A meta-assessment for elasmobranchs based on dietary data and Bayesian networks

We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.