Are we failing to protect threatened mangroves in the Sundarbans world heritage ecosystem?

The Sundarbans, the largest mangrove ecosystem in the world, is under threat from historical and future human exploitation and sea level rise. Limited scientific knowledge on the spatial ecology of the mangroves in this world heritage ecosystem has been a major impediment to conservation efforts. Here, for the first time, we report on habitat suitability analyses and spatial density maps for the four most prominent mangrove species - Heritiera fomes, Excoecaria agallocha, Ceriops decandra and Xylocarpus mekongensis. Globally endangered H. fomes abundances declined as salinity increased. Responses to nutrients, elevation, and stem density varied between species. H. fomes and X. mekongensis preferred upstream habitats. E. agallocha and C. decandra preferred down-stream and mid-stream habitats. Historical harvesting had negative influences on H. fomes, C. decandra and X. mekongensis abundances. The established protected area network does not support the most suitable habitats of these threatened species. We therefore recommend a reconfiguration of the network to include these suitable habitats and ensure their immediate protection. These novel habitat insights and spatial predictions can form the basis for future forest studies and spatial conservation planning, and have implications for more effective conservation of the Sundarbans mangroves and the many other species that rely on them

Snow cover is a neglected driver of Arctic biodiversity loss

Snow has far-reaching effects on ecosystem processes and biodiversity in high-latitude ecosystems, but these have been poorly considered in climate change impact models1,2. Here, to forecast future trends in species occurrences and richness, we fitted species-environment models with temperature data from three climate scenarios and simulated up to a 40% decrease in snow cover duration (SCD)3. We used plot-scale data on 273 vascular plant, moss and lichen species in 1,200 study sites spanning a wide range of environmental conditions typical for mountainous Arctic landscapes (within 165 km2). According to the models, a rise in temperature increased overall species richness and caused only one species to lose all suitable habitat. In contrast, a shorter SCD tempered the effect of increasing temperature on species richness and led to accelerated rates of species’ local extinctions after a tipping point at 20-30% SCD decrease. All three species groups showed similar extinction rates but contrasting species richness responses. Our simulations indicate that future biodiversity patterns in Arctic regions are highly dependent on the evolution of snow conditions. Climate impact models that ignore the effects of snow cover change may provide biased biodiversity projections, with potentially erratic implications for Arctic nature conservation planning.Peer reviewe

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels

A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study