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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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Briscoe, N. J., Elith, J., Salguero‐Gómez, R., Lahoz‐Monfort, J. J., Camac, J., Giljohann, K. M., Holden, M. H., Hradsky, B. A., Kearney, M., McMahon, S. M., et al. (2019) · Ecology Letters · doi:10.1111/ele.13348 · Cited by 249
Self-citationWebber, B. L., Yates, C. J., Maître, D. C. L., Scott, J. K., Kriticos, D. J., Ota, N., McNeill, A., Roux, J. J. L., & Midgley, G. F. (2011) · Diversity and Distributions · doi:10.1111/j.1472-4642.2011.00811.x · Cited by 242
Consequences of Range Contractions and Range Shifts on Molecular Diversity
Arenas, M., Ray, N., Currat, M., & Excoffier, L. (2012) · Molecular Biology and Evolution · doi:10.1093/molbev/msr187 · Cited by 236
Padalia, H., Srivastava, V., & Kushwaha, S. P. S. (2014) · Ecological Informatics · doi:10.1016/j.ecoinf.2014.04.002 · Cited by 236
Optimizing ensembles of small models for predicting the distribution of species with few occurrences
Breiner, F. T., Nobis, M. P., Bergamini, A., & Guisan, A. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12957 · Cited by 234
“…Maxent models by applying a betamultiplier value of 2.5 because higher values were performing best with rare species according to recent literature (Elith et al., 2010; Moreno- Amat et al., 2015; Muscarella et al., 2014; Shcheglovitova & Anderson, 2013), but it might be worth to compare ESMs also…”
MethodInfluentialMajor restructuring of marine plankton assemblages under global warming
Benedetti, F., Vogt, M., Elizondo, U. H., Righetti, D., Zimmermann, N. E., & Gruber, N. (2021) · Nature Communications · doi:10.1038/s41467-021-25385-x · Cited by 255
Fordham, D. A., Akçakaya, H. R., Araújo, M. B., Elith, J., Keith, D. A., Pearson, R. G., Auld, T. D., Mellin, C., Morgan, J. W., Regan, T. J., et al. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02614.x · Cited by 236
Self-citationResearch on the ecology of ticks and tick-borne pathogens—methodological principles and caveats
Estrada‐Peña, A., Gray, J., Kahl, O., Lane, R. S., & Nijhof, A. M. (2013) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2013.00029 · Cited by 226
Protected areas offer refuge from invasive species spreading under climate change
Gallardo, B., Aldridge, D. C., González‐Moreno, P., Pergl, J., Pizarro, M., Pyšek, P., Thuiller, W., Yesson, C., & Vilà, M. (2017) · Global Change Biology · doi:10.1111/gcb.13798 · Cited by 230
Climate change impact on seaweed meadow distribution in the North Atlantic rocky intertidal
Jueterbock, A. O., Tyberghein, L., Verbruggen, H., Coyer, J. A., Olsen, J., & Hoarau, G. G. (2013) · Ecology and Evolution · doi:10.1002/ece3.541 · Cited by 235
Global marine bacterial diversity peaks at high latitudes in winter
Ladau, J., Sharpton, T. J., Finucane, M. M., Jospin, G., Kembel, S. W., O’Dwyer, J. P., Koeppel, A. F., Green, J. L., & Pollard, K. S. (2013) · The ISME Journal · doi:10.1038/ismej.2013.37 · Cited by 219
A deep active learning system for species identification and counting in camera trap images
Norouzzadeh, M. S., Morris, D., Beery, S., Joshi, N., Jojić, N., & Clune, J. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13504 · Cited by 249
“This information facilitates the modeling of population sizes, distributions, and environmental interactions [1, 2, 3].”
BackgroundDo Himalayan treelines respond to recent climate change? An evaluation of sensitivity indicators
Schickhoff, U., Bobrowski, M., Böhner, J., Bürzle, B., Chaudhary, R. P., Gerlitz, L., Heyken, H., Lange, J., Müller, M., Scholten, T., et al. (2015) · Earth System Dynamics · doi:10.5194/esd-6-245-2015 · Cited by 209
Zhao, Y., Deng, X., Xiang, W., Chen, L., & Ouyang, S. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101393 · Cited by 214
Using niche models with climate projections to inform conservation management decisions
Schwartz, M. W. (2012) · Biological Conservation · doi:10.1016/j.biocon.2012.06.011 · Cited by 209
Mechanistic species distribution modelling as a link between physiology and conservation
Evans, T. G., Diamond, S. E., & Kelly, M. W. (2015) · Conservation Physiology · doi:10.1093/conphys/cov056 · Cited by 215
“Predictions regarding the future distribution of invasive cane toads in Australia also deviate widely depending on whether correlative or mechanistic models are used (Elith et al., 2010).”
BackgroundInfluentialUsing species distribution models in paleobiogeography: A matter of data, predictors and concepts
Varela, S., Lobo, J. M., & Hortal, J. (2011) · Palaeogeography Palaeoclimatology Palaeoecology · doi:10.1016/j.palaeo.2011.07.021 · Cited by 200
Amat, E. M., Mateo, R. G., Nieto‐Lugilde, D., Morueta‐Holme, N., Svenning, J., & García‐Amorena, I. (2015) · Ecological Modelling · doi:10.1016/j.ecolmodel.2015.05.035 · Cited by 197
Anderson, R. P. (2012) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.06440.x · Cited by 199
Species distribution modelling supports the study of past, present and future biogeographies
Franklin, J. (2023) · Journal of Biogeography · doi:10.1111/jbi.14617 · Cited by 187
The global distribution of known and undiscovered ant biodiversity
Kass, J. M., Guénard, B., Dudley, K. L., Jenkins, C. N., Azuma, F., Fisher, B. L., Parr, C. L., Gibb, H., Longino, J. T., Ward, P. S., et al. (2022) · Science Advances · doi:10.1126/sciadv.abp9908 · Cited by 194
Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish
Zeng, Y., Low, B. W., & Yeo, D. C. J. (2016) · Ecological Modelling · doi:10.1016/j.ecolmodel.2016.09.019 · Cited by 193
Martínez, B. D. ‐., Radford, B., Thomsen, M. S., Connell, S. D., Carreño, F., Bradshaw, C. J. A., Fordham, D. A., Russell, B. D., Gurgel, C. F. D., & Wernberg, T. (2018) · Diversity and Distributions · doi:10.1111/ddi.12767 · Cited by 197
MaxEnt versus MaxLike: empirical comparisons with ant species distributions
Fitzpatrick, M. C., Gotelli, N. J., & Ellison, A. M. (2013) · Ecosphere · doi:10.1890/es13-00066.1 · Cited by 187
Ahmadi, M., Hemami, M., Kaboli, M., & Shabani, F. (2023) · Ecology and Evolution · doi:10.1002/ece3.9827 · Cited by 187
“In general, manipulating the input data (Chauvier et al., 2021; Elith et al., 2010), and parameterizing the modeling method (Fithian et al., 2015; Muscarella et al., 2014) are two strategies that have been used to take into account the bias in SDM efforts.”
BackgroundMethodGlobal models and predictions of plant diversity based on advanced machine learning techniques
Cai, L., Kreft, H., Taylor, A., Denelle, P., Schrader, J., Essl, F., van Kleunen, M., Pergl, J., Pyšek, P., Stein, A., et al. (2022) · New Phytologist · doi:10.1111/nph.18533 · Cited by 202
“Machine learning models are known to not extrapolate well under such conditions (Elith et al ., 2010).”
BackgroundStephens, P. R., Altizer, S., Smith, K. F., Aguirre, A. A., Brown, J. H., Budischak, S. A., Byers, J. E., Dallas, T., Davies, T. J., Drake, J. M., et al. (2016) · Ecology Letters · doi:10.1111/ele.12644 · Cited by 187
“Niche modelling and related methods can be used to predict shifts in host or parasite geographic ranges in response to climate change (e.g., Morin & Thuiller 2009; Elith et al. 2010), and thus potential novel patterns of co-occurrence.”
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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