Résumé: Questions Forest fragmentation affects biodiversity locally (alpha diversity) and beyond – at relatively larger scales (gamma diversity) – by increasing dispersal and recruitment limitations. Yet, does an increase in fragmentation affect the relationship between alpha and gamma diversity and what can we learn from it? Location Northern France. Methods We surveyed 116 forest patches across three fragmentation levels: none (continuous forest); intermediate (forest patches connected by hedgerows); and high (isolated forest patches). Plant species richness of both forest specialists and generalists was surveyed at five nested spatial resolutions across each forest patch: 1 m(2); 10 m(2); 100 m(2); 1,000 m(2); and total forest patch area. First, we ran log-ratio models to quantify the alpha-gamma relationship. We did that separately for all possible combinations of fragmentation level (none vs intermediate vs high) x spatial scale (e.g., alpha-1 m(2) vs gamma-10 m(2)) x species type (e.g., alpha-specialists vs gamma-specialists). We then used linear mixed-effects models to analyze the effect of fragmentation level, spatial scale, species type and all two-way interaction terms on the slope coefficient extracted from all log-ratio models. Results We found an interaction effect between fragmentation level and species type, such that forest specialists shifted from a linear (i.e., proportional sampling) to a curvilinear plateau (i.e., community saturation) relationship at low and high fragmentation, respectively, while generalists shifted from a curvilinear to a linear pattern. Conclusions The impact of forest fragmentation on the alpha-gamma relationship supports generalist species persistence over forest specialists, with contrasting mechanisms for these two guilds. As fragmentation increases, forest specialists shift from proportional sampling towards community saturation, thus reducing alpha diversity likely due to dispersal limitation. Contrariwise, generalists shift from community saturation towards proportional sampling, thus increasing alpha diversity likely due to an increase in the edge:core ratio. To ensure long-term conservation of forest specialists, one single large forest patch should be preferred over several small ones.

Résumé: AimTo assess contemporary and historical determinants of taxonomic and ecological trait turnover in birds worldwide. We tested whether taxonomic and trait turnover (1) are structured by regional bioclimatic conditions, (2) increase in relationship with topographic heterogeneity and environmental turnover and change according to current and historical environmental conditions, and (3) decrease with human impact. Major TaxaBirds. LocationGlobal. MethodsWe used computationally efficient algorithms to map the taxonomic and trait turnover of 8,040 terrestrial bird assemblages worldwide, based on a grid with 110km x 110 km resolution overlaid on the extent-of-occurrence maps of 7,964 bird species, and nine ecological traits reflecting six key aspects of bird ecology (diet, habitat use, thermal preference, migration, dispersal and body size). We used quantile regression and model selection to quantify the influence of biomes, environment (temperature, precipitation, altitudinal range, net primary productivity, Quaternary temperature and precipitation change) and human impact (human influence index) on bird turnover. ResultsBird taxonomic and trait turnover were highest in the north African deserts and boreal biomes. In the tropics, taxonomic turnover tended to be higher, but trait turnover was lower than in other biomes. Taxonomic and trait turnover exhibited markedly different or even opposing relationships with climatic and topographic gradients, but at their upper quantiles both types of turnover decreased with increasing human influence. Main conclusionsThe influence of regional, environmental and anthropogenic factors differ between bird taxonomic and trait turnover, consistent with an imprint of niche conservatism, environmental filtering and topographic barriers on bird regional assemblages. Human influence on these patterns is pervasive and demonstrates global biotic homogenization at a macroecological scale.

Résumé: Local species richness (SR) is a key characteristic affecting ecosystem functioning. Yet, the mechanisms regulating phytoplankton diversity in freshwater ecosystems are not fully understood, especially in peri-urban environments where anthropogenic pressures strongly impact the quality of aquatic ecosystems. To address this issue, we sampled the phytoplankton communities of 50 lakes in the Paris area (France) characterized by a large gradient of physico-chemical and catchment-scale characteristics. We used large phytoplankton datasets to describe phytoplankton diversity patterns and applied a machine-learning algorithm to test the degree to which species richness patterns are potentially controlled by environmental factors. Selected environmental factors were studied at two scales: the lake-scale (e.g. nutrients concentrations, water temperature, lake depth) and the catchment-scale (e.g. catchment, landscape and climate variables). Then, we used a variance partitioning approach to evaluate the interaction between lake-scale and catchment-scale variables in explaining local species richness. Finally, we analysed the residuals of predictive models to identify potential vectors of improvement of phytoplankton species richness predictive models. Lake-scale and catchment-scale drivers provided similar predictive accuracy of local species richness (R2 = 0.458 and 0.424, respectively). Both models suggested that seasonal temperature variations and nutrient supply strongly modulate local species richness. Integrating lake- and catchment-scale predictors in a single predictive model did not provide increased predictive accuracy; therefore suggesting that the catchment-scale model probably explains observed species richness variations through the impact of catchment-scale variables on in-lake water quality characteristics. Models based on catchment characteristics, which include simple and easy to obtain variables, provide a meaningful way of predicting phytoplankton species richness in temperate lakes. This approach may prove useful and cost-effective for the management and conservation of aquatic ecosystems.