An open science automatic workflow for multi-model species distribution estimation

Created March 18, 2026

Updated on June 23, 2026

approved

Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems and help understand how to manage the effects of the unsustainable exploitation of ocean resources. However, these models have long implementation times, data and model interoperability issues and require heterogeneous competencies. Therefore, they would benefit from simplification, automatisation, and enhanced integrability of the underlying models. Artificial Intelligence can help overcome several limitations by speeding up the modelling of crucial functional parts, e.g. estimating the environmental conditions fostering a species’ persistence and proliferation in an area (the species’ ecological niche) and, consequently, its geographical distribution. This paper presents a full-automatic workflow to estimate species’ distributions through statistical and machine learning models. It embeds four ecological niche models with complementary approaches, i.e. Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps. It automatically estimates the optimal model parametrisations and decision thresholds to distinguish between suitable- and unsuitable-habitat locations and combines the models within one ensemble model. Finally, it combines several ensemble models to produce a species richness map (biodiversity index). The software is open-source, Open Science compliant, and available as a Web Processing Service-standardised cloud computing service that enhances efficiency, integrability, cross-domain reusability, and experimental reproduction and repetition. We first assess workflow stability and sensitivity and then demonstrate effectiveness by producing a biodiversity index for the Mediterranean based on 1500 species data. Moreover, we predict the spread of the invasive Siganus rivulatus in the Mediterranean and its current and future overlap with the native Sarpa salpa under different climate change scenarios.

itineris-hub
Tags:
Alien and Invasive Species
Artificial Intelligence
Ecological Niche Modelling
Marine Science
Open Science
Publication - Article
Basic
Language
English
MainTitle
An open science automatic workflow for multi-model species distribution estimation
Original ids
https://doi.org/10.1007/s41060-024-00517-w
Type
publication
bestAccessRight
OPEN
contributors
  • Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Consiglio Nazionale delle Ricerche (ISTI-CNR), Via Moruzzi 1, 56124 Pisa, Italy;
  • Istituto di Geoscienze e Georisorse, Consiglio Nazionale delle Ricerche (IGG-CNR), Via Moruzzi 1, 56124 Pisa, Italy
countries
Italy
Creator/Author
Full name
  • Coro, Gianpaolo, orcid: 0000-0001-7232-191x;
  • Sana, Lorenzo;
  • Bove, Pasquale
Other
Description
Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems and help understand how to manage the effects of the unsustainable exploitation of ocean resources. However, these models have long implementation times, data and model interoperability issues and require heterogeneous competencies. Therefore, they would benefit from simplification, automatisation, and enhanced integrability of the underlying models. Artificial Intelligence can help overcome several limitations by speeding up the modelling of crucial functional parts, e.g. estimating the environmental conditions fostering a species’ persistence and proliferation in an area (the species’ ecological niche) and, consequently, its geographical distribution. This paper presents a full-automatic workflow to estimate species’ distributions through statistical and machine learning models. It embeds four ecological niche models with complementary approaches, i.e. Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps. It automatically estimates the optimal model parametrisations and decision thresholds to distinguish between suitable- and unsuitable-habitat locations and combines the models within one ensemble model. Finally, it combines several ensemble models to produce a species richness map (biodiversity index). The software is open-source, Open Science compliant, and available as a Web Processing Service-standardised cloud computing service that enhances efficiency, integrability, cross-domain reusability, and experimental reproduction and repetition. We first assess workflow stability and sensitivity and then demonstrate effectiveness by producing a biodiversity index for the Mediterranean based on 1500 species data. Moreover, we predict the spread of the invasive Siganus rivulatus in the Mediterranean and its current and future overlap with the native Sarpa salpa under different climate change scenarios.
Publication Date
2024-03-07
Publisher
Springer Science and Business Media LLC
Subjects
  • 0106 biological sciences;
  • 13. Climate action;
  • 14. Life underwater;
  • 15. Life on land;
  • 01 natural sciences;
  • Ecological niche modelling, Marine science, Artificial intelligence, Open Science, Alien and invasive species;
isGreen
false
isInDiamondJournal
false
Software
Publication
Ending page
1150
Name
International Journal of Data Science and Analytics
Publication
Article
Starting page
1131
issnOnline
2364-4168
issnPrinted
2364-415X
vol
20
Other Research Product
Detailed informations
system:type
Research Product
Management Info
Author
Catalogue Manager ITINERIS
Version
1
Last Updated
June 23, 2026, 10:42 (UTC)
Created
March 18, 2026, 23:31 (UTC)
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