Jarvis Salmon QBA

• Main Author: Jarvis, Susan
• Other Authors: University of Stirling, Scotland’s Rural College (SRUC)
• Format: Dataset
• Language: English
• Published: University of Edinburgh. Vet School. Global Academy of Agriculture and Food Security 2021
• Subjects: Qualitative Behavioural Assessment; fish; salmon; aquaculture; welfare; Veterinary Sciences Agriculture and related subjects; Atlantic salmon; Salmo salar
• Online Access: https://hdl.handle.net/10283/3902; https://doi.org/10.7488/ds/3036

There is a growing scientific and legislative consensus that fish are sentient, and therefore have the capacity to experience pain and suffering. The assessment of the welfare of farmed fish is challenging due to the aquatic environment and the number of animals housed together. However, with increasing global production and intensification of aquaculture comes greater impetus for developing effective tools which are suitable for the aquatic environment to assess the emotional experience and welfare of farmed fish. This study therefore aimed to investigate the use of Qualitative Behavioural Assessment (QBA), originally developed for terrestrial farmed animals, in farmed salmon and evaluate its potential for use as a welfare monitoring tool. QBA is a ‘whole animal’ approach based on the description and quantification of the expressive qualities of an animal’s dynamic style of behaving, using descriptors such as relaxed, agitated, lethargic, or confident. A list of twenty qualitative descriptors was generated by fish farmers after viewing video-footage showing behaviour expressions representative of the full repertoire of salmon in this context. A separate, non-experienced group of ten observers subsequently watched twenty-five video clips of farmed salmon, and scored the twenty descriptors for each clip using a Visual Analogue Scale (VAS). To assess intra-observer reliability each observer viewed the same twenty-five video clips twice, in two sessions 10 days apart, with the second clip set presented in different order. The observers were unaware that the two sets of video clips were identical. Data were analysed using Principal Component (PC) Analysis (correlation matrix, no rotation), revealing four dimensions that together explained 79% of the variation between video clips, with PC1 (tense/anxious/skittish – calm/mellow/relaxed) explaining the greatest percentage of variation (56%). PC1 was the only dimension to show acceptable inter- and intra-observer reliability, and mean PC1 scores correlated significantly ...