A simulation‐based approach to understand how metacommunity characteristics influence emergent biodiversity patterns |
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| Authors: | Eric R Sokol Bryan L Brown J E Barrett |
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| Affiliation: | 1. Dept of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA;2. INSTAAR, Univ. of Colorado, Boulder, CO, USA |
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| Abstract: | To understand controls over biodiversity, it is necessary to take a multi‐scale approach to understand how local and regional factors affect the community assembly processes that drive emergent patterns. This need is reflected in the growing use of the metacommunity concept to interpret multi‐scale measures of biodiversity, including metrics derived from diversity partitioning (e.g. α, β and γ diversity) and variation partitioning (e.g. spatial and environmental components of compositional turnover) techniques. However, studies have shown limited success using these metrics to characterize underlying community assembly dynamics. Here we demonstrate how a metacommunity simulation package (MCSim) can be used to evaluate when and how biodiversity metrics can be used to make inferences about metacommunity characteristics. We examined a wide range of parameter settings representing ecologically relevant scenarios. We used artificial neural networks (ANNs) to assess the sensitivity of diversity and variation partitioning metrics (calculated from simulation outcomes) to metacommunity parameter settings. In the scenarios examined in this study, the niche‐neutral gradient strongly influenced most biodiversity metrics, metacommunity size exhibited a marginal influence over some metrics, and dispersal dynamics only affected a subset of variation partitioning outcomes. Variation partitioning response curves along the niche‐neutral gradient were not monotonic; however, simulation outcomes suggest other biodiversity metrics (e.g. dissimilarity saturation) can be used in combination with variation partitioning metrics to make inferences about metacommunity properties. With the growing availability of archived ecological data, we expect future work will apply simulation‐based techniques to better understand links between biodiversity and the metacommunity characteristics that are presumed to control the underlying community assembly processes. |
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