Aim\ud \ud We studied global variation in beta diversity patterns of lake macrophytes using regional data from across the world. Specifically, we examined (1) how beta diversity of aquatic macrophytes is partitioned between species turnover and nestedness within each study region, and (2) which environmental characteristics structure variation in these beta diversity components.\ud Location\ud \ud Global.\ud Methods\ud \ud We used presence–absence data for aquatic macrophytes from 21 regions distributed around the world. We calculated pairwise-site and multiple-site beta diversity among lakes within each region using Sørensen dissimilarity index and partitioned it into turnover and nestedness coefficients. Beta regression was used to correlate the diversity coefficients with regional environmental characteristics.\ud Results\ud \ud Aquatic macrophytes showed different levels of beta diversity within each of the 21 study regions, with species turnover typically accounting for the majority of beta diversity, especially in high-diversity regions. However, nestedness contributed 30–50% of total variation in macrophyte beta diversity in low-diversity regions. The most important environmental factor explaining the three beta diversity coefficients (total, species turnover and nestedness) was elevation range, followed by relative areal extent of freshwater, latitude and water alkalinity range.\ud Main conclusions\ud \ud Our findings show that global patterns in beta diversity of lake macrophytes are caused by species turnover rather than by nestedness. These patterns in beta diversity were driven by natural environmental heterogeneity, notably variability in elevation range (also related to temperature variation) among regions. In addition, a greater range in alkalinity within a region, likely amplified by human activities, was also correlated with increased macrophyte beta diversity. These findings suggest that efforts to conserve aquatic macrophyte diversity should primarily focus on regions with large numbers of lakes that exhibit broad environmental gradients

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We studied community–environment relationships of lake macrophytes at two metacommunity scales using data from 16 regions across the world. More specifically, we examined (a) whether the lake macrophyte communities respond similar to key local environmental factors, major climate variables and lake spatial locations in each of the regions (i.e., within-region approach) and (b) how well can explained variability in the community–environment relationships across multiple lake macrophyte metacommunities be accounted for by elevation range, spatial extent, latitude, longitude, and age of the oldest lake within each metacommunity (i.e., across-region approach). In the within-region approach, we employed partial redundancy analyses together with variation partitioning to investigate the relative importance of local variables, climate variables, and spatial location on lake macrophytes among the study regions. In the across-region approach, we used adjusted R2 values of the variation partitioning to model the community–environment relationships across multiple metacommunities using linear regression and commonality analysis. We found that niche filtering related to local lake-level environmental conditions was the dominant force structuring macrophytes within metacommunities. However, our results also revealed that elevation range associated with climate (increasing temperature amplitude affecting macrophytes) and spatial location (likely due to dispersal limitation) was important for macrophytes based on the findings of the across-metacommunities analysis. These findings suggest that different determinants influence macrophyte metacommunities within different regions, thus showing context dependency. Moreover, our study emphasized that the use of a single metacommunity scale gives incomplete information on the environmental features explaining variation in macrophyte communities.Electronic supplementary materialThe online version of this article (10.1007/s00442-018-4294-0) contains supplementary material, which is available to authorized users.

Questions What are the geographic patterns of γ‐diversity of aquatic plants and what are the main driving factors? Are richness trends for aquatic plants similar to total plant richness? Is the Mediterranean area a hot spot for aquatic plants? Location Europe and the Mediterranean Basin. Material We listed vascular aquatic plant presence or absence for 44 countries. We also compiled total plant species richness and geographic and environmental variables for each country. Methods We first analysed country ordination based on their aquatic flora constrained by environmental variables (dbRDA), and selected the environmental variables best explaining species patterns (BEST analysis). Total species richness patterns were studied using maps and latitudinal gradients. We used generalized additive models (GAM) to detect the main environmental factors driving species richness, both for aquatic plants and total plants. Results The BEST analysis identified a single variable that best explains aquatic plant species distribution: evapotranspiration. However, richness of aquatic plants vs latitude varies and no clear trend was observed. No relation was found between total plant and aquatic plant richness. Aquatic and total plant richness peak between 40° and 50°N, and values were intermediate at low latitudes. GAM related aquatic plant richness with water resources and rainfall, while total plant richness is mainly driven by evapotranspiration and temperature. Hydrophytes were relatively more abundant at higher latitudes than helophytes and the ratio correlated with evapotranspiration. Conclusion Southern and western Europe hold the highest aquatic plant diversity, although no clear latitudinal species richness patterns were found. Aquatic plant richness is mainly driven by water‐related variables. Total plant richness exhibits a latitudinal pattern influenced by the Sahara desert, which depresses richness at low latitudes. Best predictors of total plant richness patterns are water–energy variables.