John Wiedenmann

An Evaluation of Harvest Control Rules for Data-Poor Fisheries

Abstract For federally managed fisheries in the USA, National Standard 1 requires that an acceptable biological catch be set for all fisheries and that this catch avoid overfishing. Achieving this goal for data-poor stocks, for which stock assessments are not possible, is particularly challenging. A number of harvest control rules have very recently been developed to set sustainable catches in data-poor fisheries, but the ability of most of these rules to avoid overfishing has not been tested. We conducted a management strategy evaluation to assess several control rules proposed for data-poor situations. We examined three general life histories (“slow,” “medium,” and “fast”) and three exploitation histories (under-, fully, and overexploited) to identify control rules that balance the competing objectives of avoiding overfishing and maintaining high levels of harvest. Many of the control rules require information on species life history and relative abundance, so we explored a scenario in which unbiased kn...

Sustainable exploitation and management of autogenic ecosystem engineers: application to oysters in Chesapeake Bay

Autogenic ecosystem engineers are critically important parts of many marine and estuarine systems because of their substantial effect on ecosystem services. Oysters are of particular importance because of their capacity to modify coastal and estuarine habitats and the highly degraded status of their habitats worldwide. However, models to predict dynamics of ecosystem engineers have not previously included the effects of exploitation. We developed a linked population and habitat model for autogenic ecosystem engineers undergoing exploitation. We parameterized the model to represent eastern oyster (Crassostrea virginica) in upper Chesapeake Bay by selecting sets of parameter values that matched observed rates of change in abundance and habitat. We used the model to evaluate the effects of a range of management and restoration options including sustainability of historical fishing pressure, effectiveness of a newly enacted sanctuary program, and relative performance of two restoration approaches. In general, autogenic ecosystem engineers are expected to be substantially less resilient to fishing than an equivalent species that does not rely on itself for habitat. Historical fishing mortality rates in upper Chesapeake Bay for oysters were above the levels that would lead to extirpation. Reductions in fishing or closure of the fishery were projected to lead to long-term increases in abundance and habitat. For fisheries to become sustainable outside of sanctuaries, a substantial larval subsidy would be required from oysters within sanctuaries. Restoration efforts using high-relief reefs were predicted to allow recovery within a shorter period of time than low-relief reefs. Models such as ours, that allow for feedbacks between population and habitat dynamics, can be effective tools for guiding management and restoration of autogenic ecosystem engineers.

A Framework for Exploring the Role of Bioeconomics on Observed Fishing Patterns and Ecosystem Dynamics

ABSTRACT Understanding the patterns of development of fisheries across trophic levels and their effects on ecosystems is essential for sustainable harvests. We develop an age-structured food web model to explore some of the bioeconomic causes and consequences of fishing patterns. We illustrate some of the model behaviors using a food chain ecosystem, parameterized using species found in the northwest Atlantic. We explore the effects of different relationships between profitability (defined as total profit per unit fishing effort) and trophic level of the target species on ecosystem and fishing dynamics. Across the profitability scenarios we explore, different patterns in ecosystem and fishery dynamics emerge, with greater variability and depletion in ecosystem biomass, greater variability and less yield to the fishery, and more variable profit when lower trophic level are more profitable and subject to more intense fishing pressure. For all scenarios we calculate the mean trophic level of the catch (TLC) in each year (where trends in this metric are often assumed to be an indicator of fishing patterns and ecosystem health) and compare it with the mean trophic level of the ecosystem. The relationship between the TLC and trophic level of the ecosystem varies with the way in which the fishery develops, and also with the particular species, suggesting that the TLC may not be the best indicator of ecosystem dynamics.