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 401–500 of 1,008

Numerical methods for the analysis of diatom assemblage data
Birks, H. J. B. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511763175.004 · Cited by 105
A machine learning approach for prediction of pregnancy outcome following IVF treatment
Hassan, M. R., Alinsaif, S., Hossain, M. I., & Kamruzzaman, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3693-9 · Cited by 110
A roadmap towards predicting species interaction networks (across space and time)
Strydom, T., Catchen, M., Banville, F., Caron, D., Dansereau, G., Desjardins-Proulx, P., Forero, N., Higino, G., Mercier, B., Gonzalez, A., et al. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2021.0063 · Cited by 116
Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning
Yin, X. S., Liu, Q., Huang, X., & Pan, Y. (2021) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2021.104285 · Cited by 105
Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes
Fukuda, S., Spreer, W., Yasunaga, E., Yuge, K., Sardsud, V., & Müller, J. (2012) · Agricultural Water Management · doi:10.1016/j.agwat.2012.07.003 · Cited by 105
Threat to the point: improving the value of comparative extinction risk analysis for conservation action
Murray, K. A., Arregoitia, L. D. V., Davidson, A. D., Marco, M. D., & Fonzo, M. M. I. D. (2013) · Global Change Biology · doi:10.1111/gcb.12366 · Cited by 106
Unsupervised random forest: a tutorial with case studies
Afanador, N. L., Smolinska, A., Tran, T. N., & Blanchet, L. (2016) · Journal of Chemometrics · doi:10.1002/cem.2790 · Cited by 104
Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973–2015)
Vogeler, J. C., Braaten, J. D., Slesak, R. A., & Falkowski, M. J. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.02.046 · Cited by 104
Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument
Yaworsky, P. M., Vernon, K. B., Spangler, J. D., Brewer, S. C., & Codding, B. F. (2020) · PLOS One · doi:10.1371/journal.pone.0239424 · Cited by 104
Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods
Tariq, A., Shu, H., Siddiqui, S., Munir, I., Sharifi, A., Li, Q., & Lu, L. (2021) · Journal of Forestry Research · doi:10.1007/s11676-021-01354-4 · Cited by 104
Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: effects of scale and climate change
Fullerton, A. H., Torgersen, C. E., Lawler, J. J., Steel, E. A., Ebersole, J. L., & Lee, S. Y. (2017) · Aquatic Sciences · doi:10.1007/s00027-017-0557-9 · Cited by 103
Self-citation
The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis
Torres, P., Rodes‐Blanco, M., Viana‐Soto, A., Nieto, H., & Garcı́a, M. (2021) · Forests · doi:10.3390/f12081134 · Cited by 115
Evaluating the effectiveness of conservation site networks under climate change: accounting for uncertainty
Bagchi, R., Crosby, M., Huntley, B. J., Hole, D. G., Butchart, S. H. M., Collingham, Y. C., Kalra, M., Rajkumar, J., Rahmani, A. R., Pandey, M., et al. (2012) · Global Change Biology · doi:10.1111/gcb.12123 · Cited by 103
Densification and State Transition Across the Missouri Ozarks Landscape
Hanberry, B. B., Kabrick, J. M., & He, H. S. (2013) · Ecosystems · doi:10.1007/s10021-013-9707-7 · Cited by 103
Current practices in the identification of critical habitat for threatened species
Camaclang, A. E., Maron, M., Martin, T. G., & Possingham, H. P. (2014) · Conservation Biology · doi:10.1111/cobi.12428 · Cited by 102
The distribution of global tidal marshes from Earth observation data
Worthington, T. A., Spalding, M. D., Landis, E., Maxwell, T. L., Navarro, A., Smart, L. S., & Murray, N. (2024) · Global Ecology and Biogeography · doi:10.1111/geb.13852 · Cited by 102
The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers Castor fiber
Graf, P. M., Wilson, R. P., Qasem, L. A., Hackländer, K., & Rosell, F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0136751 · Cited by 111
A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes
Debats, S. R., Luo, D., Estes, L. D., Fuchs, T. J., & Caylor, K. K. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.03.010 · Cited by 101
Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia
Feng, P., Wang, B., Liu, D. L., Xing, H., Ji, F., Macadam, I., Ruan, H., & Yu, Q. (2018) · Climatic Change · doi:10.1007/s10584-018-2170-x · Cited by 103
BREIZHCROPS: A TIME SERIES DATASET FOR CROP TYPE MAPPING
Rußwurm, M., Pelletier, C., Zollner, M., Lefèvre, S., & Körner, M. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2020-1545-2020 · Cited by 115
Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery
Pachêco, A. D. P., da Silva, J. A., Ruiz-Armenteros, A. M., & Henriques, R. (2021) · Remote Sensing · doi:10.3390/rs13071345 · Cited by 101
Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions
Bharati, S., & Podder, P. (2022) · Security and Communication Networks · doi:10.1155/2022/8951961 · Cited by 110
BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes
Pennekamp, F., Schtickzelle, N., & Petchey, O. L. (2015) · Ecology and Evolution · doi:10.1002/ece3.1529 · Cited by 101
Warning times for species extinctions due to climate change
Stanton, J. C., Shoemaker, K. T., Pearson, R. G., & Akçakaya, H. R. (2014) · Global Change Biology · doi:10.1111/gcb.12721 · Cited by 101
Species Distribution Modeling for Machine Learning Practitioners: A Review
Beery, S., Cole, E., Parker, J., Perona, P., & Winner, K. (2021) · ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3460112.3471966 · Cited by 100
Exploring factors associated with pressure ulcers: A data mining approach
Raju, D. A., Su, X., Patrician, P. A., Loan, L. A., & McCarthy, M. S. (2014) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2014.08.002 · Cited by 100
Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests
Kühnlein, M., Appelhans, T., Thies, B., & Nauß, T. (2014) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-14-0082.1 · Cited by 100
Importance of regional variation in conservation planning: a rangewide example of the Greater Sage‐Grouse
Doherty, K. E., Evans, J. S., Coates, P. S., Juliusson, L. M., & Fedy, B. C. (2016) · Ecosphere · doi:10.1002/ecs2.1462 · Cited by 100
Forest fire forecasting using ensemble learning approaches
Xie, Y., & Peng, M. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3515-0 · Cited by 100
Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data
Ruescas, A. B., Hieronymi, M., Mateo‐García, G., Koponen, S., Kallio, K. Y., & Camps‐Valls, G. (2018) · Remote Sensing · doi:10.3390/rs10050786 · Cited by 100
Predicting walleye recruitment as a tool for prioritizing management actions
Hansen, G. J. A., Carpenter, S. R., Gaeta, J. W., Hennessy, J. M., & Zanden, M. J. V. (2015) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2014-0513 · Cited by 100
Using Random Forest to Improve the Downscaling of Global Livestock Census Data
Nicolas, G., Robinson, T. P., Wint, W., Conchedda, G., Cinardi, G., & Gilbert, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150424 · Cited by 100
Predicting the standing stock of organic carbon in surface sediments of the North–West European continental shelf
Diesing, M., Kröger, S., Parker, R. E., Jenkins, C. J., Mason, C., & Weston, K. (2017) · Biogeochemistry · doi:10.1007/s10533-017-0310-4 · Cited by 103
Current and future distribution of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in WHO Eastern Mediterranean Region
Ducheyne, E. I., Minh, N. N. T., Haddad, N., Bryssinckx, W., Buliva, E., Simard, F., Malik, M. R., Charlier, J., Waele, V. D., Mahmoud, O., et al. (2018) · International Journal of Health Geographics · doi:10.1186/s12942-018-0125-0 · Cited by 99
A novel citizen science approach for large-scale standardised monitoring of bat activity and distribution, evaluated in eastern England
Newson, S. E., Evans, H. E., & Gillings, S. (2015) · Biological Conservation · doi:10.1016/j.biocon.2015.06.009 · Cited by 99
Geospatial tools address emerging issues in spatial ecology: a review and commentary on the Special Issue
Skidmore, A. K., Franklin, J., Dawson, T. P., & Pilesjö, P. (2011) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2011.554296 · Cited by 98
Reducing the babel in plant volatile communication: using the forest to see the trees
Ranganathan, Y., & Borges, R. M. (2009) · Plant Biology · doi:10.1111/j.1438-8677.2009.00278.x · Cited by 98
Classification and regression with random forests as a standard method for presence-only data SDMs: A future conservation example using China tree species
Zhang, L., Huettmann, F., Liu, S., Sun, P., Yu, Z., Zhang, X., & Mi, C. (2019) · Ecological Informatics · doi:10.1016/j.ecoinf.2019.05.003 · Cited by 98
What variables are important in predicting bovine viral diarrhea virus? A random forest approach
Machado, G., Recamonde‐Mendoza, M., & Corbellini, L. G. (2015) · Veterinary Research · doi:10.1186/s13567-015-0219-7 · Cited by 97
Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning
Nelson, N. G., Muñoz‐Carpena, R., Phli̇ps, E. J., Kaplan, D. A., Sucsy, P. V., & Hendrickson, J. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.7b05884 · Cited by 97
Climate and human water use diminish wetland networks supporting continental waterbird migration
Donnelly, J., King, S. L., Silverman, N. L., Collins, D. P., Carrera-González, E., Lafón‐Terrazas, A., & Moore, J. N. (2020) · Global Change Biology · doi:10.1111/gcb.15010 · Cited by 101
Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States
Calovi, M., Grozinger, C. M., Miller, D. A., & Goslee, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-81051-8 · Cited by 97
The use of random forest to identify climate and human interference on vegetation coverage changes in southwest China
Wang, Y., Chen, X., Gao, M. K., & Dong, J. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109463 · Cited by 97
Spatial heterogeneity of ambient sound at the habitat type level: ecological implications and applications
Bormpoudakis, D., Sueur, J., & Pantis, J. D. (2013) · Landscape Ecology · doi:10.1007/s10980-013-9849-1 · Cited by 97
Hydrologic filtering of fish life history strategies across the United States: implications for stream flow alteration
McManamay, R. A., & Frimpong, E. A. (2014) · Ecological Applications · doi:10.1890/14-0247.1 · Cited by 97
imageRF – A user-oriented implementation for remote sensing image analysis with Random Forests
Waske, B., van der Linden, S., Oldenburg, C., Jakimow, B., Rabe, A., & Hostert, P. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.01.014 · Cited by 96
Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness
Li, J., Tran, M., & Siwabessy, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149089 · Cited by 96
A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping
Bui, D. T., Shirzadi, A., Chapi, K., Shahabi, H., Pradhan, B., Pham, B. T., Singh, V. P., Chen, W., Khosravi, K., Ahmad, B. B., et al. (2019) · Water · doi:10.3390/w11102013 · Cited by 96
Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB
Norallahi, M., & Kaboli, H. S. (2021) · Natural Hazards · doi:10.1007/s11069-020-04453-3 · Cited by 96
The current and future uses of machine learning in ecosystem service research
Scowen, M., Athanasiadis, I. N., Bullock, J. M., Eigenbrod, F., & Willcock, S. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149263 · Cited by 104
Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms
Rihan, M., Bindajam, A. A., Talukdar, S., Shahfahad, Naikoo, M. W., Mallick, J., & Rahman, A. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.03.026 · Cited by 99
Direct and indirect effects of climate change on projected future fire regimes in the western United States
Liu, Z., & Wimberly, M. C. (2015) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2015.10.093 · Cited by 96
Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier
Deschamps, B., McNairn, H. E., Shang, J., & Jiao, X. (2012) · Canadian Journal of Remote Sensing · doi:10.5589/m12-012 · Cited by 96
Overcoming data deficiency in reptiles
Bland, L. M., & Böhm, M. (2016) · Biological Conservation · doi:10.1016/j.biocon.2016.05.018 · Cited by 101
Rapid multi-nation distribution assessment of a charismatic conservation species using open access ensemble model GIS predictions: Red panda (Ailurus fulgens) in the Hindu-Kush Himalaya region
Kandel, K., Huettmann, F., Suwal, M. K., Regmi, G. R., Nijman, V., Nekaris, K. A. I., Lama, S. T., Thapa, A. K., Sharma, H. P., & Subedi, T. R. (2014) · Biological Conservation · doi:10.1016/j.biocon.2014.10.007 · Cited by 95
Multiple factors drive regional agricultural abandonment
Osawa, T., Kohyama, K., & Mitsuhashi, H. (2015) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2015.10.067 · Cited by 95
Species’ thermal ranges predict changes in reef fish community structure during 8 years of extreme temperature variation
Day, P. B., Stuart‐Smith, R. D., Edgar, G. J., & Bates, A. E. (2018) · Diversity and Distributions · doi:10.1111/ddi.12753 · Cited by 101
Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data
Farrell, A. V., Wang, G., Rush, S. A., Martin, J. A., Belant, J. L., Butler, A. B., & Godwin, D. (2019) · Ecology and Evolution · doi:10.1002/ece3.5177 · Cited by 95
The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning
Hoffman, A. L., Kemanian, A. R., & Forest, C. E. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/ab7b22 · Cited by 95
Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests
Guo, F., Zhang, L., Jin, S., Tigabu, M., Su, Z., & Wang, W. (2016) · Forests · doi:10.3390/f7110250 · Cited by 94
Defining a spectrum of integrative trait‐based vegetation canopy structural types
Fahey, R. T., Atkins, J. W., Gough, C. M., Hardiman, B. S., Nave, L. E., Tallant, J. M., Nadelhoffer, K. J., Vogel, C. S., Scheuermann, C. M., Stuart‐Haëntjens, E. J., et al. (2019) · Ecology Letters · doi:10.1111/ele.13388 · Cited by 94
Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy
He, J., Chen, Y., Wu, J., Stow, D. A., & Christakos, G. (2019) · Water Research · doi:10.1016/j.watres.2019.115403 · Cited by 95
Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half
Liu, J., Zhou, Y., Valach, A. C., Shortt, R., Kasak, K., Rey‐Sánchez, C., Hemes, K. S., Baldocchi, D., & Lai, D. Y. F. (2020) · Global Change Biology · doi:10.1111/gcb.15247 · Cited by 95
Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms
Li, X., Du, H., Mao, F., Zhou, G., Chen, L., Xing, L., Fan, W., Xu, X., Liu, Y., Cui, L., et al. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.04.002 · Cited by 94
Assessing the vulnerability of European butterflies to climate change using multiple criteria
Heikkinen, R. K., Luoto, M., Leikola, N., Pöyry, J., Settele, J., Kudrna, O., Marmion, M., Fronzek, S., & Thuiller, W. (2009) · Biodiversity and Conservation · doi:10.1007/s10531-009-9728-x · Cited by 93
Sewer Condition Prediction and Analysis of Explanatory Factors
Laakso, T., Kokkonen, T., Mellin, I., & Vahala, R. (2018) · Water · doi:10.3390/w10091239 · Cited by 93
PACES: prediction of N4-acetylcytidine (ac4C) modification sites in mRNA
Zhao, W., Zhou, Y., Cui, Q., & Zhou, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-47594-7 · Cited by 93
Patterns and predictions of drinking water nitrate violations across the conterminous United States
Pennino, M. J., Leibowitz, S. G., Compton, J. E., Hill, R. A., & Sabo, R. D. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137661 · Cited by 93
Does Garbage in the Diet Improve Reproductive Output of Glaucous Gulls?
Weiser, E. L., & Powell, A. N. (2010) · Ornithological Applications · doi:10.1525/cond.2010.100020 · Cited by 93
Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data
Na, X., Zhang, S., Li, X., Yu, H., & Liu, C. (2010) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.76.7.833 · Cited by 93
Comparative genetic responses to climate for the varieties of Pinus ponderosa and Pseudotsuga menziesii: Realized climate niches
Rehfeldt, G. E., Jaquish, B. C., López-Upton, J., Sáenz‐Romero, C., Clair, J. B. S., Leites, L. P., & Joyce, D. G. (2014) · Forest Ecology and Management · doi:10.1016/j.foreco.2014.02.035 · Cited by 92
Spatial analysis and characteristics of pig farming in Thailand
Thanapongtharm, W., Linard, C., Chinson, P., Kasemsuwan, S., Visser, M., Gaughan, A. E., Epprech, M., Robinson, T. P., & Gilbert, M. (2016) · BMC Veterinary Research · doi:10.1186/s12917-016-0849-7 · Cited by 92
The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014
Hurni, K., & Fox, J. (2018) · Journal of Land Use Science · doi:10.1080/1747423x.2018.1499830 · Cited by 92
Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping
Kalantar, B., Al-Najjar, H. A. H., Pradhan, B., Saeidi, V., Halin, A. B. A., Ueda, N., & Naghibi, S. A. (2019) · Water · doi:10.3390/w11091909 · Cited by 92
Representativeness of FLUXNET Sites Across Latin America
Villarreal, S., & Vargas, R. (2021) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2020jg006090 · Cited by 92
A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data
Speiser, J. L. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103763 · Cited by 95
Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia
Leggesse, E. S., Zimale, F. A., Sultan, D., Enku, T., Srinivasan, R., & Tilahun, S. A. (2023) · Hydrology · doi:10.3390/hydrology10050110 · Cited by 98
Towards Quantitative Spatial Models of Seabed Sediment Composition
Stephens, D., & Diesing, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142502 · Cited by 92
Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery
García, F. C., Alonso‐Sarría, F., Gomariz‐Castillo, F., & Oñate-Valdivieso, F. (2017) · Computers & Geosciences · doi:10.1016/j.cageo.2017.02.012 · Cited by 91
wsrf : An R Package for Classification with Scalable Weighted Subspace Random Forests
Zhao, H., Williams, G. J., & Huang, J. Z. (2017) · Journal of Statistical Software · doi:10.18637/jss.v077.i03 · Cited by 91
Areas of plant diversity—What do we know?
Brummitt, N. A., Araújo, A. C., & Harris, T. (2020) · Plants People Planet · doi:10.1002/ppp3.10110 · Cited by 103
Support vector machines to map rare and endangered native plants in Pacific islands forests
Pouteau, R., Meyer, J., Taputuarai, R., & Stoll, B. (2012) · Ecological Informatics · doi:10.1016/j.ecoinf.2012.03.003 · Cited by 91
Modeling the spatial distribution of the striped dolphin ( Stenella coeruleoalba ) and common bottlenose dolphin ( Tursiops truncatus ) in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea)
Carlucci, R., Fanizza, C., Cipriano, G., Paoli, C., Russo, T., & Vassallo, P. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.05.035 · Cited by 91
Estimating population size of a nocturnal burrow-nesting seabird using acoustic monitoring and habitat mapping
Oppel, S., Hervías‐Parejo, S., Oliveira, N., Pipa, T., Silva, C., Geraldes, P. L., Goh, M. Z., Immler, E., & McKown, M. (2014) · Nature Conservation · doi:10.3897/natureconservation.7.6890 · Cited by 98
Urbanization and anticoagulant poisons promote immune dysfunction in bobcats
Serieys, L. E. K., Lea, A. J., Epeldegui, M., Armenta, T. C., Moriarty, J. G., VandeWoude, S., Carver, S., Foley, J. E., Wayne, R. K., Riley, S. P. D., et al. (2018) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2017.2533 · Cited by 90
Normalizing Gas‐Chromatography–Mass Spectrometry Data: Method Choice can Alter Biological Inference
Noonan, M. J., Tinnesand, H. V., & Buesching, C. D. (2018) · BioEssays · doi:10.1002/bies.201700210 · Cited by 90
A comprehensive examination of the network position hypothesis across multiple river metacommunities
Henriques‐Silva, R., Logez, M., Reynaud, N., Tedesco, P. A., Brosse, S., Januchowski-Hartley, S. R., Oberdorff, T., & Argillier, C. (2018) · Ecography · doi:10.1111/ecog.03908 · Cited by 92
Comparing spatial regression to random forests for large environmental data sets
Fox, E. W., Hoef, J. M. V., & Olsen, A. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229509 · Cited by 90
Estimating Barcelona's metropolitan daytime hot and cold poles using Landsat-8 Land Surface Temperature
Lemus‐Canovas, M., Vide, J. M., Moreno-García, M. C., & Bustins, J. A. L. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134307 · Cited by 90
RETRACTED ARTICLE: Towards an efficient model for network intrusion detection system (IDS): systematic literature review
Abdulganiyu, O. H., Tchakoucht, T. A., & Saheed, Y. K. (2023) · Wireless Networks · doi:10.1007/s11276-023-03495-2 · Cited by 95
Retracted
HYDROGEOMORPHIC CLASSIFICATION OF WASHINGTON STATE RIVERS TO SUPPORT EMERGING ENVIRONMENTAL FLOW MANAGEMENT STRATEGIES
Liermann, C. R., Olden, J. D., Beechie, T. J., Kennard, M. J., Skidmore, P. B., Konrad, C. P., & Imaki, H. (2011) · River Research and Applications · doi:10.1002/rra.1541 · Cited by 94
A genetic algorithm approach to optimising random forests applied to class engineered data
Elyan, E., & Gaber, M. M. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.007 · Cited by 90
Validation of a national hydrological model
McMillan, H., Booker, D. J., & Cattoën, C. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.07.043 · Cited by 89
Local and landscape scale determinants of macroinvertebrate assemblages and their conservation value in ponds across an urban land-use gradient
Thornhill, I., Batty, L. C., Death, R. G., Friberg, N. R., & Ledger, M. E. (2017) · Biodiversity and Conservation · doi:10.1007/s10531-016-1286-4 · Cited by 95
Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm
Graw, J. H., Wood, W. T., & Phrampus, B. J. (2020) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb020135 · Cited by 89
Assessment of the effectiveness of China’s protected areas in enhancing ecosystem services
Yu, C., Zhang, Z., Jeppesen, E., Gao, Y., Liu, Y., Liu, Y., Lu, Q., Wang, C., & Sun, X. (2023) · Ecosystem Services · doi:10.1016/j.ecoser.2023.101588 · Cited by 91
Evaluating performance of macroinvertebrate-based adjusted and unadjusted multi-metric indices (MMI) using multi-season and multi-year samples
Chen, K., Hughes, R. M., Xu, S. B., Zhang, J., Cai, D., & Wang, B. (2013) · Ecological Indicators · doi:10.1016/j.ecolind.2013.07.006 · Cited by 89
Fragmentation and thermal risks from climate change interact to affect persistence of native trout in the Colorado River basin
Roberts, J. J., Fausch, K. D., Peterson, D. P., & Hooten, M. B. (2013) · Global Change Biology · doi:10.1111/gcb.12136 · Cited by 91
Hidden Markov Models: The Best Models for Forager Movements?
Joo, R., Bertrand, S., Tam, J., & Fablet, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071246 · Cited by 89
Disentangling the effects of land use and geo-climatic factors on diversity in European freshwater ecosystems
Feld, C. K., Birk, S., Eme, D., Gerisch, M., Hering, D., Kernan, M., Maileht, K., Mischke, U., Ott, I., Borgwardt, F., et al. (2015) · Ecological Indicators · doi:10.1016/j.ecolind.2015.06.024 · Cited by 96

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