Species Distribution Models (MaxEnt)
Also known as: MaxEnt, SDM, Maximum Entropy Model
Species Distribution Models (SDMs) using Maximum Entropy (MaxEnt) are statistical methods developed by Phillips, Anderson, and Schapire (2004) to predict where species are likely to occur based on known occurrence points and environmental variables. MaxEnt has become one of the most widely used algorithms in conservation biology and biogeography for mapping suitable habitat and assessing climate change impacts.
Key highlights
- Presence-only modelling: does not require labelled absence or background data; useful for rare species or citizen science records
- Robust performance: consistently outperforms many alternatives in benchmark studies across diverse taxa
- Interpretability: produces variable importance rankings and response curves showing how suitability changes with each environmental factor
- Flexible predictions: extends beyond the observed range to project habitat under different climate scenarios
Intuition
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How it works
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When to use it
Use MaxEnt for species conservation planning, invasive species risk mapping, assessing climate change impacts on biodiversity, and understanding habitat requirements. It works well with presence-only data (common in ecology) and is robust to moderate sample sizes. Avoid it when occurrence records are highly biased geographically, or when you lack reliable environmental data, or when the focal species has a very small range with few records.
Strengths & limitations
- Presence-only modelling: does not require labelled absence or background data; useful for rare species or citizen science records
- Robust performance: consistently outperforms many alternatives in benchmark studies across diverse taxa
- Interpretability: produces variable importance rankings and response curves showing how suitability changes with each environmental factor
- Flexible predictions: extends beyond the observed range to project habitat under different climate scenarios
- Assumption of equilibrium: assumes species occur in all suitable habitat and have not reached all suitable areas (problematic for invasive species or range-restricted endemics)
- Ecological realism: does not explicitly model dispersal, biotic interactions, or demographic processes
- Extrapolation uncertainty: predictions become unreliable in novel environments far from training data ranges (analogue-setting problem)
Common pitfalls
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Applications
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Frequently asked
What is the difference between MaxEnt and other SDM algorithms like Generalized Linear Models (GLM)?
GLM requires absence or background data and assumes linear relationships; MaxEnt needs only presence data and can model complex nonlinear habitat relationships. MaxEnt generally performs better with small or biased datasets.
How many occurrence records do I need for a reliable MaxEnt model?
Ideally 30+ records, though performance improves with more. Below 10, models are unreliable. Ensure records are spatially scattered and geographically unbiased. A few well-distributed records often outperform many clustered records.
How do I choose which environmental variables to include?
Start with theory: include variables that ecologically influence the species (climate, habitat structure). Screen for multicollinearity (correlation > 0.7) and remove one of each pair. Avoid survey artefacts (e.g. human population density). Use jackknife or permutation importance tests.
Can MaxEnt be used to project species to new continents or future climates?
Yes, but with caution. Extrapolation beyond the range of training data variables increases error (analogue-setting problem). Always report uncertainty. For future climates, use multiple climate models and discuss multimodel range. For new continents, assess whether the ecological context is comparable.
Sources
- 1.Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modelling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.
- 2.Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.
- 3.Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to MaxEnt for modelling species' distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058-1069.
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Cite this page
ScholarGate. (2026, June 3). Species Distribution Models (MaxEnt). ScholarGate. https://scholargate.app/sustainability/species-distribution-models