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Population ecology and juvenile density hotspots of thornback ray (Raja clavata) around the Shetland Islands, Scotland.

Mia McAllisterShaun FraserLea-Anne Henry
Published in: Journal of fish biology (2023)
Elasmobranchs are facing global decline, and so there is a pressing need for research into their populations to inform effective conservation and management strategies. Little information exists on the population ecology of skate species around the British Isles, presenting an important knowledge gap that this study aimed to reduce. The population ecology of thornback ray (Raja clavata) around the Shetland Islands, Scotland was investigated in two habitats: inshore (50-150 m deep) and shallow coastal (20-50 m deep), from 2011-2022, and 2017-2022 respectively. Using trawl -survey data from the annual Shetland Inshore Fish Survey, the size composition of R. clavata catches was compared between shallow and inshore habitats across 157 trawl sets, and 885 individuals, over the years 2017-2022. Catch per unit effort (CPUE) of R. clavata was significantly higher in shallow than inshore areas (ANOVA, F = 72.52, df = 1, 5, P < 0.001). Size composition also significantly differed between the two habitats (ANOSIM, R = 0.96, P = 0.002), with R. clavata being smaller in shallow areas and where juveniles (<60 cm) occurred more frequently. Spatial distribution maps confirmed density hotspots of juveniles in shallow habitats, with repeated use of certain locations consistent over time. The results of this study provide the first evidence for R. clavata using shallow areas for potential nurseries in Shetland, which can inform the International Union for Conservation of Nature's Important Shark and Ray Area process. Furthermore, this study provides important new population ecology information for R. clavata around Shetland which may have important conservation implications and be valuable for informing species and fisheries stock assessments in this region. This article is protected by copyright. All rights reserved.
Keyphrases
  • healthcare
  • machine learning
  • cross sectional
  • big data
  • high resolution
  • genetic diversity