The Tonle Sap Lake (TSL) is a flood-pulse system. It is the largest natural lake in South-East Asia and constitutes one of the largest fisheries over the world, supporting the livelihood of million peoples. Nonetheless, the Mekong River Basin is changing rapidly due to accelerating water infrastructure development (hydropower, irrigation, flood control, and water supply) and climate change, bringing considerable modifications to the annual flood-pulse of the TSL. Such modifications are expected to have strong impacts on fish biodiversity and abundance. This paper aims to characterize the spatio-temporal variations of fish taxonomic composition and to highlights the underlying determinants of these variations. For this purpose, we used data collected from a community catch monitoring program conducted at six sites during 141 weeks, covering two full hydrological cycles. For each week, we estimated beta diversity as the total variance of the site-by-species community matrix and partitioned it into Local Contribution to Beta Diversity (LCBD) and Species Contribution to Beta Diversity (SCBD). We then performed multiple linear regressions to determine whether species richness, species abundances and water level explained the temporal variation in the contribution of site and species to beta diversity. Our results indicate strong temporal variation of beta diversity due to differential contributions of sites and species to the spatial variation of fish taxonomic composition. We further found that the direction, the shape and the relative effect of species richness, abundances and water level on temporal variation in LCBD and SCBD values greatly varied among sites, thus suggesting spatial variation in the processes leading to temporal variation in community composition. Overall, our results suggest that fish taxonomic composition is not homogeneously distributed over space and time and is likely to be impacted in the future if the flood-pulse dynamic of the system is altered by human activities.

Genotypes that hedge their bets can be favored by selection in an unpredictably varying environment. Bet hedging can be achieved by systematically expressing several phenotypes, such as one that readily attempts to reproduce and one that procrastinates in a dormant stage. But how much of each phenotype should a genotype express? Theory predicts that evolving bet-hedging strategies depend on local environmental variation, on how the population is regulated, and on exchanges with neighboring populations. Empirically, however, it remains unknown whether bet hedging can evolve to cope with the ecological conditions experienced by populations. Here we study the evolution of bet-hedging dormancy frequencies in two neighboring populations of the chestnut weevil, Curculio elephas. We estimate the temporal distribution of demographic parameters together with the form of the relationship between fecundity and population density and use both to parameterize models that predict the bet-hedging dormancy frequency expected to evolve in each population. Strikingly, the observed dormancy frequencies closely match predictions in their respective localities. We also found that dormancy frequencies vary randomly across generations, likely due to environmental perturbations of the underlying physiological mechanism. Using a model that includes these constraints, we predict the whole distribution of dormancy frequencies whose mean and shape agree with our observed data. Overall, our results suggest that dormancy frequencies have evolved according to local ecological conditions and physiological constraints.

International audienceSpatial synchrony in population dynamics has been identified in most taxonomic groups. Numerous studies have reported varying levels of spatial synchrony among closely-related species, suggesting that species' characteristics may play a role in determining the level of synchrony. However, few studies have attempted to relate this synchrony to the ecological characteristics and/or life-history traits of species. Yet, as to some extent the extinction risk may be related to synchrony patterns, identifying a link between species' characteristics and spatial synchrony is crucial, and would help us to define effective conservation planning. Here, we investigated whether species attributes and temperature synchrony (i.e. a proxy of the Moran effect) account for the differences in spatial population synchrony observed in 27 stream fish species in France. After measuring and testing the level of synchrony for each species, we performed a comparative analysis to detect the phylogenetic signal of these levels, and to construct various multi-predictor models with species traits and temperature synchrony as covariates, while taking phylogenetic relatedness into account. We then performed model averaging on selected models to take model uncertainty into account in our parameter estimates. Fifteen of the 27 species displayed a significant level of synchrony. Synchrony was weak, but highly variable between species, and was not conserved across the phylogeny. We found that some species' characteristics significantly influenced synchrony levels. Indeed, the average model indicated that species associated with greater dispersal abilities, lower thermal tolerance, and opportunistic strategy displayed a higher degree of synchrony. These findings indicate that phylogeny and spatial temperature synchrony do not provide information pertinent for explaining the variations in species' synchrony levels, whereas the dispersal abilities, the life-history strategies and the upper thermal tolerance limits of species do appear to be quite reliable predictors of synchrony levels

Binomial N‐mixture models are commonly applied to analyse population survey data. By estimating detection probabilities, N‐mixture models aim at extracting information about abundances in terms of absolute 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 has hindered evaluations of their performance. We use simulations and a case study to assess the sensitivity of binomial N‐mixture models to overdispersion in abundance and in detection, develop computationally efficient graphical goodness of fit checks to detect it, and evaluate the ability of the checks to identify overdispersion. The simulations show that if the parametric assumptions are not exact the bias in estimated abundances can be severe: underestimation if there is overdispersion in abundance relative to the fitted model and overestimation if there is overdispersion in detection. Our goodness‐of‐fit checks performed well in detecting lack of fit when the abundance distribution was 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 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 about abundance. 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. We show that problems can occur even when estimated detection probabilities are high, and that previously reported problems with negative binomial models cannot always be diagnosed by checking the sensitivity of abundance estimates to numerical cutoff values used in likelihood computations.

Before‐After‐Control‐Impact (BACI) designs are powerful tools to derive inferences about environmental perturbations (e.g., hurricanes, restoration programs) when controlled experimental designs are unfeasible. Applications of BACI designs mostly rely on testing for a significant interaction between periods and treatments (so‐called BACI contrast) to demonstrate the effects of the perturbation. However, significant interactions can emerge for several reasons, including when changes are larger in control sites, such that additional diagnostics must be performed to determine the full complexity of system changes. We propose two measures that detail the nature of change implied by BACI contrasts, along with its uncertainty. CI‐divergence (Control‐Impact divergence) quantifies to what extent control and impact sites have diverged between the after and the before period, whereas CI‐contribution (Control‐Impact contribution) quantifies to what extent the change between periods is stronger in impact sites relative to control sites. To illustrate how these two CI measures can be combined with BACI contrast to gain insights about effects of environmental perturbations, we used count data from the Swedish Breeding Bird Survey to investigate how hurricane Gudrun affected the long‐term abundances of four bird species in forested areas of southern Sweden. Before‐After‐Control‐Impact contrasts suggested the hurricane affected all four species. However, the values of the two CI measures strongly differed, even among species showing similar BACI contrasts. Those differences highlight qualitatively distinct population trajectories between periods and treatments requiring different ecological explanations. Overall, we show that BACI contrasts do not provide the full story in assessing the effects of environmental perturbations. The two CI measures can be used to assist ecological interpretations, or to specify detailed hypotheses about effects of restoration actions to allow stronger confirmatory inference about their outcomes. By providing a framework to develop more detailed explanations and hypotheses about ecological changes, the two CI measures can improve conclusions and strengthen evidence of effects of conservation actions and impact assessments under BACI designs.

Many species distribution models (SDMs) are built with precise but geographically restricted presence–absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen‐science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species’ niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger‐scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger‐scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

One key hypothesis explaining the fate of exotic species introductions posits that the establishment of a self-sustaining population in the invaded range can only succeed within conditions matching the native climatic niche. Yet, this hypothesis remains untested for individual release events. Using a dataset of 979 introductions of 173 mammal species worldwide, we show that climate-matching to the realized native climatic niche, measured by a new Niche Margin Index (NMI), is a stronger predictor of establishment success than most previously tested life-history attributes and historical factors. Contrary to traditional climatic suitability metrics derived from species distribution models, NMI is based on niche margins and provides a measure of how distant a site is inside or, importantly, outside the niche. Besides many applications in research in ecology and evolution, NMI as a measure of native climatic niche-matching in risk assessments could improve efforts to prevent invasions and avoid costly eradications.

The delivery of rigorous and unbiased evidence on the effects of interventions lay at the heart of the scientific method. Here we examine scientific papers evaluating agri-environment schemes, the principal instrument to mitigate farmland biodiversity declines worldwide. Despite previous warnings about rudimentary study designs in this field, we found that the majority of studies published between 2008 and 2017 still lack robust study designs to strictly evaluate intervention effects. Potential sources of bias that arise from the correlative nature are rarely mentioned, and results are still promoted by using a causal language. This lack of robust study designs likely results from poor integration of research and policy, while the erroneous use of causal language and an unwillingness to discuss bias may stem from publication pressures. We conclude that scientific reporting and discussion of study limitations in intervention research must improve and propose some practices toward this goal. K E Y W O R D S agri-environment scheme, before after control impact, biodiversity | causal language, evaluation of conservation interventions, meta-analysis, organic farming, study design, systematic review This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.