Institute of Biology

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Global decoupling of functional and phylogenetic diversity in plant communities
(Springer Science and Business Media LLC, 2024-12-03)
Hähn, Georg J. A.
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Damasceno, Gabriella
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Alvarez-Davila, Esteban
;
Aubin, Isabelle
;
Bauters, Marijn
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Land‐Use Impacts on Plant Functional Diversity Throughout Europe
(Wiley, 2024-12-13)
Rosa, Francesca
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van Bodegom, Peter M.
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Hellweg, Stefanie
;
Pfister, Stephan
;
Biurrun, Idoia
ABSTRACT Aim Global biodiversity loss resulting from anthropogenic land‐use activities is a pressing concern, requiring precise assessments of impacts at large spatial extents. Existing models mainly focus on species richness and abundance, lacking insights into ecological mechanisms and species' roles in ecosystem functioning. To bridge this gap, we conducted an extensive analysis of the impact of human land use on vascular plant functional diversity across diverse land‐use classes and bioregions in Europe, comparing it to traditional metrics. Location Europe. Time Period 1992–2019. Major Taxa Studied Vascular plants. Methods Integrating extensive databases of vegetation plots with spatial data on land use and land cover, we paired plots from areas actively used and modified by humans with plots from natural habitats under similar environmental conditions. Using species occurrences and traits, in each plot we computed three complementary functional diversity metrics (functional richness, evenness and divergence), species richness and abundance. We assessed the impact of land use by comparing the metrics in the paired plots. Results Our findings revealed that, compared to natural habitats, anthropogenic land use exhibits lower functional richness and divergence but higher functional evenness across most land‐use classes and bioregions. The response of functional richness was more marked than the other two metrics and especially pronounced in croplands and urban areas and in northern bioregions. Functional richness exhibited a pattern that did not fully overlap with the trend in species richness, providing useful complementary information. Main Conclusions We provide a large‐scale precise assessment of anthropogenic land‐use impacts on functional diversity across Europe. Our findings indicate that: (i) human disturbance significantly alters plant functional diversity compared to natural habitats; (ii) this alteration goes in the direction of functional homogenisation within sites; (iii) functional diversity metrics complement traditional metrics by offering deeper insights into the ecological mechanisms in response to anthropogenic land use.
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Six Decades of Losses and Gains in Alpha Diversity of European Plant Communities
(Wiley, 2025-11)
Midolo, Gabriele
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Clark, Adam Thomas
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Chytrý, Milan
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Essl, Franz
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Dullinger, Stefan
ABSTRACT
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Learning the syntax of plant assemblages
(Springer Science and Business Media LLC, 2025-10-13)
Leblanc, César
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Bonnet, Pierre
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Servajean, Maximilien
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Thuiller, Wilfried
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Chytrý, Milan
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Annual Vegetation of Saline Seasonal Wetlands (
<i>Crypsietea aculeatae</i>
) in Europe
(Wiley, 2025-10)
Iemelianova, Svitlana
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Dítě, Daniel
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Dítě, Zuzana
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Willner, Wolfgang
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Bergmeier, Erwin
ABSTRACT Aims The class Crypsietea aculeatae comprises pioneer ephemeral dwarf‐graminoid vegetation occurring in periodically flooded saline habitats. Although numerous regional studies have described this vegetation, a unified classification across Europe has been lacking. This study aims to establish a formalized, continent‐wide classification at the association level, identify its biogeographical patterns, and assess relationships between species composition and environmental gradients. Location Europe (except Armenia, Azerbaijan, Georgia, and Russia). Methods We compiled a dataset of 2538 vegetation plots from the European Vegetation Archive (EVA), private databases, and literature. A hierarchical expert system was developed using formal definitions of syntaxa based on species cover. Nonmetric multidimensional scaling ( NMDS ) was applied to evaluate compositional differences and reveal biogeographical patterns. Distance‐based redundancy analysis (db‐ RDA ) and unweighted community means of Ellenberg‐type indicator values ( EIVs ) were used to assess environmental drivers. Results We present a revised syntaxonomic framework for the class Crypsietea aculeatae and propose updates to the EuroVegChecklist . One alliance ( Cypero‐Spergularion marinae ) and four associations ( Crypsietum aculeatae , Crypsietum schoenoidis , Cyperetum pannonici , and Oxybasio chenopodioidis‐Crypsietum aculeatae ) were formally defined, with basic descriptions of their species composition, ecological characteristics, and geographical distribution. The main gradients in vegetation variability within the class Crypsietea aculeatae are driven by climatic gradient and edaphic factors, including soil moisture, soil acidity, salinity, and nutrient availability. Conclusions We established the first formal classification of annual vegetation in European saline seasonal wetlands. The syntaxonomic nomenclature was revised, and modifications to the EuroVegChecklist were proposed. The resulting classification system, accompanied by an expert system, enables consistent application in nature conservation and ecological research.
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EUNIS habitat maps: enhancing thematic and spatial resolution for Europe through machine learning
(Springer Science and Business Media LLC, 2025-12-11)
Si-Moussi, Sara
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Hennekens, Stephan
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Mücher, Sander
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De Keersmaecker, Wanda
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Chytrý, Milan
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Land‐Use Impacts on Plant Functional Diversity Throughout Europe
(Wiley, 2024-12-13)
Rosa, Francesca
;
van Bodegom, Peter M.
;
Hellweg, Stefanie
;
Pfister, Stephan
;
Biurrun, Idoia
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Aim</jats:title><jats:p>Global biodiversity loss resulting from anthropogenic land‐use activities is a pressing concern, requiring precise assessments of impacts at large spatial extents. Existing models mainly focus on species richness and abundance, lacking insights into ecological mechanisms and species' roles in ecosystem functioning. To bridge this gap, we conducted an extensive analysis of the impact of human land use on vascular plant functional diversity across diverse land‐use classes and bioregions in Europe, comparing it to traditional metrics.</jats:p></jats:sec><jats:sec><jats:title>Location</jats:title><jats:p>Europe.</jats:p></jats:sec><jats:sec><jats:title>Time Period</jats:title><jats:p>1992–2019.</jats:p></jats:sec><jats:sec><jats:title>Major Taxa Studied</jats:title><jats:p>Vascular plants.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Integrating extensive databases of vegetation plots with spatial data on land use and land cover, we paired plots from areas actively used and modified by humans with plots from natural habitats under similar environmental conditions. Using species occurrences and traits, in each plot we computed three complementary functional diversity metrics (functional richness, evenness and divergence), species richness and abundance. We assessed the impact of land use by comparing the metrics in the paired plots.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Our findings revealed that, compared to natural habitats, anthropogenic land use exhibits lower functional richness and divergence but higher functional evenness across most land‐use classes and bioregions. The response of functional richness was more marked than the other two metrics and especially pronounced in croplands and urban areas and in northern bioregions. Functional richness exhibited a pattern that did not fully overlap with the trend in species richness, providing useful complementary information.</jats:p></jats:sec><jats:sec><jats:title>Main Conclusions</jats:title><jats:p>We provide a large‐scale precise assessment of anthropogenic land‐use impacts on functional diversity across Europe. Our findings indicate that: (i) human disturbance significantly alters plant functional diversity compared to natural habitats; (ii) this alteration goes in the direction of functional homogenisation within sites; (iii) functional diversity metrics complement traditional metrics by offering deeper insights into the ecological mechanisms in response to anthropogenic land use.</jats:p></jats:sec>
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Global decoupling of functional and phylogenetic diversity in plant communities
(Springer Science and Business Media LLC, 2025-02)
Hähn, Georg J A
;
Damasceno, Gabriella
;
Alvarez-Davila, Esteban
;
Aubin, Isabelle
;
Bauters, Marijn
Plant communities are composed of species that differ both in functional traits and evolutionary histories. As species' functional traits partly result from their individual evolutionary history, we expect the functional diversity of communities to increase with increasing phylogenetic diversity. This expectation has only been tested at local scales and generally for specific growth forms or specific habitat types, for example, grasslands. Here we compare standardized effect sizes for functional and phylogenetic diversity among 1,781,836 vegetation plots using the global sPlot database. In contrast to expectations, we find functional diversity and phylogenetic diversity to be only weakly and negatively correlated, implying a decoupling between these two facets of diversity. While phylogenetic diversity is higher in forests and reflects recent climatic conditions (1981 to 2010), functional diversity tends to reflect recent and past climatic conditions (21,000 years ago). The independent nature of functional and phylogenetic diversity makes it crucial to consider both aspects of diversity when analysing ecosystem functioning and prioritizing conservation efforts.
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A deep‐learning framework for enhancing habitat identification based on species composition
(Wiley, 2024-07)
Leblanc, César
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Bonnet, Pierre
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Servajean, Maximilien
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Chytrý, Milan
;
Aćić, Svetlana
<jats:title>Abstract</jats:title><jats:sec><jats:title>Aims</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Location</jats:title><jats:p>The framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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 <jats:italic>k</jats:italic>‐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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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 European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state‐of‐the‐art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats.</jats:p></jats:sec>
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Structural, ecological and biogeographical attributes of European vegetation alliances
(Wiley, 2024-01)
Preislerová, Zdenka
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Marcenò, Corrado
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Loidi, Javier
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Bonari, Gianmaria
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Borovyk, Dariia
<jats:title>Abstract</jats:title><jats:p>The first comprehensive phytosociological classification of all vegetation types in Europe (EuroVegChecklist; <jats:italic>Applied Vegetation Science</jats:italic>, 2016, <jats:bold>19</jats:bold>, 3–264) contained brief descriptions of each type. However, these descriptions were not standardized and mentioned only the most distinct features of each vegetation type. The practical application of the vegetation classification system could be enhanced if users had the option to select sets of vegetation types based on various combinations of structural, ecological, and biogeographical attributes. Based on a literature review and expert knowledge, we created a new database that assigns standardized categorical attributes of 12 variables to each of the 1106 alliances dominated by vascular plants defined in EuroVegChecklist. These variables include dominant life form, phenological optimum, substrate moisture, substrate reaction, salinity, nutrient status, soil organic matter, vegetation region, elevational vegetation belt, azonality, successional status and naturalness. The new database has the potential to enhance the usefulness of phytosociological classification for researchers and practitioners and to help understand this classification to non‐specialists.</jats:p>

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