spatialRF In The Wild

Summary

This article lists known applications of the R package spatialRF found in the literature.

This page is maintained by hand and may be incomplete. If you’ve used spatialRF in a publication, talk, or blog post and would like it listed here, please open an issue at the package repository.

Research Papers

2026

Andrus, R. A., Steed, B., Egan, J., Lestina, J., Steed, P., Bennett, A. J. H., Meddens, A., & Goodburn, J. (2026). A multi-scale assessment of interior Douglas-fir tree mortality for hazard and risk assessments. Ecosphere, 17(1), e70490. https://doi.org/10.1002/ecs2.70490

Chappell, E. N., Hendriks, C., Hoeks, S., Huijbregts, M. A. J., Hanssen, S. V., & Wicke, B. (2026). Carbon offsetting in city climate action: Role, determinants and characteristics. Climate Policy, 26(2), 253–268. https://doi.org/10.1080/14693062.2025.2499606

Davtalab, M., & Byčenkienė, S. (2026). Synergistic effects of spatial urban form on PM2.5 concentration and urban heat islands: A multi-scale explanatory machine learning. Journal of Cleaner Production, 538, 147226. https://doi.org/10.1016/j.jclepro.2025.147226

Jafari, A., Sarmadian, F., & Heidari, A. (2026). Enhancing digital mapping of soil organic carbon through spatial modeling and validation. Scientific Reports, 16, 8810. https://doi.org/10.1038/s41598-026-39496-2

Karanasios, P. I., Schmeller, D. S., Hsu, Z.-T., & Lin, Y.-P. (2026). Precise roadkill environmental factors identification for sustainable planning and design through spatial and temporal modelling. Nature Conservation, 61, 1–27. https://doi.org/10.3897/natureconservation.61.171624

Mimi, M. S., Das, S., & Dutta, A. K. (2026). Non-spatial AI modeling to estimate traffic volume measures on local roadways. International Journal of Urban Sciences, 1–30. https://doi.org/10.1080/12265934.2025.2612094

Pan, H., Hui, Y., & Wu, W. (2026). Critical thresholds for co-benefits of carbon accumulation and biodiversity conservation under global nitrogen enrichment. Nature Communications, 17, 1336. https://doi.org/10.1038/s41467-025-68090-9

Starck, J., et al. (2026). Limited microclimatic buffering capacity in boreal forests calls for sustainable management strategies. Environmental Research Letters, 21, 014011. https://doi.org/10.1088/1748-9326/ae2d79

Wang, X., Tian, C., Jia, X., Zhao, Y., & Xing, Y. (2026). The impact of significant geographical barriers on the invasion risk of non-native aquatic animals: A case study of the Qinling Mountains, China. Biology, 15(4), 329. https://doi.org/10.3390/biology15040329

Wang, Y., Gao, G., Huang, Y., & Fu, B. (2026). Divergent responses of ecosystem water and carbon use efficiency to atmosphere-vegetation-soil moisture factors in the northern drylands of China. Journal of Geophysical Research: Biogeosciences, 131, e2025JG009271. https://doi.org/10.1029/2025JG009271

Xiang, T., Arranz, I., Kuczynski, L., et al. (2026). Increasing functional or phylogenetic distance from native fish communities promotes non-native fish invasions in global rivers. Global Change Biology, 32(3), e70814. https://doi.org/10.1111/gcb.70814

Xiao, S., Adams, J. M., Li, S., Slik, F., et al. (2026). Analogous environments across the tropics have similar levels of tree species alpha diversity. National Science Review, 13(2), nwaf465. https://doi.org/10.1093/nsr/nwaf465

2025

Bartholomée, C., Taconet, P., Mercat, M., Grail, C., Bouhsira, E., Fournet, F., et al. (2025). Investigating the role of urban vegetation alongside other environmental variables in shaping Aedes albopictus presence and abundance in Montpellier, France. PLoS One, 20(11), e0335793. https://doi.org/10.1371/journal.pone.0335793

Brady, O. J., Bastos, L. S., Caldwell, J. M., Cauchemez, S., Clapham, H. E., Dorigatti, I., et al. (2025). Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections. PLoS Computational Biology, 21(4), e1012771. https://doi.org/10.1371/journal.pcbi.1012771

Cao, G., Dai, L., & Qiu, J. (2025). The importance of soil depth in determining the impacts of mixed plantations with thinning on soil multifunctionality. Plant and Soil, 517, 509–524. https://doi.org/10.1007/s11104-025-07872-y

Croft, S., Warren, D., & Blanco-Aguiar, J. (2025). Predicting the distribution of common wild mammal species across Europe: Are there sufficient occurrence data? European Journal of Wildlife Research, 71, 133. https://doi.org/10.1007/s10344-025-02014-2

Fernández-García, V., Phelps, L. N., Strydom, T., Muando, P. J., Ranaivonasy, J., Lehmann, C. E. R., & Kull, C. A. (2025). High-resolution satellite data improve insights into landscape fires and their drivers in southeastern Africa. Journal of Geophysical Research: Biogeosciences, 130, e2024JG008635. https://doi.org/10.1029/2024JG008635

Gia Barboza-Salerno, T., Yang, G. E. B., et al. (2025). Development and spatial validation of a random forest prediction model for firearm-related injury risk in Chicago census tracts [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7190389/v1

Howard, J. K., Barnett, A. R., Fesenmyer, K. A., & Anderson, M. G. (2025). From fragmentation to resilience: Connectivity and habitat diversity as drivers of fish persistence in California watersheds. PLoS One, 20(12), e0339212. https://doi.org/10.1371/journal.pone.0339212

Jiang, B., Chen, H., & Wei, Z. (2025). Higher temperature sensitivity of forest soil methane oxidation in colder climates. Nature Communications, 16, 2428. https://doi.org/10.1038/s41467-025-57763-0

Lagacé, M., Boudreau, D. R., Tousignant, L., & Moreau, G. (2025). Ecological factors underlying the spatiotemporal dynamics in a key forest beetle pollinator. Agricultural and Forest Entomology, 27(4), 590–599. https://doi.org/10.1111/afe.12687

Larocque, S. M., Bzonek, P. A., Brownscombe, J. W., Martin, G. K., Brooks, J. L., Boston, C. M., Doka, S. E., Cooke, S. J., & Midwood, J. D. (2025). Application of telemetry-based fish habitat models to predict spatial habitat availability and inform ecological restoration. Journal of Fish Biology, 106(5), 1601–1618. https://doi.org/10.1111/jfb.15899

Le Geay, M., Mayers, K., & Sytiuk, A. (2025). Uncovering diversity and abundance patterns of CO2-fixing microorganisms in peatlands. npj Biodiversity, 4, 30. https://doi.org/10.1038/s44185-025-00099-1

Lutz, L., Amendt, J., & Moreau, G. (2025). Baited traps as flawed proxies for carcass colonization. Scientific Reports, 15, 6267. https://doi.org/10.1038/s41598-025-90522-1

Ma, D., Peng, S., Lin, Z., Zhu, J., Niu, L., Ding, X., Pan, X., Jiao, Y., Cui, B., & Cai, F. (2025). A deviation-frequency-trend framework for multi-scale assessment of soil erosion dynamics. Ecological Indicators, 180, 114341. https://doi.org/10.1016/j.ecolind.2025.114341

Ma, Y., Ma, L., & Yang, L. (2025). Coexistence patterns of biocrust-vascular plant in the Loess Plateau. Ecosystems, 28, 30. https://doi.org/10.1007/s10021-025-00976-7

Magneville, C., Cartereau, M., Hernández-Agüero, J. A., et al. (2025). Echoes of the past: Long-term climate stability shapes functional and phylogenetic diversity in Euro-Mediterranean forests. Global Ecology and Biogeography, 34(12), e70177. https://doi.org/10.1111/geb.70177

Mahapatra, S., Majhi, B. K., Sarkar, M. S., Datta, D., Mishra, A. P., & Rathnayake, U. (2025). Understanding forest fragmentation dynamics and identifying drivers for forest cover loss using random forest models to develop effective forest management strategies in North-East India. Results in Engineering, 26, 104640. https://doi.org/10.1016/j.rineng.2025.104640

Marchand, W., Daubrée, J.-B., & Depardieu, C. (2025). Viscum album in French forests: Distribution, environmental drivers and impacts on tree health and growth. Forest Ecology and Management, 598, 123226. https://doi.org/10.1016/j.foreco.2025.123226

Mathews-Martin, L., Metras, R., Boucher, J. M., et al. (2025). Meteorological and environmental factors associated with the exposure to tick-borne encephalitis virus (TBEV) in cattle, north-eastern France, 2018–2019. Veterinary Research, 56, 157. https://doi.org/10.1186/s13567-025-01588-8

Matta, R., Stritih, A., & Lecina-Diaz, J. (2025). Identifying wind and bark beetle disturbance predictors across German forests. Ecological Indicators, 179, 114248. https://doi.org/10.1016/j.ecolind.2025.114248

Mikryukov, V., et al. (2025). Connecting the multiple dimensions of global soil fungal diversity. Science Advances, 9, eadj8016. https://doi.org/10.1126/sciadv.adj8016

Mondal, S., Gluck-Thaler, E., Grabowski Ocampos, C. J., Hahn Villalba, E., Niblack, T. L., Orrego Fuente, A. L., Pedrozo, L. M., Ralston, T. I., Soilan, L. C., & Lopez-Nicora, H. D. (2025). Geostatistical modeling improves prediction of Macrophomina phaseolina abundance and distribution in soybean fields. Phytopathology, 115(3), 247–259. https://doi.org/10.1094/PHYTO-04-24-0139-R

Nikolaou, D., Ziakopoulos, A., Kontaxi, A., Theofilatos, A., & Yannis, G. (2025). Spatial analysis of telematics-based surrogate safety measures. Journal of Safety Research, 92, 98–108. https://doi.org/10.1016/j.jsr.2024.09.012

Parishwad, O., Gao, K., & Najafi, A. (2025). Modeling e-scooter sharing demand and its influencing factors: A spatial machine learning approach [Preprint]. SSRN. https://doi.org/10.2139/ssrn.5232197

Putkiranta, P., Räsänen, A., & Luoto, M. (2025). Mapping plant diversity in a northern boreal landscape using remotely sensed spectral variation. Landscape Ecology, 40, 206. https://doi.org/10.1007/s10980-025-02240-8

Schiller, J., Stiller, S., & Ryo, M. (2025). Artificial intelligence in environmental and Earth system sciences: Explainability and trustworthiness. Artificial Intelligence Review, 58, 316. https://doi.org/10.1007/s10462-025-11165-2

Shayle, E. S., & Zeuss, D. (2025). Temporal and spatial upscaling with PlanetScope data: Predicting relative canopy dieback in the piñon-juniper woodlands of Utah. Remote Sensing, 17(19), 3323. https://doi.org/10.3390/rs17193323

Stewart, J. D., Kiers, E. T., Chomicki, G., & Weedon, J. T. (2025). Crop yields are not greater outside centers of origin. One Earth, 8(8), 101346. https://doi.org/10.1016/j.oneear.2025.101346

Suleymanov, A., Kuzyakov, Y., Asylbaev, I., Rusakov, I., Suleymanov, R., Tuktarova, I., & Belan, L. (2025). Mechanisms and drivers of soil pH assessed by Shapley additive explanation. CATENA, 259, 109301. https://doi.org/10.1016/j.catena.2025.109301

Teles, J. N., & Mantelatto, F. L. (2025). Decapod biodiversity hotspots and environmental drivers: A macroecological approach about bycatch species in Brazil. Journal of Biogeography, 52(12), e70076. https://doi.org/10.1111/jbi.70076

Terkper, K. A., Moomen, M., Rahman, M. A., Mohammed, N.-H., Khan, W. A., & Codjoe, J. (2025). Understanding vehicle availability patterns using census data with spatially-hybrid machine learning models. Case Studies on Transport Policy, 22, 101641. https://doi.org/10.1016/j.cstp.2025.101641

Wang, W., & Wang, C. (2025). Divergent urban ozone responses to straw burning in northern China from observational data: Roles of meteorology and photochemistry. Atmosphere, 16(11), 1296. https://doi.org/10.3390/atmos16111296

Wei, P., Luo, X., Pie, M. R., Sucharitakul, P., Zhou, W., & Yuan, Z. (2025). Global diversity patterns in anurans are determined by terrestrial and arboreal species. Integrative Zoology. Advance online publication. https://doi.org/10.1111/1749-4877.13032

Withnell, E., Celik, C., & Secrier, M. (2025). Integrative spatial modelling of cellular plasticity using graph neural networks and geostatistics [Preprint]. bioRxiv. https://doi.org/10.1101/2025.09.24.678189

Yang, X., Zhang, Z., Zhou, H., Liu, F., Yu, H., Zhao, B., Wang, X., Li, H., & Shi, Z. (2025). Social–ecological factors and ecosystem service trade-offs/synergies in vegetation change zones of Qilian Mountain National Park during 2000–2020. Remote Sensing, 17(8), 1402. https://doi.org/10.3390/rs17081402

Zhang, J., Mu, L., Zhang, D., Chen, Z., Rajbhandari-Thapa, J., Pagán, J. A., & Zhou, Z. (2025). SpaCE: A spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome. International Journal of Geographical Information Science, 1–32. https://doi.org/10.1080/13658816.2024.2443757

2024

Berv, J. S., et al. (2024). Genome and life-history evolution link bird diversification to the end-Cretaceous mass extinction. Science Advances, 10, eadp0114. https://doi.org/10.1126/sciadv.adp0114

Fernández-García, V., Phelps, L. N., Strydom, T., et al. (2024). Fire regimes of southeastern Africa are better predicted by Sentinel-2 than MODIS [Preprint]. ESS Open Archive. https://doi.org/10.22541/essoar.173282212.20545644/v1

Gilman, E., & Chaloupka, M. (2024). Evidence from interpretable machine learning to inform spatial management of Palau’s tuna fisheries. Ecosphere, 15(2), e4751. https://doi.org/10.1002/ecs2.4751

Gokhale, S. S. (2024). County-level associations between social and sleep deprivation conditioned by regional effects. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) (pp. 710–717). IEEE. https://doi.org/10.1109/ICHI61247.2024.00113

Quinn, C. A., Burns, P., Jantz, P., Salas, L., Goetz, S. J., & Clark, M. L. (2024). Soundscape mapping: Understanding regional spatial and temporal patterns of soundscapes incorporating remotely-sensed predictors and wildfire disturbance. Environmental Research: Ecology, 3(2), 025002. https://doi.org/10.1088/2752-664X/ad4bec

Rueda, M., González-Suárez, M., & Revilla, E. (2024). Global biogeographical regions reveal a signal of past human impacts. Ecography, 2024, e06762. https://doi.org/10.1111/ecog.06762

Salako, G., Zaitsev, A., Betancur-Corredor, B., & Russell, D. J. (2024). Modelling and spatial prediction of earthworms ecological-categories distribution reveal their habitat and environmental preferences. Ecological Indicators, 169, 112832. https://doi.org/10.1016/j.ecolind.2024.112832

Teillet, C., Devillers, R., Tran, A., et al. (2024). Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites. International Journal of Health Geographics, 23, 18. https://doi.org/10.1186/s12942-024-00378-3

Wang, X., Bocksberger, G., & Arandjelovic, M. (2024). Strontium isoscape of sub-Saharan Africa allows tracing origins of victims of the transatlantic slave trade. Nature Communications, 15, 10891. https://doi.org/10.1038/s41467-024-55256-0

Wang, Z., Fu, B., Wu, X., Wang, S., Li, Y., & Feng, Y. (2024). Distinguishing trajectories and drivers of vegetated ecosystems in China’s Loess Plateau. Earth’s Future, 12, e2023EF003769. https://doi.org/10.1029/2023EF003769

Wei, Y., Wang, M., Viscarra Rossel, R. A., Chen, H., & Luo, Z. (2024). Extreme climate as the primary control of global soil organic carbon across spatial scales. Global Biogeochemical Cycles, 38, e2024GB008200. https://doi.org/10.1029/2024GB008200

Zhi, R., Boughton, E. H., Li, H., Petticord, D. F., Saha, A., Sparks, J. P., Reddy, K. R., & Qiu, J. (2024). Soil legacy phosphorus and loss risk in subtropical grasslands. Journal of Environmental Management, 366, 121656. https://doi.org/10.1016/j.jenvman.2024.121656

2023

Guirado, E., Delgado-Baquerizo, M., Benito, B. M., Molina-Pardo, J. L., Berdugo, M., Martínez-Valderrama, J., & Maestre, F. T. (2023). The global biogeography and environmental drivers of fairy circles. Proceedings of the National Academy of Sciences, 120(40), e2304032120. https://doi.org/10.1073/pnas.2304032120

Kang, L., Chen, L., Li, Z., Wang, J., Xue, K., Deng, Y., Delgado-Baquerizo, M., Song, Y., Zhang, D., Yang, G., Zhou, W., Liu, X., Liu, F., & Yang, Y. (2023). Patterns and drivers of prokaryotic communities in thermokarst lake water across Northern Hemisphere. Global Ecology and Biogeography, 32, 2244–2256. https://doi.org/10.1111/geb.13764

Lauer, D. A., & McGuire, J. L. (2023). [Article]. Environmental Research Letters, 18, 074029. https://doi.org/10.1088/1748-9326/acde90

Mathias, S., van Galen, L. G., Jarvie, S., & Larcombe, M. J. (2023). Range reshuffling: Climate change, invasive species, and the case of Nothofagus forests in Aotearoa New Zealand. Diversity and Distributions, 29, 1402–1419. https://doi.org/10.1111/ddi.13767

Potapov, A. M., Guerra, C. A., van den Hoogen, J., et al. (2023). Globally invariant metabolism but density-diversity mismatch in springtails. Nature Communications, 14, 674. https://doi.org/10.1038/s41467-023-36216-6

Rodman, K. C., Davis, K. T., Parks, S. A., Chapman, T. B., Coop, J. D., Iniguez, J. M., Roccaforte, J. P., Sánchez Meador, A. J., Springer, J. D., Stevens-Rumann, C. S., Stoddard, M. T., Waltz, A. E. M., & Wasserman, T. N. (2023). Refuge-yeah or refuge-nah? Predicting locations of forest resistance and recruitment in a fiery world. Global Change Biology, 29, 7029–7050. https://doi.org/10.1111/gcb.16939

Sáez-Sandino, T., García-Palacios, P., Maestre, F. T., et al. (2023). The soil microbiome governs the response of microbial respiration to warming across the globe. Nature Climate Change, 13, 1382–1387. https://doi.org/10.1038/s41558-023-01868-1

Zhu, H., Liu, H., Zhou, Q., & Cui, A. (2023). Towards an accurate and reliable downscaling scheme for high-spatial-resolution precipitation data. Remote Sensing, 15(10), 2640. https://doi.org/10.3390/rs15102640

2022

Brame, J. E., Liddicoat, C., Abbott, C. A., Edwards, R. A., Robinson, J. M., Gauthier, N. E., & Breed, M. F. (2022). [Preprint]. bioRxiv. https://doi.org/10.1101/2022.10.07.510278

Ferguson, T. P., Chen, J., & Jorgensen, P. D. (2022). Trump and the Republican base: A machine learning approach. Institute for New Economic Thinking. https://www.ineteconomics.org/uploads/papers/Trump-and-the-Republican-Base.pdf

Liu, Z., Jin, Y., Yang, L., Yan, L., Zhang, Y., Xu, M., Tang, J., Zhou, Y., Hu, F., & Cheng, J. (2022). Incorporating egg-transporting pathways into conservation plans of spawning areas: An example of small yellow croaker (Larimichthys polyactis) in the East China Sea zone. Frontiers in Marine Science, 9, 941411. https://doi.org/10.3389/fmars.2022.941411

Lotz, T., Su, S., & Opp, C. (2022). Multi-metal distribution patterns in soils of the Sacramento River floodplain and their controlling factors. Applied Sciences, 12(17), 8462. https://doi.org/10.3390/app12178462

Monnier-Corbel, A., Monnet, A.-C., Bacon, L., Benito, B. M., Robert, A., & Hingrat, Y. (2022). Density-dependence of reproductive success in a Houbara bustard population. Global Ecology and Conservation, 35, e02071. https://doi.org/10.1016/j.gecco.2022.e02071

Patoine, G., Eisenhauer, N., Cesarz, S., et al. (2022). Drivers and trends of global soil microbial carbon over two decades. Nature Communications, 13, 4195. https://doi.org/10.1038/s41467-022-31833-z

Santiago-Rosario, L. Y., Harms, K. E., & Craven, D. (2022). Contrasts among cationic phytochemical landscapes in the southern United States. Plant-Environment Interactions, 3, 226–241. https://doi.org/10.1002/pei3.10093

Wayman, J. P., Sadler, J. P., Pugh, T. A. M., Martin, T. E., Tobias, J. A., & Matthews, T. J. (2022). Assessing taxonomic and functional change in British breeding bird assemblages over time. Global Ecology and Biogeography, 31, 925–939. https://doi.org/10.1111/geb.13468

Others

Eduardo, R. (n.d.). Awesome-Geospatial [Online resource, R section]. GitHub. https://github.com/sacridini/Awesome-Geospatial

2025

Nowosad, J. (2025, June 25). Specialized R packages for spatial machine learning: An introduction to RandomForestsGLS, spatialRF, and Meteo. geocompx. https://geocompx.org/post/2025/sml-bp5/

2022

Poloni, A., Tonini, M., & Lambiel, C. (2022, November 18–20). Advanced spatial learning technique for automatic mapping of geomorphological features in alpine periglacial environment [Conference abstract]. In P. Dèzes (Ed.), Abstract volume of the 20th Swiss Geoscience Meeting, Symposium 21+22: Spatial Data Science + Virtual Representation of Forests. Swiss Academy of Sciences (SCNAT), Lausanne, Switzerland. https://geoscience-meeting.ch/sgm2022/wp-content/uploads/abstracts/Poloni_Alessio_08-17-22-12-08-18.pdf