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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“…and (ii) a larger proportion of non-analog conditions is likely to occur in the future leading to prediction errors also in loca - tions that are included within the training area but where no prediction errors are expected under current conditions (Elith et al., 2010 ; Velazco et al., 2024 ).”
Signatures of niche conservatism and niche shift in the North American kudzu (Pueraria montana) invasion
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Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau
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“, 2003) andpredictive ecological modeling (PEM) (Chee et al., 2016; Elith et al., 2010; Guisan and Zimmermann, 2000; MacMillan et al., 2007) offer a wide range of geographic tools for creating both a spatial and thematic linkage between field sites for reference identification and critical zone assessment (Bui, 2016).”
Background
Modelling the Risk Posed by the Zebra Mussel Dreissena polymorpha: Italy as a Case Study
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“To decrease the number of variables for the final distribution models, we first eliminated the highly correlated predictors and retained those with a Pearson’s |r| < 0.80 (Elith et al. 2010).”
MethodResultInfluential
Climatic Suitability Derived from Species Distribution Models Captures Community Responses to an Extreme Drought Episode
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Time of activity is a better predictor of the distribution of a tropical lizard than pure environmental temperatures
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Predicting the dynamic distribution of Sphagnum bogs in China under climate change since the last interglacial period
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“SDMs identify relationships between the known occurrence of a species (presence or presence/absence data) and environmental data, and use these relationships to make predictions for all unsampled areas in the study region [14,15].”
Method
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Finding what you don’t know: Testing SDM methods for poorly known species
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“Negative values of MESS indicate extrapolation outside the range of predictors during model calibration, which could have negative consequences on model performance (Elith et al., 2010).”
BackgroundMethod
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“…models of direct climate change effects, presumably as an effect of combined factors decreasing each model's performance, all indi‐ ces are above the threshold for good model performance (AUC > 0.8, Elith et al., 2010; Pearce & Ferrier, 2000; TSS > 0.5, Allouche et al., 2006; Herkt et al., 2017).”
BackgroundInfluential
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“However, the way distribution shifts are quantified has changed relatively little (Elith et al. 2010).”
Background
Species distribution modelling for plant communities: stacked single species or multivariate modelling approaches?
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“These issues can be somewhat ameliorated by recent developments in the wider SDM literature, where how to improve predicted distributions is now widely discussed (Araujo and Guisan 2006; Beaumont et al. 2008; Elith and Leathwick 2009; Elith et al. 2010; Beale and Lennon 2012; Porfirio et al. 2014; Jarnevich et al. 201…”
Background
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“…AUC (Supplementary Table 1), utilizes absence data, which may be biased in the early stages of an invasion when absence points may not be true absences and may become presences as the organism spreads to fill its new niche (Václavı́k and Meentemeyer 2009; Elith et al. 2010; Robinson et al. 2010).”
BackgroundMethod
Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China
Zhang, K., Liu, H., Pan, H., Shi, W., Zhao, Y., Li, S., Liu, J., & Tao, J. (2020) · Ecology and Evolution · doi:10.1002/ece3.6236 · Cited by 45
“This file was generated using occurrence point by deriving a Gaussian kernel density map that was rescaled from 1 to 20 based on Elith et al. (2010).”
BackgroundMethod
Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling
Jing, W. A. N., Qi, G., Ma, J., Ren, Y., Rui, W. A. N. G., & McKirdy, S. (2020) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(19)62840-6 · Cited by 46
The way bioclimatic variables are calculated has impact on potential distribution models
Bede‐Fazekas, Á., & Somodi, I. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13488 · Cited by 46
“This finding provides additional support that extrapolation is a major issue of PDMs (Braunisch et al., 2013; Chakraborty et al., 2019; Elith et al., 2010).”
Result
Rapid shifts in Arctic tundra species' distributions and inter‐specific range overlap under future climate change
van Beest, F. M., Beumer, L. T., Andersen, A. S., Hansson, S. V., & Schmidt, N. M. (2021) · Diversity and Distributions · doi:10.1111/ddi.13362 · Cited by 47
“Following Elith et al. (2010), we used presence locations with associated bioclimatic values under current conditions (1979– 2013) as input points and then estimated (dis)similarities in bioclimatic conditions across the study area extent (i.e. vegetated part of the Northeast Greenland National…”
Method

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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