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The art of modelling range-shifting species: The art of modelling range-shifting species
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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“The present habitat suitability and distribution of each species were estimated using the MaxEnt modelling technique, which employs maximum entropy methods and is particularly resilient when dealing with presence-only species records (Elith et al., 2010).”
From Projecting future climate change impacts on the distribution of the ‘Octopus vulgaris species complex’ (2022) · cites this paper as Elith et al., 2010
In the citing paper
“Without clear records on survey effort across the landscape, we cannot distinguish between areas that are environmentally unsuitable and those that are under-sampled (Elith, Kearney & Phillips 2010: Appendix S1).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney & Phillips 2010
In the citing paper
“The executable Maxent software (v.3.4.4) (Phillips, Dudík & Schapire 2021) has several modifiable parameters that influence the model performance, and ultimately the effectiveness of the predictive model (e.g., Barbet-Massin et al. 2012; Elith, Kearney & Phillips 2010; Fourcade et al. 2014; Merow, Smith & Silander 2013; Morales, Fernández & Baca-González 2017; Phillips et al. 2017).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney & Phillips 2010
In the citing paper
“The executable Maxent software (v.3.4.4) (Phillips, Dudík & Schapire 2021) has several modifiable parameters that influence the model performance, and ultimately the effectiveness of the predictive model (e.g., Barbet-Massin et al. 2012; Elith, Kearney & Phillips 2010; Fourcade et al. 2014; Merow, Smith & Silander 2013; Morales, Fernández & Baca-González 2017; Phillips et al. 2017).”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney & Phillips 2010
In the citing paper
“The executable Maxent software (v.3.4.4) (”
From A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina (2021) · cites this paper as Elith, Kearney \u0026 Phillips 2010
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Swinnen, K. R., Strubbe, D., Matthysen, E., & Leirs, H. (2017) · Biodiversity and Conservation · doi:10.1007/s10531-017-1333-9 · Cited by 40
“SDMs are applied to a wide range of questions in conservation biology, such as providing information for managing threatened species, to determine the effects of climate change on species distributions and to assess the potential of invasive species (Guisan and Thuiller 2005; Elith et al. 2011; Guillera-Arroita et al.…”
BackgroundMethodInfluentialMorales‐Barbero, J., Martínez, P. A., Ferrer‐Castán, D., & Olalla‐Tárraga, M. Á. (2017) · Ecography · doi:10.1111/ecog.02647 · Cited by 38
Rocca, F. D., Bogliani, G., Breiner, F. T., & Milanesi, P. (2018) · Biodiversity and Conservation · doi:10.1007/s10531-018-1670-3 · Cited by 40
Back to the future: conserving functional and phylogenetic diversity in amphibian-climate refuges
Lourenço‐de‐Moraes, R., Campos, F. S., Ferreira, R. B., Solé, M., Beard, K. H., & Bastos, R. P. (2019) · Biodiversity and Conservation · doi:10.1007/s10531-019-01706-x · Cited by 42
Yu, W., Chen, X., Zhang, Y., & Qian, Y. T. (2019) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsz051 · Cited by 40
Bowen, A. K. M., & Stevens, M. H. H. (2020) · Ecology and Evolution · doi:10.1002/ece3.6735 · Cited by 40
“…the area from which background points were selected using a buffer indicating how far WLBG could have reached if conditions were suitable (Elith et al., 2010) using the distance between the earliest record of WLBG (K. Kyde, pers. communication) and the maximum distance from that record…”
BackgroundMethodInfluentialPrieto-Torres, D. A., Nori, J., Rojas‐Soto, O., & Navarro-Sigüenza, A. G. (2021) · Biological Conservation · doi:10.1016/j.biocon.2021.109083 · Cited by 42
Tonini, J. F. R., Costa, L. P., & Carnaval, A. C. (2013) · Journal of Zoological Systematics & Evolutionary Research · doi:10.1111/jzs.12014 · Cited by 40
Mapping from heterogeneous biodiversity monitoring data sources
Sardà‐Palomera, F., Brotons, L., Villero, D., Sierdsema, H., Newson, S. E., & Jiguet, F. (2012) · Biodiversity and Conservation · doi:10.1007/s10531-012-0347-6 · Cited by 40
Davis, M. A., Douglas, M. R., Webb, C. T., Collyer, M. L., Holycross, A. T., Painter, C. W., Kamees, L. K., & Douglas, M. E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131067 · Cited by 40
Gámez, N., Escalante, T., Espinosa, D., Eguiarte, L. E., & Morrone, J. J. (2013) · Journal of Biogeography · doi:10.1111/jbi.12249 · Cited by 39
Wielstra, B., Babik, W., & Arntzen, J. W. (2015) · Biological Journal of the Linnean Society · doi:10.1111/bij.12446 · Cited by 39
Assessing the Risk of Invasion by Tephritid Fruit Flies: Intraspecific Divergence Matters
Godefroid, M., Cruaud, A., Rossi, J., & Rasplus, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135209 · Cited by 39
Habitat preferences of baleen whales in a mid-latitude habitat
Prieto, R., Tobeña, M., & Silva, M. A. (2016) · Deep Sea Research Part II Topical Studies in Oceanography · doi:10.1016/j.dsr2.2016.07.015 · Cited by 42
“We used the multivariate similarity surface (MESS) function in MaxEnt (Elith et al., 2010) to test the similarity among environmental conditions found during model fitting and the prediction area.”
MethodSteen, V. A., Sofaer, H. R., Skagen, S. K., Ray, A. J., & Noon, B. R. (2017) · Ecology and Evolution · doi:10.1002/ece3.3403 · Cited by 39
Becerra-López, J. L., Esparza‐Estrada, C. E., Romero-Méndez, U., Sigala‐Rodríguez, J. J., Goyenechea, I., & Castillo-Cerón, J. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185086 · Cited by 39
de la Vega, G., & Corley, J. C. (2019) · International Journal of Pest Management · doi:10.1080/09670874.2018.1547460 · Cited by 39
“A MESS analysis (multivariate environmental similarity surface) and the limiting factor mapping technique was performed to determine limiting factors that affect the distribution boundary of SWD populations (Elith et al. 2010; Hill et al. 2012).”
BackgroundMethodInfluentialEndangered species management and climate change: When habitat conservation becomes a moving target
Wilkening, J. L., Pearson‐Prestera, W., Mungi, N. A., & Bhattacharyya, S. (2019) · Wildlife Society Bulletin · doi:10.1002/wsb.944 · Cited by 39
Henríquez, W. A., Urbina-Cardona, N., & Armenteras, D. (2019) · Perspectives in Ecology and Conservation · doi:10.1016/j.pecon.2019.11.002 · Cited by 44
“All SDM created for the selected species showed high values for the partial ROC test (1.30 – 1.87; P < 0.05), with a mean omission rate of only 10 %, indicating that the models of potential distribution were satisfactory.”
InfluentialPréau, C., Grandjean, F., Sellier, Y., Gailledrat, M., Bertrand, R., & Isselin‐Nondedeu, F. (2020) · Scientific Reports · doi:10.1038/s41598-020-60479-4 · Cited by 39
Briscoe, N. J., Zurell, D., Elith, J., König, C., Fandós, G., Malchow, A., Kéry, M., Schmid, H., & Guillera‐Arroita, G. (2021) · Global Change Biology · doi:10.1111/gcb.15723 · Cited by 39
“Correlative (static) SDMs describe the likely occurrence or abundance of a species as a function of environmental covariates (Elith & Leathwick, 2009).”
BackgroundSelf-citationTournebize, R., Borner, L., Manel, S., Meynard, C. N., Vigouroux, Y., Crouzillat, D., Fournier, C., Kassam, M., Descombes, P., Tranchant‐Dubreuil, C., et al. (2022) · Global Change Biology · doi:10.1111/gcb.16191 · Cited by 39
Where wolves were: setting historical baselines for wolf recovery in Spain
Clavero, M., García-Reyes, A., Fernández‐Gil, A., Revilla, E., & Fernández, N. (2022) · Animal Conservation · doi:10.1111/acv.12814 · Cited by 45
Modelling of the potential distribution of Limnoperna fortunei (Dunker, 1857) on a global scale
Campos, M. D. C. S., de Andrade, A. F. A., & Kunzmann, B. (2014) · Aquatic Invasions · doi:10.3391/ai.2014.9.3.03 · Cited by 39
Lim, J. Y., Huang, H., Farnsworth, A., Lunt, D. J., Baker, W. J., Morley, R. J., Kissling, W. D., & Hoorn, C. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13436 · Cited by 38
“For example, the Eocene occurrences of Mauritiinae-like pollen in North America and Europe (Figure 2 (3) issues with projecting niche models to non-analogue climates (Elith et al., 2010; Yates et al., 2018); and (4) inherent uncertain - ties in the palaeoclimatic models used.”
BackgroundForecasting species’ responses to climate change using space-for-time substitution
Kharouba, H. M., & Williams, J. L. (2024) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2024.03.009 · Cited by 38
Future trends of marine fish biomass distributions from the North Sea to the Barents Sea
Gordó−Vilaseca, C., Costello, M. J., Marta Coll, M., Jüterbock, A. O., Reiss, H., & Stephenson, F. (2024) · Nature Communications · doi:10.1038/s41467-024-49911-9 · Cited by 42
Cross‐validation matters in species distribution models: a case study with goatfish species
Huang, H., Zhang, Z., Bede‐Fazekas, Á., Mammola, S., Gu, J., Zhou, J., Qu, J., & Lin, Q. (2024) · Ecography · doi:10.1111/ecog.07354 · Cited by 41
Wachter, G. A., Papadopoulou, A., Muster, C., Arthofer, W., Knowles, L. L., Steiner, F. M., & Schlick-Steiner, B. C. (2016) · Molecular Ecology · doi:10.1111/mec.13634 · Cited by 41
“…were performed in MAXENT using the clamping option (i.e. restricting the extrapolation to the range of values encountered during training), and variables outside their training range were identified using multivariate environmental similarity surfaces (‘MESS maps’; Elith et al. 2010, 2011).”
Method
Metadata
- Authors
- Elith, J., Kearney, M., & Phillips, S.
- Affiliations
- The University of Melbourne (AU)
- AT&T (United States) (US)
- Corresponding author
- Elith, Jane
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2010-05-24
- Volume
- 1
- Issue
- 4
- Pages
- 330-342
- ISSN
- 2041-210X (ISSN-L)
- 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
- 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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