Producing distribution maps for a spatially-explicit ecosystem model using large monitoring and environmental databases and a combination of interpolation and extrapolation

Arnaud Grüss, Michael D. Drexler, Cameron H. Ainsworth, Elizabeth A Babcock, Joseph H. Tarnecki, Matthew S. Love

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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 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.

Original languageEnglish (US)
Article number16
JournalFrontiers in Marine Science
Volume5
Issue numberJAN
DOIs
StatePublished - Jan 31 2018

Fingerprint

Extrapolation
Ecosystems
Gulf of Mexico
interpolation
Interpolation
Monitoring
ecosystems
monitoring
ecosystem
Functional groups
Spatial distribution
functional group
Fish
spatial distribution
Fisheries
methodology
invertebrate
prediction
invertebrates
gulf

Keywords

  • Atlantis
  • Distribution maps
  • Generalized additive models
  • Geostatistical generalized linear mixed models
  • Gulf of Mexico
  • Large monitoring database
  • Spatially-explicit ecosystem model
  • Species distribution models

ASJC Scopus subject areas

  • Oceanography
  • Global and Planetary Change
  • Aquatic Science
  • Water Science and Technology
  • Environmental Science (miscellaneous)
  • Ocean Engineering

Cite this

Producing distribution maps for a spatially-explicit ecosystem model using large monitoring and environmental databases and a combination of interpolation and extrapolation. / Grüss, Arnaud; Drexler, Michael D.; Ainsworth, Cameron H.; Babcock, Elizabeth A; Tarnecki, Joseph H.; Love, Matthew S.

In: Frontiers in Marine Science, Vol. 5, No. JAN, 16, 31.01.2018.

Research output: Contribution to journalArticle

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