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Resource managers worldwide are being asked to consider the ecosystem while making management decisions. Integrated Ecosystem Assessment (IEA) provides a flexible framework for addressing ecosystem...

In the management of natural resources, regulation often induces behavioral responses by resource users that ultimately undermine stated policy objectives. Examples of these unintended consequences have been associated with regulations ranging from the Endangered Species Act to laws governing clean air. In this paper we investigate an unintended behavioral response that can be triggered by conservation measures in multispecies fishery management, which leads to increased targeting of the species the conservation measures are meant to protect. Harvest is subject to stochastic variation, with output partially determined by the probability of encountering species of interest, either due to targeting or avoidance. Given the right conditions, an intertemporal arbitrage opportunity arises due to the fact that by targeting a stock in the current period, the probability of encountering that stock in the next period, when announced conservation measures are implemented, decreases. We present an empirical case study that supports the findings of the theoretical model. The results indicate that, by targeting so called weak stocks, some New England fishermen are willing to trade off increased costs today for increased expected profits in the future. To prevent the potentially harmful effects of this behavioral response, a manager may adopt precautionary provisions at the time a quota reduction is announced, or alternatively allow the industry to bank part of its current season's quota in order to alleviate the consequences of the reduction in the ensuing period. These results highlight the challenge of developing effective conservation strategies.

There has been a proliferation of coupled social-ecological systems (SES) models created and published in recent years. However, the degree of coupling between natural and social systems varies widely across the different coupled models and is often a function of the disciplinary background of the team conducting the research. This manuscript examines models developed for and used by NOAA Fisheries in support of Ecosystem Based Fisheries Management (EBFM) in the United States. It provides resource managers and interdisciplinary scientists insights on the strengths and weaknesses of the most commonly used SES models: end-to-end models, conceptual models, bioeconomic models, management strategy evaluations (MSEs), fisher behavior models, integrated social vulnerability models, and regional economic impact models. These model types are not unique to the literature, but allow us to differentiate between one-way coupled models – where outputs from one model are inputs into a second model of another discipline with no feedback to the first model, and two-way coupled models – where there are linkages between the natural and social system models. For a model to provide useful strategic or tactical advice, it should only be coupled to the degree necessary to understand the important dynamics/responses of the system and to create management-relevant performance metrics or potential risks from an (in)action. However, one key finding is to not wait to integrate! This paper highlights the importance of “when” the coupling happens, as timing affects the ability to fully address management questions and multi-sectoral usage conflicts that consider the full SES for EBFM or ecosystem based management (EBM) more generally.

Modeling tools that can demonstrate possible consequences of strategies designed to operationalize ecosystem-based fisheries management (EBFM) should be able to address tradeoffs over a wide suite of considerations representing the scope of marine management objectives. Coupled ecological-economic modeling, where models for ecological and economic subsystems are linked through their inputs and outputs, allows for quantification of such tradeoffs. Here, we link the harvest output from fishery management scenarios implemented in an end-to-end ecosystem model (Atlantis) to an input–output regional economic model for the Northeast United States to calculate changes in socio-economic indicators, including the consequences of management action for regional sales, wages, and employment. We implement three simple scenarios (maintain, decrease, or increase current fishing effort), and compare model-projected values for systematic and sector-specific indicators. Systematic indicators revealed different ecological and economic outcomes, with large ecological responses and clear tradeoffs among the catch and biomass of species groups. Economic indicators for the region responded similarly to fishery yield; however, changes in total sales did not match those in landed catch. Under increased fishing effort, a lower proportional increase in sales relative to total landed catch arose due to increased yield from lower value species groups. Average fisheries income changed little among scenarios, but was highest when effort was maintained at current levels, likely a reflection of fleet and catch stability. Our results serve to demonstrate that consequences of management may be felt disproportionately among species through the region and across different fisheries sectors. With our coupled modeling approach of passing Atlantis ecosystem model outputs to an input–output economic model, we were able to assess effects of fisheries management across a broader suite of indicators that have relevance for policymakers across multiple objectives.

Portfolio management has been suggested as a tool to help implement ecosystem-based fisheries management. The portfolio approach involves the application of financial portfolio theory to multispecies fishery management to account for species interdependencies, uncertainty, and sustainability constraints. By considering covariance among species, this approach allows economic risks and returns to be calculated across varying combinations of stock sizes. Trade-offs between expected aggregate returns and portfolio risk can thus be assessed. We develop a procedure for constructing portfolio models to help implement ecosystem-based fisheries management in the northeastern United States, using harvest data from the National Marine Fisheries Service. Extending the work of Sanchirico et al. (2008), we propose a measure of excessive risk taking, which may be used by managers to monitor signals of nonoptimal harvests. In addition, we conduct portfolio assessments of historical commercial fishing performance ...

Fishery managers worldwide are evaluating methods for incorporating climate, habitat, ecological, social, and economic factors into current operations in order to implement Ecosystem Approaches to Fishery Management (EAFM). While this can seem overwhelming, it is possible to take practical steps towards EAFM implementation that make use of existing information and provide managers with valuable strategic advice. Here, we describe the process used by the U.S. Mid-Atlantic Fishery Management Council (Council) to develop an ecosystem-level risk assessment, the initial step proposed in their recently adopted EAFM guidance document. The Council first defined five types of Risk Elements (ecological, economic, social, food production, management) and identified which management objectives aligned with each element. Based on an existing ecosystem status report for the region and other existing sources (including expert opinion), potential ecological, social, economic, and management indicators were identified for each risk element. Finally, low, low-moderate, moderate-high, and high risk criteria were defined for each indicator, and the indicator data were used to score each risk element using the criteria. The ultimate outcome is a ranked risk assessment in order to focus on the highest risk issues for further evaluation and mitigation. The risk assessment highlights certain species and certain management issues as posing higher cumulative risks to meeting Council management objectives when considering a broad range of ecological, social, and economic factors. Tabular color coded summaries of risk assessment results will be used by the Council to prioritize further EAFM analyses as well as research plans over the coming five years. As ecosystem reporting and operational EAFM continue to evolve in future years, the Council foresees integrating these efforts so that ecosystem indicators are refined to meet the needs of fishery managers in identifying and managing risks to achieving ecological, social, and economic fishery objectives. Overall, ecosystem indicator-based risk assessment is a method that can be adapted to a wide range of resource management systems and available information, and therefore represent a promising way forward in the implementation of EAFM.

Marine ecosystems are characterized by many complex interactions. Fisheries managers face the challenge of maintaining or restoring sustainability for individual living resources which are affected by both ecological and economic interactions with other species, through processes like predation and fishing fleet interactions. These species interactions are further complicated by interactions with habitats that are changing due to both human activities and climate change. Often, fishery management systems designed to promote sustainability of individual resources have few tools or processes that also address interactions between species, fleets, habitat, and climate. Here, we review existing and potential fishery assessment and management information and tools, and we develop a potential framework for addressing interactions in management at the request of the U.S. Mid-Atlantic Fishery Management Council. The structured framework can be used to first prioritize interactions, second specify key questions regarding high priority interactions, and third tailor appropriate analyses to address them. The primary tools for the initial steps in the framework are risk assessment and Management Strategy Evaluation (MSE). Finally, implemented management would be evaluated to ensure that objectives are being met, or to adjust measures as conditions change. In the final section, we outline an example to illustrate how a structured decision making process within the framework could work.