Improving the spatial allocation of functional group biomasses in spatially-explicit ecosystem models: insights from three Gulf of Mexico models

Grüss, Arnaud; Babcock, Elizabeth A.; Sagarese, Skyler R.; Drexler, Michael; Chagaris, David; Ainsworth, Cameron H.; Penta, Bradley; deRada, Sergio; Sutton, Tracey

doi:10.5343/bms.2016.1057

Cited by 25 publications

(32 citation statements)

References 32 publications

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“…The modeling platform utilizes a biological sub-model that simulates nutrient, detritus and microbial cycles as well as species ecology, and a human impacts sub-model which can represent a wide variety of fisheries activities by modeling direct and indirect functional group mortality and habitat impacts. The distribution maps and vertical distribution profiles fed into Atlantis allow the modeling platform to allocate the biomasses of functional groups and life stages in the horizontal and vertical dimension, respectively (Grüss et al, 2016a).…”

Section: Methodsmentioning

confidence: 99%

“…. , x n are the environmental covariates selected after the collinearity analysis; and year and monitoring program are "nuisance variables" (i.e., variables that are not of immediate interest but that must be accounted for in the analysis) treated as fixed effect factors (Farmer and Karnauskas, 2013;Grüss et al, 2016aGrüss et al, , 2017a. 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).…”

Section: Surface Salinity Unitless Terrain Ruggedness Index Unitlessmentioning

confidence: 99%

“…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%

“…To be able to generate long-term probability of encounter maps, we constructed 20 × 20 km prediction grids for the GOM LME, based on the depth ranges at which the functional groups/life stages are encountered by the monitoring programs of the large monitoring database for the U.S. GOM. To determine the depth at which the functional groups/life stages are encountered by monitoring programs, we used the SRTM30 PLUS global bathymetry grid to generate a depth raster with a resolution of 20 km; we obtained the SRTM30 PLUS global bathymetry grid from the Gulf of Mexico Coastal Observing System 2 We made predictions with fitted binomial GAMs, using the 20 × 20 km prediction grids, the 20 × 20 km gridded map of environmental parameters, and 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).…”

Section: Production Of Distribution Maps For the Gom Lme From The Prementioning

confidence: 99%

“…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). The simulation of predator-prey interactions is a key feature of ecosystem models (Plaganyi, 2007;Christensen and Walters, 2011;Grüss et al, 2016a). To simulate predator-prey interactions, spatially-explicit ecosystem models need to represent patterns of spatial overlap between predators and prey.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Surface Salinity Unitless Terrain Ruggedness Index Unitlessmentioning

confidence: 99%

Section: Surface Salinity Unitless Terrain Ruggedness Index Unitlessmentioning

confidence: 99%

Section: Production Of Distribution Maps For the Gom Lme From The Prementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

O’Farrell

Grüss

Sagarese

et al. 2017

Rev Fish Biol Fisheries

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Improving the spatial allocation of functional group biomasses in spatially-explicit ecosystem models: insights from three Gulf of Mexico models

Cited by 25 publications

References 32 publications

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

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

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

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

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