Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation
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2018
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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 ªidiosyncrasy in species responsesº 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.
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MARCO JUNIOR, Paulo De; NÓBREGA, Caroline Corrêa. Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. PLoS One, San Francisco, v. 13, n. 9, e0202403-25, 2018. DOI: 10.1371/journal.pone.0202403. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202403. Acesso em: 6 jan. 2023.