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

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

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

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1890/07-0539.1

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Predicting species distribution: offering more than simple habitat models.
Guisan, A., & Thuiller, W. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00792.x · Cited by 6,844
CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS
De’ath, G., & Fabricius, K. (2000) · Ecology · doi:10.1890/0012-9658(2000)081[3178:cartap]2.0.co;2 · Cited by 3,235
Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 1,987
Roads as Conduits for Exotic Plant Invasions in a Semiarid Landscape
Gelbard, J. L., & Belnap, J. (2003) · Conservation Biology · doi:10.1046/j.1523-1739.2003.01408.x · Cited by 667
Predicting climate‐induced range shifts: model differences and model reliability
Lawler, J. J., White, D., Neilson, R. P., & Blaustein, A. R. (2006) · Global Change Biology · doi:10.1111/j.1365-2486.2006.01191.x · Cited by 378
Self-citation

Show all 15 references

Research works printed but not linked: 1

Breiman L., 2001, Machine Learning, 45, 15

Sorted by citations

Items 501–600 of 1,008

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches
Delerce, S., Dorado, H., Grillon, A., Rebolledo, M. C., Prager, S. D., Patiño, V. H., Garcés-Varón, G., & Jimenez, D. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161620 · Cited by 98
Annual precipitation regulates spatial and temporal drivers of lake water clarity
Rose, K. C., Greb, S. R., Diebel, M. W., & Turner, M. G. (2016) · Ecological Applications · doi:10.1002/eap.1471 · Cited by 89
High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic
Žížala, D., Minařík, R., Skála, J., Beitlerová, H., Juřicová, A., Rojas, J. R., Penížek, V., & Zádorová, T. (2022) · CATENA · doi:10.1016/j.catena.2022.106024 · Cited by 88
The effects of model and data complexity on predictions from species distributions models
García‐Callejas, D., & Araújo, M. B. (2015) · Ecological Modelling · doi:10.1016/j.ecolmodel.2015.06.002 · Cited by 88
Species distribution modelling for conservation planning in Victoria, Australia
Liu, C., White, M. D., Newell, G. R., & Griffioen, P. A. (2012) · Ecological Modelling · doi:10.1016/j.ecolmodel.2012.07.003 · Cited by 88
Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation
Maxwell, A. E., Warner, T. A., Strager, M. P., Conley, J., & Sharp, A. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.1001086 · Cited by 88
Random forests to evaluate biotic interactions in fish distribution models
Vezza, P., Muñoz‐Mas, R., Martínez‐Capel, F., & Mouton, A. (2015) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2015.01.005 · Cited by 88
A global model of avian influenza prediction in wild birds: the importance of northern regions
Herrick, K. A., Huettmann, F., & Lindgren, M. A. (2013) · Veterinary Research · doi:10.1186/1297-9716-44-42 · Cited by 88
Beyond buzz‐pollination – departures from an adaptive plateau lead to new pollination syndromes
Dellinger, A. S., Chartier, M., Fernández‐Fernández, D., Penneys, D. S., Alvear, M., Almeda, F., Michelangeli, F. A., Staedler, Y. M., Armbruster, W. S., & Schönenberger, J. (2018) · New Phytologist · doi:10.1111/nph.15468 · Cited by 87
Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness
Carrasco, L., Giam, X., Papeş, M., & Sheldon, K. S. (2019) · Remote Sensing · doi:10.3390/rs11070743 · Cited by 87
Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique
Tsai, Y.-L. S., Dietz, A. J., Oppelt, N., & Kuenzer, C. (2019) · Remote Sensing · doi:10.3390/rs11080895 · Cited by 87
Biogeographic structure of the northeastern Pacific rocky intertidal: the role of upwelling and dispersal to drive patterns
Fenberg, P. B., Menge, B. A., RAIMONDI, P. T., & Rivadeneira, M. M. (2014) · Ecography · doi:10.1111/ecog.00880 · Cited by 91
Vegetation change and land tenure in Mexico: A country-wide analysis
Bonilla‐Moheno, M., Redo, D., Aide, T. M., Clark, M. L., & Grau, H. R. (2012) · Land Use Policy · doi:10.1016/j.landusepol.2012.04.002 · Cited by 87
Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps
Cracknell, M. J., Reading, A. M., & McNeill, A. W. (2013) · Australian Journal of Earth Sciences · doi:10.1080/08120099.2014.858081 · Cited by 87
Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data
Juel, A., Groom, G. B., Svenning, J., & Ejrnæs, R. (2015) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2015.05.008 · Cited by 87
Disentangling the effects of compositional and structural diversity on forest productivity
Silva Pedro, M., Rammer, W., & Seidl, R. (2017) · Journal of Vegetation Science · doi:10.1111/jvs.12505 · Cited by 86
Linking land use, in-stream stressors, and biological condition to infer causes of regional ecological impairment in streams
Laan, J. J. V., Hawkins, C. P., Olson, J. R., & Hill, R. A. (2013) · Freshwater Science · doi:10.1899/12-186.1 · Cited by 86
Automated High-Throughput Identification and Characterization of Clinically Important Bacteria and Fungi using Rapid Evaporative Ionization Mass Spectrometry
Bolt, F., Cameron, S. J. S., Karancsi, T., Simon, D. A., Schaffer, R., Rickards, T., Hardiman, K., Burke, A., Bodai, Z., Perdones-Montero, Á., et al. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01016 · Cited by 86
Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya
Mbaabu, P. R., Ng, W., Schaffner, U., Gichaba, M., Olago, D. O., Choge, S., ORIASO, S. O., & Eckert, S. (2019) · Remote Sensing · doi:10.3390/rs11101217 · Cited by 86
Performance predictions of ground source heat pump system based on random forest and back propagation neural network models
Lu, S., Li, Q., Bai, L. M., & Wang, R. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111864 · Cited by 86
Bolt-looseness detection by a new percussion-based method using multifractal analysis and gradient boosting decision tree
Wang, F., & Song, G. (2020) · Structural Health Monitoring · doi:10.1177/1475921720912780 · Cited by 86
Soil as an extended composite phenotype of the microbial metagenome
Neal, A. L., Bacq-Labreuil, A., Zhang, X., Clark, I. M., Coleman, K., Mooney, S. J., Ritz, K., & Crawford, J. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-67631-0 · Cited by 86
Soil and Aquifer Properties Combine as Predictors of Groundwater Uranium Concentrations within the Central Valley, California
Lopez, A. M., Wells, A. A., & Fendorf, S. E. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c05591 · Cited by 86
Characterizing forest species composition using multiple remote sensing data sources and inventory approaches
Ørka, H. O., Dalponte, M., Gobakken, T., Næsset, E., & Ene, L. T. (2013) · Scandinavian Journal of Forest Research · doi:10.1080/02827581.2013.793386 · Cited by 86
The ConMap approach for terrain‐based digital soil mapping
Behrens, T., Schmidt, K., Zhu, A., & Scholten, T. (2009) · European Journal of Soil Science · doi:10.1111/j.1365-2389.2009.01205.x · Cited by 86
Assessing the effectiveness of China's protected areas to conserve current and future amphibian diversity
Chen, Y. F., Zhang, J., Jiang, J., Nielsen, S. E., & He, F. (2016) · Diversity and Distributions · doi:10.1111/ddi.12508 · Cited by 86
Impacts of land use/cover change on terrestrial carbon stocks in Uganda
Zhang, F., Zhan, J., Zhang, Q., Yao, L., & Liu, W. (2017) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2017.03.005 · Cited by 85
Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics
Paz‐Kagan, T., Silver, M., Panov, N., & Karnieli, A. (2019) · Remote Sensing · doi:10.3390/rs11080953 · Cited by 85
Marine fish traits follow fast-slow continuum across oceans
Beukhof, E., Frelat, R., Pécuchet, L., Maureaud, A. A., Dencker, T. S., Sólmundsson, J., Punzón, A., Primicerio, R., Hidalgo, M., Möllmann, C., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-53998-2 · Cited by 91
Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine
Luo, C., Zhang, X., Wang, Y., Men, Z., & Liu, H. (2022) · Soil and Tillage Research · doi:10.1016/j.still.2022.105325 · Cited by 88
An intelligent approach for predicting overbreak in underground blasting operation based on an optimized XGBoost model
Hong, Z., Tao, M., Liu, L., Zhao, M., & Wu, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107097 · Cited by 87
Predictions of avian Plasmodium expansion under climate change
Loiseau, C., Harrigan, R. J., Bichet, C., Julliard, R., Garnier, S., Lendvai, Á. Z., Chastel, O., & Sorci, G. (2013) · Scientific Reports · doi:10.1038/srep01126 · Cited by 91
Impact of food predictability on social facilitation by foraging scavengers
Deygout, C., Gault, A., Duriez, O., Sarrazin, F., & Bessa‐Gomes, C. (2010) · Behavioral Ecology · doi:10.1093/beheco/arq120 · Cited by 85
Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales
Li, A., Dhakal, S., Glenn, N. F., Spaete, L., Shinneman, D. J., Pilliod, D. S., Arkle, R. S., & McIlroy, S. K. (2017) · Remote Sensing · doi:10.3390/rs9090903 · Cited by 84
Developing climate model ensembles: A comparative case study
Crawford, J., Venkataraman, K., & Booth, J. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.10.054 · Cited by 84
Global Estimates of Marine Gross Primary Production Based on Machine Learning Upscaling of Field Observations
Huang, Y., Nicholson, D., Huang, B., & Cassar, N. (2021) · Global Biogeochemical Cycles · doi:10.1029/2020gb006718 · Cited by 84
Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy
Jin, Y., Lan, A., Dai, Y., Jiang, L., & Liu, S. (2023) · European journal of medical research · doi:10.1186/s40001-023-01361-7 · Cited by 84
Classification of Reynolds phytoplankton functional groups using individual traits and machine learning techniques
Kruk, C., Devercelli, M., Huszar, V. L. M., Atilano, E. H., Beamud, G., Diaz, M., da Silva, L. H. S., & Segura, A. M. (2017) · Freshwater Biology · doi:10.1111/fwb.12968 · Cited by 83
Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors
Sanikhani, H., Deo, R. C., Samui, P., Kişi, Ö., Mert, C., Mirabbasi, R., Gavili, S., & Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Z. M. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.07.008 · Cited by 83
Modelling the current fractional cover of an invasive alien plant and drivers of its invasion in a dryland ecosystem
Shiferaw, H., Schaffner, U., Bewket, W., Alamirew, T., Zeleke, G., Teketay, D., & Eckert, S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36587-7 · Cited by 83
Stand structure drives disparities in carbon storage in northern hardwood-conifer forests
Thom, D., & Keeton, W. S. (2019) · Forest Ecology and Management · doi:10.1016/j.foreco.2019.03.053 · Cited by 83
Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model
Wang, M., Wan, W. J., & Zhao, Y. (2020) · Comptes Rendus Mécanique · doi:10.5802/crmeca.3 · Cited by 84
An improved cardiac arrhythmia classification using an RR interval-based approach
Rahul, J., Sora, M., Sharma, L. D., & Bohat, V. K. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.04.004 · Cited by 83
Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models
Zhang, A., Tariq, A., Quddoos, A., Naz, I., Aslam, R. W., Barboza, E., Ullah, S., & Abdullah-Al-Wadud, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-92034-4 · Cited by 83
Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests
Timm, B. C., & McGarigal, K. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.08.033 · Cited by 83
Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA
Risch, D., Clark, C., Dugan, P., Popescu, M., Siebert, U., & Parijs, S. V. (2013) · Marine Ecology Progress Series · doi:10.3354/meps10426 · Cited by 83
Explaining relationships between coke quality index and coal properties by Random Forest method
Chelgani, S. C., Matin, S. S., & Hower, J. C. (2016) · Fuel · doi:10.1016/j.fuel.2016.06.034 · Cited by 83
Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China
Chen, T., Trinder, J. C., & Niu, R. (2017) · Remote Sensing · doi:10.3390/rs9040333 · Cited by 83
Testing a Landsat-based approach for mapping disturbance causality in U.S. forests
Schroeder, T. A., Schleeweis, K. G., Moisen, G. G., Toney, C., Cohen, W. B., Freeman, E. A., Yang, Z., & Huang, C. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.03.033 · Cited by 83
Identifying the controls on coastal cliff landslides using machine-learning approaches
Dickson, M. E., & Perry, G. L. W. (2015) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2015.10.029 · Cited by 82
Comparison of hyperspectral imaging and computer vision for automatic differentiation of organically and conventionally farmed salmon
Xu, J., Riccioli, C., & Sun, D. (2016) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2016.10.021 · Cited by 82
Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection
Seo, D. K., Kim, Y., Eo, Y. D., Park, W. Y., & Park, H. (2017) · Remote Sensing · doi:10.3390/rs9111163 · Cited by 82
Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation
Koch, J., Stisen, S., Refsgaard, J. C., Ernstsen, V., Jakobsen, P. R., & Højberg, A. L. (2019) · Water Resources Research · doi:10.1029/2018wr023939 · Cited by 82
Comparison of daily streamflow forecasts using extreme learning machines and the random forest method
Li, X., Sha, J., & Wang, Z. (2019) · Hydrological Sciences Journal · doi:10.1080/02626667.2019.1680846 · Cited by 82
Application of deep learning in ecological resource research: Theories, methods, and challenges
Guo, Q., Jin, S., Li, M., Yang, Q., Xu, K., Ju, Y., Zhang, J., Xuan, J., Liu, J., Su, Y., et al. (2020) · Science China Earth Sciences · doi:10.1007/s11430-019-9584-9 · Cited by 85
Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm
Mohammadi, B., Guan, Y., Aghelpour, P., Emamgholizadeh, S., Zolá, R. P., & Zhang, D. (2020) · Water · doi:10.3390/w12113015 · Cited by 82
Random forests with parametric entropy-based information gains for classification and regression problems
Ignatenko, V., Surkov, A., & Koltcov, S. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1775 · Cited by 82
Statistical Learning in Palaeolimnology
Simpson, G. L., & Birks, H. J. B. (2012) · Developments in paleoenvironmental research · doi:10.1007/978-94-007-2745-8_9 · Cited by 82
Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models
Conlisk, E. E., Syphard, A. D., Franklin, J., Flint, L. E., Flint, A. L., & Regan, H. M. (2012) · Global Change Biology · doi:10.1111/gcb.12090 · Cited by 83
Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier
Wang, H., Zhao, Y., Pu, R., & Zhang, Z. (2015) · Remote Sensing · doi:10.3390/rs70709020 · Cited by 81
Spring predictability explains different leaf‐out strategies in the woody floras of North America, Europe and East Asia
Zohner, C. M., Benito, B. M., Fridley, J. D., Svenning, J., & Renner, S. S. (2017) · Ecology Letters · doi:10.1111/ele.12746 · Cited by 81
Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) — A multivariate modeling approach
Meßenzehl, K., Meyer, H., Otto, J., Hoffmann, T., & Dikau, R. (2016) · Geomorphology · doi:10.1016/j.geomorph.2016.01.008 · Cited by 80
The influence of socioeconomic, environmental, and demographic factors on municipality-scale land-cover change in Mexico
Bonilla‐Moheno, M., Aide, T. M., & Clark, M. L. (2011) · Regional Environmental Change · doi:10.1007/s10113-011-0268-z · Cited by 80
Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics
Frazier, R. J., Coops, N. C., Wulder, M. A., & Kennedy, R. E. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.03.003 · Cited by 80
Greenspace spatial characteristics and human health in an urban environment: An epidemiological study using landscape metrics in Sheffield, UK
Mears, M., Brindley, P., Jørgensen, A., TONYALOĞLU, E. E., & Maheswaran, R. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105464 · Cited by 82
Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva
Derot, J., Yajima, H., & Jacquet, S. (2020) · Harmful Algae · doi:10.1016/j.hal.2020.101906 · Cited by 81
Climate change impact on medicinal plants in Indonesia
Cahyaningsih, R., Phillips, J., Brehm, J. M., Gaisberger, H., & Maxted, N. (2021) · Global Ecology and Conservation · doi:10.1016/j.gecco.2021.e01752 · Cited by 81
Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions
Elbeltagi, A., Kushwaha, N. L., Rajput, J., Vishwakarma, D. K., Kulimushi, L. C., Kumar, M., Zhang, J., Pande, C. B., Choudhari, P., Meshram, S. G., et al. (2022) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-022-02196-0 · Cited by 79
Decomposing environmental, spatial, and spatiotemporal components of species distributions
Hothorn, T., Müller, J. C., Schröder, B., Kneib, T., & Brandl, R. (2010) · Ecological Monographs · doi:10.1890/10-0602.1 · Cited by 79
Large-Scale Examination of Spatio-Temporal Patterns of Drifting Fish Aggregating Devices (dFADs) from Tropical Tuna Fisheries of the Indian and Atlantic Oceans
Maufroy, A., Chassot, E., Joo, R., & Kaplan, D. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128023 · Cited by 79
Meta‐analysis of the effects of rice‐field abandonment on biodiversity in Japan
Koshida, C., & Katayama, N. (2018) · Conservation Biology · doi:10.1111/cobi.13156 · Cited by 79
Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?
Bertani, I., Steger, C., Obenour, D. R., Fahnenstiel, G. L., Bridgeman, T. B., Johengen, T. H., Sayers, M. J., Shuchman, R. A., & Scavia, D. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.10.023 · Cited by 82
Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods
Machado, G., Vilalta, C., Recamonde‐Mendoza, M., Corzo, C. A., Torremorell, M., Perez, A., & VanderWaal, K. L. (2019) · Scientific Reports · doi:10.1038/s41598-018-36934-8 · Cited by 78
Modeling lake trophic state: a random forest approach
Hollister, J. W., Milstead, W. B., & Kreakie, B. J. (2016) · Ecosphere · doi:10.1002/ecs2.1321 · Cited by 80
Natural flow regime, temperature and the composition and richness of invertebrate assemblages in streams of the western United States
Chinnayakanahalli, K., Hawkins, C. P., Tarboton, D. G., & Hill, R. A. (2011) · Freshwater Biology · doi:10.1111/j.1365-2427.2010.02560.x · Cited by 78
Predicting range shifts of northern bird species: Influence of modelling technique and topography
Virkkala, R., Marmion, M., Heikkinen, R. K., Thuiller, W., & Luoto, M. (2010) · Acta Oecologica · doi:10.1016/j.actao.2010.01.006 · Cited by 78
Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds
Carlisle, D. M., Falcone, J. A., & Meador, M. R. (2008) · Environmental Monitoring and Assessment · doi:10.1007/s10661-008-0256-z · Cited by 78
Comparison of historical and current forest surveys for detection of homogenization and mesophication of Minnesota forests
Hanberry, B. B., Palik, B. J., & He, H. S. (2012) · Landscape Ecology · doi:10.1007/s10980-012-9805-5 · Cited by 78
Development of a microbial community-based index of biotic integrity (MC-IBI) for the assessment of ecological status of rivers in the Taihu Basin, China
Niu, L., Li, Y., Wang, P., Zhang, W., Wang, C., Li, J., & Wu, H. (2017) · Ecological Indicators · doi:10.1016/j.ecolind.2017.10.051 · Cited by 81
A double decomposition-based modelling approach to forecast weekly solar radiation
Prasad, R., Ali, M., Xiang, Y., & Khan, H. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.01.005 · Cited by 78
Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy
Ferreira, M. P., Féret, J., Grau, E., Gastellu‐Etchegorry, J., do Amaral, C. H., Shimabukuro, Y. E., & Filho, C. R. D. S. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.023 · Cited by 77
Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon
Silatsa, F. B. T., Yemefack, M., Tabi, F. O., Heuvelink, G. B. M., & Leenaars, J. G. B. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114260 · Cited by 77
Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia
Dikshit, A., Pradhan, B., & Alamri, A. M. (2020) · Applied Sciences · doi:10.3390/app10124254 · Cited by 77
Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves
Zhu, Y., Liu, K., Myint, S. W., Du, Z., Li, Y., Cao, J., Liu, L., & Wu, Z. (2020) · Remote Sensing · doi:10.3390/rs12122039 · Cited by 77
A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran
Pouyan, S., Pourghasemi, H. R., Bordbar, M., Rahmanian, S., & Clague, J. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-94266-6 · Cited by 77
Random Forest Regression Models in Ecology: Accounting for Messy Biological Data and Producing Predictions with Uncertainty
Akselrud, C. A. (2024) · Fisheries Research · doi:10.1016/j.fishres.2024.107161 · Cited by 82
Predicting building ages from LiDAR data with random forests for building energy modeling
Tooke, T. R., Coops, N. C., & Webster, J. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.10.004 · Cited by 77
The impact of modelling choices in the predictive performance of richness maps derived from species‐distribution models: guidelines to build better diversity models
Benito, B. M., Cayuela, L., & de Albuquerque, F. S. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12022 · Cited by 77
Strategies for Reforestation under Uncertain Future Climates: Guidelines for Alberta, Canada
Gray, L. K., & Hamann, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022977 · Cited by 77
Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods
Doktor, D., Lausch, A., Spengler, D., & Thurner, M. (2014) · Remote Sensing · doi:10.3390/rs61212247 · Cited by 77
Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA
Pierce, A. D., Farris, C. A., & Taylor, A. H. (2012) · Forest Ecology and Management · doi:10.1016/j.foreco.2012.05.010 · Cited by 77
Predicting the occurrence of cold-water patches at intermittent and ephemeral tributary confluences with warm rivers
Ebersole, J. L., Wigington, P. J., Leibowitz, S. G., Comeleo, R. L., & Sickle, J. V. V. (2014) · Freshwater Science · doi:10.1086/678127 · Cited by 76
Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil
Nunes, M. H., & Görgens, E. B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154738 · Cited by 76
Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm
Xu, J., & Sun, D. (2016) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2016.10.014 · Cited by 76
Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change
Prasad, A., Pedlar, J., Peterson, D. L., McKenney, D. W., Iverson, L. R., Matthews, S., & Adams, B. T. (2020) · Diversity and Distributions · doi:10.1111/ddi.13078 · Cited by 76
Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods
Deur, M., Gašparović, M., & Balenović, I. (2020) · Remote Sensing · doi:10.3390/rs12233926 · Cited by 82
Object-Based Wetland Vegetation Classification Using Multi-Feature Selection of Unoccupied Aerial Vehicle RGB Imagery
Zhou, R., Yang, C., Li, E., Cai, X., Yang, J., & Xia, Y. (2021) · Remote Sensing · doi:10.3390/rs13234910 · Cited by 77
Safety, Security and Privacy in Machine Learning Based Internet of Things
Abbas, G., Mehmood, A., Maple, C. R., Epiphaniou, G., & Lloret, J. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11030038 · Cited by 82
Effect of biochar application rate on changes in soil labile organic carbon fractions and the association between bacterial community assembly and carbon metabolism with time
Qiu, H., Liu, J., Boorboori, M. R., Li, D., Chen, S., Ma, X., Cheng, P., & Zhang, H. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.158876 · Cited by 77
Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages
Maloney, K. O., Schmid, M. C., & Weller, D. E. (2011) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00124.x · Cited by 76

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks