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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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“The extent of extrapolation in the future projection was examined using multivariate environmental similarity surface (MESS) (Elith and Leathwick, 2009; Elith et al., 2010).”
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“To enforce smooth response curves, we only enabled hinge features (see Elith et al.(9); Hill & Terblanche(41)), all other parameters were left at default.”
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“PeerJ reviewing PDF | (2017:06:18607:2:1:NEW 29 Mar 2018) 281 In addition to the resulting suitability maps, the output of multivariate 282 environmental similarity surfaces (MESS) analysis was examined (Elith et al, 2010).”
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“…focus on the spatial structure of data, rely on machine-learning algorithms to understand non-linear relationships between multiple variables (Elith et al. 2010), allow parameters to be defined as probabilities (Wikle and Royle 1999, Fuentes et al. 2007), or describe patterns from data…”
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“Finally, we generated a multivariate environmental similarity surface to measure the degree and location of extrapolation and interpolation across the study area (Elith, Kearney, and Phillips 2010).”
MethodSpatial distribution and impacts of climate change on Milicia excelsa in Benin, West Africa
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“Modelling mobile species presents a particularly challenging situation because, to be meaningful, predictive models must capture both a seasonally dynamic landscape and associated species movements, which traditional methods are unable to account for (Elith et al., 2010).”
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“SDMs suffer from low transferability into novel environmental conditions due to non-linear response curves (Bell & Schlaepfer, 2016; Elith et al., 2010; Qiao et al., 2019).”
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“MESS maps visualize how environmentally similar or different areas across the globe are compared to the model training data 171 .”
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Global warming leads to habitat loss and genetic erosion of alpine biodiversity
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“Extrapolation risk associated with modelling past and future projections was assessed through a multivariate environmental similarity surface (MESS) analysis (Elith et al., 2010) using the R package ‘dismo’ v1.”
MethodNo optimal spatial filtering distance for mitigating sampling bias in ecological niche models
Lamboley, Q., & Fourcade, Y. (2024) · Journal of Biogeography · doi:10.1111/jbi.14854 · Cited by 30
“…approaches have become very common in the fields of biogeography, climate-change ecology or invasion biology, and are implemented through various statistical tools, from simple generalised linear models to advanced machine-learning algorithms such as the popular MaxEnt (Elith et al., 2006 , 2010 ).”
Goicolea, T., Adde, A., Broennimann, O., García‐Viñás, J. I., Gastón, A., Aroca‐Fernández, M. J., Guisan, A., & Mateo, R. G. (2024) · Ecography · doi:10.1111/ecog.07328 · Cited by 30
“We computed the degree of extrapolation by performing multivariate environmental similarity surfaces (MESS) analyses (Elith et al. 2010) on the bioclimatic covariates with the MESS function of the ‘modEvA’ R package (Márcia Barbosa et al. 2013).”
MethodMilanesi, P., Holderegger, R., Caniglia, R., Fabbri, E., & Randi, E. (2015) · Basic and Applied Ecology · doi:10.1016/j.baae.2015.08.008 · Cited by 30
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“We further performed a Multivariate Environmental Similarity Surfaces analysis (MESS, Elith et al. 2010) to identify climates ’non-analog’ to the calibration dataset.”
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“These grids were produced by deriving a Gaussian kernel density map of the occurrence locations (for more detail see Elith et al., 2010).”
MethodEcological niche differentiation across a wolf‐coyote hybrid zone in eastern North America
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“We considered L because one side of the unimodal curve might not be included in the background data (Elith et al., 2010).”
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Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species
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“In this sense, it is noteworthy that maximum entropy method is considered the best option for species distribution modeling when we used presence-only data (Elith et al., 2010; Espinosa et al., 2018; Phillips et al., 2006; Pliscoff and Fuentes-Castillo, 2011; Politis et al., 2011).”
MethodKochmann, J., Cunze, S., & Klimpel, S. (2021) · Mammal Review · doi:10.1111/mam.12249 · Cited by 30
“Models trained only on data from the non- native range would therefore have underestimated the niche and areas with climatically suitable conditions, due to violation of the equilibrium assumption (Elith et al. 2010).”
BackgroundMethodKariyawasam, C. S., Kumar, L., & Ratnayake, S. S. (2021) · Wetlands Ecology and Management · doi:10.1007/s11273-021-09799-4 · Cited by 30
Predicted Shifts in the Distributions of Atlantic Reef-Building Corals in the Face of Climate Change
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“The use of the ensemble modelling technique is recommended over the use of the outcomes from one modelling approach to evaluate the impact of climate changes on shifts in the ranges of species (Elith et al. 2010; Ahmadi et al. 2019; Della Rocca et al. 2019).”
BackgroundMethodChanges in plant species richness due to land use and nitrogen deposition across the globe
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Global diversity patterns of larger benthic foraminifera under future climate change
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“Potential extrapolation errors were quantified using MESS (Multivariate Environmental Similarity Surfaces; Elith et al., 2010).”
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Changing habitat areas and static reserves: challenges to species protection under climate change
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Blanchard, R., O’Farrell, P. J., & Richardson, D. M. (2014) · GCB Bioenergy · doi:10.1111/gcbb.12129 · Cited by 32
Pérez‐Navarro, M. Á., Broennimann, O., Selma, M. Á. E., Bagaria, G., Guisan, A., & Lloret, F. (2022) · Ecography · doi:10.1111/ecog.06263 · Cited by 29
Gonzalez‐Aragon, D., Rivadeneira, M. M., Lara, C., Torres, F. I., Vásquez, J. A., & Broitman, B. R. (2024) · Ecology and Evolution · doi:10.1002/ece3.10901 · Cited by 28
“MESS indicates environmental dissimilarity with negative values and similarity with positive values (Elith et al., 2010).”
BackgroundSmith, A. B. (2012) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2012.00785.x · Cited by 30
Conservation paleobiology needs phylogenetic methods
Lawing, A. M., & Matzke, N. J. (2014) · Ecography · doi:10.1111/ecog.00783 · Cited by 30
Using individual tracking data to validate the predictions of species distribution models
Pinto, C., Thorburn, J., NEAT, F. C., Wright, P. J., Wright, S. R., Scott, B. E., Cornulier, T., & Travis, J. M. J. (2016) · Diversity and Distributions · doi:10.1111/ddi.12437 · Cited by 29
“One of the main advantages of species distribution models (SDMs) is that they may be used to generate predictions for the species distribution beyond the area originally sampled, provided the prediction is performed within the environmental range sampled (Elith et al., 2010).”
BackgroundMethodNeven, L. G., Kumar, S., Yee, W. L., & Wakie, T. (2017) · Environmental Entomology · doi:10.1093/ee/nvx203 · Cited by 29
Shabani, F., Ahmadi, M., Peters, K. J., Haberle, S. G., Champreux, A., Saltré, F., & Bradshaw, C. J. A. (2019) · Ecography · doi:10.1111/ecog.04530 · Cited by 29
Modeling Current and Future Potential Distributions of Milkweeds and the Monarch Butterfly in Idaho
Svancara, L. K., Abatzoglou, J. T., & Waterbury, B. (2019) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2019.00168 · Cited by 29
“In Maxent, the feature types represent mathematical transformations of the covariates to allow complex relationships while the regularizationmultiplier imposes penalties to themodel to help prevent overfitting (Elith et al., 2010, 2011; Merow et al., 2013).”
BackgroundMethodInfluentialDistribution modelling of vegetation types based on area frame survey data
Horvath, P., Halvorsen, R., Stordal, F., Tallaksen, L. M., Tang, H., & Bryn, A. (2019) · Applied Vegetation Science · doi:10.1111/avsc.12451 · Cited by 29
Rodríguez‐Merino, A., Murillo, P. G., & Fernández‐Zamudio, R. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105738 · Cited by 29
Wiltshire, K. H., & Tanner, J. E. (2020) · Ecological Modelling · doi:10.1016/j.ecolmodel.2020.109071 · Cited by 29
Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity
Marzialetti, F., Febbraro, M. D., Malavasi, M., Giulio, S., Acosta, A. T. R., & Carranza, M. L. (2020) · Remote Sensing · doi:10.3390/rs12142315 · Cited by 29
“The degree of extrapolation on values of covariates lying outside the RF calibration range was assessed through the Multivariate Environmental Similarity Surface, (MESS, Figure 2 box 3 b) [63].”
MethodPredicting the distributions of regional endemic dragonflies using a combined model approach
Boys, W. A., Siepielski, A. M., Smith, B. D., Patten, M. A., & Bried, J. T. (2020) · Insect Conservation and Diversity · doi:10.1111/icad.12444 · Cited by 29
“This method compares the similarity between the environment used in model training to future environments the model is being projected to which allows for identification of areas where careful interpretation may be needed (Elith et al., 2010).”
MethodBerriozabal‐Islas, C., Bautista, A. R., Torres-Ángeles, F., Rodrigues, J. F. M., Macip‐Ríos, R., & Aguilar, P. O. (2020) · Hydrobiologia · doi:10.1007/s10750-020-04402-y · Cited by 29
“All data were individually reviewed and records that seemed unreliable for a particular type of information were eliminated from the data base; for example, duplicate records and occurrences far from the known ranges of distribution for each species (Elith et al., 2010).”
BackgroundMapping the current and future distributions of Onosma species endemic to Iran
Nasab, F. K., Mehrabian, A., & Mostafavi, H. (2020) · Journal of Arid Land · doi:10.1007/s40333-020-0080-z · Cited by 29
Climate influence on the distribution of the yellow plum (Ximenia Americana L.) in Burkina Faso
Lompo, O., Dimobe, K., Mbayngone, E., Salfo, S., Sambaré, O., Thiombiano, A., & Ouédraogo, A. (2021) · Trees Forests and People · doi:10.1016/j.tfp.2021.100072 · Cited by 29
Runquist, R. D. B., Lake, T. A., & Moeller, D. A. (2021) · Journal of Biogeography · doi:10.1111/jbi.14105 · Cited by 29
“Those beyond- range environments challenge model projections because species– environment relationships are not truly known (Broennimann et al., 2007; Elith et al., 2010; Guisan et al., 2014; Mesgaran et al., 2014).”
BackgroundInfluentialIs the southern crab Halicarcinus planatus (Fabricius, 1775) the next invader of Antarctica?
López‐farrán, Z., Guillaumot, C., Vargas‐Chacoff, L., Paschke, K. A., Dulière, V., Danis, B., Poulin, É., Saucède, T., Waters, J. M., & Gérard, K. (2021) · Global Change Biology · doi:10.1111/gcb.15674 · Cited by 28
“In order to spot and remove extrapolation errors, the Multivariate Similarity Environmental Estimate (MESS; Elith et al., 2010) was computed based on the presence records (Guillaumot et al., 2019, 2020).”
MethodLovrenčić, L., Temunović, M., Gross, R., Grgurev, M., & Maguire, I. (2022) · Scientific Reports · doi:10.1038/s41598-022-06027-8 · Cited by 36
Predicting the Potential Distribution of Pine Wilt Disease in China under Climate Change
Ouyang, X., Chen, A., Li, Y., Han, X., & Lin, H. (2022) · Insects · doi:10.3390/insects13121147 · Cited by 29
“The current state of the climate was favorable [39].”
BackgroundPredicting Current and Future Invasion of Solidago canadensis: a Study from China
Xu, Z., Peng, H., Feng, Z., & Abdulsalih, N. (2014) · Polish Journal of Ecology · doi:10.3161/104.062.0207 · Cited by 29
Compton, T. J., de Winton, M. D., Leathwick, J. R., & WADHWA, S. (2012) · Freshwater Biology · doi:10.1111/j.1365-2427.2012.02754.x · Cited by 29
Lowman, M. D., Devy, S. M., & Ganesh, T. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7161-5 · Cited by 29
Chignell, S. M., Luizza, M. W., Skach, S., Young, N. E., & Evangelista, P. (2017) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.63 · Cited by 29
“In addition to these statistics, we used SAHM to create a multivariate environmental similarity surface (MESS) for each run to assess whether model predictions had acceptable levels of extrapolation (Elith et al. 2010; Stohlgren et al. 2011).”
MethodVelo‐Antón, G., Martínez‐Freiría, F., Pereira, P., Crochet, P., & Brito, J. C. (2018) · Journal of Biogeography · doi:10.1111/jbi.13176 · Cited by 29
Fitzgerald, M., Coulson, R. N., Lawing, A. M., Matsuzawa, T., & Koops, K. (2018) · Primates · doi:10.1007/s10329-018-0657-8 · Cited by 29
Gerstner, B. E., Kass, J. M., Kays, R., Helgen, K. M., & Anderson, R. P. (2018) · Journal of Mammalogy · doi:10.1093/jmammal/gyy012 · Cited by 29
“…of candidate models with differing levels of complexity, 2 key parameters in Maxent were varied: feature classes and regularization multipliers (Phillips and Dudík 2008; Elith et al. 2010b; Anderson and Gonzalez 2011; Warren and Seifert 2011; Warren et al. 2013; Radosavljevic and Anderson 2014).”
BackgroundMethodInfluentialNiche shift and resource supplementation facilitate an amphibian range expansion
Davies, S., Hill, M., Mcgeoch, M., & Clusella-Trullas, S. (2018) · Diversity and Distributions: A journal of biological invasions and biodiversity · doi:10.1111/ddi.12841 · Cited by 25
Borges, F. O., Guerreiro, M., Santos, C. P., Paula, J. R., & Rosa, R. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.1018766 · Cited by 28
“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).”
MethodDobson, R., Challinor, A. J., Cheke, R., Jennings, S. A., Willis, S. G., & Dallimer, M. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14101 · Cited by 28
“From static SDMs, the drivers of species occurrence can be inferred, and it is assumed that by applying these relationships over space and time, patterns of distribution suitability can be projected, and some have even used this information to project distribution changes (Elith et al., 2010).”
BackgroundA guide and tools for selecting and accessing microclimate data for mechanistic niche modeling
Meyer, A. V., Sakairi, Y., Kearney, M., & Buckley, L. B. (2023) · Ecosphere · doi:10.1002/ecs2.4506 · Cited by 28
Self-citationAn integrated species distribution modelling framework for heterogeneous biodiversity data
Jung, M. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102127 · Cited by 28
“Yet, such naïve projections assume that species are in equilibrium with their environment and often – but not always – neglect factors such as biotic interactions, adaptation and dispersal (Araújo and Guisan, 2006; Elith et al., 2010).”
BackgroundMethod
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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