Ecosphere (Aug 2022)

Hay meadows' overriding effect shapes ground beetle functional diversity in mountainous landscapes

  • Mauro Gobbi,
  • Luca Corlatti,
  • Marco Caccianiga,
  • Cajo J. F. ter Braak,
  • Luca Pedrotti

DOI
https://doi.org/10.1002/ecs2.4193
Journal volume & issue
Vol. 13, no. 8
pp. n/a – n/a

Abstract

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Abstract Mountain regions are hotspots of biodiversity, and are particularly sensitive to human activities and global changes. Characterizing biodiversity using trait‐based approaches may improve the understanding of the evolutionary and mechanistic basis of ecological patterns in species distribution. The investigation of trait–environment relationships, however, is challenging, especially when a hierarchical sampling design is used, as this may lead to misidentification of associations. Here, we investigate how functional traits in ground beetles (Coleoptera: Carabidae), a taxon often used as a bioindicator of climate and environmental changes, vary with environmental features in a mountainous landscape. The study was conducted in the Stelvio National Park (Central Italian Alps), collecting individuals with pitfall traps deployed within plots (small spatial scale), located along altitudinal transects (large spatial scale). To investigate the trait–environment association, we used double constrained correspondence analysis, which permits the selection of important traits and environmental variables while accounting for the hierarchical structure of the sampling design. The trait–environment association was largely one‐dimensional, with hay meadow acting as main environmental driver, negatively related to brachypterous wing‐form (indicator of poor dispersal ability) and, to a lesser extent, to specialized diet and (only for the large scale) body length. Secondarily, these traits were related negatively to soil pH and, for the larger spatial scale, positively to canopy cover and elevation. Double constrained correspondence analysis with specialized permutation schemes for statistical testing was effective and robust to analyze the data of the hierarchical sampling.

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