Pacific-wide bigeye thresher shark (Alopias superciliosus) sustainability status assessment: introduction, datasets and methodology

Fu D, Roux M-J, Clarke S, et al (2016) Pacific-wide bigeye thresher shark (Alopias superciliosus) sustainability status assessment: introduction, datasets and methodology. WCPFC, Bali, Indonesia

The bigeye thresher shark, Alopias superciliosus, has been identified as one of the least productive pelagic sharks and there is concern regarding its conservation status. Although it is one of three thresher sharks designated by WCPFC as key shark species, no stock assessment has been conducted due to information gaps and changes in reporting and observer coverage over time and space, which do not yet support a traditional approach to stock assessment. As an alternative to gain new insights into the sustainability status of bigeye thresher shark in relation to pelagic longline fisheries in the Pacific, this study applies a spatially explicit and quantitative sustainability risk assessment to available data. The analytical framework evaluates sustainability risk as the ratio of current impacts from fisheries (spatially-explicit and cumulative fishing mortality F) to a maximum impact sustainable threshold (MIST) reference point based on population productivity. This approach differs from traditional stock assessments, in which F is compared to estimates of population abundance. The risk assessment approach evaluates F in terms of whether the population’s ability to withstand fishing pressure is exceeded, rather than evaluating biomass (B) and whether the population is overfished. The assessment is constrained by the available data and by some aspects of the methodology which are currently being addressed. Key components (and analytical procedures) include: 1) estimation of the species distribution or relative abundance in space; 2) calibration of population and fishery groups catchability; and 3) estimation of the maximum intrinsic population growth rate r for the species, using available life history data. The first two are used in conjunction with commercial effort (logsheet) data to quantify fishing impact. The third is used to define the MIST reference point. Observer data from the Pacific Community (SPC), United States (US) and Japan were standardized with two models, a zero-inflated negative binomial (ZINB) model and a geo-statistical delta-generalised linear mixed (delta-GLMM) model, which permitted derivation of spatial indices of relative abundance over different but overlapping areas. Population catchability (q) is statistically calibrated using a Bayesian state-space biomass dynamics model (BDM) fitted to time series of relative abundance and annual catch estimates obtained using a representative subset of the observer data. This approach is under development and serves to estimate a plausible range of values for q, which are then adjusted spatially by fishing season and targeting strategy (i.e., ‘fishery groups’). Relative F are calculated as the sum product of total effort and fishery-group specific catchability in 5x5 degree cells, weighted by the relative density of bigeye thresher shark in each cell, as obtained from the spatial standardization. Post-capture survival is not taken into account in the present assessment. The strengths and value of a spatially-explicit, sustainability risk assessment framework reside in data integration from multiple sources and the ability to map relative fishing impact and sustainability risk spatially and among fishery sectors, with uncertainty.