DOI: 10.5061/dryad.z34tmpgqc
title: Data from: Indirect effects of higher mean air temperature related to climate change on major life-history traits in a pulsed-resource consumer

description: Climate change is directly and indirectly affecting species. The degree of
 these effect types differs by species and context, with indirect effects
 likely to be stronger for consumers of pulsed resources. Here, we
 investigated how higher mean air temperature related to climate change
 affects masting, and in parallel, how this change affects life-history
 traits in edible dormice (Glis glis). We analysed 17 years of
 capture-recapture data from 2,530 individuals. We collected air
 temperature and, as a measure of seed production, pollen data from
 European beech (Fagus sylvatica). Our results show that increasing mean
 air temperature was associated with a shift in beech pollen production,
 leading to a biannual mast cycle in recent years, with alteration of years
 with very high and very low seed availability. The changed cycle in mast
 events resulted in a significant reduction in overall yearling survival in
 dormice, while overall adult survival remained stable. In parallel, both
 age classes significantly increased their litter size in this timeframe.
 Furthermore, survival probabilities in the two age classes also differed
 depending on the beech mast status (mast, mast-failure). We show that the
 observed dramatic changes in seed production had complex effects on
 life-history traits in a pulsed resource consumer.
description: # Indirect effects of higher mean air temperature related to climate
 change on major life-history traits in a pulsed-resource consumer
 [https://doi.org/10.1038/s41598-026-37071-3](https://doi.org/10.1038/s41598-026-37071-3) ## Description of the data and file structure This README file was generated on 2026-01-01 by Lukas Hochleitner ## General information ### Dataset title Indirect effects of climate change on major life-history traits in a pulsed-resource consumer ### Contributors * Lukas Hochleitner, University of Veterinary Medicine, Department of Interdisciplinary Life Sciences, Research Institute of Wildlife Ecology * Shane Morris, University of Veterinary Medicine, Department of Interdisciplinary Life Sciences, Research Institute of Wildlife Ecology * Maximilian Bastl, Medical University of Vienna, Department of Oto-Rhino-Laryngology * Thomas Ruf, University of Veterinary Medicine, Department of Interdisciplinary Life Sciences, Research Institute of Wildlife Ecology * Claudia Bieber, University of Veterinary Medicine, Department of Interdisciplinary Life Sciences, Research Institute of Wildlife Ecology ### Corresponding author Lukas Hochleitner, [lukas.hochleitner@vetmeduni.ac.at](mailto:lukas.hochleitner@vetmeduni.ac.at) ### Recommended Software for Data Analysis #### MARK (White, G. C., and Burnham, K. P. 1999, "Program MARK: survival estimation for populations of marked animals". Bird Study 46, 120-138) #### Version 9.0 #### R Environment for Statistical Computing #### Version 4.2.1 #### Packages * psych * ggplot2 * ggpubr * EnvStats * MASS * car * multcomp * dplyr * tidyr * magrittr * reshape * RMark * R.utils * car * ggplot2 * ggpubr * lme4 * lmtest * MuMIn * cvequality * olsrr * moments * fiwidat * AER * blmeco * blme ## Files and variables ### Body_mass.csv #### Table Columns * ID ... identification number of the individual * Date ... capture date (format: dd.mm.yyyy) * Age ... A = adult / Y = yearling * weight ... body mass in g * period ... 1 = period 1 (2006-2013) / 2 = period 2 (2014-2022) #### Purpose and Utility Input data for "Body_mass.R" Data to get results of "3.7 Body mass of dormice" and "Figure 5" ### Air_Soil_Tempteratures.csv #### Table Columns * date ... date (format: dd.mm.yyyy) * month ... month of the year * temp_Berndorf ... daily mean air temperature measured at weather station Berndorf * temp_NB ... daily mean air temperature measured in the study area (NA = missing value due to measurement error) * temp_air_p ... daily mean air temperature of the previous day * temp_soil ... daily soil temperature measured in the study area at approximately 60 cm underground (NA = missing value due to measurement error) #### Purpose and Utility Input data for "Correlation_Temperature.R" Data to check correlation between daily mean air temperature in study area and daily mean air temperature at nearest weather station in Berndorf Data to check correlation between daily mean air temperature of the previous day and daily mean soil temperature Data to get estimated air temperature of the missing days, and estimated soil temperature of the missing days Data to get results of Figure S1 (Supporting Information) ### Effects_of_soil_temperature.csv #### Table Columns * Year ... capture year * ID ... identification number of the individual * Date ... capture date (format: dd.mm.yyyy) * Day ... day of the year * Sex ... M = male / F = female * Age ... A = adult / Y = yearling * weight ... body mass in g * mast_prior ... 0 = the year before hibernation was a mast-failure year / 1 = the year before hibernation was a mast year * oct-may ... mean soil temperature of October to May of the respective hibernation season in °C #### Purpose and Utility Input data for "Effects_of_soil_temperature.R" Data to get the results of "3.1 Effects of soil temperature on dormice" and Table S2 (Supporting Information) ### Environmental_characteristics_I.csv #### Table Columns * year ... year of measurements * period ... p1 = Period 1 (2006-2013) / p2 = Period 2 (2014-2022) * pollen ... Annual pollen integral in pollen/m^3^ * temp ... annual mean air temperature * mast ... 1 = mast-failure year / 2 = mast year * reproF ... proportion of reproductively active females #### Purpose and Utility Input data for "Environmental_characteristics.R" Data to analyse the correlation between the proportion of reproductive active females and mast events Data to analyse the difference in the annual mean air temperature and the inter-annual variation in annual pollen integrals between periods ### Environmental_characteristics_II.csv #### Table Columns * year ... year of measurements * mast ... Y = mast year / N = mast-failure year * temp.sum.-1 ... mean air temperature of the summer (June and July) one year before observed year (in the column "year") in °C * temp.sum.-2 ... mean air temperature of the summer (June and July) two years before observed year (in the column "year") in °C * temp.-1.-2 ... mean air temperature of the summers (June and July) one and two years before observed year (in column "year") in °C * pollen ... Annual pollen integral in pollen/m³ #### Purpose and Utility Input data for "Environmental_characteristics.R" Data to analyse the relationship between annual pollen integral and summer temperatures (June and July) ### Capture_mark_recapture.csv #### Table Columns * Year ... capture year * ID ... identification number of the individual * Date ... capture date (format: dd.mm.yyyy) * NB ... nestbox, in which the individuals were captured * Sex ... M = male / F = female * Age ... J = juvenile / Y = yearling / A = adult * reproF ... 1 = female was reproductively active / 0 = female was not reproductively active (NA = female was not captured within the weeks 31 -39, or female was captured twice without juveniles in the same nestbox) * numberOffspring ... litter size * f_mast ... 0 = Period 1 (2006-2013) / 1 = Period 2 (2014-2022) * buchen.pollen.total ... Annual pollen integral in pollen/m^3^ #### Purpose and Utility Input data for "Litter_size.R" Input data for "Reproductively_active_femalesR" Data to get results of "3.6 Reproductive activity of females and litter size" ### Capture_mark_recapture_pooled.csv #### Table Columns * Year ... capture year * ID ... identification number of the individual * Event ... number of year * Sex ... M = male / F = female * Age.at.1.capture ... age at first capture (A = adult / Y = yearling) #### Purpose and Utility Input data for "Building_capture_histories_year.R" Data to create csv-file "Capture_histories.csv" to build Cormack-Jolly-Seber (CJS) models in the R-package "RMark" as well as in the program MARK ### Capture_histories.csv #### Table Columns * "" ... number of capture history * ch ... capture history consisting of "0" and "1"; each digit represents one year; 0 = individual was not captured in the respective year / 1 = individual was captured in the respective year * Sex ... M = male / F = female * Age ... A = adult / Y = yearling