Cited by 4,950Species Distribution and Climate ChangeEcology and Vegetation Dynamics StudiesStatistical Methods and Inference

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RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY

Cutler, D. Richard, Edwards, Thomas C., Beard, Karen H., Cutler, Adele, Hess, Kyle T., Gibson, Jacob, Lawler, Joshua J.·2007·Ecology

DOI 10.1890/07-0539.1

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Abstract

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

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Authors: Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J.
Affiliations: Utah State University (US)
University of Washington (US)
Corresponding author: Cutler, D. Richard
ORCID: Edwards, Thomas C.
Beard, Karen H.
Lawler, Joshua J.
Venue: Ecology
Publisher: Wiley
Published: 2007-11
Volume: 88
Issue: 11
Pages: 2783-2792
ISSN: 0012-9658 (ISSN-L)
DOI: 10.1890/07-0539.1
OpenAlex: W2139086914
Cited by: 4,950
References: 15
Citations by year: 2026: 469 · 2025: 535 · 2024: 417 · 2023: 360 · 2022: 372 · 2021: 436 · 2020: 369 · 2019: 312 · 2018: 345 · 2017: 276 · 2016: 209 · 2015: 213 · 2014: 179 · 2013: 147 · 2012: 120
FWCI: 22.77
Type: journal article
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 2861 d)
Locations: Ecology (publishedVersion)
PubMed (publishedVersion)
Digital Commons - USU (Utah State University) (submittedVersion)
Funders: National Park Service
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
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Keywords: random forest, classification accuracy, feature importance, invasive plants, rare lichens, cavity-nesting birds, nest site selection, ecological classification, classifier comparison, missing data imputation, predictor interactions
MeSH terms: Algorithms, Animals, Birds, Data Interpretation, Statistical, Demography, Ecology, Models, Theoretical, Population Density, Population Dynamics, Species Specificity, Trees, Models, Statistical, Logistic Models, Ecosystem

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