Bayesian hierarchichal model for evaluating the risk of vessel strikes on North Atlantic right whales in the SE United States

Endangered Species Research

Leslie I. Ward-Geiger¹  Christopher J. Fonnesbeck¹  Lance P. Garrison¹  René D. Baumstark¹ 

关键词: Bayesian model;    Eubalaena glacialis;    Hierarchical model;    Right whale;    Risk analysis;   

DOI  :  10.3354/esr00134

学科分类：动物科学

来源: Inter-Research

【 摘 要 】

ABSTRACT: A primary factor threatening the recovery of the North Atlantic right whale is the ongoing risk of collision with large ocean-going vessels. Hence, any viable conservation strategy must include mitigation of this risk. In particular, the critical wintering habitat off the Atlantic shores of the southeastern United States overlaps with the shipping routes of some of the region’s busiest ports. As a first step in the process of ship strike risk mitigation for this region, we estimated the risk associated with current patterns of shipping traffic, and compared this with estimates of risk for a set of hypothetical alternative routes. As a measure of risk, we selected the co-occurrence of whales and vessels within cells of a 4 km grid. We performed parametric estimation of whale encounter rate and associated risk within a Bayesian hierarchical model, using data from aerial surveys and the Mandatory Ship Reporting System of the SE United States, along with a selection of environmental covariates. Importantly, we were able to account for annual and monthly variation in encounters in our estimates. All alternative routes provided reduced overall risk, ranging from a 27 to 44% reduction, relative to the estimated risk of observed traffic. The largest marginal gains in risk reduction were attained by restricting traffic associated with the busiest port, Jacksonville, Florida, but restrictions on all ports achieved the highest reduction. We emphasize the importance of accounting for temporal as well as spatial variation in whale encounter rates, given the migratory behavior of the species.

【 授权许可】

Unknown