Cited by 2,813Species Distribution and Climate ChangeEcology and Vegetation Dynamics StudiesAnimal Ecology and Behavior Studies

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The art of modelling range-shifting species: The art of modelling range-shifting species

Elith, Jane, Kearney, Michael, Phillips, Steven·2010·Methods in Ecology and Evolution

DOI 10.1111/j.2041-210x.2010.00036.x

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Abstract

1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) are frequently applied for predicting potential future distributions of range-shifting species, despite these models’ assumptions that species are at equilibrium with the environments used to train (fit) the models, and that the training data are representative of conditions to which the models are predicted. Here we explore modelling approaches that aim to minimize extrapolation errors and assess predictions against prior biological knowledge. Our aim was to promote methods appropriate to range-shifting species. 2. We use an invasive species, the cane toad in Australia, as an example, predicting potential distributions under both current and climate change scenarios. We use four SDM methods, and trial weighting schemes and choice of background samples appropriate for species in a state of spread. We also test two methods for including information from a mechanistic model. Throughout, we explore graphical techniques for understanding model behaviour and reliability, including the extent of extrapolation. 3. Predictions varied with modelling method and data treatment, particularly with regard to the use and treatment of absence data. Models that performed similarly under current climatic conditions deviated widely when transferred to a novel climatic scenario. 4. The results highlight problems with using SDMs for extrapolation, and demonstrate the need for methods and tools to understand models and predictions. We have made progress in this direction and have implemented exploratory techniques as new options in the free modelling software, MaxEnt. Our results also show that deliberately controlling the fit of models and integrating information from mechanistic models can enhance the reliability of correlative predictions of species in non-equilibrium and novel settings. 5. Implications. The biodiversity of many regions in the world is experiencing novel threats created by species invasions and climate change. Predictions of future species distributions are required for management, but there are acknowledged problems with many current methods, and relatively few advances in techniques for understanding or overcoming these. The methods presented in this manuscript and made accessible in MaxEnt provide a forward step.

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MethodInfluential
Spatio-temporal dynamics of suitable habitats for Detarium microcarpum Guill. & Perr. (Caesalpiniaceae), a priority food tree species in Benin (West Africa)
Agbo, R. I., Idohou, R., Vihotogbé, R., Missihoun, A. A., Dagba, R. A., Assogbadjo, A. E., & Agbangla, C. (2018) · Modeling Earth Systems and Environment · doi:10.1007/s40808-018-0550-x · Cited by 34
“In this field, maximum entropy modelling is the most used algorithm yielding excellent results in comparative tests (Pearson 2007; Elith et al. 2010).”
Method
Estimating environmental suitability
Drake, J. M., & Richards, R. L. (2018) · Ecosphere · doi:10.1002/ecs2.2373 · Cited by 34
Species distribution modeling that overlooks intraspecific variation is inadequate for proper conservation of marula (Sclerocarya birrea, Anacardiaceae)
Jinga, P., Liao, Z., & Nobis, M. P. (2021) · Global Ecology and Conservation · doi:10.1016/j.gecco.2021.e01908 · Cited by 34
Accounting for uncertainty in colonisation times: a novel approach to modelling the spatio‐temporal dynamics of alien invasions using distribution data
Catterall, S., Cook, A. R., Marion, G., Butler, A., & Hulme, P. E. (2012) · Ecography · doi:10.1111/j.1600-0587.2011.07190.x · Cited by 34
Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog
Uribe‐Rivera, D. E., Azat, C., Valenzuela‐Sánchez, A., Bizama, G., Simonetti, J. A., & Pliscoff, P. (2017) · Ecological Applications · doi:10.1002/eap.1556 · Cited by 34
Global Distribution of Alien Mammals Under Climate Change
Biancolini, D., Pacifici, M., Falaschi, M., Bellard, C., Blackburn, T. M., Ficetola, G. F., & Rondinini, C. (2024) · Global Change Biology · doi:10.1111/gcb.17560 · Cited by 36
“…with such settings has shown low model complexity and high transferability compared to other methods, making it highly suited for modeling alien species distributions under climate change (Elith, Kearney, and Phillips 2010; Hao et al. 2020; Jiménez-Valverde et al. 2011; Werkowska et al. 2017).”
Trends in marine species distribution models: a review of methodological advances and future challenges
Klaassen, M., Marques, T. A., Alves, F., & Fernández, M. (2025) · Ecography · doi:10.1002/ecog.07702 · Cited by 41
Climate niche shift in invasive species: the case of the brown anole
ANGETTER, L.-S., Lötters, S., & Rödder, D. (2011) · Biological Journal of the Linnean Society · doi:10.1111/j.1095-8312.2011.01780.x · Cited by 33
Niche shift in four non‐native estrildid finches and implications for species distribution models
Stiels, D., Gaißer, B., Schidelko, K., Engler, J. O., & Rödder, D. (2014) · Ibis · doi:10.1111/ibi.12194 · Cited by 33
Forecasting Distributional Responses of Limber Pine to Climate Change at Management-Relevant Scales in Rocky Mountain National Park
Monahan, W. B., Cook, T., Melton, F., Connor, J., & Bobowski, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083163 · Cited by 38
Diversity and Distribution of Deep-Sea Shrimps in the Ross Sea Region of Antarctica
Basher, Z., Bowden, D. A., & Costello, M. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103195 · Cited by 33
Effectiveness of conservation areas for protecting biodiversity and ecosystem services: a multi-criteria approach
Márquez, J. R. G., Krueger, T., Páez, C. A., Ruíz-Agudelo, C. A., Bejarano, P., Muto, T. J., & Arjona, F. (2016) · International Journal of Biodiversity Science Ecosystems Services & Management · doi:10.1080/21513732.2016.1200672 · Cited by 35
“Absence data were represented by randomly selecting locations outside of the multivariate environmental similarity surface (MESS) (Elith et al. 2010) of the presence data.”
Method
Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake, Nepal
Shrestha, S., & Kang, T. (2017) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-017-1191-4 · Cited by 33
“This model has previously been used to predict species distributions in ecosystems (Phillips et al. 2006; Phillips and Dudik 2008; Elith et al. 2010), and is a pattern-orientated model (Paola and Leeder 2011).”
Method
Freshwater crayfish invasions in South Africa: past, present and potential future
Nunes, A. L., Zengeya, T. A., Measey, J. G., & Weyl, O. L. F. (2017) · African Journal of Aquatic Science · doi:10.2989/16085914.2017.1405788 · Cited by 33
“In addition, clamping was selected to minimise predictions to regions of environmental space outside the limits encountered during training, because extrapolation may overinflate areas of climatic suitability (Elith et al. 2010).”
Identifying priority conservation landscapes and actions for the Critically Endangered Javan leopard in Indonesia: Conserving the last large carnivore in Java Island
Wibisono, H. T., Wahyudi, H. A., Wilianto, E., Pinondang, I. M. R., Primajati, M., Liswanto, D., & Linkie, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198369 · Cited by 40
MAXIMUM ENTROPY MODELING (MAXENT) OF CURRENT AND FUTURE DISTRIBUTIONS OF IPS MANNSFELDI (WACHTL, 1879) (CURCULIONIDAE: SCOLYTINAE) IN TURKEY
Sarıkaya, O., KARACEYLAN, I. B., & Şen, İ. (2018) · Applied Ecology and Environmental Research · doi:10.15666/aeer/1603_25272535 · Cited by 33
Range contraction to a higher elevation: the likely future of the montane vegetation in South Africa and Lesotho
Bentley, L. K., Robertson, M. P., & Barker, N. P. (2018) · Biodiversity and Conservation · doi:10.1007/s10531-018-1643-6 · Cited by 33
Modeling spatially and temporally complex range dynamics when detection is imperfect
Rushing, C. S., Royle, J. A., Ziolkowski, D. J., & Pardieck, K. L. (2019) · Scientific Reports · doi:10.1038/s41598-019-48851-5 · Cited by 33
A global assessment of human influence on niche shifts and risk predictions of bird invasions
Cardador, L., & Blackburn, T. M. (2020) · Global Ecology and Biogeography · doi:10.1111/geb.13166 · Cited by 33
Spatial areas of genotype probability: Predicting the spatial distribution of adaptive genetic variants under future climatic conditions
Rochat, E., Selmoni, O. M., & Joost, S. (2021) · Diversity and Distributions · doi:10.1111/ddi.13256 · Cited by 33
“It is also possible that the forecasted environmental conditions exceed the climatic ranges observed today, leading to extrapolation (and the related uncertainty) in the calculation of future evolutionary potential (Elith et al., 2010).”
Background
Citizen science and niche modeling to track and forecast the expansion of the brown marmorated stinkbug Halyomorpha halys (Stål, 1855)
Streito, J., Chartois, M., Pierre, É., Dusoulier, F., Armand, J.-M., Gaudin, J., & Rossi, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-90378-1 · Cited by 33
New models for wild ungulates occurrence and hunting yield abundance at European scale
Illanas, S., Croft, S., Smith, G. C., López‐Padilla, S., Vicente, J., Blanco, J., Scandura, M., Apollonio, M., Ferroglio, E., Zanet, S., et al. (2022) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2022.en-7631 · Cited by 38
Plant invasion risk inside and outside protected areas: Propagule pressure, abiotic and biotic factors definitively matter
Lozano, V., Febbraro, M. D., Brundu, G., Carranza, M. L., Alessandrini, A., Ardenghi, N. M. G., Barni, E., Bedini, G., Celesti‐Grapow, L., Cianfaglione, K., et al. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162993 · Cited by 36
Using dispersal information to model the species–environment relationship of spreading non‐native species
Sullivan, M. J. P., Davies, R. G., Reino, L., & Franco, A. M. A. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00219.x · Cited by 33
Assessing current and projected suitable habitats for tree-of-heaven along the Appalachian Trail
Clark, J. P., Wang, Y., & August, P. V. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0192 · Cited by 33
Tracing the evolutionary history of the mole,Talpa europaea, through mitochondrial DNA phylogeography and species distribution modelling
Feuda, R., Bannikova, A. A., Zemlemerova, E. D., Febbraro, M. D., Loy, A., Hutterer, R., Aloise, G., Ae, Z., Annesi, F., & Colangelo, P. (2015) · Biological Journal of the Linnean Society · doi:10.1111/bij.12459 · Cited by 33
Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea
Benedetti, F., Guilhaumon, F., Adloff, F., & Ayata, S. (2016) · Ecography · doi:10.1111/ecog.02434 · Cited by 34
Evaluating the risk for Usutu virus circulation in Europe: comparison of environmental niche models and epidemiological models
Cheng, Y., Tjaden, N. B., Jaeschke, A., Lühken, R., Ziegler, U., Thomas, S. M., & Beierkuhnlein, C. (2018) · International Journal of Health Geographics · doi:10.1186/s12942-018-0155-7 · Cited by 33
Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia
Tobin, R. J., Harrison, L. E., Tully, M. K., Lubis, I. N. D., Noviyanti, R., Anstey, N. M., Rajahram, G. S., Grigg, M. J., Flegg, J. A., Price, D. J., et al. (2024) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0011570 · Cited by 32
Parameter and uncertainty estimation for process‐oriented population and distribution models: data, statistics and the niche
Marion, G., McInerny, G., Pagel, J., Catterall, S., Cook, A. R., Hartig, F., & O’Hara, R. B. (2012) · Journal of Biogeography · doi:10.1111/j.1365-2699.2012.02772.x · Cited by 32
Can Recent Global Changes Explain the Dramatic Range Contraction of an Endangered Semi-Aquatic Mammal Species in the French Pyrenees?
Charbonnel, A., Laffaille, P., Biffi, M., Blanc, F., Maire, A., Némoz, M., Sánchez-Pérez, J. M., Sauvage, S., & Buisson, L. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159941 · Cited by 32
“The use of modelling tools such as Species Distribution Models (SDMs) may help to understand how species shift their distribution over time and to predict the impacts of future climate change on these range shifts [2].”
Method
Tracing a toad invasion: lack of mitochondrial DNA variation, haplotype origins, and potential distribution of introduced Duttaphrynus melanostictus in Madagascar
Vences, M., Brown, J. L., Lathrop, A., Rosa, G. M., Cameron, A., Crottini, A., Dolch, R., Edmonds, D., Freeman, K. L. M., Glaw, F., et al. (2017) · Amphibia-Reptilia · doi:10.1163/15685381-00003104 · Cited by 34
Incorporating biophysical ecology into high‐resolution restoration targets: insect pollinator habitat suitability models
Tomlinson, S., Webber, B. L., Bradshaw, D., Dixon, K. W., & Renton, M. (2017) · Restoration Ecology · doi:10.1111/rec.12561 · Cited by 32
“There have been a number of reports urging quantification of specific constraints in addressing complex ecological management issues (e.g. Purves & Pacala 2008; Elith et al. 2010; Alagador et al. 2015).”
Background

Authors: Elith, J., Kearney, M., & Phillips, S.
Affiliations: The University of Melbourne (AU)
AT&T (United States) (US)
Corresponding author: Elith, Jane
ORCID: Elith, Jane
Kearney, Michael
Venue: Methods in Ecology and Evolution
Publisher: Wiley
Published: 2010-05-24
Volume: 1
Issue: 4
Pages: 330-342
ISSN: 2041-210X (ISSN-L)
DOI: 10.1111/j.2041-210x.2010.00036.x
OpenAlex: W2100533358
Semantic Scholar: 57fbdc5ff136d10620ea5204db024583249f9550
Cited by: 2,813
References: 79
Citations by year: 2026: 190 · 2025: 230 · 2024: 252 · 2023: 251 · 2022: 240 · 2021: 219 · 2020: 199 · 2019: 184 · 2018: 163 · 2017: 179 · 2016: 152 · 2015: 128 · 2014: 159 · 2013: 107 · 2012: 100
FWCI: 50.92
Type: journal article
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Methods in Ecology and Evolution (publishedVersion)
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 › Environmental Science › Ecology › Animal Ecology and Behavior Studies
Keywords: species distribution modeling, extrapolation error, Rhinella marina, invasive species, climate change scenarios, absence data, MaxEnt, model reliability, species spread

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The art of modelling range-shifting species: The art of modelling range-shifting species

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