Peer-Reviewed Journal Details
Mandatory Fields
Luck C.;Jessopp M.;Tully O.;Cosgrove R.;Rogan E.;Cronin M.
2020
December
Global Ecology And Conservation
Estimating protected species bycatch from limited observer coverage: A case study of seal bycatch in static net fisheries
Validated
WOS: 8 ()
Optional Fields
Bycatch Gillnet Potential biological removal Seal Static net
24
2020 The Authors Fisheries bycatch represents a major anthropogenic threat to marine megafauna worldwide. To identify populations at risk, it is essential to estimate the total number of individuals removed from a population as bycatch. However, estimating total bycatch remains challenging due to the often-limited scope of monitoring programmes. In this study, we aimed to maximise the value of limited bycatch data collected by scientific observers and self-reported by fishers to provide estimates of total seal bycatch for static net fisheries operating in Irish waters. We constructed a model of bycatch rate as a function of known predictors of seal bycatch, and used this to predict bycatch rates throughout the Irish Exclusive Economic Zone. Annual estimates of seal bycatch, from 2011 to 2016, ranged between 202 (90% CI: 2-433) and 349 (90% CI: 6-833) seals per annum. Estimated bycatch exceeded the precautionary threshold of Potential Biological Removal (PBR = 165-218; Fr=0.5) for the national grey seal population but was below less conservative threshold values (PBR = 330-437; Fr=1.0), with confidence intervals spanning both. Further research on the population structure of grey seals in the Northeast Atlantic is needed to set appropriate bycatch thresholds. Nonetheless, this study shows that by utilising predictive models to maximise the value of limited bycatch observer effort, we can produce informative estimates of protected species bycatch and highlight areas of high bycatch risk. We present this as a case study for maritime nations with comparatively limited bycatch data to fill key data gaps in protected species bycatch worldwide.
2351-9894
10.1016/j.gecco.2020.e01213
Grant Details