Using delta generalized additive models to produce distribution maps for spatially explicit ecosystem models

Grüss, Arnaud; Drexler, Michael; Ainsworth, Cameron H.

doi:10.1016/j.fishres.2014.05.005

Cited by 76 publications

(69 citation statements)

References 29 publications

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“…The usefulness of our methodology combining GAM and geostatistical GLMM predictions is exemplified by the distribution maps generated for pink shrimp and adult red grouper (Figure 4). The distribution maps produced for juvenile and adult pink shrimps from GAM and geostatistical GLMM predictions concurred with previous studies in that pink shrimp hotspots are found on the West Florida Shelf, around the Dry Tortugas and the Florida Keys, in the region off Alabama and Mississippi where depth ranges between 20 and 40 m, on the Texas-Mexico border, and on the Campeche Bank (Costello and Allen, 1970;Bielsa et al, 1983;Ramirez-Rodriguez et al, 2003;Drexler and Ainsworth, 2013;Grüss et al, 2014). However, reflecting monitoring data for the U.S. GOM, the geostatistical GLMM of adult pink shrimp predicted that the life stage has a very high probability to be encountered in the region off Alabama and Mississippi where depth ranges between 20 and 40 m, contrary to the GAM of adult pink shrimp.…”

Section: Discussionsupporting

confidence: 86%

“…This assumption is appropriate for many of the functional groups represented in Atlantis-GOM that are structure-oriented; the abundance of structure-oriented functional groups, such as red grouper and gag, may be primarily determined by the encounter/nonencounter of a suitable structure (Saul et al, 2013;Grüss et al, 2017c). Yet, spatial patterns of probability of encounter and spatial patterns of abundance often rely on slightly different determinants (Nielsen et al, 2005;Koubbi et al, 2006;Aarts et al, 2012;Grüss et al, 2014); thus, if we had produced distribution maps from abundance predictions (e.g., if we had had monitoring data for the entire GOM LME), some of those distribution maps may have been slightly different from the distribution maps constructed in the present study. In the future, if we had access to a reasonable amount of monitoring data for the southern GOM, it would be interesting to evaluate the consequences of employing abundance vs. probability of encounter maps to produce maps of relative abundance for Atlantis-GOM with our methodology.…”

Section: Discussionmentioning

confidence: 73%

“…Thus, as is often done in the mapping literature (e.g., Maxwell et al, 2009;Hattab et al, 2013;Cormon et al, 2014;Grüss et al, 2014Grüss et al, , 2017b,c), we assumed a linear relationship between probability of encounter and abundance. This assumption is appropriate for many of the functional groups represented in Atlantis-GOM that are structure-oriented; the abundance of structure-oriented functional groups, such as red grouper and gag, may be primarily determined by the encounter/nonencounter of a suitable structure (Saul et al, 2013;Grüss et al, 2017c).…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the shrinkage approach, we applied an extra penalty to each environmental covariate as the smoothing parameter approached zero, which allowed its removal when the smoothing parameter was equal to zero (Marra and Wood, 2011;Drexler and Ainsworth, 2013;Grüss et al, 2014). Once a GAM was fitted, if an environmental covariate p-value had a p > 0.05, it was removed and the model was refitted (Koubbi et al, 2006;Weber and McClatchie, 2010;Grüss et al, 2014Grüss et al, , 2016dGrüss et al, , 2017aChagaris et al, 2015). The restricted maximum likelihood (REML) optimization method was employed (Wood, 2011).…”

Section: Surface Salinity Unitless Terrain Ruggedness Index Unitlessmentioning

confidence: 99%

“…The extrapolation of spatial distribution patterns predicted by statistical models integrating environmental covariates (commonly called "species distribution models") is a useful means to produce distribution maps for spatially-explicit ecosystem models (Drexler and Ainsworth, 2013;Grüss et al, 2014Grüss et al, , 2016dHattab et al, 2014). For example, Drexler and Ainsworth (2013) fitted generalized additive models (GAMs) to monitoring data collected in the U.S. Gulf of Mexico (GOM).…”

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: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Surface Salinity Unitless Terrain Ruggedness Index Unitlessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Using historical fisheries data to predict tuna distribution within the British Indian Ocean Territory Marine Protected Area, and implications for its management

Dunn

Curnick

2019

Aquatic Conservation

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Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes

Vilas

Fletcher

Siders

et al. 2022

Ecology and Evolution

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Understanding how species respond to the environment is essential in ecology, evolution, and conservation. Abiotic factors can influence species responses and the multi‐dimensional space of abiotic factors that allows a species to grow represents the environmental niche. While niches are often assumed to be constant and robust, they are most likely changing over time and estimation can be influenced by population biology, sampling intensity, and computation methodology. Here, we used a 12‐year time series of survey data to fit annual ecological niche models (ENMs) for 10 marine fish species by using two regression and two machine learning algorithms to evaluate the variation and differentiation of environmental niches. Fitted ENMs were used to develop multi‐dimensional annual and pooled hypervolumes that were evaluated over time and across ENM algorithms, species, and years by computing volume, distance, and dissimilarity metrics for each annual estimated niche. We then investigated potential drivers of estimated hypervolume dynamics including species abundance, species occurrence, sampling effort, salinity, red tides severity, and algorithm. Overall, our results revealed that estimated niches varied over time and across ENM, species, and algorithms. Niche estimation was influenced over time by multiple factors suggesting high complexity on niche dynamics interpretation. Species with high occurrence tended to have a closer representation of the pooled niche and years with higher abundance tended to produce niche expansion. ENM algorithm, sampling effort, seawater salinity, and red tides explained the deviations from the pooled niche. Greater sampling effort led to more comprehensive and complete estimates of species niches. High red tides severity triggered niche contraction. Our results emphasize the predictable effects of population, sampling, and environment on species niche estimation and interpretation, and that each should be considered when performing and interpreting ecological niche analyses. Our niche analysis approach may contribute to effectively quantifying and assessing niche dynamics.

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Using delta generalized additive models to produce distribution maps for spatially explicit ecosystem models

Cited by 76 publications

References 29 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

Using historical fisheries data to predict tuna distribution within the British Indian Ocean Territory Marine Protected Area, and implications for its management

Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes

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