DOI: 10.5061/dryad.sj3tx96jb
title: Data from: On the spatial clustering of behavioural phenotypes: Matching movement tactics with landscape structure in a large herbivore

description: In the wild, individuals consistently differ in movement and space use
 behaviours, depending on their personality. This variation can lead to
 personality-habitat associations and spatial structuring, potentially
 generating individual niche segregation. We explored the link between
 personality and landscape composition and structure in a population of
 free-ranging roe deer (Capreolus capreolus), predicting that i)
 individuals with similar movement tactics would occupy similar ecological
 niches, which should result in ii) spatial clustering of personalities in
 heterogeneous landscapes with personality-alike individuals being closer
 to each other in space than by chance Using GPS and activity data from 263
 roe deer, we calculated five movement-based personality traits. We
 evaluated the association between movement phenotypes and habitat by
 comparing the among-individual similarity in movement behaviour to their
 similarity in home range composition and structure. Additionally, we
 conducted spatially explicit analyses to quantify the spatial clustering
 of these traits. Our results reveal that individuals with similar daytime
 use of open habitats, an indicator of boldness, occupy the same ecological
 niche with respect to woodland availability and habitat homogeneity,
 leading to strong spatial clustering in this trait. In contrast, home
 range size, average movement speed, and road diurnality (i.e, an
 individual’s propensity to get closer to a road during daytime) were
 spatially structured only at a small scale. Additionally, we found no
 spatial structure in activity level, and neither activity nor road
 diurnality was associated with landscape composition and structure.
 Matching movement-based personality traits with landscape features
 revealed spatial clustering of personalities. This non-random distribution
 could have implications for managing wild ungulate populations,
 segregating ecosystem services (e.g., nutrient fluxes) and disservices
 (e.g., road collisions) across the landscape.
description: # Data from: On the spatial clustering of behavioural phenotypes: Matching
 movement tactics with landscape structure in a large herbivore Dataset
 DOI: [10.5061/dryad.sj3tx96jb](https://doi.org/10.5061/dryad.sj3tx96jb) ##
 Description of the data and file structure Data and R code to reproduce
 the results of the article : "On the spatial clustering of
 behavioural phenotypes: matching movement tactics with landscape structure
 in a large herbivore". ### Files and variables #### File:
 spatial_matrix.RData **Description:** Barycentres of the locations of the
 roe deer individuals. *Description of the columns :* * ID : unique
 identifier of the individuals * X and Y : coordinates of the barycentres
 of all locations for each individual, projected in Lambert 93 (in m). ####
 File: data_movement_traits_spatial_analysis.RData **Description:**
 Movement traits for each month and roe deer individual. *Description of
 the columns :* * ID : unique identifier of the individuals * month : the
 month over which the movement traits are averaged * year : the year of
 monitoring of the individual and month * number_fixes_speed : the number
 of locations used for the calculation of the average movement speed during
 the corresponding month * average_daily_speed : the movement trait
 "Average movement speed (AVGspeed)" used to characterise
 exploration (in m/h) * hr_size_kernel_90 : the movement trait "Home
 range size (HR size)" used to characterise exploration (in ha) *
 number_locations_home_range : the number of GPS locations used to
 calculate the home range size during the corresponding month *
 mean_ODBA_all : the movement trait "Activity (ODBA)" used to
 characterise activity * number_fixes_activity_all : the number of GPS
 locations used to calculate the ODBA during the corresponding month *
 number_locations_day : the number of fixes recorded during daytime (at
 noon) during a given month * number_locations_night : the number of fixes
 recorded during nighttime (at midnight) during a given month *
 number_locations_day_without_manmade : the number of fixes recorded during
 daytime (at noon) during a given month without the manmade infrastructures
 * number_locations_night_without_manmade : the number of fixes recorded
 during nighttime (at midnight) during a given month without the manmade
 infrastructures * number_open_locations_day : the number of fixes recorded
 during daytime (at noon) during a given month in the open habitats *
 number_open_locations_day_with_hedge : the number of fixes recorded during
 daytime (at noon) during a given month in the open habitats with the
 hedges considered as an open feature and not a closed feature *
 number_open_locations_day_without_manmande : the number of fixes recorded
 during daytime (at noon) during a given month in the open habitats without
 the manmade infrastructures * probability_open_day_with_hedge : the
 movement trait "Probability of using open habitats during daytime
 (POH)" used to characterise boldness * number_fixes_distances_all :
 the number of locations used for the calculation of the diurnality of the
 distance to roads during the corresponding month * mean_distance_road_all
 : the mean distance in m of an individual from the road during a given
 month (daytime and nighttime both included) * mean_distance_road_day : the
 mean distance in m of an individual from the road during a given month
 during daytime * mean_distance_road_night : the mean distance in m of an
 individual from the road during a given month during nighttime *
 diurnality_road : the movement trait "Diurnality of the distance to
 roads (Droad)" used to characterise boldness * ani_sexe : the sex (M
 male and F female) of the individual * cap_age_classe : the age class of
 the individual ("adulte" for adults and "yearling" for
 yearlings) * cap_poids : the weight in kg of the individual measured at
 capture * proportion_culture_fall : the proportion of autumn crops in the
 home range of the individual * proportion_water : the proportion of water
 bodies in the home range of the individual * proportion_woodland : the
 proportion of woodland in the home range of the individual *
 proportion_meadow_natural : the proportion of natural meadows in the home
 range of the individual * proportion_hedges : the proportion of hedges in
 the home range of the individual * proportion_culture_summer : the
 proportion of summer crops in the home range of the individual *
 proportion_friche : the proportion of scrublands in the home range of the
 individual * proportion_roads : the proportion of roads and pathways in
 the home range of the individual * proportion_missing : the proportion of
 unidentified landscape features in the home range of the individual *
 proportion_manmade : the proportion of manmade infrastructures in the home
 range of the individual * mean_distance_roads : the average distance in m
 of an individual from roads depending on its home range (calculated using
 random points sampled from its home range) * mean_distance_manmade : the
 average distance in m of an individual from manmande infrastructures
 depending on its home range (calculated using random points sampled from
 its home range) * mean_distance_hedge : the average distance in m of an
 individual from hedges depending on its home range (calculated using
 random points sampled from its home range) * Contagion_Index.value : the
 contagion index calculated to index habitat homogeneity * mean_day_length
 : the average duration of the day during a given month #### File:
 spatialisation_personality.R **Description:** Code to perfom all the
 analyses used in the article. This workflow quantifies how individual
 movement traits are spatially structured by first modelling multiple
 traits jointly to extract individual-level estimates, then testing whether
 those traits show spatial autocorrelation. ## Code/software The
 visualisation of the code and data require the software R, with or without
 Rstudio. ## Access information *Capreolus capreolus* is not an endangered
 species and the population is not threatened. The coordinates of the
 barycentres were used to perform the analyses and make the map found in
 the published article. For more information on the data, do not hesitate
 to contact the authors (ines.khazar@hotmail.fr).