Identifying species complexes based on spatial and temporal clustering from joint dynamic species distribution models

Abstract

Abstract Data-limited species are often grouped into a species complex to simplify management. Commonalities between species that may indicate if species can be adequately managed as a complex include the following: shared habitat utilization (e.g., overlapping fine-scale spatial distribution), synchrony in abundance trends, consistent fishing pressure or gear susceptibility, or life history parameters resulting in similar productivity. Using non-target rockfish species in the Gulf of Alaska as a case study, we estimate spatial and temporal similarities among species to develop species complexes using the vector autoregressive spatio-temporal (VAST) model, which is a joint dynamic species distribution model. Species groupings are identified using Ward's hierarchical cluster analysis based on spatial and temporal species correlations. We then compare the spatial and temporal groupings with cluster analysis groupings that use exploitation and life history characteristics data. Based on the results, we conclude that there are some rockfish species that consistently group together, but the arrangement and number of clusters differ slightly depending on the data used. Developing species complexes for fisheries management requires a variety of analytical approaches including species distribution models and cluster analyses, and these can be applied across the full extent of available data sources.