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Pennino MG, Paradinas I, Illian JB, et al. Accounting for preferential sampling in species distribution models. Ecol Evol. 2018;9(1):653-663doi: 10.1002/ece3.4789.
Pennino, M. G., Paradinas, I., Illian, J. B., Muñoz, F., Bellido, J. M., López-Quílez, A., & Conesa, D. (2019). Accounting for preferential sampling in species distribution models. Ecology and evolution, 9(1), 653-663. https://doi.org/10.1002/ece3.4789
Pennino, Maria Grazia, et al. "Accounting for preferential sampling in species distribution models." Ecology and evolution vol. 9,1 (2019): 653-663. doi: https://doi.org/10.1002/ece3.4789
Pennino MG, Paradinas I, Illian JB, Muñoz F, Bellido JM, López-Quílez A, Conesa D. Accounting for preferential sampling in species distribution models. Ecol Evol. 2018 Dec 26;9(1):653-663. doi: 10.1002/ece3.4789. eCollection 2019 Jan. PMID: 30680145; PMCID: PMC6342115.
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Ecol Evol. 2018 Dec 26;9(1):653-663. doi: 10.1002/ece3.4789. eCollection 2019 Jan.

Accounting for preferential sampling in species distribution models.

Ecology and evolution

Maria Grazia Pennino, Iosu Paradinas, Janine B Illian, Facundo Muñoz, José María Bellido, Antonio López-Quílez, David Conesa

Affiliations

  1. Instituto Español de Oceanografía Centro Oceanográfico de Vigo Vigo Spain.
  2. Departament ?Estadística i Investigació Operativa Universitat de València Valencia Spain.
  3. Ipar Perspective Asociación Sopela Spain.
  4. School of Mathematics and Statistics Centre for Research into Ecological and Environmental Modelling (CREEM) University of St Andrews St Andrews UK.
  5. Instituto Español de Oceanografía Centro Oceanográfico de Murcia Murcia Spain.

PMID: 30680145 PMCID: PMC6342115 DOI: 10.1002/ece3.4789

Abstract

Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as

Keywords: Bayesian modelling; integrated nested Laplace approximation; point processes; species distribution models; stochastic partial differential equation

References

  1. Ecol Appl. 2006 Feb;16(1):33-50 - PubMed
  2. Conserv Biol. 2010 Oct;24(5):1388-97 - PubMed

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