---
title: "AutoMaxEnt: Automated Maximum Entropy Workflow for Species Distribution Modelling"
id: "biorxiv-15-towards-the-automation-of-species-distribution-modelling-the-automaxent-routine"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-15-towards-the-automation-of-species-distribution-modelling-the-automaxent-routine"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.22.753478v1?rss=1"
published_at: "2026-09-23T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# AutoMaxEnt: Automated Maximum Entropy Workflow for Species Distribution Modelling
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-15-towards-the-automation-of-species-distribution-modelling-the-automaxent-routine
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.22.753478v1?rss=1)
- **Published At:** 2026-09-23T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The preprint introduces **AutoMaxEnt**, a repository of functions and tools to automate maximum entropy species distribution models (SDMs). - SDMs are widely used to evaluate species spatial distributions and responses to environmental change, but typically demand substantial researcher input and decision-making. - AutoMaxEnt automates most data pre-processing and preparation steps while allowing users to explore multiple modelling scenarios automatically or with user control. - Key capabilities reported include model fit control, multiple algorithms for generating background points, automatic model and variable selection, and configuration of the study area. - The routine supports automated model fitting, evaluation, and selection across different species, study areas, and time periods, aiming to scale SDM applications to large species sets. - The authors frame AutoMaxEnt as a flexible environment that balances automation with user control over scenario generation. - A public code repository is provided (GitHub link reported in the source). Details about implementation, performance benchmarks, example workflows, and empirical results were not reported in the abstract and would require consultation of the repository or full manuscript. - The article is a preprint and has not been peer reviewed; the authors declared no competing interests. - Copyright for the preprint has been placed in the Public Domain; supplementary material and data/code links are available from the source record.
## Clinical Analysis & Structured Key Points
Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine | bioRxiv Skip to main content New Results Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine View ORCID Profile Gonzalo Albaladejo Robles , View ORCID Profile Jessica Lee Abbate , David W Redding doi: https://doi.org/10.64898/2026.09.22.753478 Gonzalo Albaladejo Robles 1 Natural History Museum; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gonzalo Albaladejo Robles For correspondence: gonalbala{at}gmail.com Jessica Lee Abbate 2 Geomatys ; University of Virginia; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica Lee Abbate David W Redding 3 University College London ; Natural History Museum Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Info/History Metrics Supplementary material Data/Code Preview PDF 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. Competing Interest Statement The authors have declared no competing interest. Footnotes https://github.com/BioDivHealth/AutoMaxent/tree/main Copyright The copyright holder has placed this preprint in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. Back to top Previous Posted September 23, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine Gonzalo Albaladejo Robles , Jessica Lee Abbate , David W Redding bioRxiv 2026.09.22.753478; doi: https://doi.org/10.64898/2026.09.22.753478 Share This Article: Copy Citation Tools Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine Gonzalo Albaladejo Robles , Jessica Lee Abbate , David W Redding bioRxiv 2026.09.22.753478; doi: https://doi.org/10.64898/2026.09.22.753478 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8021) Biochemistry (18781) Bioengineering (14921) Bioinformatics (44492) Biophysics (22625) Cancer Biology (19761) Cell Biology (26945) Clinical Trials (138) Developmental Biology (13993) Ecology (21037) Epidemiology (2067) Evolutionary Biology (25473) Genetics (16185) Genomics (23535) Immunology (18725) Microbiology (42548) Molecular Biology (18095) Neuroscience (93601) Paleontology (701) Pathology (2987) Pharmacology and Toxicology (5104) Physiology (8127) Plant Biology (16020) Scientific Communication and Education (2097) Synthetic Biology (4572) Systems Biology (10251) Zoology (2393)
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