Ecopath with Ecosim as a model-building toolbox: Source code capabilities, extensions, and variations

Steenbeek, Jeroen; Buszowski, Joe; Christensen, Villy; Akoğlu, Ekin; Aydin, Kerim; Ellis, Nick; Felinto, Dalai; Guitton, Jérôme; Lucey, Sean M.; Kearney, Kelly; Mackinson, Steven; Pan, Mike; Platts, Mark; Walters, Carl J.

doi:10.1016/j.ecolmodel.2015.06.031

Cited by 63 publications

(59 citation statements)

References 29 publications

(20 reference statements)

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“…; Steenbeek et al. ). It is now time to focus efforts on designing and using methodologies for improving the inputs used in EBFM studies, as we did in the present study (Grüss et al.…”

Section: Discussionmentioning

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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“…; Steenbeek et al. ). It is now time to focus efforts on designing and using methodologies for improving the inputs used in EBFM studies, as we did in the present study (Grüss et al.…”

Section: Discussionmentioning

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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“…Steenbeek et al . ) – to study community stability under diverse scenarios (e.g. climate warming, extreme events, management).…”

Section: Discussionmentioning

confidence: 99%

N‐dimensional hypervolumes to study stability of complex ecosystems

et al. 2016

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Although our knowledge on the stabilising role of biodiversity and on how it is affected by perturbations has greatly improved, we still lack a comprehensive view on ecosystem stability that is transversal to different habitats and perturbations. Hence, we propose a framework that takes advantage of the multiplicity of components of an ecosystem and their contribution to stability. Ecosystem components can range from species or functional groups, to different functional traits, or even the cover of different habitats in a landscape mosaic. We make use of n-dimensional hypervolumes to define ecosystem states and assess how much they shift after environmental changes have occurred. We demonstrate the value of this framework with a study case on the effects of environmental change on Alpine ecosystems. Our results highlight the importance of a multidimensional approach when studying ecosystem stability and show that our framework is flexible enough to be applied to different types of ecosystem components, which can have important implications for the study of ecosystem stability and transient dynamics.

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“…Spatially-explicit ecosystem models, especially highly sophisticated models such as applications of the Atlantis modeling platforms, rely on numerous data inputs, many of which can have a significant impact on ecosystem model predictions (Fulton et al, 2007;Shin et al, 2010;Walters et al, 2010;Steele et al, 2013). Many spatially-explicit ecosystem models have been designed worldwide (Fulton, 2010;EspinozaTenorio et al, 2012;Steele et al, 2013), and significant attention has been devoted to enhance the structure and assumptions of ecosystem modeling platforms (e.g., Shin et al, 2010;Steenbeek et al, 2013Steenbeek et al, , 2016Travers-Trolet et al, 2014;Grüss et al, 2016b,c). Therefore, it is now high time to develop and employ methodologies such as ours for ensuring that the spatially-explicit ecosystem models available around the world are provided with reliable inputs generated using the best available data and information.…”

Section: Discussionmentioning

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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Ecopath with Ecosim as a model-building toolbox: Source code capabilities, extensions, and variations

Cited by 63 publications

References 29 publications

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

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

N‐dimensional hypervolumes to study stability of complex ecosystems

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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