Improving the applicability of metabarcoding to monitor salmon farming impacts on benthic environments

Abstract

The projected expansion of the aquaculture industry in Scotland has environmental, social, and economic implications. Salmon farming is associated with several impacts that need to be monitored, including the accumulation of excess organic matter on the seabed (benthic environment) which affects sediment chemistry and fauna. The current method of impact monitoring, through morphological identification of macrofauna, is time-consuming and expensive. These limitations can be overcome with environmental DNA (eDNA) metabarcoding. To utilise metabarcoding for regulatory monitoring, an understanding is needed of the sources of variability in metabarcoding-derived data. Metabarcoding-inferred indicators of impact also need to be identified specifically for fish farm monitoring. This thesis aims to improve the applicability of metabarcoding to monitor farm impacts by: (i) quantifying and contextualising two sources of variation in library preparation; (ii) identifying metabarcoding-inferred indicators associated with benthic macrofauna and sediment sulphide concentrations; and (iii) investigating bacterial function associated with farm impact. Both the DNA extraction kits and PCR replicate pooling strategies tested here could elucidate farm impacts, suggesting that the faster, more cost-effective options could be chosen without compromising monitoring effectiveness. Metabarcoding-inferred indicators of farm impact associated with changes in the current monitoring index and sediment sulphide concentrations were identified using a machine-learning approach. Their relationships with morphologically identified macrofauna were assessed. These data were used to create a preliminary database of metabarcoding-inferred indicators of impact at farms of two different sediment types. The vertical distributions of bacteria in sub-surface sediments were also assessed along with their function. Sulphur metabolism-related functions were identified as indicators of farm impact. This thesis contributes to the knowledge base required in implementing metabarcoding in the regulatory monitoring of farms. Using more efficient monitoring methods can help mitigate and minimise fish farm environmental impacts, which is increasingly necessary due to the growth of the industry and the importance of sustainability.

Date of Award	20 Nov 2022
Original language	English
Awarding Institution	University of Highlands and Islands
Sponsors	ESF studentship
Supervisor	Tom Wilding (Supervisor) & Victoria Pritchard (Supervisor)