| Summary: | One of the key challenges to understanding patterns of β diversity is to disentangle deterministic patterns from stochastic ones. Stochastic processes may mask the influence of deterministic factors on community dynamics, hindering identification of the mechanisms causing variation in community composition. We studied temporal β diversity (among-year dissimilarity) of macroinvertebrate communities in near-pristine boreal streams across 14 years. To assess whether the observed β diversity deviates from that expected by chance, and to identify processes (deterministic vs. stochastic) through which different explanatory factors affect community variability, we used a null model approach. We observed that at the majority of sites temporal β diversity was low indicating high community stability. When stochastic variation was unaccounted for, connectivity was the only variable explaining temporal β diversity, with weakly connected sites exhibiting higher community variability through time. After accounting for stochastic effects, connectivity lost importance, suggesting that it was related to temporal β diversity via random colonization processes. Instead, β diversity was best explained by in-stream vegetation, community variability decreasing with increasing bryophyte cover. These results highlight the potential of stochastic factors to dampen the influence of deterministic processes, affecting our ability to understand and predict changes in biological communities through time. Long-term monitoring data of benthic stream invertebrate communitiesData includes annual monitoring data of benthic stream invertebrates collected from 23 near-pristine streams in northern Finland. Sampling was conducted during 14 consecutive years (2000 to 2013), always by the same field crew. A 2-min kick-net sampling was used. Samples were preserved in the field and later processed in the laboratory. All individuals were sorted, counted and identified to the lowest feasible level. Data in repository presents original abundance data for ...
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