A deep‐learning framework for enhancing habitat identification based on species composition

作者：César Leblanc, Pierre Bonnet, Maximilien Servajean, Milan Chytrý, Svetlana Aćić, Olivier Argagnon, Ariel Bergamini, Idoia Biurrun, Gianmaria Bonari, Juan Antonio Campos, Andraž Čarni, Renata Ćušterevska, Michele De Sanctis, Jürgen Dengler, Emmanuel Garbolino, В. Б. Голуб, Ute Jandt, Florian Jansen, Maria Lebedeva, Jonathan Lenoir, Jesper Erenskjold Moeslund, Aaron Pérez‐Haase, Remigiusz Pielech, Jozef Šibík, Zvjezdana Stančić, Angela Stanisci, Grzegorz Swacha, Domas Uogintas, Kiril Vassilev, Thomas Wohlgemuth, Alexis Joly · 发表于：Applied Vegetation Science · 年份：2024 · DOI：10.1111/avsc.12802 · 被引用次数：14 · 研究领域：Species Distribution and Climate Change、Wildlife Ecology and Conservation、Genetic diversity and population structure

Abstract Aims The accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation‐plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types. Location The framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus). Methods We leveraged deep‐learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k ‐fold cross‐validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation‐plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems. Results Exploration of the use of deep learning applied to species composition and plot‐location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to Europ...