Bayesian spatial predictive models for data-poor fisheries

Marie-Christine Rufener, Paul Gerhard Kinas, Marcelo Francisco Nobrega, Jorge Eduardo Lins Oliveira

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Abstract

Understanding the spatial distribution and identifying environmental variables that drive endangered fish species abundance are key factors to implement sustainable fishery management strategies. In the present study we proposed hierarchical Bayesian spatial models to quantify and map sensitive habitats for juveniles, adults and overall abundance of the vulnerable lane snapper (Lutjanus synagris) present in the northeastern Brazil. Data were collected by fishery-unbiased gillnet surveys, and fitted through the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equations (SPDE) tools, both implemented in the R environment by the R-INLA library (http://www.r-inla.org). Our results confirmed that the abundance of juveniles and adults of L synagris are spatially correlated, have patchy distributions along the Rio Grande do Norte coast, and are mainly affected by environmental predictors such as distance to coast, chlorophyll-a concentration, bathymetry and sea surface temperature. By means of our results we intended to consolidate a recently introduced Bayesian geostatistical model into fisheries science, highlighting its potential for establishing more reliable measures for the conservation and management of vulnerable fish species even when data are sparse. (C) 2017 Elsevier 'B.V. All rights reserved.
Original languageEnglish
JournalEcological Modelling
Volume348
Pages (from-to)125-134
ISSN0304-3800
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Ecological Modeling
  • Bayesian geostatistical models
  • Essential fish habitats
  • Fisheries ecology
  • Integrated Nested Laplace Approximations
  • Stochastic Partial Differential Equations
  • Atmospheric temperature
  • Conservation
  • Digital storage
  • Ecosystems
  • Fish
  • Fisheries
  • Laplace transforms
  • Oceanography
  • Partial differential equations
  • Stochastic models
  • Stochastic programming
  • Stochastic systems
  • Surface waters
  • Chlorophyll-a concentration
  • Environmental variables
  • Essential fish habitat
  • Geostatistical modeling
  • Geostatistical models
  • Laplace approximation
  • Sea surface temperature (SST)
  • Stochastic partial differential equation
  • Population distribution
  • abundance estimation
  • bathymetric survey
  • Bayesian analysis
  • chlorophyll a
  • fishery management
  • fishery survey
  • gillnet
  • patchiness
  • prediction
  • sea surface temperature
  • spatial distribution
  • Brazil
  • Rio Grande do Norte
  • Lutjanus synagris
  • General biology - Conservation and resource management
  • Mathematical biology and statistical methods
  • Ecology: environmental biology - General and methods
  • Ecology: environmental biology - Wildlife management: aquatic
  • Biophysics - Biocybernetics
  • chlorophyll-a
  • Animals, Chordates, Fish, Nonhuman Vertebrates, Vertebrates
  • species abundance
  • species distribution
  • ECOLOGY
  • SNAPPER LUTJANUS-SYNAGRIS
  • LANE SNAPPER
  • PERCIFORMES LUTJANIDAE
  • SPECIES DISTRIBUTION
  • NURSERIES
  • HABITATS
  • PISCES
  • GROWTH
  • COAST
  • AGE

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