Aim: β-Diversity, commonly defined as the compositional variation among localities that links local diversity (α‐diversity) and regional diversity (γ‐diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e. species replacement) and the nestedness of assemblages (i.e. species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream β-diversity along elevation gradients, and at contributing to the rich debate on null model approaches to identify non-random patterns in the distribution of taxa.

Location: Swiss Alpine region.

Methods: Based on presence-absence data of 78 stream invertebrate families from 309 sites, we analyzed the effect size of non-random spatial distribution of stream invertebrates on the β-diversity and its two components (i.e. turnover and nestedness). We used a modelling framework that allows exploring the complete range of existing algorithm sused in null model analysis, and to assess how distribution patterns vary according to an array of possible ecological assumptions.

Results: Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings.

Main conclusions: Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of non-random spatial distribution of taxa on β-diversity.

The surveys were carried out across the Swiss Alpine region between 2013 and 2017 within the framework of the Swiss Biodiversity Monitoring Program (BDM). Standard sampling within streams occurred during the optimal period in terms of invertebrate diversity, from March to June depending on the elevation. Each site was assigned to one of four bioclimatic stages, which were based on mean annual temperature and distributed along the altitudinal gradient.

R (v.4.1.1) was used for data analysis, including the following packages: betapart (v.1.5.4), plyr (v.1.8.6), igraph (v.1.2.9), reshape2 (v.1.4.4), foreach (v.1.5.1), doParallel (v.1.0.16), ggplot2 (v.3.3.5), ggpubr (v.0.4.0), cowplot (v.1.1.1), viridis (v.0.6.2). For the 'Tuning Peg' algorithm, we used a slightly modified version of the R script from Strona et al. (2018), in order to take into account the β-diversity indices from Baselga (2010).

Strona, G., Ulrich, W. and Gotelli, N.J. (2018), Bi-dimensional null model analysis of presence-absence binary matrices. Ecology, 99: 103-115. https://doi.org/10.1002/ecy.2043

Baselga, A. (2010), Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19: 134-143. https://doi.org/10.1111/j.1466-8238.2009.00490.x