Untangling multi-species fisheries data with species distribution models

Abstract

Long-term trends in fisheries catch are useful to monitor effects of fishing on wild populations. However, fisheries catch data are often aggregated in multi-species complexes, complicating assessments of individual species. Non-target species are often grouped together in this way, but this becomes problematic when increasingly common shifts toward targeting incidental species demand closer management focus at the species level. Species distribution models (SDMs) offer an under-utilised tool to allocate aggregated catch data among species for individual assessments. Here, we present a case study of two shovel-nosed lobsters (Thenus spp.), previously caught incidentally and recorded together in logbook records, to illustrate the design and use of catch allocation SDMs to untangle multi-species data for stock assessments of individual species. We demonstrate how catch allocation SDMs reveal previously masked species-specific catch trends from aggregated data and can identify shifts in fishing behaviour, e.g., changes in target species. Finally, we review key assumptions and limitations of this approach that may arise when applied across a broad geographic or taxonomic scope. Our aim is to provide a template to assist researchers and managers seeking to assess stocks of individual species using aggregated multi-species data.