Data and R code repository for 'Six decades of losses and gains in alpha diversity of European plant communities'

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Midolo, Gabriele

Description

The repository contains the comprehensive data outputs and R code for the study "Six decades of losses and gains in alpha diversity of European plant communities".This is a copy of the main GitHub repository (https://github.com/gmidolo/interpolated_S_change). Here, we provide large-size files (e.g., model output and predictions) necessary for full reproducibility.The GitHub repository provides a complete overview of the project, including a detailed README.The repository includes:data/ : Contains preprocessed input data, all model results (e.g., fitted Random Forest and XGBoost models, tuning results, cross-validation outputs), model predictions (plot-level species richness predictions, partial dependence curves), and spatial data. These are the files not fully included in the main GitHub repository due to size constraints.fig/ : Contains figues produced by the R code.src/ : Contains all R scripts used for data preprocessing, model tuning, training, and testing, validation, interpolation, spatial/temporal distance analyses, and figure generation.Please note: The original raw vegetation data from the European Vegetation Archive (EVA) and ReSurveyEurope are not directly deposited here. Access to these complete original datasets, and their use for additional publications, is permissible only following acceptance from the EVA Coordinating Board (more information).

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.4

FAIR Score

73%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

Assigned Domain

Subfield

Artificial Intelligence

Field

Computer Science

Domain

Physical Sciences

Confidence Score

45%

Source

Scholar Data Model

Keywords

alpha diversityautocorrelationBiogeographical regionbiogeographic regionshabitat specificitybiodiversity changelatitudinal gradientmachine learningnature conservationrandom forestsxgboostinterpolationvegetation resurvey

Normalization Factors

FT

57.69

CTw

1.00

MTw

1.00