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.

doi:10.1890/07-0539.1

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

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 forest has demonstrated advantages over other statistical classifiers including very high classification accuracy, ranking variables by importance, ability to model complex interactions, and no assumptions about the distribution of predictor or response variables.
“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]
Random forests is an ensemble method developed to mitigate the problem of overfitting in decision trees.
“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

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
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 · see citing works
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 23,834 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
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 · see citing works
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 3,125 · see citing works
Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction
Prasad, A., Iverson, L. R., & Liaw, A. I. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 2,297 · see citing works
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 · see citing works
Predicting climate‐induced range shifts: model differences and model reliability
Lawler, J., White, D., Neilson, R., & Blaustein, A. (2006) · Global Change Biology · doi:10.1111/j.1365-2486.2006.01191.x · Cited by 418 · see citing works
Self-citation
Effects of sample survey design on the accuracy of classification tree models in species distribution models
Edwards, T. C., Cutler, D. R., Zimmermann, N. E., Geiser, L. H., & Moisen, G. G. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.05.016 · Cited by 155 · see citing works
Self-citation
Combining Multiple Classifiers An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping
Steele, B. (2000) · Remote Sensing of Environment · doi:10.1016/s0034-4257(00)00145-0 · Cited by 127 · see citing works
[23] Random Forests for Microarrays
Cutler, A., & Stevens, J. R. (2006) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(06)11023-x · Cited by 92 · see citing works
Self-citation
MODEL-BASED STRATIFICATIONS FOR ENHANCING THE DETECTION OF RARE ECOLOGICAL EVENTS
Edwards, T. C., Cutler, D. R., Zimmermann, N. E., Geiser, L. H., & Alegria, J. (2005) · Ecology · doi:10.1890/04-0608 · Cited by 89 · see citing works
Self-citation
Landscape patterns as habitat predictors: building and testing models for cavity-nesting birds in the Uinta Mountains of Utah, USA
Lawler, J. J., & Edwards, T. C. (2002) · Landscape Ecology · doi:10.1023/a:1020219914926 · Cited by 66 · see citing works
Self-citation

Flagged first, then by citations

Sorted by citations

A random forest guided tour
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149 · see citing works
An assessment of the effectiveness of a random forest classifier for land-cover classification
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J. S., Chica‐Olmo, M., & Rigol-Sánchez, J. P. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.11.002 · Cited by 2,962 · see citing works
Implementation of machine-learning classification in remote sensing: an applied review
Maxwell, A. E., Warner, T. A., & Fang, F. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1433343 · Cited by 2,152 · see citing works
Where is positional uncertainty a problem for species distribution modelling?
Naimi, B., Hamm, N., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2013) · Ecography · doi:10.1111/j.1600-0587.2013.00205.x · Cited by 1,924 · see citing works
A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species' traits and environment
Pyšek, P., Jaros̆ı́k, V., Hulme, P. E., Pergl, J., Hejda, M., Schaffner, U., & Vilà, M. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02636.x · Cited by 1,454 · see citing works
Evaluation of consensus methods in predictive species distribution modelling
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., & Thuiller, W. (2008) · Diversity and Distributions · doi:10.1111/j.1472-4642.2008.00491.x · Cited by 1,449 · see citing works
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392 · see citing works
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
Tomislav Hengl, A., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler, B. (2018) · PeerJ · doi:10.7717/peerj.5518 · Cited by 1,130 · see citing works
Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models
Elith, J., & Graham, C. H. (2009) · Ecography · doi:10.1111/j.1600-0587.2008.05505.x · Cited by 1,093 · see citing works
Random Forests for Global and Regional Crop Yield Predictions
Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D., Shim, K., Gerber, J., Reddy, V. R., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156571 · Cited by 939 · see citing works
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
Boulesteix, A., Janitza, S., Kruppa, J., & König, I. R. (2012) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1072 · Cited by 930 · see citing works
A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding
Gromski, P. S., Muhamadali, H., Ellis, D. I., Yun Xu, A., Correa, E. S., Turner, M. L., & Goodacre, R. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.012 · Cited by 914 · see citing works
Random Forest for Bioinformatics
Qi, Y. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7_11 · Cited by 847 · see citing works
Machine Learning Methods Without Tears: A Primer for Ecologists
Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823 · see citing works
Self-citation
Evaluating life‐history strategies of reef corals from species traits
Darling, E. S., Álvarez‐Filip, L., Oliver, T. A., McClanahan, T. R., & Côté, I. M. (2012) · Ecology Letters · doi:10.1111/j.1461-0248.2012.01861.x · Cited by 799 · see citing works
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
Tyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 792 · see citing works
Specialized metabolic functions of keystone taxa sustain soil microbiome stability
Xun, W., Liu, Y., Li, W., Ren, Y., Xiong, W., Xu, Z., Zhang, N., Miao, Y., Shen, Q., & Zhang, R. (2021) · Microbiome · doi:10.1186/s40168-020-00985-9 · Cited by 787 · see citing works
Random forests: from early developments to recent advancements
Fawagreh, K., Gaber, M. M., & Elyan, E. (2014) · Systems Science & Control Engineering · doi:10.1080/21642583.2014.956265 · Cited by 786 · see citing works
Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia
Youssef, A. M., Pourghasemi, H. R., Pourtaghi, Z. S., & Al-Katheri, M. M. (2015) · Landslides · doi:10.1007/s10346-015-0614-1 · Cited by 772 · see citing works
Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J. M. C. (2012) · Forest Ecology and Management · doi:10.1016/j.foreco.2012.03.003 · Cited by 755 · see citing works
Geospatial big data handling theory and methods: A review and research challenges
Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., et al. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.10.012 · Cited by 749 · see citing works
On the selection of thresholds for predicting species occurrence with presence‐only data
Liu, C., Newell, G. R., & White, M. D. (2015) · Ecology and Evolution · doi:10.1002/ece3.1878 · Cited by 740 · see citing works
Object-oriented mapping of landslides using Random Forests
Stumpf, A., & Kerle, N. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.05.013 · Cited by 733 · see citing works
Applications for deep learning in ecology
Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724 · see citing works
Widespread deoxygenation of temperate lakes
Jane, S. F., Hansen, G. J. A., Kraemer, B. M., Leavitt, P. R., Mincer, J. L., North, R. L., Pilla, R. M., Stetler, J. T., Williamson, C. E., Woolway, R. I., et al. (2021) · Nature · doi:10.1038/s41586-021-03550-y · Cited by 709 · see citing works
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran
Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2015) · Environmental Monitoring and Assessment · doi:10.1007/s10661-015-5049-6 · Cited by 702 · see citing works
A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD
Hao, T., Elith, J., Guillera‐Arroita, G., & Lahoz‐Monfort, J. J. (2019) · Diversity and Distributions · doi:10.1111/ddi.12892 · Cited by 689 · see citing works
Advanced Spectral Classifiers for Hyperspectral Images: A review
Ghamisi, P., Plaza, J., Chen, Y., Li, J., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2616418 · Cited by 687 · see citing works
A review on longitudinal data analysis with random forest
Hu, J., & Szymczak, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad002 · Cited by 678 · see citing works
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677 · see citing works
Assessing transferability of ecological models: an underappreciated aspect of statistical validation
Wenger, S. J., & Olden, J. D. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00170.x · Cited by 655 · see citing works
Random Forest based hourly building energy prediction
Wang, Z., Wang, Y., Zeng, R., Srinivasan, R. S., & Ahrentzen, S. B. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.04.008 · Cited by 639 · see citing works
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
Pelletier, C., Valero, S., Inglada, J., Champion, N., & Dedieu, G. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.10.010 · Cited by 630 · see citing works
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
Wang, L., Zhou, X., Zhu, X., Dong, Z., & Guo, W. (2016) · The Crop Journal · doi:10.1016/j.cj.2016.01.008 · Cited by 623 · see citing works
On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping
Millard, K., & Richardson, M. C. (2015) · Remote Sensing · doi:10.3390/rs70708489 · Cited by 615 · see citing works
Consistency of random forests
Scornet, E., Biau, G., & Vert, J.-P. (2015) · The Annals of Statistics · doi:10.1214/15-aos1321 · Cited by 612 · see citing works
Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran
Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2015) · CATENA · doi:10.1016/j.catena.2015.10.010 · Cited by 608 · see citing works
Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest
Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S., & Mack, M. C. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2009.02051.x · Cited by 599 · see citing works
Dispersal will limit ability of mammals to track climate change in the Western Hemisphere
Schloss, C. A., Nuñez, T. A., & Lawler, J. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116791109 · Cited by 599 · see citing works
Self-citation
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
Rodriguez-Galiano, V. F., Chica‐Olmo, M., Abarca-Hernández, F., Atkinson, P. M., & Jeganathan, C. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.12.003 · Cited by 593 · see citing works
Statistical solutions for error and bias in global citizen science datasets
Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., Stuart‐Smith, R. D., Wotherspoon, S. J., Krkošek, M., Stuart‐Smith, J., et al. (2013) · Biological Conservation · doi:10.1016/j.biocon.2013.07.037 · Cited by 574 · see citing works
Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches
Powell, S. L., Cohen, W. B., Healey, S. P., Kennedy, R. E., Moisen, G. G., Pierce, K. B., & Ohmann, J. L. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.12.018 · Cited by 545 · see citing works
Gradient forests: calculating importance gradients on physical predictors
Ellis, N., Smith, S. J., & Pitcher, C. R. (2011) · Ecology · doi:10.1890/11-0252.1 · Cited by 543 · see citing works
Applications of machine learning in animal behaviour studies
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505 · see citing works
Machine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 483 · see citing works
Supervised classification of human microbiota
Knights, D., Costello, E. K., & Knight, R. D. (2010) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2010.00251.x · Cited by 482 · see citing works
Using species distribution models to predict new occurrences for rare plants
Williams, J. N., Seo, C., Thorne, J. H., Nelson, J. К., Erwin, S. A., O’Brien, J. M., & Schwartz, M. W. (2009) · Diversity and Distributions · doi:10.1111/j.1472-4642.2009.00567.x · Cited by 464 · see citing works
Projected climate‐induced faunal change in the Western Hemisphere
Lawler, J. J., Shafer, S. L., White, D., Kareiva, P., Maurer, E. P., Blaustein, A. R., & Bartlein, P. J. (2009) · Ecology · doi:10.1890/08-0823.1 · Cited by 458 · see citing works
Self-citation
Application of multivariate statistical techniques in microbial ecology
Paliy, O., & Shankar, V. (2016) · Molecular Ecology · doi:10.1111/mec.13536 · Cited by 455 · see citing works
Coupling machine learning and crop modeling improves crop yield prediction in the US corn belt
Shahhosseini, M., Hu, G., Huber, I., & Archontoulis, S. V. (2021) · Scientific Reports · doi:10.1038/s41598-020-80820-1 · Cited by 452 · see citing works
Global Patterns and Predictions of Seafloor Biomass Using Random Forests
Wei, C., Rowe, G. T., Escobar-Briones, E. G., Boëtius, A., Soltwedel, T., Caley, M. J., Soliman, Y. S., Huettmann, F., Qu, F., Yu, Z., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015323 · Cited by 449 · see citing works
Early‐life establishment of the swine gut microbiome and impact on host phenotypes
Mach, N., Berri, M., Estellé, J., Levenez, F., Lemonnier, G., Denis, C., Leplat, J. J. J. J., Chevaleyre, C., Billon, Y., Dore, J. M., et al. (2015) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12285 · Cited by 446 · see citing works
Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics
Murphy, M. A., Evans, J. S., & Storfer, A. T. (2010) · Ecology · doi:10.1890/08-0879.1 · Cited by 442 · see citing works
Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)
Trigila, A., Iadanza, C., Esposito, C., & Mugnozza, G. S. (2015) · Geomorphology · doi:10.1016/j.geomorph.2015.06.001 · Cited by 425 · see citing works
A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3075159 · Cited by 399 · see citing works
Modeling Species Distribution and Change Using Random Forest
Evans, J. S., Murphy, M. A., Holden, Z. A., & Cushman, S. A. (2010) · Springer eBooks · doi:10.1007/978-1-4419-7390-0_8 · Cited by 399 · see citing works
The science of citizen science: Exploring barriers to use as a primary research tool
Burgess, H. K., DeBey, L. B., Froehlich, H. E., Schmidt, N., Theobald, E. J., Ettinger, A. K., Lambers, J. H. R., Tewksbury, J. J., & Parrish, J. K. (2016) · Biological Conservation · doi:10.1016/j.biocon.2016.05.014 · Cited by 399 · see citing works
Application of machine learning methods to spatial interpolation of environmental variables
Li, J., Heap, A. D., Potter, A., & Daniell, J. J. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2011.07.004 · Cited by 396 · see citing works
A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest
Gibson, R. K., Danaher, T. J., Hehir, W., & Collins, L. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111702 · Cited by 391 · see citing works
Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006
Dillon, G. K., Holden, Z. A., Morgan, P., Crimmins, M. A., Heyerdahl, E. K., & Luce, C. H. (2011) · Ecosphere · doi:10.1890/es11-00271.1 · Cited by 389 · see citing works
Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak
Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., & Dungey, H. S. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.07.007 · Cited by 385 · see citing works
Multiple ecological pathways to extinction in mammals
Davidson, A. D., Hamilton, M. J., Boyer, A. G., Brown, J. H., & Ceballos, G. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901956106 · Cited by 380 · see citing works
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020) · Electronics · doi:10.3390/electronics9071177 · Cited by 378 · see citing works
Predicting individual tree attributes from airborne laser point clouds based on the random forests technique
Yu, X., Hyyppa, J. M., Vastaranta, M., Holopainen, M., & Viitala, R. (2010) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.08.003 · Cited by 372 · see citing works
Predicting the conservation status of data-deficient species
Bland, L. M., Collen, B., Orme, C. D. L., & Bielby, J. (2015) · Conservation Biology · doi:10.1111/cobi.12372 · Cited by 364 · see citing works
The evolution of critical thermal limits of life on Earth
Bennett, J. M., Sunday, J. M., Calosi, P., Villalobos, F., Martínez, B. D. ‐., Molina‐Venegas, R., Araújo, M. B., Algar, A. C., Clusella‐Trullas, S., Hawkins, B. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21263-8 · Cited by 359 · see citing works
Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review
Jiménez-Carvelo, A. M., González‐Casado, A., Bagur-González, M. G., & Cuadros-Rodrı́guez, L. (2019) · Food Research International · doi:10.1016/j.foodres.2019.03.063 · Cited by 352 · see citing works
Sensitive periods of amygdala development: the role of maltreatment in preadolescence
Pechtel, P., Lyons-Ruth, K., Anderson, C. M., & Teicher, M. H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.025 · Cited by 342 · see citing works
Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA
Falkowski, M. J., Evans, J. S., Martinuzzi, S., Gessler, P. E., & Hudak, A. T. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.01.003 · Cited by 342 · see citing works
Competitive interaction with keystone taxa induced negative priming under biochar amendments
Chen, L., Jiang, Y., Liang, C. C., Luo, Y., Xu, Q., Han, C., Qiguo, Z., & Sun, B. (2019) · Microbiome · doi:10.1186/s40168-019-0693-7 · Cited by 342 · see citing works
Gradient modeling of conifer species using random forests
Evans, J. S., & Cushman, S. A. (2009) · Landscape Ecology · doi:10.1007/s10980-009-9341-0 · Cited by 326 · see citing works
Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran
Pourghasemi, H. R., & Kerle, N. (2016) · Environmental Earth Sciences · doi:10.1007/s12665-015-4950-1 · Cited by 325 · see citing works
Global patterns in the bycatch of sharks and rays
Oliver, S., Braccini, M., Newman, S. J., & Harvey, E. S. (2015) · Marine Policy · doi:10.1016/j.marpol.2014.12.017 · Cited by 318 · see citing works
Alteration of streamflow magnitudes and potential ecological consequences: a multiregional assessment
Carlisle, D. M., Wolock, D. M., & Meador, M. R. (2010) · Frontiers in Ecology and the Environment · doi:10.1890/100053 · Cited by 315 · see citing works
Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence
Mi, C., Huettmann, F., Guo, Y., Han, X., & Wen, L. (2017) · PeerJ · doi:10.7717/peerj.2849 · Cited by 313 · see citing works
Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin
Mellor, A., Boukir, S., Haywood, A., & Jones, S. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.03.014 · Cited by 312 · see citing works
Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling
Mainali, K. P., Warren, D. L., Dhileepan, K., McConnachie, A. J., Strathie, L. W., Hassan, G. Z., Karki, D., Shrestha, B. B., & Parmesan, C. (2015) · Global Change Biology · doi:10.1111/gcb.13038 · Cited by 311 · see citing works
The avian scavenger crisis: Looming extinctions, trophic cascades, and loss of critical ecosystem functions
Buechley, E. R., & Sekercioglu, C. H. (2016) · Biological Conservation · doi:10.1016/j.biocon.2016.04.001 · Cited by 311 · see citing works
Continuous mobile authentication using touchscreen gestures
Feng, T., Liu, Z., Kwon, K.-A., Shi, W. L., Cărbunar, B., Jiang, Y., & Nguyen, N. (2012) · IEEE Conference on Technologies for Homeland Security (HST) · doi:10.1109/ths.2012.6459891 · Cited by 310 · see citing works
Evaluating urban flood risk using hybrid method of TOPSIS and machine learning
Rafiei-Sardooi, E., Azareh, A., Choubin, B., Mosavi, A. H., & Clague, J. J. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2021.102614 · Cited by 309 · see citing works
An insight into machine-learning algorithms to model human-caused wildfire occurrence
Rodrígues, M., & de la Riva, J. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.03.003 · Cited by 305 · see citing works
Complex agricultural landscapes host more biodiversity than simple ones: A global meta-analysis
Estrada-Carmona, N., Sánchez, A. C., Remans, R., & Jones, S. K. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2203385119 · Cited by 305 · see citing works
Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds
Oppel, S., Meirinho, A., Ramírez, I., Gardner, B., O’Connell, A. F., Miller, P. I., & Louzao, M. (2012) · Biological Conservation · doi:10.1016/j.biocon.2011.11.013 · Cited by 304 · see citing works
ADHD in girls and boys – gender differences in co-existing symptoms and executive function measures
Skogli, E. W., Teicher, M. H., Andersen, P. N., Hovik, K. T., & Øie, M. G. (2013) · BMC Psychiatry · doi:10.1186/1471-244x-13-298 · Cited by 304 · see citing works
Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability
Martinuzzi, S., Vierling, L. A., Gould, W. A., Falkowski, M. J., Evans, J. S., Hudak, A. T., & Vierling, K. T. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.07.002 · Cited by 302 · see citing works
A review of supervised machine learning algorithms and their applications to ecological data
Crisci, C., Ghattas, B., & Perera, G. (2012) · Ecological Modelling · doi:10.1016/j.ecolmodel.2012.03.001 · Cited by 300 · see citing works
Forecasting Corn Yield With Machine Learning Ensembles
Shahhosseini, M., Hu, G., & Archontoulis, S. V. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.01120 · Cited by 300 · see citing works
Modelling species presence‐only data with random forests
Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2021) · Ecography · doi:10.1111/ecog.05615 · Cited by 300 · see citing works
Erratum to: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia
Youssef, A. M., Pourghasemi, H. R., Pourtaghi, Z. S., & Al-Katheri, M. M. (2015) · Landslides · doi:10.1007/s10346-015-0667-1 · Cited by 294 · see citing works
Consistent response of bird populations to climate change on two continents
Stephens, P. A., Mason, L. R., Green, R. E., Gregory, R. D., Sauer, J. R., Alison, J., Auniņš, A., Brotons, L., Butchart, S. H. M., Campedelli, T., et al. (2016) · Science · doi:10.1126/science.aac4858 · Cited by 292 · see citing works
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
Li, X., Li, C., Rahaman, M. M., Sun, H., Li, X., Wu, J., Yao, Y., & Grzegorzek, M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10121-0 · Cited by 291 · see citing works
Mapping forest change using stacked generalization: An ensemble approach
Healey, S. P., Cohen, W. B., Yang, Z., Brewer, C. K., Brooks, E. B., Gorelick, N., Hernández, A. J., Huang, C., Hughes, M. J., Kennedy, R. E., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.09.029 · Cited by 288 · see citing works
Drivers and hotspots of extinction risk in marine mammals
Davidson, A. D., Boyer, A. G., Kim, H., Pompa, S., Hamilton, M. J., da Costa, D. P., Ceballos, G., & Brown, J. H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121469109 · Cited by 287 · see citing works
A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning
Houborg, R., & McCabe, M. F. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.10.004 · Cited by 287 · see citing works
Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions
Chagas, C. D. S., de Carvalho, W., Bhering, S. B., & Filho, B. C. (2016) · CATENA · doi:10.1016/j.catena.2016.01.001 · Cited by 283 · see citing works
Does the interpolation accuracy of species distribution models come at the expense of transferability?
Heikkinen, R. K., Marmion, M., & Luoto, M. (2011) · Ecography · doi:10.1111/j.1600-0587.2011.06999.x · Cited by 281 · see citing works
Predictive soil parent material mapping at a regional-scale: A Random Forest approach
Heung, B., Bulmer, C., & Schmidt, M. (2013) · Geoderma · doi:10.1016/j.geoderma.2013.09.016 · Cited by 280 · see citing works
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T. D., & Bui, D. T. (2018) · Remote Sensing · doi:10.3390/rs10020172 · Cited by 280 · see citing works
High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data
Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., & Chen, H. Y. H. (2020) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102163 · Cited by 280 · see citing works
A systematic literature review for network intrusion detection system (IDS)
Abdulganiyu, O. H., Ait Tchakoucht, T., & Saheed, Y. K. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00682-2 · Cited by 279 · see citing works

Applications of Modern Classification Methods to Predict Occurrences of Rare and Invasive Plant Species
Reynolds, S. (2021) · Digital Commons - USU (Utah State University) · doi:10.26076/b0e8-36b6
A Comprehensive Analysis of Plant Invasion Classification Using Random Forest Method
Cui, Z., Qian, X., & Cai, M. (2025) · Frontiers in artificial intelligence and applications · doi:10.3233/faia250107
RF_Modelling_POPreprint
Valavi, R. (2020) · doi:10.17605/osf.io/dkmw6
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 · see citing works
Classification in conservation biology: A comparison of five machine-learning methods
Kampichler, C., Wieland, R., Calmé, S., Weissenberger, H., & Arriaga-Weiss, S. L. (2010) · Ecological Informatics · doi:10.1016/j.ecoinf.2010.06.003 · Cited by 185 · see citing works

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

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks