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MaxEnt's parameter configuration and small samples: are we paying attention to recommendations? A systematic review
Narkis S Morales1,2, Ignacio C Fernández2,3, Victoria Baca-González4
1Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, Australia.
Peerj
|March 21, 2017
Summary
Many researchers use MaxEnt for species distribution modeling but often fail to optimize its default parameters, especially with small datasets. This can lead to inaccurate species distribution maps, impacting conservation and policy decisions.
Area of Science:
- Ecology
- Biodiversity Conservation
- Computational Biology
Background:
- Environmental Niche Modeling (ENM) is crucial for species distribution mapping.
- MaxEnt is a popular ENM tool due to its user-friendly interface and automatic settings.
- However, default MaxEnt settings can yield suboptimal models, particularly with limited species presence data.
Purpose of the Study:
- To assess if researchers optimize MaxEnt parameters (feature classes, regularization multiplier) when using small sample sizes.
- To evaluate the impact of using default versus optimized MaxEnt parameters on species distribution models.
- To highlight potential inaccuracies in published species distribution models.
Main Methods:
- Systematic review of 244 articles published between 2013-2015 using MaxEnt.
- Analysis of parameter optimization practices (feature classes, regularization multiplier).
- Comparative analysis of species distribution models generated with default vs. optimized MaxEnt parameters in 20 selected articles.
Main Results:
- Only 16% of studies evaluated optimal feature classes, 6.9% evaluated optimal regularization multipliers, and 3.7% evaluated both.
- Significant differences observed in suitable habitat areas between default and optimized MaxEnt models.
- Default parameter use can result in over-complex or over-simplistic species distribution models.
Conclusions:
- Researchers frequently use MaxEnt as a 'black box,' neglecting parameter optimization, especially with small sample sizes.
- Suboptimal MaxEnt models can lead to unreliable species distribution predictions, affecting ecological research and policy.
- Judicious application and rigorous parameter evaluation of MaxEnt are essential for accurate species distribution modeling.
Keywords:
Auto-featuresEnvironmental niche modellingMaximum entropyParameters configurationRegularization multiplierSpecies distributionUser-defined featuresMore Related Videos
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