Improving the productivity-susceptibility analysis to assess data-limited fisheries

摘要

Each year, millions of marine aquarium fish and invertebrates are harvested from coral reefs and enter the complex and largely unregulated marine aquarium trade (MAT). It is challenging to identify species at risk of overexploitation in this trade due to its data-limited and poorly monitored nature. We developed a new analytical approach based on a productivity- susceptibility analysis (PSA) to assess the vulnerability of wild-captured marine aquarium fish. The PSA was originally developed to assess food fisheries; however, species and operational characteristics between food fisheries and the MAT differ. Thus, we improved a prior PSA framework to assess the data-limited MAT through customization of productivity and susceptibility factors to align with the target fishery, improved data binning, calculation of susceptibility, and characterization of the vulnerability scores. Our vulnerability results align well with the most recent IUCN assessments, showing improved accuracy using this revised PSA compared to prior adaptions of the PSA to the MAT. Further, we show that this PSA approach can be used to assess species on either a global or country-specific scale. A Gaussian mixture model clustering algorithm was applied to the PSA results to objectively classify fish along a sustainability continuum. Among 32 species, a majority of species clustered as highly sustainable or sustainable indicating little management or over-harvest concern; however, the Bangaii cardinalfish Pterapogon kauderni and blue tang Paracanthurus hepatus indexed as unsustainable. This novel PSA method, and use of a clustering algorithm to classify results, provides a predictive tool for a wide range of fisheries. In addition to informing species management plans, the compilation of sustainability status data generated by our PSA can inform a consumer guide, allowing consumers and other stakeholders to make sustainable decisions when purchasing fish.