Species Distribution Modeling for Machine Learning Practitioners: A Review

Regularization Paths for Generalized Linear Models via Coordinate Descent

Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works

Maximum entropy modeling of species geographic distributions

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2005) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.03.026 · Cited by 17,870 · see citing works

WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas

Fick, S. E., & Hijmans, R. J. (2017) · International Journal of Climatology · doi:10.1002/joc.5086 · Cited by 17,783 · see citing works

Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models

Wood, S. N. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00749.x · Cited by 7,538 · see citing works

Predictive habitat distribution models in ecology

Guisan, A., & Zimmermann, N. E. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00354-9 · Cited by 7,359 · see citing works

Generalized Linear Models

Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344614 · Cited by 7,321 · 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

A statistical explanation of MaxEnt for ecologists

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011) · Diversity and Distributions · doi:10.1111/j.1472-4642.2010.00725.x · Cited by 6,699 · see citing works

A working guide to boosted regression trees

Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484 · see citing works

Random forests for classification in ecology

Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007) · Ecology · doi:10.1890/07-0539.1 · Cited by 4,950 · see citing works

SoilGrids250m: Global gridded soil information based on machine learning

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733 · see citing works

<i>Entropy and diversity</i>

Jost, L. (2006) · Oikos · doi:10.1111/j.2006.0030-1299.14714.x · Cited by 4,669 · see citing works

ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE

MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., & Langtimm, C. A. (2002) · Ecology · doi:10.1890/0012-9658(2002)083[2248:esorwd]2.0.co;2 · Cited by 4,481 · see citing works

Vegetation of the Siskiyou Mountains, Oregon and California

WHITTAKER, R. H. (1960) · Ecological Monographs · doi:10.2307/1943563 · Cited by 4,018 · see citing works

A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

Merow, C., Smith, M. J., & Silander, J. A. (2013) · Ecography · doi:10.1111/j.1600-0587.2013.07872.x · Cited by 3,830 · see citing works

Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., et al. (2017) · Ecography · doi:10.1111/ecog.02881 · Cited by 3,689 · see citing works

Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks

Broomhead, D. S., & Lowe, D. G. (1988) · Complex Systems · Cited by 3,429 · see citing works

Modern Applied Statistics with S-Plus.

Venables, W. N., & Ripley, B. D. (1996) · Biometrics · doi:10.2307/2532871 · Cited by 3,012 · see citing works

Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information

(2015) · Photogrammetric Engineering & Remote Sensing · doi:10.1016/s0099-1112(15)30100-2 · Cited by 2,584 · see citing works

The Human Footprint and the Last of the Wild

Sanderson, E. W., Jaiteh, M., Levy, M. A., Redford, K. H., Wannebo, A., & Woolmer, G. (2002) · BioScience · doi:10.1641/0006-3568(2002)052[0891:thfatl]2.0.co;2 · Cited by 2,472 · see citing works

PARTITIONING DIVERSITY INTO INDEPENDENT ALPHA AND BETA COMPONENTS

Jost, L. (2007) · Ecology · doi:10.1890/06-1736.1 · Cited by 2,230 · 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

Five (or so) challenges for species distribution modelling

Araújo, M. B., & Guisan, A. (2006) · Journal of Biogeography · doi:10.1111/j.1365-2699.2006.01584.x · Cited by 1,904 · see citing works

The crucial role of the accessible area in ecological niche modeling and species distribution modeling

Barve, N., Barve, V., Jiménez‐Valverde, A., Lira‐Noriega, A., Maher, S. P., Peterson, A. T., Soberón, J. M., & Villalobos, F. (2011) · Ecological Modelling · doi:10.1016/j.ecolmodel.2011.02.011 · Cited by 1,901 · see citing works

Modifiable Areal Unit Problem

Shekhar, S., Xiong, H. Y., & Zhou, X. S. (2017) · Encyclopedia of GIS · doi:10.1007/978-3-319-17885-1_100791 · Cited by 1,895 · see citing works

Vegetation of the Great Smoky Mountains

WHITTAKER, R. H. (1956) · Ecological Monographs · doi:10.2307/1943577 · Cited by 1,891 · see citing works

A review of vegetation indices

Bannari, A., Morin, D., Bonn, F., & HUETE, A. R. (1995) · Remote Sensing Reviews · doi:10.1080/02757259509532298 · Cited by 1,681 · see citing works

Selecting thresholds for the prediction of species occurrence with presence‐only data

Liu, C., White, M. D., & Newell, G. R. (2013) · Journal of Biogeography · doi:10.1111/jbi.12058 · Cited by 1,639 · see citing works

mvabund– an R package for model‐based analysis of multivariate abundance data

Wang, Y., Naumann, U., Wright, S. T., & Warton, D. I. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00190.x · Cited by 1,587 · see citing works

Population Ecology of Some Warblers of Northeastern Coniferous Forests

MacArthur, R. H. (1958) · Ecology · doi:10.2307/1931600 · Cited by 1,575 · see citing works

The iNaturalist Species Classification and Detection Dataset

Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00914 · Cited by 1,451 · see citing works

Arthropod decline in grasslands and forests is associated with landscape-level drivers

Seibold, S., Goßner, M. M., Simons, N. K., Blüthgen, N., Müller, J. C., Ambarlı, D., Ammer, C., Bauhus, J., Fischer, M., Habel, J. C., et al. (2019) · Nature · doi:10.1038/s41586-019-1684-3 · Cited by 1,333 · see citing works

Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding

Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J. E., Thomsen, P. F., Bellemain, E., Besnard, A. G., Coissac, É., Boyer, F., et al. (2015) · Molecular Ecology · doi:10.1111/mec.13428 · Cited by 1,275 · see citing works

Predicting global change impacts on plant species’ distributions: Future challenges

Thuiller, W., Albert, C. H., Araújo, M. B., Berry, P. M., Cabeza, M., Guisan, A., Hickler, T., Midgley, G. F., Paterson, J., Schurr, F. M., et al. (2007) · Perspectives in Plant Ecology Evolution and Systematics · doi:10.1016/j.ppees.2007.09.004 · Cited by 1,253 · see citing works

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230 · 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

Niche dynamics in space and time

Pearman, P. B., Guisan, A., Broennimann, O., & Randin, C. F. (2008) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2007.11.005 · Cited by 1,044 · see citing works

How to make more out of community data? A conceptual framework and its implementation as models and software

Ovaskainen, O., Tikhonov, G. N., Norberg, A., Blanchet, F. G., Duan, L. L., Dunson, D. B., Roslin, T. V., & Abrego, N. (2017) · Ecology Letters · doi:10.1111/ele.12757 · Cited by 1,036 · see citing works

A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity

Tuomisto, H. (2010) · Ecography · doi:10.1111/j.1600-0587.2009.05880.x · Cited by 1,021 · see citing works

Assessing data quality in citizen science

Kosmala, M., Wiggins, A., Swanson, A., & Simmons, B. D. (2016) · Frontiers in Ecology and the Environment · doi:10.1002/fee.1436 · Cited by 992 · see citing works

bioclim : the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies

Booth, T. H., Nix, H. A., Busby, J., & Hutchinson, M. F. (2013) · Diversity and Distributions · doi:10.1111/ddi.12144 · Cited by 991 · see citing works

Is my species distribution model fit for purpose? Matching data and models to applications

Guillera‐Arroita, G., Lahoz‐Monfort, J. J., Elith, J., Ascelin Gordon, A., Kujala, H., Lentini, P. E., Mccarthy, M. A., Tingley, R., & Wintle, B. A. (2015) · Global Ecology and Biogeography · doi:10.1111/geb.12268 · Cited by 969 · see citing works

Estimating animal density using camera traps without the need for individual recognition

Rowcliffe, J. M., Field, J., Turvey, S. T., & Carbone, C. (2008) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2008.01473.x · Cited by 907 · see citing works

Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?

VanDerWal, J. J., Shoo, L. P., Graham, C. H., & Williams, S. E. (2009) · Ecological Modelling · doi:10.1016/j.ecolmodel.2008.11.010 · Cited by 888 · see citing works

Continuous Deep Q-Learning with Model-based AccelerationPreprint

Gu, S., Lillicrap, T., Sutskever, I., & Levine, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.00748 · Cited by 770 · see citing works

Challenges and Opportunities of Open Data in Ecology

Reichman, O. J., Jones, M. B., & Schildhauer, M. P. (2011) · Science · doi:10.1126/science.1197962 · Cited by 758 · see citing works

Uncertainty in ensemble forecasting of species distribution

Buisson, L., Thuiller, W., Casajus, N., Lek, S., & Grenouillet, G. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2009.02000.x · Cited by 713 · see citing works

Species richness, hotspots, and the scale dependence of range maps in ecology and conservation

Hurlbert, A. H., & Jetz, W. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704469104 · Cited by 710 · see citing works

Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right

Peterson, A. T., & Soberón, J. (2012) · Natureza &amp; Conservação · doi:10.4322/natcon.2012.019 · Cited by 702 · see citing works

Recognition in Terra Incognita

Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679 · see citing works

Improving species distribution models for climate change studies: variable selection and scale

Austin, M. P., & Niel, K. P. V. (2010) · Journal of Biogeography · doi:10.1111/j.1365-2699.2010.02416.x · Cited by 661 · see citing works

BIOCLIM - a bioclimate analysis and prediction system

Busby, J. (1991) · Plant protection quarterly · Cited by 594 · see citing works

Integrating biodiversity distribution knowledge: toward a global map of life

Jetz, W., McPherson, J. M., & Guralnick, R. (2011) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2011.09.007 · Cited by 593 · see citing works

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels

Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J. V., Araújo, M. B., Dallas, T., Dunson, D. B., Elith, J., et al. (2019) · Ecological Monographs · doi:10.1002/ecm.1370 · Cited by 575 · see citing works

Continuum Concept, Ordination Methods, and Niche Theory

Austin, M. P. (1985) · Annual Review of Ecology and Systematics · doi:10.1146/annurev.es.16.110185.000351 · Cited by 552 · see citing works

Essential biodiversity variables for mapping and monitoring species populations

Jetz, W., McGeoch, M. A., Guralnick, R., Ferrier, S., Beck, J., Costello, M. J., Fernández, M., Geller, G. N., Keil, P., Merow, C., et al. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0826-1 · Cited by 526 · see citing works

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · cited 1× · see citing works

Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015) · Scientific Data · doi:10.1038/sdata.2015.26 · Cited by 510 · see citing works

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

Júnior, P. D. M., & Nóbrega, C. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202403 · Cited by 493 · see citing works

Moving beyond static species distribution models in support of conservation biogeography

Franklin, J. (2010) · Diversity and Distributions · doi:10.1111/j.1472-4642.2010.00641.x · Cited by 488 · see citing works

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., & Brenning, A. (2018) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.06.002 · Cited by 482 · see citing works

The niche, biogeography and species interactions

Wiens, J. J. (2011) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2011.0059 · Cited by 479 · see citing works

Plant-geography upon a physiological basis

Schimper, A. F. W., Fisher, W. R., Groom, P., & Balfour, I. B. (1903) · Clarendon Press eBooks · doi:10.5962/bhl.title.122577 · Cited by 478 · see citing works

Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology

Renner, I. W., & Warton, D. I. (2013) · Biometrics · doi:10.1111/j.1541-0420.2012.01824.x · Cited by 463 · see citing works

Climate refugia: joint inference from fossil records, species distribution models and phylogeography

Gavin, D. G., Fitzpatrick, M. C., Gugger, P. F., Heath, K. D., Rodríguez‐Sánchez, F., Dobrowski, S. Z., Hampe, A., Hu, F. S., Ashcroft, M. B., Bartlein, P. J., et al. (2014) · New Phytologist · doi:10.1111/nph.12929 · Cited by 462 · see citing works

Bias correction in species distribution models: pooling survey and collection data for multiple species

Fithian, W., Elith, J., Hastie, T. J., & Keith, D. A. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12242 · Cited by 454 · see citing works

The projection of species distribution models and the problem of non-analog climate

Fitzpatrick, M. C., & Hargrove, W. W. (2009) · Biodiversity and Conservation · doi:10.1007/s10531-009-9584-8 · Cited by 420 · see citing works

Accounting for uncertainty when mapping species distributions: The need for maps of ignorance

Rocchini, D., Hortal, J., Lengyel, S., Lobo, J. M., Jiménez‐Valverde, A., Ricotta, C., Bacaro, G., & Chiarucci, A. (2011) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133311399491 · Cited by 406 · see citing works

An evaluation of alternative algorithms for fitting species distribution models using logistic regression

Pearce, J., & Ferrier, S. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(99)00227-6 · Cited by 385 · see citing works

The ubiquity of model-based reinforcement learning

Doll, B. B., Simon, D. A., & Daw, N. D. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.08.003 · Cited by 380 · see citing works

How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates?

Sinclair, S. J., White, M., & Newell, G. R. (2010) · Ecology and Society · doi:10.5751/es-03089-150108 · Cited by 376 · see citing works

Incorporating microclimate into species distribution models

Lembrechts, J. J., Nijs, I., & Lenoir, J. (2018) · Ecography · doi:10.1111/ecog.03947 · Cited by 376 · see citing works

Projection-based model reduction: Formulations for physics-based machine learning

Swischuk, R., Mainini, L., Peherstorfer, B., & Willcox, K. E. (2018) · Computers & Fluids · doi:10.1016/j.compfluid.2018.07.021 · Cited by 364 · see citing works

Modelling ecological niches with support vector machines

Drake, J. M., Randin, C. F., & Guisan, A. (2006) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2006.01141.x · Cited by 357 · see citing works

Will remote sensing shape the next generation of species distribution models?

He, K. S., Bradley, B. A., Cord, A. F., Rocchini, D., Tuanmu, M., Schmidtlein, S., Turner, W., Wegmann, M., & Pettorelli, N. (2015) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.7 · Cited by 352 · see citing works

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Wisz, M. S., & Guisan, A. (2009) · BMC Ecology · doi:10.1186/1472-6785-9-8 · Cited by 339 · see citing works

A comparison of methods for mapping species ranges and species richness

Graham, C. H., & Hijmans, R. J. (2006) · Global Ecology and Biogeography · doi:10.1111/j.1466-822x.2006.00257.x · Cited by 339 · see citing works

Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions

Dormann, C. F., Bobrowski, M., Dehling, D. M., Harris, D. J., Hartig, F., Lischke, H., Moretti, M. D., Pagel, J., Pinkert, S., Schleuning, M., et al. (2018) · Global Ecology and Biogeography · doi:10.1111/geb.12759 · Cited by 328 · see citing works

The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

Thapa, R., Zhang, K., Snavely, N., Belongie, S., & Khan, A. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11390 · Cited by 322 · see citing works

Incorporating uncertainty in predictive species distribution modelling

Beale, C. M., & Lennon, J. J. (2011) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2011.0178 · Cited by 321 · see citing works

Conservation in a cup of water: estimating biodiversity and population abundance from environmental DNA

Lodge, D. M., Turner, C. R., Jerde, C. L., Barnes, M. A., Chadderton, L., Egan, S. P., Feder, J. L., Mahon, A. R., & Pfrender, M. E. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2012.05600.x · Cited by 313 · see citing works

Presence‐Only Data and the EM Algorithm

Ward, G., Hastie, T. J., Barry, S. C., Elith, J., & Leathwick, J. R. (2008) · Biometrics · doi:10.1111/j.1541-0420.2008.01116.x · Cited by 292 · see citing works

Disparity between range map‐ and survey‐based analyses of species richness: patterns, processes and implications

Hurlbert, A. H., & White, E. P. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00726.x · Cited by 292 · see citing works

An artificial neural network approach to spatial habitat modelling with interspecific interaction

Özesmi, S. L., & Özesmi, U. (1999) · Ecological Modelling · doi:10.1016/s0304-3800(98)00149-5 · Cited by 290 · see citing works

A practical guide for combining data to model species distributions

Fletcher, R. J., Hefley, T. J., Robertson, E. P., Zuckerberg, B., McCleery, R. A., & Dorazio, R. M. (2019) · Ecology · doi:10.1002/ecy.2710 · Cited by 287 · see citing works

Generalized joint attribute modeling for biodiversity analysis: median‐zero, multivariate, multifarious data

Clark, J. S., Nemergut, D. R., Seyednasrollah, B., Turner, P. J., & Zhang, Y. S. (2016) · Ecological Monographs · doi:10.1002/ecm.1241 · Cited by 283 · see citing works

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Pacifici, K., Reich, B. J., Miller, D. A. W., Gardner, B., Stauffer, G. E., Singh, S. P., McKerrow, A. J., & Collazo, J. A. (2016) · Ecology · doi:10.1002/ecy.1710 · Cited by 283 · see citing works

The recent past and promising future for data integration methods to estimate species’ distributions

Miller, D. A. W., Pacifici, K., Sanderlin, J. S., & Reich, B. J. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13110 · Cited by 283 · see citing works

Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses

Richards, C. L., Carstens, B. C., & Knowles, L. L. (2007) · Journal of Biogeography · doi:10.1111/j.1365-2699.2007.01814.x · Cited by 281 · see citing works

Wallace : A flexible platform for reproducible modeling of species niches and distributions built for community expansion

Kass, J. M., Vilela, B., Aiello‐Lammens, M. E., Muscarella, R., Merow, C., & Anderson, R. P. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12945 · Cited by 280 · see citing works

Advances and applications of occupancy models

Bailey, L. L., MacKenzie, D. I., & Nichols, J. D. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12100 · Cited by 266 · see citing works

Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change

Synes, N. W., & Osborne, P. E. (2011) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2010.00635.x · Cited by 246 · see citing works

The role of functional traits in species distributions revealed through a hierarchical model

Pollock, L. J., Morris, W. K., & Vesk, P. A. (2011) · Ecography · doi:10.1111/j.1600-0587.2011.07085.x · Cited by 244 · see citing works

Open science, reproducibility, and transparency in ecology

Powers, S. M., & Hampton, S. E. (2018) · Ecological Applications · doi:10.1002/eap.1822 · Cited by 224 · see citing works

COMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE

Dormann, C. F., Purschke, O., Márquez, J. R. G., Lautenbach, S., & Schröder, B. (2008) · Ecology · doi:10.1890/07-1772.1 · Cited by 222 · see citing works

Model-based machine learning

Bishop, C. (2013) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2012.0222 · Cited by 218 · see citing works

Towards global data products of Essential Biodiversity Variables on species traits

Kissling, W. D., Walls, R., Bowser, A., Jones, M., Kattge, J., Agosti, D., Amengual, J., Basset, A., van Bodegom, P. M., Cornelissen, J. H. C., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0667-3 · Cited by 217 · see citing works

Using presence‐only and presence–absence data to estimate the current and potential distributions of established invasive species

Gormley, A. M., Forsyth, D. M., Griffioen, P. A., Lindeman, M., Ramsey, D. S. L., Scroggie, M. P., & Woodford, L. (2010) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2010.01911.x · Cited by 211 · see citing works

Inference from presence‐only data; the ongoing controversy

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Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring

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Tang, W., Ye, S., Qin, S., Fan, Q., Tang, J., Zhang, H., Liu, J., Huang, Z., & Liu, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-70044-y · Cited by 9 · see citing works

Habitat suitability modeling of dominant weed in canola (Brassica napus) fields using machine learning techniques

Dastres, E., Sarvestani, G. S., Edalat, M., & Pourghasemi, H. R. (2025) · Weed Science · doi:10.1017/wsc.2025.5 · Cited by 9 · see citing works

National-scale acoustic monitoring of avian biodiversity and migration

Bick, I. A., Bakkestuen, V., Cretois, B., Hillier, B. V., Kålås, J. A., Øien, I. J., Pedersen, M., Raja, K. B., Rosten, C. M., Somveille, M., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-10389-4 · Cited by 9 · see citing works

Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Zhang, H., Li, S., Mason, J. A., Hezi Yizhaq, A., Dongwei, G. U. I., & Xu, Z. (2024) · Earth Surface Processes and Landforms · doi:10.1002/esp.5783 · Cited by 8 · see citing works

Using geographic information systems and remote sensing technique to classify land cover types and predict grassland bird abundance and distribution in Nairobi National Park, Kenya

Ong’ondo, F. J., Ambinakudige, S., Malaki, P. A., Njoroge, P., & Ahmad, H. (2025) · International Journal of Geoheritage and Parks · doi:10.1016/j.ijgeop.2025.02.003 · Cited by 8 · see citing works

Predicting species abundance using machine learning approach: a comparative assessment of random forest spatial variants and performance metrics

Mushagalusa, C. A., Fandohan, A. B., & Kakaï, R. G. (2024) · Modeling Earth Systems and Environment · doi:10.1007/s40808-024-02055-7 · Cited by 8 · see citing works

Fine‐Grain Predictions Are Key to Accurately Represent Continental‐Scale Biodiversity Patterns

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Global range extension of bioclimatic zone of Bruguiera hainesii C.G.Rogers 1919 (Rhizophoraceae)

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Monitoring protected areas by integrating machine learning, remote sensing and citizen science

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Fall armyworm habitat analysis in Africa with multi-source earth observation data

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Great Gerbils ( Rhombomys opimus ) in Central Asia Are Spreading to Higher Latitudes and Altitudes

Liu, X., Xu, L., Zheng, J., Lin, J., Li, X., Liu, L., Tian, R., & Mu, C. (2024) · Ecology and Evolution · doi:10.1002/ece3.70517 · Cited by 6 · see citing works

Distribution and behaviour of striped dolphins in the southwestern Mediterranean Sea based on whale-watching data

Cáceres, R. M. C., Gomariz‐Castillo, F., Alonso‐Sarría, F., Abel-Abellán, I., & Casalduero, F. G. (2023) · Regional Studies in Marine Science · doi:10.1016/j.rsma.2023.103256 · Cited by 6 · see citing works

Modeling the importance of subsurface environmental variables in driving swordfish ( Xiphias gladius ) catchability in the Western Indian Ocean

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Mapping environmental suitability changes for arbovirus mosquitoes in Southeast Asia: 1960–2020

Hou, W., Zhou, Y., Luo, W. P., Wang, L., Kwan, M., & Cook, A. R. (2024) · iScience · doi:10.1016/j.isci.2024.110498 · Cited by 6 · see citing works

On pseudo-absence generation and machine learning for locust breeding ground prediction in AfricaPreprint

Yusuf, I. S., Tessera, K.-A., Tumiel, T., Slim, Z., Kerkeni, A., Nevo, S., & Pretorius, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03904 · Cited by 6 · see citing works

Modelling the distribution of the Caucasian oak (Quercus macranthera) in Western Asia under future climate change scenarios

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Predicting the effect of climate change on the spatiotemporal distribution of two endangered plant species, Silene leucophylla Boiss. and Silene schimperiana Boiss., using machine learning, in Saint Catherine Protected Area, Egypt

Refaat, A. M., Youssef, A. M., Mosallam, H., & Farouk, H. (2024) · Beni-Suef University Journal of Basic and Applied Sciences · doi:10.1186/s43088-024-00553-2 · Cited by 5 · see citing works

Distribution of denitrifiers predicted by correlative niche modeling of changing environmental conditions and future climatic scenarios across the Baltic Sea

Abdelgadir, M., Alharbi, R., AlRashidi, M., Alatawi, A. S., Sjöling, S., & Dinnétz, P. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102346 · Cited by 5 · see citing works

Integrating advanced remote sensing technologies and machine learning in urban forestry: a comprehensive review and future outlook

Zeybek, M. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/addf66 · Cited by 4 · see citing works

Macroecological approaches for the prediction of zoonotic disease risk

Marco, M. D., Marcolin, L., Tonelli, A., Skinner, E. B., & Catucci, E. (2026) · Nature Sustainability · doi:10.1038/s41893-025-01749-9 · Cited by 4 · see citing works

Standardized ML and XAI Workflow for Presence-Only Species Distribution Modeling: A Case Study in the Amazon

Miyaji, R. O., de Almeida, F. V., & Corrêa, P. L. P. (2026) · IEEE Access · doi:10.1109/access.2026.3652949 · Cited by 4 · see citing works

An Empirical Evaluation of Ensemble Strategies in Habitat Suitability Modeling

Alaoui, O. E., & Idri, A. (2024) · SN Computer Science · doi:10.1007/s42979-024-02828-y · Cited by 4 · see citing works

Climate change drives spatiotemporal mismatches between bivalves and mangrove in the Colombian Pacific coast

Selvaraj, J. J., & Portilla-Cabrera, C. V. (2024) · Journal for Nature Conservation · doi:10.1016/j.jnc.2024.126679 · Cited by 4 · see citing works

Multi-scale and Multimodal Species Distribution Modeling

van Tiel, N., Zbinden, R., Dalsasso, E., Kellenberger, B., Pellissier, L., & Tuia, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_10 · Cited by 3 · see citing works

Species Distribution Modeling Reveals Future Climate Refugia and Important Areas for Rocky Plants in Brazil's Iron Quadrangle

Francisconi, A. F., Araujo, J. V. D. S. R., Anastacio, A. C. S. A., Marques, A. P. D. S., & Zucchi, M. I. (2026) · Ecology and Evolution · doi:10.1002/ece3.73355 · Cited by 3 · see citing works

Using ensemble modeling to predict the current distribution of Pistacia atlantica Desf. in Algeria

Aloui, M., Neffar, S., & Chenchouni, H. (2024) · Die Naturwissenschaften · doi:10.1007/s00114-024-01951-3 · Cited by 3 · see citing works

Modeling Current and Future Habitat Suitability for the Snow Leopard ( Panthera uncia ) Under Climate Change Scenarios in Nepal

Budha, M., Karki, J., Khadka, B., & Koju, N. P. (2025) · Ecology and Evolution · doi:10.1002/ece3.72490 · Cited by 2 · see citing works

Climate change impact on ethnomedicinal high use plants in East Himalayan and Indo-Burma hotspots, India

Jha, K. K., & Jha, R. (2025) · Discover Plants. · doi:10.1007/s44372-025-00439-2 · Cited by 2 · see citing works

Using habitat suitability models for multiscale forensic geolocation analysis

Wang, H., Miller, J. A., Grubesic, T. H., & Jha, S. (2023) · Transactions in GIS · doi:10.1111/tgis.13052 · Cited by 2 · see citing works

Comparing Commonly Used Aquatic Habitat Modeling Methods for Native Fish

Turney, E., Goodrum, G. C., Saunders, W. C., Walsworth, T. E., & Null, S. E. (2024) · Ecological Modelling · doi:10.1016/j.ecolmodel.2024.110909 · Cited by 2 · see citing works

Impacts of base learners selection of heterogeneous ensemble for habitat suitability modeling

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Study of ethnoveterinary medicine practices in a global hotspot of India using ecological modelling: why and where?

Jha, K. K., & Jha, R. (2025) · Academia Environmental Sciences and Sustainability · doi:10.20935/acadenvsci7582 · Cited by 2 · see citing works

Generating Binary Species Range Maps

Dorm, F., Lange, C. M., Loarie, S. R., & Aodha, O. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_1 · Cited by 2 · see citing works

Species habitat modeling based on image semantic segmentation

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Habitat probability prediction of umbrella species in urban ecosystems including habitat suitability of prey species

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Predicting the effect of climate change on the geographic distribution of the endemic Fritillaria aurea in Türkiye

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Interpreting ML in Ecology: A RAG-Based Approach to Explainability in Species Distribution Modeling

Miyaji, R. O., & Corrêa, P. L. P. (2025) · Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA) · doi:10.5753/wcama.2025.8219 · Cited by 1 · see citing works

Prediction of present and future spatial occurrence of cyanobacteria and the toxin nodularin in the Baltic Sea

Abdelgadir, M., Karlson, B., Dahlgren, E., & Olofsson, M. (2026) · Biogeosciences · doi:10.5194/bg-23-4321-2026 · Cited by 1 · see citing works

Random forest-based species distribution modeling reveals intensifying multi-species invasion risks of alien plants in Ethiopia under climate change

Yasin, K. H., Tulu, D., Gelete, T. B., Ahmed, B., Iguala, A. D., Tadesse, K. A., & Kebede, E. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.101869 · Cited by 1 · see citing works

Disentangling sampling bias to generate early-warning surfaces of forest ecological sensitivity: An integrated MaxEnt–CatBoost framework

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How safe are vulture safe zones in Indian subcontinent: A climate change perspective

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The Extinction Trajectory of the Crimean Juniper (Juniperus excelsa) Species in Central Anatolia Under Global Climate Change

Acarer, A. (2025) · Düzce Üniversitesi Orman fakültesi ormancılık dergisi/Düzce Üniversitesi Orman Fakültesi ormancılık dergisi · doi:10.58816/duzceod.1694206 · Cited by 1 · see citing works

From Bioclimatic Envelopes to Machine Learning: A Journey Through the History, Present, and Future of Species Distribution Modeling With Practical Tips for Use and Notes to BryophytesPreprint

Číhal, L. (2023) · Preprints.org · doi:10.20944/preprints202304.0367.v1 · Cited by 1 · see citing works

NicheFlow: Towards a foundation model for Species Distribution ModellingPreprint

Dinnage, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.15.618541 · Cited by 1 · see citing works

Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041–2100)

Wang, T., & Dong, J. (2026) · Scientific Data · doi:10.1038/s41597-026-06688-4 · Cited by 1 · see citing works

Aplicação de Técnicas de Confident Learning para Limpeza de Dados e Melhoria de Desempenho de Classificadores de Aprendizado de Máquina: um Estudo de Caso

Miyaji, R. O., de Almeida, F. V., & Corrêa, P. L. P. (2023) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd.2023.232175

Continental species distribution and biodiversity predictions depend on modeling grainPreprint

Cohen, J. M., & Jetz, W. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.25.564051

Using Geographic Information Systems and Remote Sensing Technique to Classify Land Cover Types and Predict Grassland Bird Abundance and Distribution in Nairobi National Park, KenyaPreprint

Ong’ondo, F. J., Ambinakudige, S., Malaki, P. A., & Njoroge, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4804328

Comparação e avaliação de técnicas de aprendizado de máquina para modelagem de distribuição de espécies na região da Bacia Amazônica.

Miyaji, R. O. (2024) · doi:10.11606/d.3.2024.tde-26082024-110959

Aplicação de técnicas de Co-Teaching para Limpeza de Dados e Treinamento Robusto de Redes Neurais: um Estudo de Caso de Modelagem de Distribuição de Espécies

Miyaji, R. O., de Almeida, F. V., & Corrêa, P. L. P. (2024) · Anais do Brazilian e-Science Workshop (BreSci) · doi:10.5753/bresci.2024.244114

Integrating Satellite Data for Seabird Species Distribution Modeling: Acquisition and Pre-processing of Predictor Variables

Yañez, M. B., Saldaña-Pérez, M., Guzmán, G., & Palma-Preciado, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-77290-0_12

Tackling the problem of multi-class imbalanced classification on species distribution models using machine learning techniquesPreprint

Sidumo, B. (2025) · Research Square · doi:10.21203/rs.3.rs-6258914/v1

Modelling current and future suitable cultivation areas of cashew trees in Benin (West Africa) based on the major parasite and its parasitoid distribution under global climate warming

Dotonhoué, C. F. G., Wédjangnon, A. A., Agoundé, G., & Ouinsavi, C. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101589

Predictive Analytics in Agriculture: Assessing Crop Yield Through Species Distribution Models and Machine Learning in the Context of Greenhouse Gas Dynamics Over Time

Bohra, N., & Gupta, A. K. (2025) · Studies in big data · doi:10.1007/978-981-96-7492-3_7

Modelling the Spread-And Establishment Potential of Climate-Sensitive Plant Pests for Phytosanitary Risk AnalysesPreprint

Heß, B., Waldau, T., Menz, C., Dominic, A., Wilstermann, A., & Schrader, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5389182

Modeling and Assessing the Impact of Future Climate Change on Migratory Bird Habitat Pattern Using AI and Machine Learning Algorithms

Kumar, V., Saran, S., & Jeganathan, C. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242595

Study of Biodiversity and Ecology Using a Machine Learning Approach

Jeniffer, J. T., & Thatheyus, A. J. (2026) · Earth and environmental sciences library · doi:10.1007/978-3-032-11426-6_15

Integrating Explainable AI with Species Distribution Models to Assess Anthropogenic Impacts on Amazonian Birds

Miyaji, R. O., de Almeida, F. V., & Corrêa, P. L. P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15990-8_18

CGAF-SDP:A Species Distribution Prediction Model Based on Cross Graph Attention Fusion

Sun, X., Li, S., & Du, Z. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356271

Critical Thresholds for Fish Resources in a Changing Climate

Liu, Y., Li, Q., & Wang, Z. (2026) · Marine Resource Economics · doi:10.1086/739342

İç Anadolu’daki Boylu Ardıç (Juniperus excelsa) Türünün Küresel İklim Değişikliği Altında Yok Oluşa Sürüklenişi

Acarer, A. (2025) · DergiPark (Istanbul University)

Contribution à l'extraction de contradictions techniques depuis un corpus de brevets propre à un domaine par l'usage des techniques du traitement automatique des langues

Berdyugina, D. (2022) · theses.fr (ABES) · doi:10.70675/1a9f84a9z1837z430eza530z1181fcafa585

A predictive approach to determining the joint conservation status of species

Estopinan, J. (2023) · doi:10.70675/fc734692z3098z402ez9cb5za4e234160edb

Diversity and evolution of Quaternary micromammal faunas from South Africa : novel approaches for reconstructing the paleoenvironments of early hominins

Linchamps, P. (2023) · doi:10.70675/c99ca11fz9a71z4439zbd93z1b142d561964

Modeling the spatiotemporal dynamics of Mormon crickets across the western United States using Bayesian species distribution models

Wu, D., Srygley, R. B., Li, G., & Cao, G. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103989

Learning Transferable PV Siting Logic from Urban Deployment Patterns with Explainable Deep LearningPreprint

Shen, J. F., Zheng, F., Deng, W., Chen, T., Xiong, S., Bellotti, A. G., Tesema, F. B., & Prasad, D. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6661886

Uncertainties of Predicting the Potential Geographical Distribution of Species Invasion by Considering Random Errors in Environmental Factors

Xie, J., Zhang, H., & Yang, W. (2026) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-026-09880-6

STEM-LM: Spatio-Temporal Ecological Modeling via Masked Language Model for Multi-Species DistributionPreprint

Li, J. K., Lim, W., Rheem, R., Callahan, F. M., Raskin, L. Y., Lemmon-Kishi, M., & Nielsen, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.724718

Climate change impacts on Xanthium strumarium distribution: Integrating species distribution models with rhizosphere microbiome analysis in China

Dong, J., Zhang, L., Wang, X., Zhang, Y., Zhu, M., & Jiang, H. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351471

Thermal Stress and Habitat Loss: Assessing the Vulnerability of Potamon fluviatile (Decapoda: Potamidae) to Climate Change in Italy

Sorboni, S. G., Pourebrahim, S., Chen, J. E., & Hadipour, M. (2026) · Ecology and Evolution · doi:10.1002/ece3.74256

Modelling Climate‐Resilient Habitats for Conservation and Restoration of Isoberlinia doka Craib and Stapf and Isoberlinia tomentosa (Harms) Craib and Stapf in Benin

Adjahossou, S. B., Ayena, J. I. K., Sode, A. I., Idohou, R., Olaye, A. B., Houehanou, T. D., Gouwakinnou, G. N., Toyi, S. S. M., & Houinato, M. (2026) · Climate Resilience and Sustainability · doi:10.1002/cli2.70062

Human-centric skills are essential for the responsible and rigorous application of AI in ecology

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Fusing laser scans with pretrained satellite embeddingsand habitat priors for tree species classificationPreprint

Kulicki, M., Wielgosz, M., Puliti, S., Będkowski, J., & Stereńczak, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7435257

From data to decisions: The role of machine learning in precision ecological restoration

Ahmed, Z., Dongwei, G. U. I., Liu, Q., Murtaza, G., & Ali, S. (2026) · Ecological Applications · doi:10.1002/eap.70305

Projected effects of climate change on the potential environmental suitability of Vachellia seyal (Delile) P. J. H. Hurter in Sudan using MaxEnt species distribution modeling

Yasin, E. H. E., Gadallah, N. A. H., Siddig, A. A. H., Koreň, M., & Czimber, K. (2026) · Modeling Earth Systems and Environment · doi:10.1007/s40808-026-02946-x

Shared Environmental Signatures Among Co-Occurring Ornamental Reef Fish Species in Eastern Indonesia

Rubandi, P. E., Riswantini, D., & Astuti, A. B. (2026) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina72033.2026.11714676

A sequential multi-model machine learning framework integrating environmental and management factors for mapping exchangeable potassium in rice soils

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Deep transfer learning reveals severe habitat collapse and limited upslope migration of Persian Oak in the Zagros Mountains under climate change

Masoudi, B., & Bazzi, S. (2026) · Journal of Mountain Science · doi:10.1007/s11629-026-0559-y

A Review of Machine Learning Based Species' Distribution Modelling

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SUSTAINABILITY—MORE THAN A BUZZ-WORD?

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THE CONTRIBUTION OF POPULATION AND COMMUNITY BIOLOGY TO CONSERVATION SCIENCE

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