Common mistakes in ecological niche models

Ecological niche models (ENMs) are widely used statistical methods to estimate various types of species niches. After lecturing several editions of introductory courses on ENMs and reviewing numerous manuscripts on this subject, we frequently faced some recurrent mistakes: 1) presence-background mod...

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Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2021-02, Vol.35 (2), p.213-226
Main Authors: Sillero, Neftalí, Barbosa, A. Márcia
Format: Article
Language:English
Subjects:
autocorrelation
Clustering
Coordinates
Ecological niches
Errors
filtering records
Learning algorithms
Machine learning
Maxent
Niches
Resampling
Spatial discrimination
Spatial resolution
Species
species distribution models
Statistical analysis
Statistical methods
topographical variables
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Summary:Ecological niche models (ENMs) are widely used statistical methods to estimate various types of species niches. After lecturing several editions of introductory courses on ENMs and reviewing numerous manuscripts on this subject, we frequently faced some recurrent mistakes: 1) presence-background modelling methods, such as Maxent or ENFA, are used as if they were pseudo-absence methods; 2) spatial autocorrelation is confused with clustering of species records; 3) environmental variables are used with a higher spatial resolution than species records; 4) correlations between variables are not taken into account; 5) machine-learning models are not replicated; 6) topographical variables are calculated from unprojected coordinate systems, and; 7) environmental variables are downscaled by resampling. Some of these mistakes correspond to student misunderstandings and are corrected before publication. However, other errors can be found in published papers. We explain here why these approaches are erroneous and we propose ways to improve them.
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2020.1798968