Long-term fishery management targets are typically defined in terms of optimal equilibrium productions (harvests). However, reaching the optimal target is complicated by the fact that most fisheries must pass through a stock-drawdown phase (non-equilibrium harvests), often under shifting socioeconomic and changing climatic conditions. During stock-drawdown phase when initial biomass is higher than the optimal biomass that produces optimal yield, the difference between these two biomass levels (defined as “endowment”) is progressively depleted. In this phase, both catch and fishing effort often exceed levels consistent with long-term optimal harvests, leading to substantial and persistent excess capacity. Existing dynamic bioeconomic models largely focus on optimal equilibrium and open access outcomes, providing limited guidance on the length of time it takes to reach optimal targets and how excess capacity emerges during the transition. This study develops a discrete-time dynamic bioeconomic model that explicitly incorporates both drawdown harvests and equilibrium harvests. For a given fishery, the model quantifies important fisheries dynamics, including: (1) the length of time it takes to reach optimal management targets (MSY or MEY); (2) the externality such as the magnitude (“stickiness”) and persistence of excess capacity; and (3) the potential time to stock collapse in the absence of management. Through scenario analysis, the model estimates the impacts on fishery dynamics (including economic performance) arising from climate-induced productivity declines and challenges faced by fisheries, such as nonlinear catchability driven by technological improvements, market-driven fluctuations in fish prices and harvesting costs, and local stewardship in effort controls. This model presents an operational and tractable framework for proactive fishery management.

Maximum economic yield (MEY) as derived from Schaefer's (1957) bioeconomic model was potentially a major contribution to fishery management, but it has been hard to apply to fishery management in reality. Schaefer's model with fixed catchability and associated linear CPUE does not match the variable nature of catchability resulting from technological progress and schooling behavior, leading to a biased estimation of MEY or economic optimal biomass (B-MEY). This study improves on Schaefer's model by incorporating nonlinear CPUE, where MEY depends on biomass relationships with catchability and with CPUE. When CPUE is constant, MEY is shown analytically to be the same as the biological optimal yield (maximum sustainable yield [MSY]) and the related optimal biomasses are equivalent (B-MEY = B-MSY). However, in cases of nonlinear CPUE, MEY might be closer to or further away from MSY. The less sensitive the CPUE is in response to changes in biomass, the closer the economic optimum B-MEY is to the biological optimum B-MSY. When CPUE is sensitive to changes in biomass, the benefit of leaving more fish in water (the stock effect) is more noticeable. Hence, this revised model can become an important component in matching fishery management goals to the realities of fisheries. Simulation analyses further illustrate that the traditional Schaefer economic optimum does not apply to all fisheries. This model provides the basis for fishery management to set a total catch limit (as a fishery management reference point) that could achieve MEY but also defines how MEY could be equal to, closer to, or further away from MSY based on characteristics of an individual fishery. For a fishery in overfished status, it helps fishery managers to decide a rebuilding target at B-MSY or at a higher abundance than B-MSY in order to achieve MEY and still meet biological reference points.

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.

This techno-economic performance review of selected fishing fleets in North and South America was carried out as part of the 2020 FAO Review of the techno-economic performance of the main global fishing fleets and presents the findings for four selected countries. The country studies are based on fishing fleet data from surveys conducted in the period 2012–17 in the United States of America by the National Marine Fisheries Service, and fishing vessel surveys carried out in Brazil, Chile and Peru during 2018 by national fisheries experts. The review includes financial and economic information of 21 fishing fleet segments, including shrimp and groundfish trawlers, demersal trawlers, longliners, purse seiners, dredgers as well as hook and line fishing vessels. Analysis of the costs and earnings data of these important fishing fleet segments in North and South America, showed that 81 percent of the fleet segments had a positive net cash flow. The net profit margins of 38 percent of the 21 fishing fleet segments were >10 percent. Two-thirds (67 percent) of the fleet segments presented positive results in terms of their capital productivity, as the return on fixed tangible assets (ROFTA) was positive. Twenty-four percent of the fleet segments showed return on investment (ROI) figures of twenty percent or more. A majority of the Chilean and Peruvian fleet segments had ROIs of ten percent or higher in 2018. The financial and economic performance of the fishing fleet segments is not only affected by the seafood prices, but also by the fisheries management regime in place, fish species targeted, fish stock status and fishing methods and technologies applied. The vessel age structure shows an increasing trend for most of the fishing fleet segments in this review, which is another issue that assisted in increasing profitability of vessels in these fleet segments, as replacement and depreciations costs are low or non-existent.

Fisheries harvesting yellowfin and bigeye tuna while targeting skipjack in the Eastern Pacific Ocean (EPO) are not managed optimally with respect to economic value. Bigeye tuna are generally caught at before they reach full size so cannot fetch the higher prices obtained for mature fish which are usually harvested by longline fleets and sold to the sashimi market. This study evaluates the economic and biological trade-offs of managing the fishery to determine how the economic value may increase with different harvest strategies while the spawning biomass of both species is maintained at the optimal sustainable levels. This study uses three analytical models to assess the economic and biological tradeoffs in four possible scenarios with different combinations of purse-seine and longline fishing effort. The first model evaluates the biological tradeoffs under various effort combinations of longline (LL) and purse-seine (PS) that could reach the same optimal biomass level, measured by the spawning biomass ratio (SBR). The second model evaluates the long-term optimal equilibrium economic value under various effort combinations. The third model evaluates the dynamic (short-term) trajectory of recovery path of bigeye tuna under various policy options. The analytical results show that economics and conservation are not incompatible. In one scenario, we show that reducing purse-seine effort by 26.3%, via a per-ton compensation system from longline fleets to the purse-seine, leads to net economic gain of $93 million, annually. The total value of the PS and LL fisheries in EPO increases from $1246 million to $1339 million. The study shows that the economic value of the resource is highly dependent on the allocation of effort between the longline and purse-seine fisheries. Since the longline and purse-seine fisheries in EPO are formed by multiple users in multiple countries/groups, the ideal scenarios would not be feasible without administrative measures and/or economic incentives. This study also discusses three possible ways of implementing a management strategy that would achieve higher economic value while still maintaining tuna conservation goals, such as a tradeable right-based management scheme.

We present a vessel and target-specific positive mathematical programming model (PMP) for Hawaii's longline fishing fleet. Although common in agricultural economics, PMP modeling is rarely attempted in fisheries. To demonstrate the flexibility of the PMP framework, we separate tuna and swordfish production technologies into three policy relevant fishing targets. We find the model most accurately predicts vessel-specific annual bigeye catch in the WCPO, with an accuracy of 12% to 35%, and a correlation between 0.30 and 0.53. To demonstrate the model's usefulness to policy makers, we simulate the economic impact to individual vessels from increasing and decreasing the bigeye catch limit in the WCPO by 10%. Our results suggest that such policy changes will have moderate impacts on most vessels, but large impacts on a few generating a fat tailed distribution. These results offer insights into the range of winners and losers resulting from changes in fishery policies, and therefore, which policies are more likely to gain widespread industry support. As a tool for fishery management, the calibrated PMP model offers a flexible and easy-to-use framework, capable of capturing the heterogeneous response of fishing vessels to evaluate policy changes.

NOAA’s National Marine Fisheries Service (NMFS) recently completed two reports on excess harvesting capacity, National Assessment of Excess Harvesting Capacity in Federally Managed Commercial Fisheries and Excess Harvesting Capacity in U.S. Fisheries: A Report to Congress. This paper presents the definitions of harvesting capacity, excess capacity, and overcapacity used in the two reports; summarizes the method used to estimate harvesting capacity; and presents some of the findings and policy recommendations in the two reports. The National Assessment was used in preparing the Report to Congress. The Report to Congress includes harvesting capacity assessments for 25 fisheries, 60 fleets, and 127 species groups; identifies and described the fisheries with the most severe examples of excess harvesting capacity; and discusses measures to reduce excess harvesting capacity.

This study examines spillover effects resulting from US fishing regulations instituted to protect sea turtles. Sea turtles, along with US and foreign fisheries for swordfish co-occur on the high seas in the North and Central Pacific and that allows for "spillover effects." When one fishery is required to curtail fishing activity to reduce incidental fishing mortality on sea turtle populations, the activity of other, unregulated fleets may change in ways that adversely affect the very species intended for protection. This study provides an empirical model that estimates these "spillover effects" on sea turtle bycatch resulting from production displacement between regulated US and less-regulated non-US fleets in the North and Central Pacific Ocean. The study demonstrates strong spillover effects, resulting in more sea turtle interaction due to increased foreign fleet activity when Hawaii swordfish production declines.

From abstract: This report presents findings from the Pacific Islands Fisheries Science Center (PIFSC) cost-earnings study of the Hawaii-based longline fishery fleet which primarily targets bigeye tuna and swordfish...This report also compares 2012 results with the previous cost-earnings studies of the Hawaii longline fleet that examines the economic profiles of the fleet for 2000 and 2005 operations.

There is a growing body of literature positively linking dimensions of social capital to economic benefits. Yet recent research also points to a potential "dark side" of social capital, where over-embeddedness in networks and the pressures associated with brokerage are hypothesized to constrain actors, having a negative effect on economic outcomes. This dichotomy suggests that context is important, yet the overwhelming majority of existing empirical evidence stems from socially homogenous populations in corporate and organizational settings, limiting a broader understanding of when and how context matters. We advance this discourse to a socially fragmented, ethnically diverse common-pool resource system where information is highly valuable and competition is fierce. Merging several unique datasets from Hawaii's pelagic tuna fishery, we find that network prominence, i.e., being well connected locally, has a significant, positive effect on economic productivity. In contrast, we find that brokerage, defined here as ties that bridge either structurally distinct or ethnically distinct groups, has a significant, negative effect. Taken together, our results provide empirical support to widespread claims of the value of information access in common-pool resource systems, yet suggest that in ethnically diverse, competitive environments, brokers may be penalized for sharing information across social divides. Our results thus contribute to an emerging theory on the fragile nature of brokerage that recognizes its potential perils and the importance of context. (C) 2015 Elsevier B.V. All rights reserved.

Conservation measures of setting annual caps on sea turtle, Cheloniidae, interactions and other regulations have resulted in a signifi cant reduction in sea turtle interactions in the Hawaii-based longline fi shery. On the other hand, the conservation measures created a limitation on swordfi sh, Xiphias gladius, production and created uncertainty for participants in the fi shery because the fi shery would be closed whenever the cap is reached. This study explores the trade-offs between the risks of sea turtle interactions and economic returns from swordfi sh fi shing, and identifi es examples of alternative management options that could allow the swordfi sh fi shery to operate throughout the year with Introduction The Hawaii shallow-set longline fi shery primarily targets swordfi sh, Xiphias gladius, in waters north of the Hawaiian Archipelago. These fi shing grounds are also key pelagic habitat for protected species of sea turtles, particularly loggerhead sea turtles, Caretta caretta, and occasionally longline vessels will incidentally catch them. In 2004, under provisions of the Endangered Species Act, NOAA’s National Marine Fisheries Service (NMFS) issued a series of regulations for the fi shery including caps on incidental captures of sea turtles (called sea turtle “interactions”) allowed each year (NOAA, 2009). These regulations included an annual fi shing effort limit which is the total number of fi shing days (sets) that the fl eet could utilize throughout the year. If and when the fl eet-wide fi shing effort limit is reached, or the incidental sea turtle catch limit is reached, NMFS will close the swordfi sh fi shery for the remainder of the year. Subject to these constraints, and other regulations, swordfi sh vessels are free to set their gear in any month and anywhere in the swordfi sh grounds. The location and timing of swordfi sh fi shing operations has consequences for sea turtles as well as for the economic returns of the vessels. Decisions on where and when to fi sh take into account expected swordfi sh catch rates and may take into account the likelihood of interactions with sea turtles, both of which vary spatially and temporally, as well as the costs incurred in fi shing, which are largely a function of trip length (days) and distance of fi shing locations from port. Two natural questions that arise are “can sea turtle hot spots be identifi ed and avoided without reduced or negative impact on the economic returns of the fi shery?” And, if so, “what timearea fi shing strategy should be pursued to maximize net economic returns subject to the fl eet-wide constraint on turtle interactions and fi shing effort?” To study these questions from a fl eetwide perspective, a bioeconomic model was developed and used to examine trade-offs between the risk of interacting with sea turtles and economic returns to the fl eet (Li and Pan, 2007). This paper describes the bioeconomic model and demonstrates how it can be applied to evaluate potential policy choices. The model was used to search for possible policy alternatives in order to maximize swordfi sh fi shing opportunities subject to the constraints on fi shing effort and the annual cap on interactions with loggerhead sea turtles. If the swordfi sh fi shery is closed, Hawaii longline fi shermen who are engaged in the shallow-set fi shery for swordfi sh can redirect their effort to target bigeye tuna, Thunnus obesus, using deep-set longline gear. Thus, the foregone swordfi sh fi shing opportunity may not have a negative impact on the Hawaii longline fi shery if fi shermen could continue their fi shing operation by targeting bigeye tuna for the remainder of the year (at some fi xed and operational costs). However, the fi shery also faces restrictions on bigeye tuna due to overfi shing of the stocks in the Pacifi c Ocean.1 Reduced fi shing opportuni1Bigeye catch limits imposed on the Hawaii longline fi shery are determined by two Regional Fisheries Management Organizations (RFMO’s): the Western and Central Pacifi c Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC). Each RFMO allocates a region-specifi c bigeye quota for the U.S. pelagic longline fi shery operating in its a reduced risk of exceeding the cap on loggerhead sea turtle, Caretta caretta, interactions. In addition, the study compares the trade-offs in terms of foregone swordfi sh production based on one interaction reduction before and after the implementation of the conservation measures. A spatial bioeconomic model is developed to conduct simulation analyses. A Generalized Additive Model (GAM) is applied to Hawaii longline logbook data to examine and predict sea turtle interactions in response to changes in spatial and temporal distributions of fi shing effort and oceanographic conditions. A cost function is built into the model for making economic analyses to estimate net revenue returns.

Fisheries productivity is the result of many factors, including endogenous and exogenous elements, such as regulation and stock condition. Understanding changes in productivity and the factors affecting that change is important to fishery management and a sustainable fishing industry. However, no study has been conducted to measure productivity change in the Hawaii longline fishery, the largest fresh bigeye tuna and swordfish producer in the United Stated. Using a Lowe productivity index, productivity change in the Hawaii longline fleet between 2000 and 2012 is measured in this study. In addition, a biomass quantity index is constructed to disentangle biomass impacts in a pelagic environment in order to arrive at an “unbiased” productivity metric. This is particularly important in the Hawaii longline fishery where catches rely mostly on transboundary (shared) stocks with little control on the total amount of extraction. As resource depletion of the transboundary stocks occurs, productivity loss may follow if less output is obtained from the same input usage, or more inputs are used to extract the same catch level from the fishery. Finally, the study compares productivity change under different fishing technologies.

To date, none of the fisheries in the U.S. Pacific Islands Region is managed under a catch share program. In light of the NOAA policy to encourage the use of catch shares as a fishery management tool, the Western Pacific Fishery Management Council (WPFMC) listed six commercial fisheries, including the Hawaii pelagic longline fishery, the largest in the region, as potential candidates for catch share programs. This study examines the baseline economic characteristics and the main challenges facing the Hawaii pelagic longline fishery and evaluates the impact of these on the desirability and feasibility of a catch share program for this particular fishery.

Social Accounting Matrix (SAM) models include a comprehensive accounting of regional income and institutional factors, and are intended to be capable of assessing distributional analysis. However, in practice, studies generally construct models that are not appropriately designed to capture income linkages. In this study, we demonstrate the use of SAM modeling to assess the income distribution linkages of Hawaii's commercial fishery sector. We identify the distributional characteristics of the economic impact from the fishery industry by mapping industry labor inputs to a state level occupational matrix prior to the linking of household accounts. The distributional analysis of the SAM indicates that Hawaii's longline sectors impact middle income groups most significantly with modest linkages to lower income groups.