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Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

Author

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  • Paulo De Marco Júnior
  • Caroline Corrêa Nóbrega

Abstract

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls − collinearity among environmental predictors − is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called “idiosyncrasy in species responses” to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables.

Suggested Citation

  • Paulo De Marco Júnior & Caroline Corrêa Nóbrega, 2018. "Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-25, September.
  • Handle: RePEc:plo:pone00:0202403
    DOI: 10.1371/journal.pone.0202403
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    1. Pimenta, Mayra & Andrade, André Felipe Alves de & Fernandes, Fernando Hiago Souza & Amboni, Mayra Pereira de Melo & Almeida, Renata Silva & Soares, Ana Hermínia Simões de Bello & Falcon, Guth Berger &, 2022. "One size does not fit all: Priority areas for real world problems," Ecological Modelling, Elsevier, vol. 470(C).
    2. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    3. Abdulwahab, Umarfarooq A. & Hammill, Edd & Hawkins, Charles P., 2022. "Choice of climate data affects the performance and interpretation of species distribution models," Ecological Modelling, Elsevier, vol. 471(C).
    4. Mendes, Poliana & Velazco, Santiago José Elías & Andrade, André Felipe Alves de & De Marco, Paulo, 2020. "Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy," Ecological Modelling, Elsevier, vol. 431(C).
    5. Bryanna Norlin & Andrew E. Scholl & Andrea L. Case & Timothy J. Assal, 2024. "Mapping an Indicator Species of Sea-Level Rise along the Forest–Marsh Ecotone," Land, MDPI, vol. 13(10), pages 1-20, September.
    6. Dany A. Cotrina Sánchez & Elgar Barboza Castillo & Nilton B. Rojas Briceño & Manuel Oliva & Cristóbal Torres Guzman & Carlos A. Amasifuen Guerra & Subhajit Bandopadhyay, 2020. "Distribution Models of Timber Species for Forest Conservation and Restoration in the Andean-Amazonian Landscape, North of Peru," Sustainability, MDPI, vol. 12(19), pages 1-20, September.
    7. Sillero, Neftalí & Arenas-Castro, Salvador & Enriquez‐Urzelai, Urtzi & Vale, Cândida Gomes & Sousa-Guedes, Diana & Martínez-Freiría, Fernando & Real, Raimundo & Barbosa, A.Márcia, 2021. "Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling," Ecological Modelling, Elsevier, vol. 456(C).

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