Year: 2025
Type: preprint
Abstract: To address the urgent biodiversity crisis, it is crucial to understand the nature of plant assemblages. The distribution of plant species is not only shaped by their broad environmental requirements, but also by micro-environmental conditions, dispersal limitations, and direct and indirect species interactions. While predicting species composition and habitat identity is essential for conservation and restoration purposes, it thus remains challenging. In this study, we propose a novel approach inspired by advances in large language models to learn the "syntax" of abundance-ordered plant species sequences in communities. Our method, which captures latent associations between species across diverse ecosystems, can be fine-tuned for diverse tasks. In particular, we show that our methodology is able to outperform other approaches to (i) predict species that might occur in an assemblage given the other listed species, despite being originally missing in the species list (+16.53% compared to co-occurrence matrices and +6.56% compared to neural networks) and (ii) classify habitat types from species assemblages (+5.54% compared to expert systems and +1.14% compared to deep learning). The proposed application has a vocabulary that covers over ten thousand plant species from Europe and adjacent countries and provides a powerful methodology for improving biodiversity mapping, restoration, and conservation biology.
Source: Research Square
Authors: César Leblanc, Pierre Bonnet, Maximilien Servajean, Wilfried Thuiller, Milan Chytrý, Svetlana Aćić, Olivier Argagnon, Idoia Biurrun, Gianmaria Bonari, Helge Bruelheide, Juan Campos, Andraž Čarni, Renata Ćušterevska, Michele De Sanctis, Jürgen Dengler, Tetiana Dziuba, Jesper Moeslund, Emmanuel Garbolino, Ute Jandt, Florian Jansen, Jonathan Lenoir, Aaron Haase, Remigiusz Pielech, Jozef Sibik, Zvjezdana Stančić, Domas Uogintas, Thomas Wohlgemuth, Alexis Joly
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Montpellier, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Université Grenoble Alpes, Masaryk University, University of Belgrade, Conservatoire botanique national méditerranéen, University of the Basque Country, University of Siena, Martin Luther University Halle-Wittenberg, Jovan Hadži Institute of Biology, Research Centre of the Slovenian Academy of Sciences and Arts, Ss. Cyril and Methodius University in Skopje, Sapienza University of Rome, ZHAW Zurich University of Applied Sciences, National Academy of Sciences of Ukraine, M.G. Kholodny Institute of Botany, Aarhus University, Institut Supérieur d'Ingénierie et Gestion de l'Environnement - ISIGE, École Nationale Supérieure des Mines de Paris, University of Rostock, Universitat de Barcelona, Jagiellonian University, Slovak Academy of Sciences, Plant Science and Biodiversity Centre of the Slovak Academy of Sciences, University of Zagreb, Nature Research Centre, Swiss Federal Institute for Forest, Snow and Landscape Research, Centre Inria d'Université Côte d'Azur
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: green
OpenAlex ID: W4409207775