Cited by 433Species Distribution and Climate ChangeEcology and Vegetation Dynamics StudiesBotany and Plant Ecology Studies

Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models

Varela, Sara, Anderson, Robert P., García‐Valdés, Raúl, Fernández‐González, Federico·2014·Ecography

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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Crossref · 10.1111/j.1600-0587.2013.00441.x

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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).”

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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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