Quantifying risk of whale–vessel collisions across space, time, and management policies

• Published in: Ecosphere
• Main Authors: Crum, Nathan, Gowan, Timothy, Krzystan, Andrea, Martin, Julien
• Other Authors: National Oceanic and Atmospheric Administration
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2019
• Subjects: Ecology; Ecology, Evolution, Behavior and Systematics; North Atlantic
• Online Access: http://dx.doi.org/10.1002/ecs2.2713; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecs2.2713; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.2713; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ecs2.2713; https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.2713

Abstract Transportation industries can negatively impact wildlife populations, including through increased risk of mortality. To mitigate this risk successfully, managers and conservationists must estimate risk across space, time, and alternative management policies. Evaluating this risk at fine spatial and temporal scales can be challenging, especially in systems where wildlife–vehicle collisions are rare or imperfectly detected. The sizes and behaviors of wildlife and vehicles influence collision risk, as well as how much they co‐occur in space and time. We applied a modeling framework based on encounter theory to quantify the risk of lethal collisions between endangered North Atlantic right whales and vessels. Using Automatic Identification System vessel traffic data and spatially explicit estimates of right whale abundance that account for imperfect detection, we modeled risk at fine spatiotemporal scales before and after implementation of a vessel speed rule in the southeastern United States. The expected seasonal mortality rates of right whales decreased by 22% on average after the speed rule was implemented, indicating that the rule is effective at reducing lethal collisions. The rule's effect on risk was greatest where right whales were abundant and vessel traffic was heavy, and its effect varied considerably across time and space. Our framework is spatiotemporally flexible, process‐oriented, computationally efficient and accounts for uncertainty, making it an ideal approach for evaluating many wildlife management policies, including those regarding collisions between wildlife and vehicles and cases in which wildlife may encounter other dangerous features such as wind farms, seismic surveys, or fishing gear.