John T. Froeschke

Hierarchical and interactive habitat selection in response to abiotic and biotic factors: The effect of hypoxia on habitat selection of juvenile estuarine fishes

Habitat selection is a shared process among animals where individuals choose areas that differ in biotic and abiotic characteristics to maximize individual fitness. We used manipulative laboratory mesocosm choice experiments to examine hierarchical and interactive relationships influencing this habitat selection process of estuarine fishes. We assessed selection among substrate, dissolved oxygen (DO) concentration, food availability, and predation risk using two common juvenile estuarine fish species, pinfish (Lagodon rhomboides) and Atlantic croaker (Micropogonias undulatus). For both species oxygen concentration greatly influenced selection patterns; fishes strongly avoided low DO conditions, while in higher levels of DO factors such as substrate or food influenced selection patterns. However, both species strongly avoided predators even when alternative habitat was severely oxygen limited. These results suggest that predation risk may be the greatest determinant of habitat selection of the factors considered. Expansion of low DO areas in the world’s oceans is a major anthropogenic disturbance and is rapidly increasing. Assessing impacts of hypoxia on habitat usage of mobile organisms is critical as changes in environmental metrics including predator distribution and DO levels may alter habitat selection patterns disrupting critical ecosystem processes and trophic interactions. Our results indicate that juvenile fishes may forgo emigration from hypoxia due to predation risk. If similar patterns occur for juvenile fishes in estuaries they may potentially suffer from reduced growth, reproductive output, and survivorship.

Spatiotemporal Predictive Models for Juvenile Southern Flounder in Texas Estuaries

Abstract Southern Flounder Paralichthys lethostigma supports a multimillion dollar commercial and recreational fishery in the Gulf of Mexico. Despite its economic importance, the Southern Flounder population has been declining for decades. To improve the management of this fishery, both population trends and changes in environmental conditions need to be considered. Using two different statistical modeling techniques, boosted regression tree (BRT) and artificial neural network (ANN), a 29-year fisheries-independent record of juvenile Southern Flounder abundance in Texas was examined to illustrate how environmental factors influence the temporal and spatial distribution of juvenile Southern Flounder. Boosted regression trees show the presence of juvenile Southern Flounder is closely associated with relatively low temperatures, low salinity levels, and high dissolved oxygen concentrations. Both ANN and BRT models resulted in high predictive performance with slight spatial differences in predicted distributi...

Recommendations on the Use of Ecosystem Modeling for Informing Ecosystem‐Based Fisheries Management and Restoration Outcomes in the Gulf of Mexico

AbstractEcosystem-based fisheries management (EBFM) and ecosystem restoration are gaining momentum worldwide, including in U.S. waters of the Gulf of Mexico (GOM). Ecosystem models are valuable tools for informing EBFM and restoration activities. In this paper, we provide guidance and a roadmap for ecosystem modeling in the GOM region, with an emphasis on model development and use of model products to inform EBFM and the increasing investments in restoration. We propose eight “best practices” for ecosystem modeling efforts, including (1) identification of priority management questions, (2) scenarios as simulation experiments, (3) calibration and validation needs, (4) sensitivity and uncertainty analyses, (5) ensuring transparency, (6) improving communication between ecosystem modelers and the various stakeholders, (7) documentation of modeling efforts, and (8) maintaining the ecosystem models and codes. Fisheries management in the USA adheres to a prescriptive set of calculations. Therefore, the use of ec...

Two-Stage Boosted Regression Tree Model to Characterize Southern Flounder Distribution in Texas Estuaries at Varying Population Sizes

Abstract Linking trends in fish population abundance to environmental characteristics is often difficult because fish use avariety of habitats throughout their ontogeny and may exhibit large interannual fluctuations in abundance. Wedeveloped a two-stage boosted regression tree model to investigate spatiotemporal patterns of Southern Flounder Paralichthys lethostigma abundance and distribution in Texas estuaries. We used a 36-year fishery-independent data set (1977–2012) to correlate distribution with environmental conditions and seasonal or long-term changes in abundance. Adult Southern Flounder were sampled with gill nets using a random-stratified design. Predictions of abundance were made to grids of environmental data to identify “hot spots” as well as seasonal or decadal shifts in distribution. Models were fit using cross validation, and variance was estimated using nonparametric bootstrapping. Depth, temperature, distance to a tidal inlet, and salinity were the primary environmental determinants of Southern Flounder distribution and abundance. Because distribution and response to environmental conditions can depend on population abundance, we also developed a standardized index of annual abundance using the same two-stage boosted regression tree model. The index identifies a long-term decline in abundance punctuated by recent recovery (possibly in response to management actions). Mapped results identify a coastwide decline in abundance between 1980–1984 and 2005–2009 but the magnitude varied substantially, suggesting disproportionate changes in abundance across the study area.

Forecasting for Recreational Fisheries Management: What's the Catch?

AbstractThe Magnuson–Stevens Fishery Conservation and Management Reauthorization Act of 2006 required regional fishery management councils to implement annual catch limits (ACLs) for nearly all stocks under U.S. federal management. Since 2011, the number of stocks requiring ACLs (and monitoring) has increased nearly 10-fold, with strict accountability measures requiring either in-season quota closures or shortening of subsequent seasons to avoid ACL overages. Robust forecasts of landings can also provide a projected baseline for evaluation of proposed management alternatives. We compared generalized linear models (GLMs), generalized additive models (GAMs), and seasonal autoregressive integrated moving average (SARIMA) models in terms of fit, accuracy, and ability to forecast landings of four representative fish stocks that support recreational fisheries in the southeastern United States. All models were useful in developing reliable forecasts to inform management. The GAMs provided the best fit to the obs...