Global patterns in the metacommunity structuring of lake macrophytes: regional variations and driving factors.

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.

Nitrogen stable isotopes in primary uptake compartments across streams differing in nutrient availability.

High variability in the natural abundance of nitrogen stable isotopes (δ(15)N) has been reported for primary uptake compartments (PUCs; e.g., epilithon, filamentous algae, bryophytes, macrophytes) in human-impacted aquatic ecosystems, but the origin of this variability is not yet well understood. We examined how δ(15)N of different PUC types relate to δ(15)N of dissolved inorganic nitrogen (DIN) species (nitrate and ammonium) and to the stream nutrient concentrations in which they grow. We selected 25 reaches located across the fluvial network of La Tordera catchment (NE Spain, 868.5 km(2)), encompassing a gradient of human pressures from headwaters to the river valley. δ(15)N-PUC variability was mostly explained by location within the fluvial network and was strongly related to the δ(15)N of DIN species, especially of ammonium. Models were stronger for PUCs growing within the stream channel and thus using streamwater as their main source of nutrients. Regression models including nutrient concentrations improved the prediction power for δ(15)N-PUCs, suggesting that nutrient concentrations and stoichiometry cannot be ignored in explaining the natural abundance of nitrogen isotopes in PUCs. These results provide insights into what controls variability in δ(15)N of PUCs within a stream network, with implications for the application of stables isotopes as an ecological tool.

The optimal sampling design for littoral habitats modelling: A case study from the north-western Mediterranean.

Species distribution models (SDMs) have been used to predict potential distributions of habitats and to model the effects of environmental changes. Despite their usefulness, currently there is no standardized sampling strategy that provides suitable and sufficiently representative predictive models for littoral marine benthic habitats. Here we aim to establish the best performing and most cost-effective sample design to predict the distribution of littoral habitats in unexplored areas. We also study how environmental variability, sample size, and habitat prevalence may influence the accuracy and performance of spatial predictions. For first time, a large database of littoral habitats (16,098 points over 562,895 km of coastline) is used to build up, evaluate, and validate logistic predictive models according to a variety of sampling strategies. A regularly interspaced strategy with a sample of 20% of the coastline provided the best compromise between usefulness (in terms of sampling cost and effort) and accuracy. However, model performance was strongly depen upon habitat characteristics. The proposed sampling strategy may help to predict the presence or absence of target species or habitats thus improving extensive cartographies, detect high biodiversity areas, and, lastly, develop (the best) environmental management plans, especially in littoral environments.