Evaluating stakeholder‐derived strategies to reduce the risk of ships striking whales

Abstract

Aim: Ship strikes are one of the largest sources of human-caused mortality for baleen whales on the West Coast of the United States. Reducing ship-strike risk in this region is complicated by changes in ship traffic that resulted from air pollution regulations and economic factors. A diverse group of stakeholders was convened to develop strategies to reduce ship-strike risk in the Southern California Bight. Strategies proposed by some stakeholders included: (a) adding a shipping route; (b) expanding the existing area to be avoided (ATBA); and (c) reducing ship speeds. Location: Southern California Bight, off the coast of California, United States. Methods:We developed methods to estimate ship traffic in the stakeholder-derived strategies using 8 years of ship traffic data. To assess ship-strike risk for fin, humpback, and blue whales, we used habitat models developed from 7 years of survey data and home ranges derived from 53 blue whale tags. We defined collision risk as the co-occurrence between whales and ships. The risk of a lethal collision was calculated by multiplying collision risk by the probability that a collision is lethal, which is estimated using ship speed.Results: Speed reductions resulted in a large decrease in the risk of a lethal ship strike.Creating a shipping route or expanding the ATBA reduced the risk of a strike by removing traffic from a whale feeding area. Creating a shipping route was opposed by the United States Navy and the shipping industry, but expanding the ATBA was broadly supported. Main conclusions:Our analyses suggest that speed reductions and expanding the ATBA may provide an optimal solution for addressing stakeholder needs and reducing ship strikes in the Southern California Bight. The methods we developed can be used to address the global issue of balancing human use of the marine environment with the protection of whale populations.

Effects of Variability in Ship Traffic and Whale Distributions on the Risk of Ships Striking Whales

“…Moore et al (2018) used Conn and Silber's (2013) equation relating ship speed to the probability that a ship strike is fatal to estimate that reductions in ship speeds in the Santa Barbara Channel (i.e., the 2008 traffic pattern in the southern California region; Figure 1) represent a 20% reduction in the probability of a fatal strike. Consequently, ship speeds should be considered when designing strategies that can mitigate risk (e.g., Redfern et al, 2019). Our analyses show that static, spatial management strategies can be used to mitigate ship-strike risk from nearshore versus offshore traffic off California.…”

“…Consequently, more detailed and fine-scale analyses are needed to design strategies that can mitigate risk for all species in these regions. For example, Redfern et al (2019) developed methods to estimate ship-strike risk in strategies proposed by stakeholders to reduce risk in the Southern California Bight and found that speed reductions and expanding the existing area to be avoided may provide an optimal solution for addressing stakeholder needs and reducing ship strikes. Analyses are also needed to address risk from January-June because studies have found seasonal changes in fin (Scales et al, 2017) and humpback (Becker et al, 2017) whale distributions off California.…”

“…Ship strikes are one of the largest sources of human-caused mortality for fin (Balaenoptera physalus), humpback (Megaptera novaeangliae), and blue (B. musculus) whales on the United States West Coast (Carretta et al, 2017). The risk of ships striking whales (hereafter, ship-strike risk) has been assessed for these species in several regions: for all three species off the entire United States West Coast (Rockwood et al, 2017) and southern California (Redfern et al, 2013(Redfern et al, , 2019, for blue whales off the entire United States West Coast (Hazen et al, 2017;Abrahms et al, 2019b), and for humpback whales off San Francisco (Dransfield et al, 2014). Many of these risk assessments and those conducted for various locations around the world (e.g., Sri Lanka and Canada; Williams and O'Hara, 2010;Priyadarshana et al, 2016) use a single year of ship traffic data and predictions of whale distributions that are averaged over several years.…”

“…Many of these risk assessments and those conducted for various locations around the world (e.g., Sri Lanka and Canada; Williams and O'Hara, 2010;Priyadarshana et al, 2016) use a single year of ship traffic data and predictions of whale distributions that are averaged over several years. Redfern et al (2013) and Redfern et al (2019) are exceptions because they used more than one year of ship traffic data; Hazen et al (2017) and Abrahms et al (2019b) developed near real-time tools to predict blue whale distributions. However, most ship-strike risk assessments do not account for variability in ship traffic, species distributions, or both.…”

Using modelled prey to predict the distribution of a highly mobile marine mammal

“…Correlative SDMs associate species presence or presence-absence data with environmental predictor variables, fit a relationship between predictor and response, and make predictions for an area of interest (Redfern et al, 2006). These types of SDMs have been used to conduct near real-time monitoring (Abrahms et al, 2019;Hazen et al, 2017) and address a number of conservation challenges, including evaluating and mitigating ship-strike risk (Redfern, Becker, & Moore, 2020;Redfern et al, 2019), and they have shown potential in forecasting species distributions (Becker et al, 2018). Many studies that model marine animal density or distributions use oceanographic proxies for prey (e.g., Becker et al, 2016;Redfern et al, 2006;Sequeira, Mellin, Rowat, Meekan, & Bradshaw, 2012), which is expected to be the true driver of species' migratory phenology and their distribution on foraging grounds.…”