DOI: 10.5061/dryad.c2fqz61kw
title: Data from: Landscape context modulates the effect of local canopy cover on forest multidiversity across elevations

description: Declining forest biodiversity has increased focus on forest conservation
 and restoration. Many efforts to conserve and restore tree cover focus on
 the local scale, but their outcomes are frequently modulated by landscape
 context. While the diversity and composition of communities are strongly
 driven by local-scale canopy cover, landscape-scale habitat
 characteristics affect dispersal pathways and determine the species pool
 available for colonization of local patches. Moreover, local and
 landscape-scale habitat attributes vary with elevation, but how their
 effects on biodiversity change with elevation remains poorly understood.
 We examined how local canopy cover affects forest biodiversity, and how
 these effects are modulated by the amount of total and disturbed forest
 available in the surrounding landscape along an elevational gradient. We
 used remote sensing and multi-taxa biodiversity data covering plants,
 aculeata, moths, beetles, and birds (a total of 2,319 species) across 150
 plots in naturally developing forests in a forest-rich region in the
 northern European Alps. We calculated multidiversity across all species
 and for three habitat affinity guilds (forest, mixed, open-habitat) to
 test for differences based on varying habitat associations. Local canopy
 cover negatively affected multidiversity, with the weakest effect observed
 for forest species. An increasing amount of forest in the surrounding
 landscape amplified this negative effect, while an increasing amount of
 disturbed forest reduced it. The negative effect of local canopy cover on
 multidiversity weakened with elevation and became neutral across all
 guilds close to the tree line. Our findings highlight that disturbances
 promote forest biodiversity via two fundamental pathways: reducing local
 canopy cover and creating a more open and diverse landscape context.
 Moreover, the effects of canopy cover on forest biodiversity are modulated
 by environmental conditions that change with elevation. Conservation and
 restoration efforts should consider landscape context more explicitly when
 planning specific management measures. Our results suggest that canopy
 openings benefit biodiversity especially in landscapes with high forest
 cover and in low elevation areas, while conserving and re-establishing
 tree cover is important in landscapes with low forest cover and close to
 the upper tree line.cover and high elevation areas.
description: <strong>Biodiversity data</strong>
 We collected data from five taxonomic groups across all 150 plots
 in 2021, except light trapping, which was conducted in 2022. We provide a
 comprehensive description of the sampling and identification methods in
 the supplementary material of the publication, but in brief: The
 vegetation was sampled on 200 m<sup>2</sup> quadratic plots,
 separately for the herb (&lt;1 m height) and shrub layer (&gt;1-5
 m height). We used a combination of two pitfall traps, one malaise, and
 one light trap to collect beetles, moths, and aculeata (bees, wasps,
 ants). While beetles from pitfall and light traps and moths from light
 traps were identified to species level by experts, we used DNA
 metabarcoding to identify beetles, moths, and aculeata from malaise trap
 samples. We recorded birds with bioacoustics recorders, identified species
 using BirdNET (Kahl <em>et al.</em> 2021, version 2.4), and
 applied species-specific thresholds in order to improve the accuracy of
 identification (Seibold <em>et al.</em> 2024). We then
 classified all species into three broad habitat-affinity guilds, namely
 forest, mixed, and open-habitat affinity, based on Schmidt <em>et
 al.</em> (2011) and Dorow <em>et al.</em> (2020), and
 calculated multidiversity based on Allan <em>et al.</em>
 (2014) for an overall dataset and for each affinity guild.
 <strong>Local and landscape-scale forest
 characteristics</strong> We assessed local canopy
 cover as the proportion of returns 5 m above ground within a radius of
 12.6 m using high-resolution LiDAR data with an average point density of
 ~50 points m<sup>2</sup>, acquired under leaf-on conditions in
 September 2021 (Mandl <em>et al.</em> 2023).
 To quantify the proportion of forest relative to sample area, we
 used the 2018 High Resolution Layer Tree Cover Density product from the EU
 Copernicus programme (https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density). To quantify the proportion of disturbed forest relative to the area classified as forest by the Copernicus product, we used the 2020 pan-European forest disturbance maps derived from Landsat data (Senf & Seidl 2021). We resampled the disturbance map products (30 m resolution) to match the 10 m resolution of the Copernicus product and selected each pixel that was disturbed since 2006. We quantified both metrics across multiple radii (50 m, 100 m, 200 m, 500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m) to capture varying responses across the studied taxa inhabiting the complex study landscape. <strong>Statistical analysis</strong> We analysed how local and landscape-scale forest characteristics affect multidiversity along elevation. We fitted Bayesian multilevel models using the <em>brms</em> package (Bürkner 2017) with a beta probability distribution for each habitat affinity guild and radius quantifying landscape-scale forest proportions. We used multidiversity as a response and canopy cover, elevation, proportion of forest relative to sample area, and proportion of disturbed forest relative to forest area as predictor variables. To analyse the interactive effect of local and landscape-scale forest characteristics and to capture the effect of their changes with elevation, we included an interaction term between canopy cover and proportion of forest, between canopy cover and proportion of disturbed forest, as well as between elevation and each of the other predictors. We z-transformed all predictor variables to increase sampling efficiency, and we added a variable that groups plots of the same sample area as a random intercept to account for spatial autocorrelation (Dormann <em>et al.</em>, 2007). We then used Pareto smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) in combination with Bayesian stacking to select the model with the best estimated predictive performance across landscape radii (Vehtari <em>et al.</em> 2024b; Yao <em>et al.</em> 2018). 
description: # Data from: Landscape context modulates the effect of local canopy cover
 on forest biodiversity [https://doi.org/10.5061/dryad.c2fqz61kw
 ](https://doi.org/10.5061/dryad.c2fqz61kw) ## Description of the data and
 file structure We aimed to assess how the effects of local canopy cover on
 biodiversity across guilds with varying forest affinities are modified by
 landscape context. We sampled multitrophic diversity covering plants,
 aculeata (bees, wasps, and ants), moths, beetles, and birds, at 150 study
 plots in the German Alps, spanning elevations from 605 to 1725 m a.s.l. We
 used remote sensing to quantify local canopy cover (radius = 12.6m) as
 well as the landscape-scale (between 50 and 3000m) proportion of forest
 and of disturbed forest relative to forest area. We assessed the
 landscape-scale variables for multiple radii as the spatial scale
 affecting habitat selection is species-specific, depends on the landscape
 context, and is sensitive to measurement and analysis. Furthermore, given
 that species responses to predictor variables can vary depending on their
 habitat preferences, we categorized species into three broad habitat
 affinity guilds, namely species with forest, mixed, and open-habitat
 affinities. To obtain a simple and comprehensive index of m-dependent
 diversity, we calculated multidiversity (average proportional species
 richness across taxa) for each affinity guild. The repository contains 2
 zip folders, data.zip and Rscripts.zip, and a renv.lock file, as well as a
 folder landscape_data stored as supplementary material at Zenodo. The
 **data.zip** contains 4 folders: biodiv, traits, and local. The biodiv
 folder contains 6 files with **biodiversity** data and 1 file containing
 species-specific confidence thresholds used to increase the accuracy of
 bird species detection using BirdNet. The **traits** folder contains 5
 files containing forest affinity data for plants from Schmidt et al.
 (2011) and animals from Dorow et al. (2020), 1 file containing data about
 plant life traits used to exclude tree species, as well as another folder
 "no_speci" containing trait data for species that could not
 classified based on the two main affinity sources but expert knowledge and
 additional literature. The **local** folder contains 2 files, one
 containing lidar data used to quantify canopy cover and the other
 containing additional data about the study plots (location, elevation,
 etc.). The **landscape_data** folder available on Zenodo contains the
 spectral data files used to characterize landscape-scale forest features.
 To run the R scripts, place a copy of the landscape_data folder in the
 same directory as the biodiversity, trait, and local data folders, and
 rename it to landscape. The **Rscripts.zip** contains 3 files used for (1)
 data preparation (Rscript_1_data_preparation.R), (2) formal analysis,
 model checks, and predictions
 (Rscript_2_analysis_model_checks_predictions.R), and (3) preparation of
 figures and tables used in the main body and supplement
 (Rscript_3_figures_tables.R). The **renv.lock** file can be used to load
 the exact packages and package versions used for the analysis. ### Files
 and variables #### Folder: data.zip **folder biodiv data**: * File
 plants_2021.csv: The vegetation was surveyed within 200 m^2^ quadratic
 plots, separately for the herb (&lt;1 m height) and shrub layer (&gt;1-5 m
 height). The file contains the following columns: * plot = plot ID * date
 = sample date * layer = height layer * spec = species name * method =
 sampling method * File biodiv_coleoptera_2021.csv: We sampled beetles
 using 2 pitfall traps, and files were identified by experts. The file
 contains the following columns: * plot = plot ID * trap = trap ID *
 sampling = sampling ID * start = start date of the sampling period * end
 the = end date of the sampling period * species = species name * method =
 sampling method * Files biodiv_coleoptera_lt_2022.csv &
 biodiv_moths_2022.csv: We sampled beetles and moths using 1 light-trap,
 and species were identified by experts. The file contains the following
 columns: * plot = plot ID * date = sampling date * species = species name
 * Method = sampling methodFile barcoding_0.01p.csv: We sampled beetles,
 moths, and aculeata using 1 Malaise trap and identified species through
 DNA metabarcoding. The file contains the following columns: * plot = plot
 ID * sampling = sampling ID * spec_sci = scientific species name * phylum
 = taxonomic phylum * class = taxonomic class * order = taxonomic order *
 family = taxonomic family * genus = taxonomic genus * File
 birdnet_annotated_2021.csv: We sampled birds using audio recorders, and
 species were identified through BirdNET. The file contains the following
 columns: * plot = plot ID * sampling = sampling ID * date = sampling date
 * start_rec = start time of the recording * start_voc = start time of the
 vocalization used for identification * end_voc = end time of the
 vocalization used for identification * annot_interval = time interval of
 the vocalization used for identification * spec = species name *
 confidence = BirdNET confidence score * model = BirdNET version * period =
 recording period * File bird_selection.csv: We used species-specific
 thresholds to improve the accuracy of identification from Seibold et al.
 2024. The file contains the following columns: * spec_com = common species
 name * spec_sci = scientific species name * CNN_min_conf =
 species-specific confidence threshold * included = column indicating
 whether the species was included in Seibold et al. 2024 **File trait
 data:** * file plants_forest_speci_raw.csv: * Wissenschaftlicher Name =
 scientific name * ABBREVIAT = abbreviation of scientific name * A = forest
 affinity for species from the Alps * files
 [animal_taxon]_forest_speci_raw.csv: * family/species = family/species
 name * forest affinity = forest affinity * file
 plants_lookup_lifeform.csv: * species_name = species name * TRYSpeciesID =
 species ID from the TRY database * Family = taxonomic family *
 PlantGrowthForm = plant growth form **folder local data:** * file
 lidar_metrix.csv: * plot = plot ID * radius = sampling radius Alps *
 pzabove5 = canopy cover quantified as the percentage of lidar returns
 above 5 m * file study_plots.csv: * plot = plot ID * E = Esting UTM
 coordinates * N = Northing in UTM coordinates * elev = elevation in m asl
 * elev_class = elevationa zone (submontame, montane, subalpine) * area =
 sampling area used as a grouping variable to adjust for spatial
 autocorrelation **folder landscape data:** * File AOI.shp: shapefile
 determining the area of interest * Files
 TCD_2018_010m_E45N26_03035_v020.tif &
 TCD_2018_010m_E45N27_03035_v020.tif: 2018 High Resolution Layer Tree Cover
 Density product from the EU Copernicus programme
 ([https://land.copernicus.eu/en/products/high-resolution-layer-treea](https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density)) from Germany and Austria used to quantify the proportion of forest relative to the sample area. * Files disturbance_year_1986-Filesgermany.tif & disturbance_year_1986-2020_austria.tif: 2020 pan-European forest disturbance maps derived from Landsat data (Senf & Seidl 2021) used to quantify the proportion of disturbed forest relative to forest area. ## Code/software All code is written in R (version 4.3.2), and we provide a renv.lock file that enables us to load each R package in the exact version we used for the analysis. ## Access information Data from other sources was derived from: * Seibold et al. (2024) Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as a driver of bird diversity and community composition. [https://,doi.org/10.1111/ddi.13905 ](https://doi.org/10.1111/ddi.13905) * Dorow et al. (2020). Waldbindung ausgewhlter Tiergruppen Deutschlands. Lumbricidae, Araneae, Opiliones, Pseudoscorpiones, Heteroptera, Coleoptera, Aculeata, Macrolepidoptera, Aves. Bundesamt für Naturschutz, Bonn. * Schmidt et al. (2011). Waldartenlisten der Farn-und Bltenpflanzen, Moose und Flechten Deutschlands. Bundesamt für Naturschutz, Bonn. * Senf, C. & Seidl, R. (2021). Mapping the forest disturbance regimes of Europe. Nat Sustain, 4, 6370. [https://doi.org/10.1038/s41893-020-00609-y ](https://doi.org/10.1038/s41893-020-00609-y)