Assessing the risk of whale entanglement with fishing gear debris

• Published in: Marine pollution bulletin Vol. 161; no. Pt A; p. 111720
• Main Authors: Brown, Anita H., Niedzwecki, John M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.12.2020; Elsevier BV
• Subjects: Animals; Aquatic mammals; Data; Distribution functions; Entanglement; Fault tree framework; Fault trees; Field data; Fisheries; Fishing; Fishing equipment; Fishing gear; Fishing gear debris; Marine environment; Marine mammals; Marine pollution; Risk; Risk analysis; Risk assessment; US new England and Pacific regions; Whale entanglement; Whales; Fault tree framework; Whale entanglement; US new England and Pacific regions; Field data; Risk analysis; Fishing gear debris
• ISSN: 0025-326X; 1879-3363; 1879-3363
• DOI: 10.1016/j.marpolbul.2020.111720

The loss and abandonment of fishing gear has resulted in one of the most visible signs of growing pollution in the marine environment. The entanglement of whales in fishing gear has been the subject of increasing documentation. The interpretation of the documented incidents to address the risk of whale entanglement is presented. An initial risk-based model is derived that reflects published information on multi-year fishing gear accumulation rates and entanglement data. A fault tree framework is adopted to organize the data, allowing for the continual improvement of the risk-based model predictions through the incorporation of new data and inclusion of additional sub-events. Analytic distribution functions are introduced to augment incomplete data and explore hypothetical scenarios. Data reported for the US Atlantic and Pacific coastlines are used in illustrative examples, that address both regional and multi-regional applications, and the sensitivity of the risk-based predictions to the reported field data. •Risk-based model of Whale entanglement in fishing gear•Fault tree framework for organizing reported field data and sub-events•Equations derived to describe relationships between sub-events•Probability functions introduced to augment incomplete data interpretation•Sensitivity of the model predictions presented using graphical visualizations