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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
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“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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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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