DOI: 10.5061/dryad.5qfttdzh7
title: Data from: Effects of summer weather and heatwaves on wild boar activity

description: Climate change threatens wildlife species, negatively affecting their
 fitness through environmental change, i.e., through increased severity of
 droughts and summer heatwaves. Wild boar, a species with limited
 physiological thermoregulation abilities, is potentially vulnerable to
 high temperatures during summer. Yet, little is known about the
 behavioural reactions of this species to heat stress. A detailed
 understanding of wild boar behavioural adaptations to their environment
 might help understand their future population growth and change in the
 geographical range. We used multisensory collars on 24 individual wild
 boar in the Czech Republic, calculating the dynamic body acceleration as a
 proxy for energy expenditure to detect activity changes in response to
 high temperatures on two temporal scales (daily and seasonal) and
 heatwaves. Our results revealed that overall, under higher temperatures,
 wild boar reduce their activity, unless it rained. Heatwave intensity did
 not affect wild boar activity. We suggest that wild boars adapt their
 activity to weather conditions and presume the importance of sufficient
 precipitation for thermoregulation in this species. Additionally, this
 research shows the potential of remote-sensing technologies to monitor
 wildlife behaviour, particularly in challenging observational scenarios,
 offering valuable insights into the behavioural responses of wildlife in
 the face of a changing climate.
description: We collected tri-axial acceleration data from multisensory
 collars mounted on free-ranging wild boar. The tags (Wildbytes
 Technologies Ltd.) recorded data in 10 Herz frequency. The raw data was
 then processed using the supporting DDMT software (Wildbytes Technologies
 Ltd.) and exported as 30-minute sums of the vectorial sum of the dynamic
 body acceleration (VeDBA). All further processing was performed within the
 R environment (R version 4.2.2). Weather data was downloaded from the
 Visual Crossing Weather Query Builder. We first build a data set
 containing the hourly sums of the VeDBA, the hourly temperature, and the
 hourly sum of precipitation volume. In the second data set, we generated
 the average daily VeDBA, adding the daily maximum and mean temperature,
 the sum of precipitation, and if the date fell within or outside of
 heatwaves (days over 24 degrees Celsius). The third data set contains the
 average daily mean of VeDBA only within heatwaves, as well as the length
 of the heatwave, the maximum temperature within each heatwave, and the
 total precipitation.  Ultimately, we used Generalised
 Additive Mixed Models (GAMMs) and Generalised Linear Mixed Models (GLMMs)
 to statistically test the effects of weather on the VeDBA in two temporal
 scales and the intensity of heatwaves.
description: # Effects of summer weather and heatwaves on wild boar activity ## Authors
 Justine Güldenpfennig, Niccolò Fattorini, Miloš Ježek, Kevin Morelle,
 Tomasz Podgórski ## Description of the data and file structure This README
 describes the columns of the CSV files used to generate statistics and
 figures in the above-mentioned publication.
 "GAM1_data_daily.csv" collects the data used in the first model
 to predict the effects of daily temperature and time on the hourly
 vectorial sum of dynamic body acceleration (VeDBA).
 "GAM2_data_seasonal.csv" collects the data used in the second
 model to predict the effects of seasonal temperature and time on the daily
 vectorial sum of dynamic body acceleration (VeDBA).
 "GLM_data_heatwaves.csv" collects the data used in the third
 Model used to examine the effect of heatwave intensity on the vectorial
 sum of dynamic body acceleration (VeDBA). "Rscript" analyzes how
 animal activity (measured as VeDBA) is influenced by temperature, time of
 day, seasonality, and precipitation using generalized additive models
 (GAMs) and GLMs. It includes model fitting, predictions, visualizations,
 and post-hoc analyses to assess behavioral responses to weather and
 heatwaves. "temp_precip_history.csv" collects the data used to
 visualise historical data about temperature and precipitation in the Czech
 Republic. Cells with "NA" represent not unavailable data. The
 columns are as follows: "GAM1_data_daily.csv" animalID - Unique
 identifier for each tracked animal datetime - Date and time the VeDBA is
 summarised for VeDBA_sum - The hourly vectorial sum of dynamic body
 acceleration in g temp - The temperature measurement in °C precip - The
 measurement of precipitation volume in mm daynight - Categorises if the
 hour falls within daytime ("day"; sunrise to sunset) or
 nighttime ("night"; sunset to sunrise) year - The year of the
 date "GAM2_data_seasonal.csv" animalID - Unique identifier for
 each tracked animal date - The date the VeDBA is summarised for
 VeDBA_mean_daily - The daily average of the hourly vectorial sum of
 dynamic body acceleration in g Tmax - The daily maximum temperature in °C
 Tmean - The daily average temperature in °C precip_total - The daily total
 precipitation volume in mm Julian - Day of the Year heatwave - Binary
 category for within heatwave (1) or outside of heatwave (0) seq_id -
 Continuous identifier for each heatwave seq_nr - Continuous number of days
 within each heatwave (per seq_id) seq_length - Maximum number of days
 within each heatwave (per seq_id) SL-sum - daily distance traveled
 calculated using the sum of step length (distance between consecutive GPS
 locations) NSD_max - daily maximum net squared displacement calculated
 using GPS locations speed_mean - daily average speed calculated using GPS
 locations "GLM_data_heatwaves.csv" animalID - Unique identifier
 for each tracked animal seq_id - Continuous identifier for each heatwave
 VeDBA_mean_mean - The mean daily average of the hourly vectorial sum of
 dynamic body acceleration within each heatwave in g Tmax_mean - The
 average daily maximum temperature within each heatwave in °C precip_total
 - The total precipitation volume within each heatwave in mm seq_length -
 Maximum number of days within each heatwave (per seq_id) year - The year
 each heatwave was registered "temp_precip_history.csv" month -
 The month of the year year - The year of measurement Tmean - The average
 temperature in °C precip - The total precipitation in mm ## Code/Software
 R environment version 4.2.2