DOI: 10.5281/zenodo.16611858
title: Research Dataset: Duflot et al. (2025). Sustainable forest planning: assessing biodiversity effects of Triad zoning based on empirical data and virtual landscapes.

description: The data package contains basic information on the plots from which biodiversity observations were obtained, biodiversity aggregate indicators of the resampled data produced specifically for the study, and the R scripts that are needed to reproduce the results of the linked publication.

In the study, we quantified the effects of Triad zoning on biodiversity in (sub)montane eutrophic European beech forests. The Triad framework organises forest management by dividing a landscape into three spatial zones: (1) intensively managed forests (INT) focusing mainly on timber production; (2) extensively managed forests (EXT) promoting multiple ecosystem services and supporting biodiversity while producing timber; and (3) protected areas primarily aiming at biodiversity conservation and the promotion of natural processes (i.e., unmanaged forest; UNM). We evaluated how the proportion of Triad management categories in a landscape affected the landscape-level alpha and gamma species diversity. We used a subset of a European-wide biodiversity database, the Bottoms-Up platform (Burrascano et al. 2023), which gathered information on forest multi-taxonomic diversity to inform sustainable forest management (www.bottoms-up.eu). We selected a subset of sampled plots that fit the purpose of the study (see publication for details) and analysed them using a resampling process. We created ‘virtual’ landscapes across all possible compositions of Triad management categories by varying the share of INT, EXT, and UNM forests in steps of 10%, creating 66 compositionally distinct ‘virtual’ Triad landscapes. Each landscape was represented by 20 randomly drawn plots and replicated 5,000 times avoiding identical plot combinations.

The dataset contains:

1.      Basic info of the 222 sampled plots used in the study, with a KeyID referring to the Bottoms-up database (Site_Info.csv). The KeyID is a unique plot identifier allowing to track the data from the Bottoms-up databases.

2.      The R script that performs the resampling process (Code_Resample_Triad_Landscapes) and the results of that analysis (data_resample). In the results file (one for each taxon), each entry is a virtual landscape made of 20 resampled plots (rowIDs from Site_Info.csv are tracked), with specific proportions of EXT, INT and UNM forests, and is associated with landscape-level biodiversity indicators: alpha, gamma richness (gamma_0D), gamma Shannon (gamma_1D) and beta.

3.      Three annotated R scripts making the statistical analyses and producing the graphical representation of the results. There is a separate file for the taxonomic level analysis (Code_Figs_1_2_S1_TaxaTriangles), the multi-diversity level (Code_Fig_3_MultidivTriangles), and the standard deviation version of the taxonomic level analyses (Code_Figs_S3_S4_Taxa_SD_Triangles). These R scripts allow to reproduce the figures and statistical outputs presented in the linked publication. Some of the figures of the manuscript are also included as examples of the R script outputs.

S. Burrascano, et al., Where are we now with European forest multi-taxon biodiversity and where can we head to? Biological Conservation 284, 110176 (2023).