The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY
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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“We used random forest (RF) modeling because of demonstrated advantages of RF over other types of statistical classifiers that include: (1) very high classification accuracy; (2) a method of ranking variables according to their importance; (3) the ability to model complex interactions; and (4) RF makes no assumptions about the distribution of predictor or response variables [30].”
From Landscape Analysis of Adult Florida Panther Habitat (2015) · cites this paper as [30]
In the citing paper
“Random forests (Breiman, 2001a;Cutler et al., 2007) is an ensemble method developed to mitigate the problem of overfitting in decision trees.”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Cutler et al., 2007
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Cites
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Research works printed but not linked: 1
- Breiman L., 2001, Machine Learning, 45, 15
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Landscape Analysis of Adult Florida Panther Habitat
Frakes, R. A., Belden, R. C., Wood, B. E., & James, F. E. (2015) · PLOS ONE · doi:10.1371/journal.pone.0133044 · Cited by 30
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Rao, W., Qian, X., Fan, Y., & Liu, T. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.161543 · Cited by 15
Decoding methane concentration in Alberta oil sands: A machine learning exploration
Sysoeva, L., Bouderbala, I., Kent, M. H., Saha, E., Zambrano-Luna, B. A., Milne, R., & Wang, H. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112835 · Cited by 11
Space use and residency patterns of largemouth bass relative to a freshwater protected area
Zolderdo, A. J., Brownscombe, J. W., Abrams, A. E. I., Suski, C. D., & Cooke, S. J. (2024) · Aquatic Sciences · doi:10.1007/s00027-023-01026-x · Cited by 9
Comments on: A random forest guided tour
Hooker, G., & Mentch, L. (2016) · Test · doi:10.1007/s11749-016-0485-3 · Cited by 7
Hussain, B., Elmedany, W., & Sharif, M. S. (2022) · 2022 International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi56818.2022.10041508 · Cited by 4
Evaluation of Deep Species Distribution Models Using Environment and Co-occurrences
Deneu, B., Servajean, M., Botella, C., & Joly, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-28577-7_18 · Cited by 8
Metadata
- 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
- Venue
- Ecology
- Publisher
- Wiley
- Published
- 2007-11
- Volume
- 88
- Issue
- 11
- Pages
- 2783-2792
- ISSN
- 0012-9658 (ISSN-L)
- 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
- 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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