Machine learning applied to species occurrence and interactions: the missing link in biodiversity assessment and modelling of Antarctic plankton distribution

Aug 5, 2026

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Antarctic plankton forms the base of aquatic trophic networks and serves as a sensitive indicator of environmental change, yet most work examines single species distributions without capturing real-world biological interactions. This open-access study analyzes phytoplankton, mesozooplankton, and microzooplankton interactions and co-occurrences using field data, environmental descriptors, and machine-learning species distribution models. Models generate predictive occurrence maps for selected assemblages and copepod species with strong ROC performance and reveal spatial heterogeneity relevant to conservation management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Introduction

Study context: Antarctic ecosystem and threats

Conservation framework: Ross Sea Marine Protected Area

Results

Interaction and coexistence data overview

Selected assemblages and species

Environmental descriptors and modeling approach

Predictive maps and model performance

Conclusion

Grillo et al. Ecological Processes (2024) 13:56 [https://doi.org/10.1186/s13717-024-00532-6](https://doi.org/10.1186/s13717-024-00532-6) Ecological Processes RESEARCH Open Access Machine learning applied to species occurrence and interactions: the missing linkin biodiversity assessment and modelling of Antarctic plankton distribution Marco Grillo1,2* , Stefano Schiaparelli2,3, Tiziana Durazzano4, Letterio Guglielmo5,6, Antonia Granata7 and Falk Huettmann8 Abstract Background Plankton is the essential ecological category that occupies the lower levels of aquatic trophic networks, representing a good indicator of environmental change. However, most studies deal with distribution of single species or taxa and do not take into account the complex of biological interactions ofthe real world that rule the ecological processes. Results This study focused on analyzing Antarctic marine phytoplankton, mesozooplankton, and microzooplankton, examining their biological interactions and co-existences. Field data yielded 1053 biological interaction values, 762 coexistence values, and 15 zero values. Six phytoplankton assemblages and six copepod species were selected based on their abundance and ecological roles. Using 23 environmental descriptors, we modelled the distribution of taxa to accurately represent their occurrences. Sampling was conducted during the 2016–2017 Italian National Antarctic Programme (PNRA)‘P-ROSE’ project in the East Ross Sea. Machine learning techniques were applied to the occurrence data to generate 48 predictive species distribution maps (SDMs), producing 3D maps for the entire Ross Sea area. These models quantitatively predicted the occurrences of each copepod and phytoplankton assemblage, providing crucial insights into potential variations in biotic and trophic interactions, with significant implications for the management and conservation of Antarctic marine resources. The Receiver Operating Characteristic (ROC) results indicated the highest model efficiency, for Cyanophyta (74%) among phytoplankton assemblages and Paralabidocera antarctica (83%) among copepod communities. The SDMs revealed distinct spatial heterogeneity in the Ross Sea area, with an average Relative Index of Occurrence values of 0.28 (min: 0; max: 0 . 65) for phytoplankton assemblages and 0.39 (min: 0; max: 0 . 71) for copepods. Conclusion The results of this study are essential for a science-based management for one of the world’s most pristine ecosystems and addressing potential climate-induced alterations in species interactions. Our study emphasizes the importance of considering biological interactions in planktonic studies, employing open access and machine learning for measurable and repeatable distribution modelling, and providing crucial ecological insights for informed conservation strategies in the face of environmental change. Keywords Copepoda, Phytoplankton, Ross sea, Terra Nova Bay, Species Distribution Model, Marine trophic web *Correspondence: Marco Grillo[m.grillo@student.unisi.it](m.grillo@student.unisi.it) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://cre

FAQs

What is the main research focus of this study?

The study models Antarctic plankton species occurrences and biological interactions to improve biodiversity assessment by capturing co-existences rather than single-species distributions.

How was the modeling performed and what outputs were produced?

Using 23 environmental descriptors, machine-learning species distribution models generated 48 predictive species distribution maps, including 3D maps for the Ross Sea area.

What do the results indicate about model performance and spatial patterns?

Receiver Operating Characteristic results show high efficiency for both phytoplankton assemblages and copepod communities, while the maps reveal distinct spatial heterogeneity with measurable occurrence indices.

Aug 5, 2026

Upload by Hazel