Generalized Additive Models Used to Predict Species Abundance in the Gulf of Mexico: An Ecosystem Modeling Tool

Drexler, Michael; Ainsworth, Cameron H.

doi:10.1371/journal.pone.0064458

Cited by 105 publications

(83 citation statements)

References 20 publications

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“…Table 2) for most significantly explaining R and RPS trend were GAM models. This is consistent with the findings that GAM performs better than other types of population modeling (Drexler and Ainsworth, 2013;Moisen and Frescino, 2002;Guisan et al, 2002;Walsh and Kleiber, 2001). …”

Section: Figure 8 the Linear Relationship Between The Predicted Recrsupporting

confidence: 91%

Environmental Conditions Are Important Influences on the Recruitment of North Pacific Albacore Tuna, Thunnus Alalunga

Singh¹

2017

Appl Ecol Env Res

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Abstract. The effects of changing climate on numerous commercially and ecologically important fish species including the South Pacific albacore tuna, Thunnus alalunga have been documented over the past decades. The objective of this study was to explore and elucidate the relationship of environmental variables with the stock parameters of albacore tuna. The relationship of the North Pacific albacore tuna recruitment (R), female spawning stock biomass (SSB) and recruits per spawning biomass (RPS) from 1970 to 2012 with the environmental factors of sea surface temperature (SST), Pacific decadal oscillation (PDO) and El Niño southern oscillation (ENSO) was construed. SST and PDO were used as independent variables with SSB to construct stock reproduction models for R and RPS as they showed most significant relationship with the dependent variables. Model selections were based on Akaike Information Criterion (AIC) with the condition of significant parameter estimates at p<0.05. Models with single independent variables of SST, PDO and ENSO were also constructed to illuminate their individual effect on albacore R and RPS. From the results it can be stated that SST and PDO resulted in the most significant models for reproducing North Pacific albacore tuna R and RPS time series.

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Section: Figure 8 the Linear Relationship Between The Predicted Recrsupporting

confidence: 91%

Environmental Conditions Are Important Influences on the Recruitment of North Pacific Albacore Tuna, Thunnus Alalunga

Singh¹

2017

Appl Ecol Env Res

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“…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%

“…For example, Drexler and Ainsworth (2013) fitted generalized additive models (GAMs) to monitoring data collected in the U.S. Gulf of Mexico (GOM). The authors then extrapolated the predictions made by their GAMs to the entire GOM Large Marine Ecosystem (LME) to obtain distribution maps for some of the functional groups (all life stages combined) represented in the Atlantis model of the GOM LME ("Atlantis-GOM"; Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…The authors then extrapolated the predictions made by their GAMs to the entire GOM Large Marine Ecosystem (LME) to obtain distribution maps for some of the functional groups (all life stages combined) represented in the Atlantis model of the GOM LME ("Atlantis-GOM"; Figure 1). While Drexler and Ainsworth's (2013) methodology is more sophisticated than usual methodologies for generating distribution maps for ecosystem models, it has some limitations. Firstly, Drexler and Ainsworth (2013) employed only one groundfish trawl survey dataset to produce all their distribution maps, which resulted in unreliable predictions of spatial distributions for those functional groups that are poorly sampled by groundfish trawl (e.g., some of the pelagic functional groups represented in Atlantis-GOM).…”

Section: Introductionmentioning

confidence: 99%

“…While Drexler and Ainsworth's (2013) methodology is more sophisticated than usual methodologies for generating distribution maps for ecosystem models, it has some limitations. Firstly, Drexler and Ainsworth (2013) employed only one groundfish trawl survey dataset to produce all their distribution maps, which resulted in unreliable predictions of spatial distributions for those functional groups that are poorly sampled by groundfish trawl (e.g., some of the pelagic functional groups represented in Atlantis-GOM). Secondly, Drexler and Ainsworth (2013) used the same set of environmental covariates (dominant sediment type, bottom depth, bottom temperature, bottom dissolved oxygen (DO) concentration, and surface chlorophyll-a concentration) in all their GAMs; however, other environmental parameters may have a stronger influence of the spatial distribution patterns of the functional groups represented in Atlantis-GOM (e.g., sea surface temperature (SST) in the case of most pelagic functional groups).…”

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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Appraisal of culture‐based fisheries of giant freshwater prawn (Macrobrachium rosenbergii, De Man, 1879) in reservoirs of Sri Lanka

Digamadulla

Asoka

Jones

et al. 2023

Aquaculture Fish & Fisheries

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The introduction of high economic value species like giant freshwater prawn (GFWP) makes culture-based fisheries (CBF) a substantial source of income and simultaneously supports the livelihood development of rural fishers in Sri Lanka. However, this CBF strategy has been implemented without any scientific basis, and therefore, this paper evaluated the stocking and production data of GFWP in Sri Lankan reservoirs from 2011 to 2017 to identify interventions for achieving future sustainability. We used two distinct approaches to appraise the CBF strategies practised so far and decrypt the identified shortcomings in the initiatives. The first approach was a straight-head univariate generalized additive model (GAM) analysis of stocking density (SD), production (PRD), recapture efficiency (RCE) and reservoir surface area (A). The latter approach was a hybrid PCoA-GAM for detained data mining for a better management perspective. The relationships among SD, PRD RCE and A were significant and, stochastic PRD and RCE were reasoned by the biased stocking practices identified in the GAM analysis.The hybrid PCoA-GAM is seemingly robust in predicting and explaining the deviances of PRD and RCE of GFWP CBF. Results indicated that the present practices of CBF of GFWP in Sri Lankan reservoirs suffer several weaknesses, overstocking in minor and understocking in both medium and major reservoirs are primarily under consideration, which includes inter alia, the need for effective tools for identification of suitable reservoirs, the inefficient harvesting practices, non-optimal SDs coupled with the possible vulnerability of stocked postlarvae to high mortalities. According to the study, we prefer to set the optimum SD as 1500 PLs ha −1 to all reservoirs. A comprehensive study has been launched for the enhancement of CBF of GFWP in Sri Lankan reservoirs.

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Generalized Additive Models Used to Predict Species Abundance in the Gulf of Mexico: An Ecosystem Modeling Tool

Cited by 105 publications

References 20 publications

Environmental Conditions Are Important Influences on the Recruitment of North Pacific Albacore Tuna, Thunnus Alalunga

Environmental Conditions Are Important Influences on the Recruitment of North Pacific Albacore Tuna, Thunnus Alalunga

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

Appraisal of culture‐based fisheries of giant freshwater prawn (Macrobrachium rosenbergii, De Man, 1879) in reservoirs of Sri Lanka

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