Data from: Landscape context modulates the effect of local canopy cover on forest multidiversity across elevations
Abstract
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.
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 m2 quadratic plots, separately for the herb (<1 m height) and shrub layer (>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) 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
- 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
Biodiversity data
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 m2 quadratic plots, separately for the herb (<1 m height) and shrub layer (>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 et al. 2021, version 2.4), and applied species-specific thresholds in order to improve the accuracy of identification (Seibold et al. 2024). We then classified all species into three broad habitat-affinity guilds, namely forest, mixed, and open-habitat affinity, based on Schmidt et al. (2011) and Dorow et al. (2020), and calculated multidiversity based on Allan et al. (2014) for an overall dataset and for each affinity guild.
Local and landscape-scale forest characteristics
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 m2, acquired under leaf-on conditions in September 2021 (Mandl et al. 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.
Statistical analysis
We analysed how local and landscape-scale forest characteristics affect multidiversity along elevation. We fitted Bayesian multilevel models using the brms 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 et al., 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 et al. 2024b; Yao et al. 2018).