Modelling the distribution of vulnerable skate from fisheries dependent data using imperfect detection

Progress in Oceanography(2022)

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摘要
Little is still known about the biology and ecology of many elasmobranchs which often inhibits species specific management measures from being implemented. The primary aim of this study was to improve the knowledge on the distribution and habitat use of the threatened and data deficient shagreen ray, Leucoraja fullonica, using fisheries dependent data. To model its distribution, we used Bayesian hierarchical modelling, taking into consideration imperfect capture from the non-random nature of fishing gear type and spatial autocorrelation. Our second objective was to identify the potential functional role of the high occurrence area by analysing spatial length segregation using a generalised additive mixed model.From five environmental variables, depth, distance to coast, and seabed sediment type were used to model its habitat. L. fullonica was found to mainly inhabit an area of high concentration between the southern Celtic Seas and the northern Bay of Biscay. Within this area, smaller individuals were found in the deeper south-western part and larger individuals in shallower waters, closer to the coast, suggesting ontogenetic shift or spawning migration. L. fullonica were mainly caught by bottom trawl fishing gears. The isolated habitat occupancy of this species may increase its vulnerability, particularly since high fishing pressure has been observed in this area. These results highlight the importance of fisheries-dependent data for data-poor species and provide valuable new information on its ecology when considering management or conservation measures at a species level.
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关键词
Hierarchical Bayesian model,Fishery-dependent data,Habitat,Elasmobranch,IUCN Red List Species,Data-poor species
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