DOI: 10.5061/dryad.crjdfn3hj
title: Data from: Magellanic woodpeckers as indicators of wood-dwelling beetle diversity in trees with different levels of decay and under changing environmental conditions

description: 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.
description: # Data from: Magellanic woodpeckers as indicators of wood-dwelling beetle
 diversity in trees with different levels of decay and under changing
 environmental conditions Dataset DOI:
 [10.5061/dryad.crjdfn3hj](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/](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