions where variability in vole density was higher. This finding is, at first sight, in contradiction with the predictions of life-history theory in stochastic environments. Since the stork productivity-vole density relationship is concave, theory predicts a negative association between the temporal variability in vole density and stork productivity. Here, we illustrate this negative effect of vole variability on stork productivity with a simple mathematical model relating expected stork productivity to vole dynamics. When comparing model simulations to the observed mean density and variability of thirteen Czech and Polish vole populations, we find that the observed positive effect of vole variability on stork numerical response is most likely due to an unusual positive correlation between mean and variability of vole density.

Using models for unstructured populations, we investigate the effect of environmental variability on population growth when the environment affects vital rates through nonlinear functions. We focus here especially on interannual variation in food resources availability, for which sigmoid functions are relevant. Considering first unregulated populations in stochastic environments, we show that classic sigmoid annual growth rates cannot lead to positive effects of increased environmental variability on population growth. This is true even when the temporal average of food availability is low, and Jensen’s inequality predicts an increased arithmetic mean of the annual growth rate. The result is due to the log-concavity of many sigmoid (and other accelerating) functions, as convexity of the logarithm of the annual growth rate is needed for positive effects of variability to appear. Then, separating the effects of a food availability variable on reproduction and survival rates, we show that populations with less sensitive survival rate to food are more likely to benefit from food variability—as opposed to populations that have survival rates accelerating with food availability, which is rather counterintuitive given Jensen’s inequality. Again, this is explained by log-convexity properties of nonlinear functions. We further extend these results to regulated populations, in which similar positive effects of food variability can affect average population size. Positive variability effects seem however more likely to occur in regulated populations. Finally, we extend our results to stage-structured populations. We connect to the previous work showing positive effects of environmental variability with matrix models, and show that these effects are well captured by simpler unstructured models.

Dispersal theory generally predicts kin competition, inbreeding, and temporal variation in habitat quality should select for dispersal, whereas spatial variation in habitat quality should select against dispersal. The effect of predation on the evolution of dispersal is currently not well-known: because predation can be variable in both space and time, it is not clear whether or when predation will promote dispersal within prey. Moreover, the evolution of prey dispersal affects strongly the encounter rate of predator and prey individuals, which greatly determines the ecological dynamics, and in turn changes the selection pressures for prey dispersal, in an eco-evolutionary feedback loop. When taken all together the effect of predation on prey dispersal is rather difficult to predict. We analyze a spatially explicit, individual-based predator-prey model and its mathematical approximation to investigate the evolution of prey dispersal. Competition and predation depend on local, rather than landscape-scale densities, and the spatial pattern of predation corresponds well to that of predators using restricted home ranges (e.g. central-place foragers). Analyses show the balance between the level of competition and predation pressure an individual is expected to experience determines whether prey should disperse or stay close to their parents and siblings, and more predation selects for less prey dispersal. Predators with smaller home ranges also select for less prey dispersal; more prey dispersal is favoured if predators have large home ranges, are very mobile, and/or are evenly distributed across the landscape.

Land use change and land management intensification are major drivers of biodiversity loss, especially in agricultural landscapes, that cover a large and increasing share of the world's surface. Incentive-based agrienvironmental policies are designed to influence farmers' land-use decisions in order to mitigate environmental degradation. This paper evaluates the effectiveness of agri-environmental schemes for biological conservation in a dynamic agricultural landscape under economic uncertainty. We develop a dynamic ecological economic model of agricultural land-use and spatially explicit population dynamics. We then relate policies (subsidies to grassland, taxation of agricultural intensity) to the ecological outcome (probability of persistence of a species of interest). We also analyze the associated trade-offs between agricultural production (in value) and biological conservation (in probability of persistence) at the landscape scale.; Le changement d'usage des sols et l'intensification des usages contrôles sont deux facteurs majeurs de l'érosion de la biodiversité, tout particulièrement dans les paysages agricoles qui couvrent une part de plus en plus importante des surfaces mondiales. Des mesures environnementales incitatives sont mises en place pour limiter les dégradations environnementales. Cet article évalue l'efficacité de mesures agro-environnementales pour la préservation de la biodiversité dans les paysages agricoles dynamiques soumis aux incertitudes économiques. Les auteurs développent un modèle écologique-économique dynamique décrivant l'usage des sols agricoles et la dynamique spatialement explicite d'une population animale. Les auteurs relient alors l'effet des politiques publiques (subventions aux prairies et taxations des intrants) à l'état écologique du paysage (probabilité de survie persistance de l'espèce). Les auteurs décrivent également les arbitrages nécessaires à l'échelle du paysage entre production agricole (en valeur) et conservation écologique (en probabilité de persistance).

Land use change is a main driver of biodiversity erosion, especially in agricultural landscapes. Incentive-based land-use policies aim at influence land-use pattern, and are usually evaluated with habitat suitability scores, without accounting explicitly for the ecology of the studied population. In this paper, we propose a methodology to define and evaluate agricultural land-use policies with respect to their ecological outcomes directly. We use an ecological-economic model to link the regional abundance of a bird species to the economic context. Policies based on such ecological economics approaches appear to be more efficient than that based on landscape evaluation, from both economic and ecological viewpoints.

International audience; Because of the heterogeneity of natural landscapes, animals have to move through various types of areas that are more or less suitable with respect to their current needs. The locations of the profitable places actually used, which may be only a subset of the whole set of suitable areas available, are usually unknown, but can be inferred from movement analysis by assuming that these places correspond to the limited areas where the animals spend more time than elsewhere. Identifying these intensively used areas makes it possible, through subsequent analyses, to address both how they are distributed with respect to key habitat features, and the underlying behavioral mechanisms used to find these areas and capitalize on such habitats. We critically reviewed the few previously published methods to detect changes in movement behavior likely to occur when an animal enters a profitable place. As all of them appeared to be too narrowly tuned to specific situations, we designed a new, easy-to-use method based on the time spent in the vicinity of successive path locations. We used computer simulations to show that our method is both quite general and robust to noisy data.

Population response functions based on climatic and phenotypic data from common gardens have long been the gold standard for predicting quantitative trait variation in new environments. However, prediction accuracy might be enhanced by incorporating genomic information that captures the neutral and adaptive processes behind intra-population genetic variation. We used five clonal common gardens containing 34 provenances (523 genotypes) of maritime pine (Pinus pinaster Aiton) to determine whether models combining climatic and genomic data capture the underlying drivers of height-growth variation, and thus improve predictions at large geographical scales. The plastic component explained most of the height-growth variation, probably resulting from population responses to multiple environmental factors. The genetic component stemmed mainly from climate adaptation, and the distinct demographic and selective histories of the different maritime pine gene pools. Models combining climate-of-origin and gene pool of the provenances, and positive-effect height-associated alleles (PEAs) captured most of the genetic component of height-growth and better predicted new provenances compared to the climate-based population response functions. Regionally-selected PEAs were better predictors than globally-selected PEAs, showing high predictive ability in some environments, even when included alone in the models. These results are therefore promising for the future use of genome-based prediction of quantitative traits.