Modular Structure in Fish Co-occurrence Networks: A Comparison across Spatial Scales and Grouping Methodologies
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Network modules are used for diverse purposes, ranging from delineation of biogeographical provinces to the study of biotic interactions. We assess spatial scaling effects on modular structure, using a multi-step process to compare fish co-occurrence networks at three nested scales. We first detect modules with simulated annealing and use spatial clustering tests (interspecific distances among species' range centroids) to determine if modules consist of species with broadly overlapping ranges; strong spatial clustering may reflect environmental filtering, while absence of spatial clustering may reflect positive interspecific relationships (commensalism or mutualism). We then use non-hierarchical, multivariate cluster analysis as an alternative method to identify fish subgroups, we repeat spatial clustering tests for the multivariate clusters, then compare spatial clustering results among modules and clusters. Next, we compare species lists within modules and clusters, and estimate congruence as the proportion of species assigned to the same groups by the two methods. Finally, we use a well-documented nest associate complex (fishes that deposit eggs in the gravel nests of a common host) to assess whether strong within-group associations may, in fact, reflect positive interspecific relationships. At each scale, 2-4 network modules were detected but a consistent relationship between scale and the number of modules was not observed. Significant spatial clustering was detected at all scales for network modules and multivariate clusters but was less prevalent at smaller scales. Congruence between modules and clusters was always < 90% and generally decreased as the number of groups increased. At all scales, the complete nest associate complex was completely preserved within a single network module, but not within a single multivariate cluster. Collectively, our results suggest that network modules are promising tools for studying positive interactions and that smaller scales may be preferable in this research.