Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models
Abstract
Ecological niche models represent key tools in biogeography but the effects of biased sampling hinder their use. Here, we address the utility of two forms of filtering the calibration data set (geographic and environmental) to reduce the effects of sampling bias. To do so we created a virtual species, projected its niche to the Iberian Peninsula and took samples from its binary geographic distribution using several biases. We then built models for various sample sizes after applying each of the filtering approaches. While geographic filtering did not improve discriminatory ability (and sometimes worsened it), environmental filtering consistently led to better models. Models made with few but climatically filtered points performed better than those made with many unfiltered (biased) points. Future research should address additional factors such as the complexity of the species’ niche, strength of filtering, and ability to predict suitability (rather than focus purely on discrimination).
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Environmental filtering of occurrence data removes clustered or oversampled records while maintaining the range of environments in which a species was found.
“This method allows the removal of clustered or oversampled records, while still maintaining the range of environments in which a species was found (Varela et al., 2014).”
From Projecting future climate change impacts on the distribution of the ‘Octopus vulgaris species complex’ (2022) · cites this paper as Varela et al., 2014
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“, 2010) and which decreases the overall accuracy of SDMs (Phillips et al., 2009; Beck et al., 2013; Varela et al., 2014).”
BackgroundMethodInfluential
- Authors
- Varela, S., Anderson, R. P., García‐Valdés, R., & Fernández‐González, F.
- Affiliations
- Charles University (CZ)
- City College of New York (US)
- Consejo Superior de Investigaciones Científicas (ES)
- University of Castilla-La Mancha (ES)
- Venue
- Ecography
- Publisher
- Wiley
- Published
- 2014-01-20
- Volume
- 37
- Issue
- 11
- Pages
- 1084-1091
- ISSN
- 0906-7590 (ISSN-L)
- 1600-0587
- OpenAlex
- W2094836499
- Cited by
- 433
- References
- 44
- Citations by year
- 2026: 37 · 2025: 52 · 2024: 38 · 2023: 52 · 2022: 45 · 2021: 59 · 2020: 40 · 2019: 27 · 2018: 23 · 2017: 22 · 2016: 11 · 2015: 17 · 2014: 10
- FWCI
- 13.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecography (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Botany and Plant Ecology Studies
- Keywords
- ecological niche modeling, sampling bias, environmental filtering, species distribution modeling, Iberian Peninsula, virtual species, model discrimination
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