Consistent inter-individual variability in movement traits shapes the wild boar movement syndrome
Data files
May 08, 2025 version files 365.98 KB
-
README.md
3.62 KB
-
wildboar_personality_dataset.csv
362.36 KB
Abstract
Consistent intraspecific variation in behavior directly impacts reactions to environmental challenges, including life in human-altered landscapes. Yet, it is rarely considered in free-ranging species thriving in anthropogenic landscapes and causing human-wildlife conflict. Here, we examine the consistent inter-individual variability in wild boar movement traits and highlight its potential for ecology and practical management. We used satellite telemetry data from 95 GPS-tracked wild boars monitored in Czechia and computed weekly movement rate (activity), intensity of space use (exploration), and diurnality (boldness). Using a variance partitioning approach, we tested whether these traits were repeatable over time and therefore considered personality traits, and using Bayesian multivariate mixed-effect models, we examined the correlations among these traits to describe a behavioral syndrome while controlling for external and internal sources of variation. Wild boar showed significant consistent inter-individual variation in all three traits, with repeatability ranging from 0.16 to 0.35. We found significant correlations between traits, indicating the existence of a remarkable movement syndrome. Individuals staying within familiar areas were less nocturnal and moved slower, as opposed to individuals roaming more outside familiar areas, faster and with striking nocturnality. The movement syndrome and, most importantly, its variability, with individuals ranging in between extremes of activity and exploration, likely contributes to the success of this species and helps them thrive in human-dominated landscapes while maximizing resource acquisition. Integrating intraspecific behavioral variation into ecology and practical population management could improve models predicting wild boar movement and alleviate the biodiversity and economic loss caused by expanding wild boar populations.
Data for Masilkova et al. "Consistent inter-individual variability in movement traits shapes the wild boar movement syndrome" (Behavioral Ecology 2025).
Dataset "wildboar_personality_dataset.csv" (one row = mean weekly values of movement traits for individual wild boar, including model covariates) contains the following columns:
study_areas = two study areas (doupov, kostelec)
sex = sex of the wild boar (female, male)
age_class = age class of the wild boar (2 = juvenile, 3 = subadult, 4 = adult)
uniqueID = unique ID of GPS deployment (some individuals were tracked with different devices, so each tracking event is identified here)
animalID = ID of individual wild boar
week = sequential number of week in year (from 1 to 53)
mean_p2ptime = time between fixes; time in minutes elapsed between one GPS fix and the next GPS fix
mean_forestCover = forest cover; mean forest cover of all the areas traversed by the animal in that week (from 0 = non-forested area to 100 = area entirely covered by trees)
mean_hfi = human footprint index; measure of the human pressure (direct and indirect) on the environment for the year 2009. Values are means of all the areas traversed by the animal in that week. (values from 0 = no human impact to 50 = heavy human impact)
mean_distRoads = distance to roads; distance in meters to car/asphalted roads (i.e. classified as "main roads" in the Open Street Maps dataset). Values are means of all the areas traversed by the animal in that week; computed using osmdata package
mean_distPaths = distance to paths; distance in meters to walkable path restricted to offroad vehicles (i.e. classified as "paths" in the Open Street Maps dataset). Values are means of all the areas traversed by the animal in that week; computed using osmdata package
mean_tri = terrain ruggedness index; Absolute differences between the elevation of a cell and its eight surrounding cells, derived from the digital elevation model. Values are means of all the areas traversed by the animal in that week; computed using terra package
mean_dayLength = day length; weekly mean of the time in hours between sunrise and sunset according to the specific location and date; computed using suncalc package
mean_mvmntrate = Euclidean distance (in meters) that the wild boar traveled between relocations. Values represent the means of all point to point speeds in a week, and are expressed in meters per 30 min. Larger values represent faster individuals.
mean_intensity_use = mean weekly intensity of use computed by dividing the total movement distance in a week by the square root of the area of the movement of the week (i.e. the minimum convex polygon of all locations) with the function intensity_use from the package amt (Signer et al. 2019) (lower value = wild boar moving in more explorative manner, higher value = wild boar residing in restricted areas)
mean_intensity_use_trans = intensity of use data transformed for normality (i.e. log(intensity_of_use + 1)
mean_diurnality = mean weekly diurnality index reflecting an individual´s preference for moving during daylight hours while controlling for the lenght of day and night computed following Hertel et al. (2019) and using movement rate (values from -1 = nocturnal to +1 = diurnal)
mean_diurnality_bin = mean weekly diurnality index transformed into a binary variable by splitting at the median value (0 = more nocturnal than the median, 1 = less nocturnal than the median)