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Bayesian spatial predictive models for data-poor fisheries

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

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.

Suggested Citation

  • Rufener, Marie-Christine & Kinas, Paul Gerhard & Nóbrega, Marcelo Francisco & Lins Oliveira, Jorge Eduardo, 2017. "Bayesian spatial predictive models for data-poor fisheries," Ecological Modelling, Elsevier, vol. 348(C), pages 125-134.
    • Handle: RePEc:eee:ecomod:v:348:y:2017:i:c:p:125-134
    DOI: 10.1016/j.ecolmodel.2017.01.022
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    References listed on IDEAS

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    1. Zaida C. Quiroz & Marcos O. Prates & Håvard Rue, 2015. "A Bayesian approach to estimate the biomass of anchovies off the coast of Perú," Biometrics, The International Biometric Society, vol. 71(1), pages 208-217, March.
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    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    1. Martínez-Minaya, Joaquín & Conesa, David & Bakka, Haakon & Pennino, Maria Grazia, 2019. "Dealing with physical barriers in bottlenose dolphin (Tursiops truncatus) distribution," Ecological Modelling, Elsevier, vol. 406(C), pages 44-49.

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