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Metacommunity ecology includes connectivity in the investigation of local and regional processes to understand community assembly. The elements of metacommunity structure (EMS) framework classifies metacommunities into categorical archetypes based on patterns of three metrics indexing β diversity: coherence, turnover, and boundary clumping. Although the EMS framework is most commonly used to classify metacommunity types, an elements‐based approach of examining how factors affect each specific continuous EMS variable could provide a more mechanistic understanding of how metacommunities are structured at the landscape scale. Moreover, few studies have sought to quantify how methodological issues such as number and spacing of local communities affect observed outcomes of EMS‐based analyses. Using a large dataset of stream fish occurrences in the eastern U.S.A., we used mixed effects models to investigate how (1) landscape‐scale variables and (2) methodological issues such as number and location of sampling sites affect coherence, turnover, and boundary clumping individually; as well as (3) how landscape‐scale variables influence overall metacommunity patterns derived from the EMS framework. Coherence, turnover, and boundary clumping were related to temperature, density of dams, developed land use, and γ diversity. Interestingly, distance among sampled sites in metacommunities negatively affected turnover, and the number of sampling sites positively affected all three variables. Elevation affected overall observed metacommunity patterns, with metacommunities transitioning from Clementsian to clumped species loss patterns with increasing elevation. Our results suggest that metacommunity structure is affected by both natural and anthropogenic landscape‐scale variables. Observed metacommunity properties are also influenced by sampling density and site location within the catchment. Accounting for important natural, anthropogenic, and methodological issues will be critical for improving the inferential power of metacommunity analyses to begin understanding which landscape‐scale variables should be the focus of conservation and management of fish communities at a catchment scale.

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IntroductionBeta diversity represents changes in community composition among locations across a landscape. While the effects of human activities on beta diversity are becoming clearer, few studies have considered human effects on the three dimensions of beta diversity: taxonomic, functional, and phylogenetic. Including anthropogenic factors and multiple dimensions of biodiversity may explain additional variation in stream fish beta diversity, providing new insight into how metacommunities are structured within different spatial delineations.MethodsIn this study, we used a 350 site stream fish abundance dataset from South Carolina, United States to quantify beta diversity explainable by spatial, natural environmental, and anthropogenic variables. We investigated three spatial delineations: (1) a single whole-state metacommunity delineated by political boundaries, (2) two metacommunities delineated by a natural geomorphic break separating uplands from lowlands, and (3) four metacommunities delineated by natural watershed boundaries. Within each metacommunity we calculated taxonomic, functional, and phylogenetic beta diversity and used variation partitioning to quantify spatial, natural environmental, and anthropogenic contributions to variations in beta diversity.ResultsWe explained 25–81% of the variation in stream fish beta diversity. The importance of these three factors in structuring metacommunities differed among the diversity dimensions, providing complementary perspectives on the processes shaping beta diversity in fish communities. The effect of spatial, natural environmental, and anthropogenic factors varied among the spatial delineations, which indicate conclusions drawn from variation partitioning may depend on the spatial delineation chosen by researchers.DiscussionOur study highlights the importance of considering human effects on metacommunity structure, quantifying multiple dimensions of beta diversity, and careful consideration of user-defined metacommunity boundaries in beta diversity analyses.

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When investigating metacommunity dynamics, functional differences among species are often assumed to be as important as environmental differences between sites in determining β‐diversity. However, few studies have examined the influence of functional diversity on β‐diversity. We examine the relative importance of regional functional diversity partitioned by niche dimensions and environmental variation in structuring taxonomic β‐diversity of stream fishes using a large dataset of stream fish assemblages (hereafter, simply β‐diversity). We predicted that both functional diversity and environmental variation play a role in determining β‐diversity. We tested this prediction by modelling the patterns of stream fish β‐diversity as a function of environmental variation, functional diversity and γ‐richness across 10,220 sites for 329 fish species using a series of conceptual path models. Environmental variation consistently affected β‐diversity across all models, whereas functional diversity and γ‐richness influenced β‐diversity only in some models. We show that including relevant trait differences among species in path models can improve their ability to explain β‐diversity, suggesting that functional traits influence β‐diversity. The ability of path models to explain β‐diversity varied depending on the trait grouping included in the model, demonstrating that specific path models representing different niche dimensions can improve the ability of a model to explain β‐diversity. In addition, parsing traits into different niche dimensions revealed alternative patterns of functional diversity–β‐diversity relationships that otherwise would have been missed. The selection of relevant traits and linked niche dimensions is critical for detecting relationships between functional diversity and β‐diversity. Using traits associated with different niche dimensions allows for the identification of niche dimensions most strongly associated with species sorting and the detection of patterns missed by focusing on a single niche dimension. Determining the niche dimensions that influence β‐diversity could provide insights into the processes driving biodiversity and metacommunity dynamics, improving our ability to conserve or restore aquatic communities.

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