Ecosystem modeling in the Gulf of Mexico: current status and future needs to address ecosystem-based fisheries management and restoration activities

O’Farrell, Halie; Grüss, Arnaud; Sagarese, Skyler R.; Babcock, Elizabeth A.; Rose, Kenneth A.

doi:10.1007/s11160-017-9482-1

Cited by 24 publications

(17 citation statements)

References 90 publications

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“…; O'Farrell et al. ). In this context, it will be important to recognize platforms as unique areas in which fishes can alter their vertical distribution to avoid water layers containing stressors (e.g., extremes in temperature or DO concentration) that may have extirpated them from other habitats, allowing them to remain associated with a structure and retain their horizontal biogeographic distributions.…”

Section: Discussionmentioning

confidence: 99%

Environmental and Structural Drivers of Fish Distributions among Petroleum Platforms across the U.S. Gulf of Mexico

et al. 2020

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Petroleum platforms in the U.S. Gulf of Mexico (GOM) are important habitats for fishes and support regional fisheries. However, drivers of the horizontal (i.e., latitudinal and longitudinal) and vertical (i.e., position in the water column) distribution patterns of fishes associated with these artificial habitats are not fully understood on a GOMwide scale. To build upon previous studies on a large spatial scale and focus on species-specific drivers, we conducted 114 submersible rotating drop-camera and water quality sonde surveys at 54 platforms throughout the GOM. We then fitted two sets of binomial generalized additive mixed models integrating environmental and structural (i.e., characteristics of platforms) predictors to encounter/nonencounter data for 17 fish species so as to understand their horizontal and vertical distribution patterns throughout the GOM in platform habitat. Significant predictors for horizontal distribution included distance from shore (for Bermuda Chub Kyphosus saltatrix, Greater Amberjack Seriola dumerili, Vermilion Snapper Rhomboplites aurorubens), salinity (Bermuda Chub, Red Snapper Lutjanus campechanus), the number of platforms within 5 km (Blue Runner Caranx crysos, Crevalle Jack Caranx hippos), and dissolved oxygen concentration (Red Snapper). Significant predictors for vertical distribution included temperature

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Section: Discussionmentioning

confidence: 99%

Environmental and Structural Drivers of Fish Distributions among Petroleum Platforms across the U.S. Gulf of Mexico

et al. 2020

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“…2015b, ) and to map distribution hotspots for informing future MPA planning (O'Farrell et al. ). However, our statistical modeling framework could be employed to assist other timely EBFM efforts in the Gulf of Mexico, including, among others: (1) investigations of the degree of spatial overlap between assessed species and red tide Karenia brevis (a type of harmful algal bloom) to determine whether severe red tide events pose a threat to assessed species and should be considered in stock assessment and fisheries management (SEDAR 2009a, 2009b; Sagarese et al.…”

Section: Discussionmentioning

confidence: 99%

“…The statistical modeling framework we implemented can assist a number of EBFM efforts in the Gulf of Mexico. In this study, we employed GAM predictions to produce preference functions for an Ecospace ecosystem model used to evaluate the impacts of fisheries policies and efforts to mitigate lionfish invasion (Chagaris et al 2015b(Chagaris et al , 2017 and to map distribution hotspots for informing future MPA planning (O'Farrell et al 2017). However, our statistical modeling framework could be employed to assist other timely EBFM efforts in the Gulf of Mexico, including, among others: (1) investigations of the degree of spatial overlap between assessed species and red tide Karenia brevis (a type of harmful algal bloom) to determine whether severe red tide events pose a threat to FIGURE 5.…”

Section: Advancing Ebfmmentioning

confidence: 99%

Assisting Ecosystem‐Based Fisheries Management Efforts Using a Comprehensive Survey Database, a Large Environmental Database, and Generalized Additive Models

et al. 2018

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Statistical habitat models, such as generalized additive models (GAMs), are key tools for assisting ecosystembased fisheries management (EBFM) efforts. Predictions from GAMs can be used, for example, to produce preference functions for the ecosystem-modeling platform Ecospace; preference functions permit a flexible representation of spatial distribution patterns in Ecospace by defining the preferences of marine organisms for certain environmental parameter values. Generalized additive model predictions can also be used to map species distributions for assisting marine protected area (MPA) planning. In this study, we applied a recently proposed methodology to produce preference functions for the fish and invertebrates represented in an Ecospace model of the West Florida Shelf (WFS) and to map the hotspots of juveniles and adults of three economically important species for informing future MPA planning in the WFS region. This proposed methodology consists of (1) compiling a comprehensive survey database blending all of the encounter and nonencounter data of the study ecosystem collected by the fisheries-independent and fisheries-dependent surveys that employ random sampling schemes, (2) developing a large environmental database to store all of the environmental parameters influencing the spatial distribution patterns of the marine organisms of the study ecosystem, (3) using the comprehensive survey database and the large environmental database to fit binomial GAMs that integrate the confounding effects of survey and year, and (4) making predictions with fitted GAMs to Subject editor: Donald Noakes,

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“…The fact that year and monitoring program are fixed effect factors entails that it will be necessary to choose a given year and a given monitoring program to make predictions with fitted GAMs (in this case, the average year effect and the monitoring program effect with the highest selectivity; Punt et al, 2000;Maunder and Punt, 2004;Farmer and Karnauskas, 2013;Grüss et al, 2016a; see subsection Production of Distribution Maps for the GOM LME from the Predictions Made by Fitted Binomial GAMs). Ecosystem models such as Atlantis-GOM require longterm averages as input (Grüss et al, 2016a;O'Farrell et al, 2017), so encounter probability predictions for an average year is a desired output of the GAMs. However, to avoid having to make predictions using the monitoring program effect with the highest selectivity, we could have developed generalized additive mixed models (GAMMs; Lin and Zhang, 1999) treating gear as a random effect rather than GAMs.…”

Section: Surface Salinity Unitless Terrain Ruggedness Index Unitlessmentioning

confidence: 99%

“…Ecosystem simulation models are valuable tools for understanding the impacts of environmental and anthropogenic stressors on marine ecosystems and for informing resource management (Fulton, 2010;Christensen and Walters, 2011;O'Farrell et al, 2017). A wide diversity of ecosystem modeling platforms is now available, including the spatially-explicit modeling frameworks Atlantis (Fulton et al, 2004(Fulton et al, , 2007(Fulton et al, , 2011, Ecopath with Ecosim with Ecospace (Pauly et al, 2000;Christensen and Walters, 2004;Walters et al, 2010) and OSMOSE Cury, 2001, 2004;Grüss et al, 2016c).…”

Section: Introductionmentioning

confidence: 99%

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

et al. 2018

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To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM) Large Marine Ecosystem (LME) ("Atlantis-GOM"). This methodology consists of: (1) compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S.) GOM since 2000; (2) compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3) fitting binomial generalized additive models (GAMs) to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs) to the large monitoring database; and (4) employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by the data from the large monitoring database; for those, only GAM predictions were employed to obtain distribution maps for Atlantis-GOM. Pearson residuals were Grüss et al.Maps for an Ecosystem Model computed to validate geostatistical binomial GLMMs. Ultimately, 53 annual maps and 64 seasonal maps (for 32 different functional groups/life stages) were produced for Atlantis-GOM. Our methodology could serve other world's regions characterized by a large surface area, particularly LMEs bordered by several countries.

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Ecosystem modeling in the Gulf of Mexico: current status and future needs to address ecosystem-based fisheries management and restoration activities

Cited by 24 publications

References 90 publications

Environmental and Structural Drivers of Fish Distributions among Petroleum Platforms across the U.S. Gulf of Mexico

Environmental and Structural Drivers of Fish Distributions among Petroleum Platforms across the U.S. Gulf of Mexico

Assisting Ecosystem‐Based Fisheries Management Efforts Using a Comprehensive Survey Database, a Large Environmental Database, and Generalized Additive Models

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

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