Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine
- 1Natural History Museum
Abstract
Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.
Related
- Species distribution modelling of three Aacia species in the Negev Desert: predicting future range shifts and identifying climatic drivers
- sabinaMBM: An R package for Multiscale Bayesian species distribution Modelling using INLA
- Advancing Spatial Pattern Integration in Species Distribution Modelling With Computer Vision: How Grain Size and Extent Concepts Shape Predictions of Emerging Aquatic Insects
- Species Distribution, Antifungal Susceptibility, and Machine Learning-Based Prediction of Optimal Therapies Against Candida Species
- Technological approach to invasive species: the role of remote sensing and predictive modelling
BibTeX
@misc{robles2026towards,
title = {{Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine}},
author = {G. Albaladejo Robles and J. L. Abbate and D. W. Redding},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.22.753478},
url = {https://www.biorxiv.org/content/10.64898/2026.09.22.753478v1},
note = {Public domain},
}
RIS
TY - UNPB
TI - Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine
AU - G. Albaladejo Robles
AU - J. L. Abbate
AU - D. W. Redding
PY - 2026
DA - 2026/09/22
PB - bioRxiv
DO - 10.64898/2026.09.22.753478
UR - https://www.biorxiv.org/content/10.64898/2026.09.22.753478v1
AB - Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.
ER -