Management Strategy Evaluation: Allowing the Light on the Hill to Illuminate More Than One Species

Management strategy evaluation (MSE) is a simulation approach that serves as a “light on the hill” (Smith, 1994) to test options for marine management, monitoring, and assessment against simulated ecosystem and fishery dynamics, including uncertainty in ecological and fishery processes and observations. MSE has become a key method to evaluate trade-offs between management objectives and to communicate with decision makers. Here we describe how and why MSE is continuing to grow from a single species approach to one relevant to multi-species and ecosystem-based management. In particular, different ecosystem modeling approaches can fit within the MSE process to meet particular natural resource management needs. We present four case studies that illustrate how MSE is expanding to include ecosystem considerations and ecosystem models as ‘operating models’ (i.e., virtual test worlds), to simulate monitoring, assessment, and harvest control rules, and to evaluate tradeoffs via performance metrics. We highlight United States case studies related to fisheries regulations and climate, which support NOAA’s policy goals related to the Ecosystem Based Fishery Roadmap and Climate Science Strategy but vary in the complexity of population, ecosystem, and assessment representation. We emphasize methods, tool development, and lessons learned that are relevant beyond the United States, and the additional benefits relative to single-species MSE approaches

Detecting somatic growth trends for summer flounder (Paralichthys dentatus) using a state-space approach

In the past four decades, summer flounder abundance in the Northwest Atlantic shifted with no definitive explanation for this shift. Here, we extract patterns in population-level size variability from summer flounder mean length-at-age data from 1992 – 2015 using an autoregressive state-space modeling approach and annual fishing and oceanographic covariates. We found that summer flounder length-at-age varies annually, suggesting that productivity can vary annually due to variable sizes. We found that location and depth of the observed fish, exploitation, and the Gulf Stream appeared to influence the magnitude of length-at-age variation, whereby lengths-at-age were above the mean length at greater depth, northern latitudes, and during periods characterized by a northerly Gulf Stream position or higher fishing exploitation. These factors should be considered as indicators to track size and more accurately understand productivity as the summer flounder population changes and the fishery adapts in response. This study brings us closer to annual proxies for summer flounder length-at-age variation, an important tool for fisheries managers and stock-assessment scientists to more accurately predict fish stock abundances and productivity.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Optimal Survey Design for Forest Carbon Monitoring in Remote Regions Using Multi-Objective Mathematical Programming

Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inventories (NFI) utilizing a large sample of field plots, in remote regions the lack of transportation infrastructure often requires heavier reliance on remote sensing technologies, such as airborne lidar. The challenge motivating our research is that the efficacy of estimating carbon with lidar varies across the various carbon pools within forest ecosystems. Lidar measurements are typically highly correlated with aboveground tree carbon but are less strongly correlated with other carbon pools, such as down woody materials (DWM) and soil. Field measurements are essential to both (1) estimate soil and DWM carbon directly and (2) develop regression models to estimate tree carbon indirectly using lidar. With limited budgets and time, however, decision makers must find an optimal way to combine field measurements with lidar to minimize standard errors in carbon estimates for the various pools. We introduce a multi-objective binary programming formulation that quantifies the tradeoffs behind the competing objectives of minimizing standard errors for tree carbon, DWM carbon, and soil carbon. Using NFI and airborne lidar data from a remote boreal forest region of interior Alaska, we demonstrate the operational feasibility of the method and suggest that it is generalizable to other carbon sampling projects because of its generic mathematical structure

Optimal Survey Design for Forest Carbon Monitoring in Remote Regions Using Multi-Objective Mathematical Programming

Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inventories (NFI) utilizing a large sample of field plots, in remote regions the lack of transportation infrastructure often requires heavier reliance on remote sensing technologies, such as airborne lidar. The challenge motivating our research is that the efficacy of estimating carbon with lidar varies across the various carbon pools within forest ecosystems. Lidar measurements are typically highly correlated with aboveground tree carbon but are less strongly correlated with other carbon pools, such as down woody materials (DWM) and soil. Field measurements are essential to both (1) estimate soil and DWM carbon directly and (2) develop regression models to estimate tree carbon indirectly using lidar. With limited budgets and time, however, decision makers must find an optimal way to combine field measurements with lidar to minimize standard errors in carbon estimates for the various pools. We introduce a multi-objective binary programming formulation that quantifies the tradeoffs behind the competing objectives of minimizing standard errors for tree carbon, DWM carbon, and soil carbon. Using NFI and airborne lidar data from a remote boreal forest region of interior Alaska, we demonstrate the operational feasibility of the method and suggest that it is generalizable to other carbon sampling projects because of its generic mathematical structure

Fishing amplifies forage fish population collapses.

Forage fish support the largest fisheries in the world but also play key roles in marine food webs by transferring energy from plankton to upper trophic-level predators, such as large fish, seabirds, and marine mammals. Fishing can, thereby, have far reaching consequences on marine food webs unless safeguards are in place to avoid depleting forage fish to dangerously low levels, where dependent predators are most vulnerable. However, disentangling the contributions of fishing vs. natural processes on population dynamics has been difficult because of the sensitivity of these stocks to environmental conditions. Here, we overcome this difficulty by collating population time series for forage fish populations that account for nearly two-thirds of global catch of forage fish to identify the fingerprint of fisheries on their population dynamics. Forage fish population collapses shared a set of common and unique characteristics: high fishing pressure for several years before collapse, a sharp drop in natural population productivity, and a lagged response to reduce fishing pressure. Lagged response to natural productivity declines can sharply amplify the magnitude of naturally occurring population fluctuations. Finally, we show that the magnitude and frequency of collapses are greater than expected from natural productivity characteristics and therefore, likely attributed to fishing. The durations of collapses, however, were not different from those expected based on natural productivity shifts. A risk-based management scheme that reduces fishing when populations become scarce would protect forage fish and their predators from collapse with little effect on long-term average catches

Fishing amplifies forage fish population collapses.

Forage fish support the largest fisheries in the world but also play key roles in marine food webs by transferring energy from plankton to upper trophic-level predators, such as large fish, seabirds, and marine mammals. Fishing can, thereby, have far reaching consequences on marine food webs unless safeguards are in place to avoid depleting forage fish to dangerously low levels, where dependent predators are most vulnerable. However, disentangling the contributions of fishing vs. natural processes on population dynamics has been difficult because of the sensitivity of these stocks to environmental conditions. Here, we overcome this difficulty by collating population time series for forage fish populations that account for nearly two-thirds of global catch of forage fish to identify the fingerprint of fisheries on their population dynamics. Forage fish population collapses shared a set of common and unique characteristics: high fishing pressure for several years before collapse, a sharp drop in natural population productivity, and a lagged response to reduce fishing pressure. Lagged response to natural productivity declines can sharply amplify the magnitude of naturally occurring population fluctuations. Finally, we show that the magnitude and frequency of collapses are greater than expected from natural productivity characteristics and therefore, likely attributed to fishing. The durations of collapses, however, were not different from those expected based on natural productivity shifts. A risk-based management scheme that reduces fishing when populations become scarce would protect forage fish and their predators from collapse with little effect on long-term average catches