Bycatch Estimates From a Pacific Tuna Longline Fishery Provide a Baseline for Understanding the Long-Term Benefits of a Large, Blue Water Marine Sanctuary

Bycatch on pelagic tuna longlines has contributed to population declines in several far-ranging, oceanic species and presents a conservation challenge that area-based management tools are increasingly promoted to address. In January 2020 the Republic of Palau, concerned about the impacts of longline fishing in its waters, closed 80% of its exclusive economic zone to all extractive activities, reserving the remaining 20% for a domestic fishing zone (DFZ). One of a growing number of very large marine protected areas, the Palau National Marine Sanctuary (PNMS) spans ∼500,000 km2 and was established inter alia to allow for the recovery of fish stocks adversely impacted by tuna longline fleets. Given that the main tuna stocks targeted in the western Pacific are not overexploited, the benefits of protection potentially afforded by the sanctuary are likely greater for vulnerable bycatch species. Evaluations of the sanctuary’s performance require, in part, a baseline of historical catch rates and effort distribution in the distant-water fleet (DWF) and locally based fleet (LBF) operating in Palau prior to sanctuary implementation. We describe the fishing effort, catch rates, catch estimates and fishing mortality in Palau’s longline fishery based on logbook, observer and electronic monitoring data. We defined bycatch as any species, retained or discarded, other than targeted tunas. Between 2010 and 2020, 104.8 million hooks were deployed, catching over 2 million individuals from 117 taxa at an overall target:bycatch ratio of 1:1, with a retention rate of ∼62%. Pronounced differences in fishing strategies and spatial distribution of effort between fleets were associated with large variations in catch rates and composition. The LBF had a larger effect on populations of at-risk species relative to the DWF, with higher catch rates and magnitudes for several vulnerable species and higher observable fishing mortality rates (64% vs 50% in the DWF). The sanctuary reshaped Palau’s longline fishery, contracting the fishery’s area and capacity. The relocation of the DFZ eliminated the LBF and constrained the DWF to an area where the fleet’s total catch rates and those of a number of vulnerable species were historically lower relative to former fishing grounds now closed by the sanctuary. Our results highlight the importance of consistent bycatch monitoring and emphasize the need for regional area-based approaches for managing longline fisheries.

Connecting post-release mortality to the physiological stress response of large coastal sharks in a commercial longline fishery

Bycatch mortality is a major factor contributing to shark population declines. Post-release mortality (PRM) is particularly difficult to quantify, limiting the accuracy of stock assessments. We paired blood-stress physiology with animal-borne accelerometers to quantify PRM rates of sharks caught in a commercial bottom longline fishery. Blood was sampled from the same individuals that were tagged, providing direct correlation between stress physiology and animal fate for sandbar (Carcharhinus plumbeus, N = 130), blacktip (C. limbatus, N = 105), tiger (Galeocerdo cuvier, N = 52), spinner (C. brevipinna, N = 14), and bull sharks (C. leucas, N = 14). PRM rates ranged from 2% and 3% PRM in tiger and sandbar sharks to 42% and 71% PRM in blacktip and spinner sharks, respectively. Decision trees based on blood values predicted mortality with >67% accuracy in blacktip and spinner sharks, and >99% accuracy in sandbar sharks. Ninety percent of PRM occurred within 5 h after release and 59% within 2 h. Blood physiology indicated that PRM was primarily associated with acidosis and increases in plasma potassium levels. Total fishing mortality reached 62% for blacktip and 89% for spinner sharks, which may be under-estimates given that some soak times were shortened to focus on PRM. Our findings suggest that no-take regulations may be beneficial for sandbar, tiger, and bull sharks, but less effective for more susceptible species such as blacktip and spinner sharks.

Fishing trip cost modeling using generalized linear model and machine learning methods – A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis

Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower.

Highly migratory species predictive spatial modeling (PRiSM): an analytical framework for assessing the performance of spatial fisheries management

Spatial management for highly migratory species (HMS) is difficult due to many species’ mobile habits and the dynamic nature of oceanic habitats. Current static spatial management areas for fisheries in the United States have been in place for extended periods of time with limited data collection inside the areas, making any analysis of their efficacy challenging. Spatial modeling approaches can be specifically designed to integrate species data from outside of closed areas to project species distributions inside and outside closed areas relative to the fishery. We developed HMS-PRedictive Spatial Modeling (PRiSM), which uses fishery-dependent observer data of species’ presence–absence, oceanographic covariates, and gear covariates in a generalized additive model (GAM) framework to produce fishery interaction spatial models. Species fishery interaction distributions were generated monthly within the domain of two HMS longline fisheries and used to produce a series of performance metrics for HMS closed areas. PRiSM was tested on bycatch species, including shortfin mako shark (Isurus oxyrinchus), billfish (Istiophoridae), and leatherback sea turtle (Dermochelys coriacea) in a pelagic longline fishery, and sandbar shark (Carcharhinus plumbeus), dusky shark (C. obscurus), and scalloped hammerhead shark (Sphyrna lewini) in a bottom longline fishery. Model validation procedures suggest PRiSM performed well for these species. The closed area performance metrics provided an objective and flexible framework to compare distributions between closed and open areas under recent environmental conditions. Fisheries managers can use the metrics generated by PRiSM to supplement other streams of information and guide spatial management decisions to support sustainable fisheries.