Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

By Carsten F. Dormann, Jane Elith, S. Bacher, Carsten M. Buchmann, G. Carl, Gabriel Carré, J. Márquez, Bernd Gruber, Bruno Lafourcade, Pedro J. Leitão and 8 more. 2013

9,730 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9eb715fe0347445a2d63518cbb476d345ba86233

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Antoine Guisan
2. Jane Elith
3. Wilfried Thuiller
4. Miguel B. Araújo
5. A. Townsend Peterson
6. Janet Franklin
7. Jorge M. Lobo
8. N. E. Zimmermann

In Ecology

1. Jane Elith
2. Antoine Guisan
3. Wilfried Thuiller
4. Miguel B. Araújo
5. Janet Franklin
6. A. Townsend Peterson
7. Jorge M. Lobo
8. N. E. Zimmermann

In Nature and Landscape Conservation

1. Antoine Guisan
2. Jane Elith
3. Wilfried Thuiller
4. Miguel B. Araújo
5. A. Townsend Peterson
6. Janet Franklin
7. N. E. Zimmermann
8. Jorge M. Lobo

Citations in the corpus

• A practical guide to environmental association analysis in landscape genomics (2015, methodology): “One way to avoid collinearity is to reduce the number of factors (for a review of methods, see, e.g. Dormann et al. 2013).”
• Global use of ecosystem service models (2016, neutral): “For habitat model use, the Biodiversity PC and EPI variables had positive and significant correlations.”
• Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, background): “All environmental variables were examined for cross-correlations to address potential problems due to multicollinearity (Dormann et al. 2013).”
• Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (2014, methodology): “, 2004), sample size (Stockwell and Peterson, 2002), multicollinearity (Dormann et al., 2013), and spatial autocorrelation (Segurado et al.”
• Climatic Risk and Distribution Atlas of European Bumblebees (2015, background): “Such collinearities among environmental variables can cause problems for the assessment of the climatic requirements of single species (Dormann et al., 2013).”
• Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models (2014, background): “For example, correlation coefficients as high as Pearson’s r = 0.8 are common in the literature (Elith et al., 2006, 2010), and indeed, (multi)collinearity is considered as a serious challenge to most of the correlative modelling methods (Graham, 2003; Freckleton, 2011; Dormann et al., 2013).”