MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

Aim Species distribution modelling, a family of statistical methods that predicts species distributions from a set of occurrences and environmental predictors, is now routinely applied in many macroecological studies. However, the reliability of evaluation metrics usually employed to validate these models remains questioned. Moreover, the emergence of online databases of environmental variables with global coverage, especially climatic, has favoured the use of the same set of standard predictors. Unfortunately, the selection of variables is too rarely based on a careful examination of the species' ecology. In this context, our aim was to highlight the importance of selecting ad hoc variables in species distribution models, and to assess the ability of classical evaluation statistics to identify models with no biological realism. Innovation First, we reviewed the current practices in the field of species distribution modelling in terms of variable selection and model evaluation. Then, we computed distribution models of 509 European species using pseudo‐predictors derived from paintings or using a real set of climatic and topographic predictors. We calculated model performance based on the area under the receiver operating curve (AUC) and true skill statistics (TSS), partitioning occurrences into training and test data with different levels of spatial independence. Most models computed from pseudo‐predictors were classified as good and sometimes were even better evaluated than models computed using real environmental variables. However, on average they were better discriminated when the partitioning of occurrences allowed testing for model transferability. Main conclusions These findings confirm the crucial importance of variable selection and the inability of current evaluation metrics to assess the biological significance of distribution models. We recommend that researchers carefully select variables according to the species' ecology and evaluate models only according to their capacity to be transfered in distant areas. Nevertheless, statistics of model evaluations must still be interpreted with great caution.

Climate and land-use change are recognised as the two main drivers of the ongoing reorganisation of Earth's biodiversity, but understanding precisely their role in shaping species' distributions and communities remains challenging. In mountainous regions, we typically observe an uphill shift of species' altitudinal ranges caused by increasing temperatures, but it is difficult to predict how this process interacts with land-use change. Here, we replicated an inventory of bumblebees that took place in the 1960s in Norway. Focusing on subalpine areas, we reported changes in species richness and community temperature index (CTI), a measure of the relative proportion of warm-and cold-adapted species, at low and high altitude. Using aerial photographs and meteorological data, we tested the relationship between climate and land-cover changes and changes in species richness and CTI. We observed an overall increase in CTI consistent with a gradual species turnover driven by climate change. There was on average an increase in species richness at high altitudes, while low-altitudes communities tended to become less species-rich. Moreover, we observed a negative correlation between species richness and temperature and precipitation trends, suggesting a detrimental effect of climate change. Thanks to the replication of an historical inventory, we were able to show evidence for an effect of climate, and possibly land-cover, change on subalpine bumblebee assemblages. These results can contribute to a better understanding of the processes driving biodiversity changes in subalpine areas in a context of global climate and landscape changes.

Identifying local adaptation is crucial in conservation biology to define ecotypes and establish management guidelines. Local adaptation is often inferred from the detection of loci showing a high differentiation between populations, the so-called FST outliers. Methods of detection of loci under selection are reputed to be robust in most spatial population models. However, using simulations we showed that FST outlier tests provided a high rate of false-positives (up to 60%) in fractal environments such as river networks. Surprisingly, the number of sampled demes was correlated with parameters of population genetic structure, such as the variance of FST s, and hence strongly influenced the rate of outliers. This unappreciated property of river networks therefore needs to be accounted for in genetic studies on adaptation and conservation of river organisms.

No abstract

No abstract

Aim: We test whether geographical variation in the length of appendages in rodent species follows predictions of Allen's rule (a positive relationship between appendage length and temperature) at a broad taxonomic scale (order Rodentia). We also test whether the applicability of this rule varies based on the unit of analysis (species or assemblage), the appendage examined (tail, hind foot, ear), body size, occupied habitat, geographical range size, life mode and saltatorial ability. Location: Worldwide. Time period: Current. Major taxa studied: Rodents (order Rodentia). Methods: We assembled data on the morphology, ecology and phylogeny for ≤ 2,212 rodent species, representing c. 86% of all the described rodent species and c. 95% of the described genera. We tested the predicted Allen's rule associations among sizecorrected appendage lengths and both latitudinal and climatic variables (temperature and precipitation). We applied a cross-species approach based on phylogenetic regressions and a cross-assemblage approach based on spatial regressions in equalarea 1.5° grid cells. Results: Support for Allen's rule was greatest for the tail and was stronger across assemblages than across species. We detected a negative relationship between tail length and (absolute) latitude, which was accounted for by a positive association between tail length and temperature of the coldest month. This association was greatest in desert species. In addition, we observed a negative relationship between ear length and precipitation. Main conclusions: In rodents, Allen's rule is confirmed only for tails, and this association seems to be driven by adaptation to the cold, rather than warm temperatures. Habitat type seems to influence conformity to this rule. Conformity to Allen's rule is likely to be the result of complex evolutionary trade-offs between temperature regulation and other essential species traits.

Habitat fragmentation may present a major impediment to species range shifts caused by climate change, but how it affects local community dynamics in a changing climate has so far not been adequately investigated empirically. Using long‐term monitoring data of butterfly assemblages, we tested the effects of the amount and distribution of semi‐natural habitat (SNH), moderated by species traits, on climate‐driven species turnover. We found that spatially dispersed SNH favoured the colonisation of warm‐adapted and mobile species. In contrast, extinction risk of cold‐adapted species increased in dispersed (as opposed to aggregated) habitats and when the amount of SNH was low. Strengthening habitat networks by maintaining or creating stepping‐stone patches could thus allow warm‐adapted species to expand their range, while increasing the area of natural habitat and its spatial cohesion may be important to aid the local persistence of species threatened by a warming climate.