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Environmental factors impact species richness differently across taxonomic groups, and understanding the geographic patterns and drivers influencing alpine plant richness remains limited. This study compiled global distribution data of 404 species of Gentiana, an alpine genus, and analyzed the relative effects of different environmental factors and several previously proposed models on the variation of Gentiana richness. By evaluating the effects of range size and regions on the relationships between Gentiana richness and environmental factors, we found that all tested environmental factors had weak effects on richness variation for all species and wide‐ranging species, while habitat heterogeneity was the best predictor for narrow‐ranging species. Habitat heterogeneity was the main driver of richness variation in Europe and Asia, but not in North America. The multiple regression model that included variables for energy, water, seasonality, habitat heterogeneity and past climate change had the highest explanatory power, but it still explained less than 50% of the variation in species richness for all Gentiana species at both global and regional scale, except for Europe. The limited explanatory power of environmental factors in explaining species richness patterns for all species, along with the variations observed among regions, suggest that other factors, such as evolutionary processes and biogeographic history may have also influenced the geographic patterns of Gentiana species richness. In conclusion, our results indicate a limited influence of climate factors on alpine species richness, while habitat heterogeneity, along with its impacts on speciation and dispersal, likely play significant roles in shaping the richness of alpine Gentiana species. [ABSTRACT FROM AUTHOR]

Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model ( GLM) and boosted regression trees ( BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian-based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co-occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes' distribution under eutrophication stress. [ABSTRACT FROM AUTHOR]