Six decades of losses and gains in alpha diversity of European plant communities

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Abstract

Biodiversity change forecasts rely on long-term time series, but such data are often scarce in space and time. Here, we interpolated spatiotemporal changes in species richness using a novel machine learning method without requiring temporal replication at sites. Using 698,692 one-time survey vegetation plots, we estimated trends in vascular plant alpha diversity across Europe from 1960 to 2020 and validated our approach against 22,852 independent time series. We found an overall near-zero net change in species richness. However, species richness generally declined from 1960 to 1980 across habitats, followed by an increase from 2000 to 2020. Declines were most pronounced in forests, but trends varied significantly across habitats and regions, with overall increases at higher latitudes and elevations, and declines or stable trends elsewhere. Our findings demonstrate how data without temporal replication can be used to reveal context-dependent biodiversity dynamics, underscoring the importance of such forecasts for conservation and management.
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Abstract

Biodiversity change forecasts rely on long-term time series, but such data are often scarce in space and time. Here, we interpolated spatiotemporal changes in species richness using a novel machine learning method without requiring temporal replication at sites. Using 698,692 one-time survey vegetation plots, we estimated trends in vascular plant alpha diversity across Europe from 1960 to 2020 and validated our approach against 22,852 independent time series. We found an overall near-zero net change in species richness. However, species richness generally declined from 1960 to 1980 across habitats, followed by an increase from 2000 to 2020. Declines were most pronounced in forests, but trends varied significantly across habitats and regions, with overall increases at higher latitudes and elevations, and declines or stable trends elsewhere. Our findings demonstrate how data without temporal replication can be used to reveal context-dependent biodiversity dynamics, underscoring the importance of such forecasts for conservation and management. DOI https://doi.org/10.32942/X2164H Subjects Biodiversity, Ecology and Evolutionary Biology

Keywords

alpha diversity, autocorrelation, biogeographic regions, habitat specificity, latitudinal gradient, machine learning, nature conservation, random forests, statistical interpolation, vegetation resurvey, autocorrelation, biogeographic regions, habitat specificity, latitudinal gradient, machine learning, nature conservation, Random Forest, statistical interpolation, vegetation resurvey Dates Published: 2025-07-23 02:10 Last Updated: 2025-07-23 02:10 License CC-BY Attribution-NonCommercial 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: The data and R code to fully reproduce the analyses are available in the GitHub repository: https://github.com/gmidolo/interpolated_S_change. Output files including model fits are available on Zenodo: https://doi.org/10.5281/zenodo.15836616. An interactive map exploring interpolated spatiotemporal changes in species richness can be accessed at: https://gmidolo.shinyapps.io/interpolated_s_change_app (GitHub repository: https://github.com/gmidolo/interpolated_S_change_app). The use of the data for additional publications, and the access to complete and original vegetation data, are only possible through a request to EVA Coordinating Board (see https://euroveg.org/eva-database/). Language: English

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License: CC-BY-NC-4.0