Data from: Magellanic woodpeckers as indicators of wood-dwelling beetle diversity in trees with different levels of decay and under changing environmental conditions
Data files
Sep 11, 2025 version files 360.78 KB
-
Data.Vergara.2025.csv
305.39 KB
-
Environment.csv
30.12 KB
-
README.md
6.60 KB
-
Script.Vergara.2025.R
8.41 KB
-
Taxa.csv
10.27 KB
Abstract
Although woodpeckers are considered ecological indicators, their relationships with wood-dwelling beetle communities are scarcely known. Woodpeckers and wood-dwelling beetles respond to wood decay and forest disturbances; however, it is unclear how these effects propagate through saproxylic networks.
We investigated whether the trees used by Magellanic woodpeckers (Campephilus magellanicus) have a greater diversity of wood-dwelling beetles of different functional groups. We also examined the direct and indirect effects of environmental conditions and wood decay on beetles and woodpeckers.
We sampled beetles using emergence traps and characterized wood decay using tomography, comparing stem sections excavated by woodpeckers with those of control trees.
The abundance and taxonomic richness of beetles belonging to different guilds (predators, wood borers, and mycophages) were higher in sections where woodpeckers foraged. However, these factors were also influenced by remote sensing environmental variables and their interactions with woodpeckers. Wood borers positively influenced beetles of other guilds (predators and mycophages).
Climatic conditions, vegetation structure and biophysical properties had direct effects on wood decay and indirect effects on woodpeckers and wood-dwelling beetles via decay. Wood decay had positive direct and indirect effects on predators, mycophages and woodpeckers, but not on wood borers.
These results suggest Magellanic woodpeckers can serve as indicators of wood-dwelling beetle communities. Forest degradation and climate change have the potential to exert bottom-up control over interactions among woodpeckers and functional groups of wood-dwelling beetles.
Dataset DOI: 10.5061/dryad.crjdfn3hj
Description of the data and file structure
Summary
Data concerning the abundance and taxonomic richness of wood-dwelling beetles were obtained from emergence traps installed on trees with woodpecker foraging holes (wood treatment) and trees used as control. The data were also correlated with environmental variables, encompassing remote sensing environment variables and internal wood decay in the tree sections where traps were installed. Subsequently, the beetle data were analysed using Poisson and zero-inflated Poisson (ZIP) models.
Files and variables
1) Data.Vergara.2025.csv
Description: This file contains remote sensing and environmental variable data estimated for each of the studied trees (control and treatment). The data format is similar to that in the Datos.csv file. The methods used to estimate these variables are detailed in the 'Materials nd Methods' section of the article, and a more detailed description of the variables can be found in Table S1 of the supplementary data.
Variables
- The first 297 columns (from “Tomoderus melanocephalus” to “Trogossittidae Gn sp2”) contain continuous positive variables showing the estimated abundance (i.e. the number of individuals) of each beetle species per trap and season.
- Wood: a two-level categorical variable showing the woodpecker treatment (P: a tree with foraging holes; C: a control tree).
- Land: a 22-level categorical variable containing the landscape code in which the traps were deployed.
- Plot: a 3-level categorical variable showing the plot within the landscape where the traps were set. This comprises control trees and trees used for woodpecker foraging.
- Season: a column showing a five-level categorical variable representing the season during which the data were collected. The following codes represent the sampling seasons: 1 for summer 2023, 2 for autumn 2023, 3 for winter 2023, 4 for spring 2023, and 5 for summer 2024.
- Tree: a column showing a three-level categorical variable containing the species of tree on which the traps were installed, including Lenga (Nothofagus pumilio), Coigüe (Nothofagus dombeyi) and evergreen trees.
2) Environment.csv
Description: This file contains remote sensing and environmental variable data estimated for each of the studied trees (control and treatment). The data format is similar to that in the Datos.csv file. The methods used to estimate these variables are detailed in the 'Materials and Methods' section of the article.
Variables:
- Red: a positive continuous variable representing the estimated percentage of internal wood decay (%), as determined by sonic tomography of the tree section where the emergence traps were deployed. This variable took on values between 3.3% and 83.3%.
- DAP: a positive continuous variable that represents the tree diameter (cm). This variable took values between 14.6 and 140.9 cm.
- PSRI: Plant Senescence Reflectance Index, which is a continuous variable derived from remote sensing that took values between -0.02 and 0.13.
- VCF: Vegetation Continuous Fields, which is a positive continuous variable derived from remote sensing taking values between 53 and 79.5
- VCF.COV: Temporal change in VCF (ΔVCF) over the past 10 years, with values ranging from -14 to 50.
- Elevation: a positive continuous variable representing the altitude (masl) of the tree, taking values between 371 and 1,067 masl.
- TEMP: Mean annual temperature (°C), which is a continuous variable derived from remote sensing that took values between 9.8 and 19.3°C
- CAMBIO.TEMP: Change in mean temperature (°C) over the past 10 years, with values ranging from 0.8 to 3.1°C.
- SOC: Soil Organic Carbon, which is a positive continuous variable derived from remote sensing, taking values between 583 and 1452, in Mg C/10 ha.
- TDI: Topographic Diversity Index, which is a positive continuous variable derived from remote sensing, taking values between 0.30 and 0.94
- MSI: Moisture Stress Index, which is a positive continuous variable derived from remote sensing, taking values between 0.27 and 0.77
- HLI: Continuous Heat-Insolation Load Index, which is a positive continuous variable derived from remote sensing, taking values between 0.48 and 0.98
- EVI: Enhanced Vegetation Index, which is a positive continuous variable derived from remote sensing, taking values between 0.30 and 0.66
3) Taxa.csv
Description: This file is required to estimate the abundance and taxonomic richness of wood-dwelling beetle species. Taxonomic and functional information is provided for each beetle species. The methods used to estimate these variables are detailed in the 'Materials and Methods' section of the article.
Variables:
- Family: The family of each beetle species
- Guild: The trophic guild of each beetle species. These guilds are detailed in the 'Materials and Methods' section of the article and include Mycophagous, Necrophagous, Phytophagous, Saprophagous, Saproxylophagous, Xylomycetophagous, Xylophagous, and Zoophagous.
- Spp: The scientific name of each beetle species (n=297 species).
4) Script.Vergara.2025.R
Description: This file contains R codes for estimating and analyzing the abundance and taxonomic richness of wood-dwelling beetles captured in emergence traps installed in control and foraging trees. Text files are loaded for analysis (Datos.csv, Environment.csv, and Taxa.csv). First, the derivation of the abundance and richness data is described. Next, a test is performed to check for overdispersion and zero inflation in Poisson and zero-inflated Poisson (ZIP) models fitted to abundance and taxonomic richness data. Next, the process of building and selecting models begins with an initial assessment of univariate models. Next, the code to perform backward stepwise model selection based on the Akaike information criterion is provided, along with the model averaging output.
Code/software
The analysis of all data was conducted using R software version 4.4.3 (R Core Team 2025).
R Core Team (2025). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Access information
Other publicly accessible locations of the data:
- n/a
Data was derived from the following sources:
- n/a
