DOI: 10.5061/dryad.v6wwpzh8p
title: Sex drives intraspecific scaling of home range size in mammals

description: This dataset contains derived home range estimates
 from individual-level GPS tracking data of 349 resident mammals
 across 18 species in Brazil, as well as individuals' body mass, sex,
 and habitat quality estimations. The dataset was compiled to investigate
 intraspecific variation in home range (HR) size and its relationship with
 body mass (BM) and sex. The study addresses a key knowledge gap in
 movement ecology: while interspecific allometric scaling of mammal home
 ranges is well documented, it is unclear whether the same patterns hold
 within species and for both sexes. This dataset and the associated code
 allow researchers to reproduce the analyses made by Giroux et al.,
 visualize results, explore sex-specific and species-specific patterns, and
 integrate these data into broader comparative studies.
description: <strong>Animal data collection</strong>
 We conducted this study across savanna and grassland ecosystems
 in Brazil (S 58°11'40" - 46°9'50", W
 16°9'27" - 32°20'54"), under a tropical climate. The
 studied landscapes were composed of mosaics of natural forested and
 non-forested areas (e.g., woody and shrubby savannas, and open natural
 grasslands) as well as anthropogenic land uses such as exotic forests,
 crops (mainly rice and soybean), pasture, and highways. From 2007 until
 2024, we captured, sexed, weighed, and GPS tracked 349 adult, healthy,
 free-living individuals, encompassing 18 mammal species from 9 families
 and 5 orders. The monitored species were puma (<em>Puma
 concolor</em>; 5 females and 15 males), ocelot (<em>Leopardus
 pardalis</em>; 5 females and 6 males), Geoffroy’s cat
 (<em>Leopardus geoffroyi</em>; 17 females and 9 males), margay
 (<em>Leopardus wiedii</em>; 5 females and 6 males), jaguar
 (<em>Panthera onca</em>; 13 females and 8 males), crab-eating
 fox (<em>Cerdocyon thous</em>; 14 females and 19 males), hoary
 fox (<em>Lycalopex vetulus</em>; 9 females and 12 males),
 maned wolf (<em>Chrysocyon brachyurus</em>; 17 females and 20
 males), coati (<em>Nasua nasua</em>; 10 females and 7 males),
 six-banded armadillo (<em>Euphractus sexcintus</em>; 8 females
 and 10 males), southern three-banded armadillo (<em>Tolypeutes
 matacus</em>; 9 females and 8 males), pampas deer
 (<em>Ozotoceros bezoarticus</em>; 19 females and 15 males),
 wild boar (<em>Sus scrofa</em>; 13 females and 13 males),
 giant armadillo (<em>Priodontes maximus</em>; 15 females and 6
 males), brown brocket deer (<em>Subulo gouazoubira</em>; 2
 females and 4 males), white-lipped peccary (<em>Tayassu
 pecari</em>; 7 females and 3 males), giant anteater
 (<em>Myrmecophaga tridactyla</em>; 8 females and 6 males), and
 capybara (<em>Hydrochoerus hydrochaeris</em>; 3 females and 3
 males). <strong>Home range
 estimation</strong> We used the ninety-five
 percent area kernel density estimator corrected for autocorrelation (AKDEc
 95%) to estimate HR (ctmm R package). AKDEc is a nonparametric HR
 estimator that assumes that movement data represent a sample from a
 nonstationary and continuous process. This estimator was designed to deal
 not only with the autocorrelated structure of movement data but also with
 irregularly sampled data, allowing the comparison of HR estimates from
 different sampling regimes and periods.
 <strong>Habitat quality estimation</strong>
 To estimate habitat quality, we relied on the 30 x 30 m MapBiomas
 land-use land-cover classification (LULC; Collection 7; https://mapbiomas.org/en). For each species, we determined the most used habitat type (forest or non-forest) based on the LULC classification of individuals' location points, reflecting species-specific habitat preferences. Within each individual home range, we then calculated the proportion of the habitat type most used by its species as a proxy of habitat quality. <strong>Statistical model</strong> We assessed intraspecific HR scaling by regressing individuals’ HR against individuals’ BM. However, because species vary substantially in HR and BM magnitude, we first standardized HR and BM by dividing these variables by their species-specific mean. Because greater variance in HR and BM can facilitate the detection of scaling relationships, we checked whether, within species, one sex consistently exhibited higher variance in these traits than the other. We then modeled the log of the standardized HR with a Gaussian multiple linear regression model, allowing for species-specific intercept and slope parameters. Covariates in the regression model were: habitat quality, sex (coded as a binary variable where 0’s were females and 1’s were males), log standardized BM, and the interaction between log standardized BM and sex. The residual variance consisted of the sum of an overall variance parameter and the variance of the log standardized HR. To calculate HR variance, we relied on the lower and upper 95% confidence intervals for each individual’s HR. Finally, we allowed for the species-specific intercepts and slopes to be phylogenetically correlated. To this end, we calculated phylogenetic distances using the mammal supertree and the “phytools” R package. We fitted this model within a  Bayesian framework using JAGS, within the R package jagsUI.
description: # Sex drives intraspecific scaling of home range size in mammals Dataset
 DOI: [10.5061/dryad.v6wwpzh8p](https://doi.org/10.5061/dryad.v6wwpzh8p) ##
 Description of the data and file structure Understanding variation in home
 range size (HR) provides important insights into the underlying ecological
 processes driving space use. At an interspecific level, HR is expected to
 scale with body mass because larger animals can move further and need more
 resources. We aimed to investigate whether the interspecific allometric
 scaling of mammals’ HR is also observed at an intraspecific level, and the
 influence of individuals’ sex on both HR and HR scaling.  The dataset
 contains derived home range estimates from individual-level GPS tracking
 data of 349 resident mammals across 18 species in Brazil, as well as
 individuals' body mass, sex, and habitat quality estimations.
 Considering the uncertainty of HR estimations, we used a Bayesian approach
 to estimate the relationship between HR and BM, investigating the
 potential influence of sex on both HR and HR scaling. We controlled for
 habitat quality and phylogenetic autocorrelation in our model. The code
 allows statistical analysis, reproduction, and results visualization.  
 ### Files and variables #### File: data0.RData **Description:** A data
 frame d containing 349 observations (individuals) of 10 variables (data). 
 Columns contain:  ID: unique combinations of individuals' species,
 site, and names.  sp: individuals' species  site: monitoring site 
 id: individuals' names  low: lower limit of the ninety-five percent
 area kernel density estimator corrected for autocorrelation (AKDEc 95%)
 for home range (square kilometers)  hr: mean home range estimation based
 on the AKDEc 95% (square kilometers)  high: higher limit of the AKDEc 95%
 for home range (square kilometers)  sex: individuals' sex (M for
 males and F for females) weight: individual's body mass (kg) habitat:
 proportion of the most used habitat type (forest or open) by the species
 within of individuals' home range (0 to 1) #### File:
 MamPhy_BDvr_Completed_5911sp_topoCons_FBDasZhouEtAl_v2_tree0000.tre
 **Description:** The mammal supertree used to calculate phylogenetic
 distances between species #### File: Code_01_Standardization.R
 **Description:** Species vary substantially in home range (HR) and body
 mass (BM) magnitude. This code uses 'data0' to standardize HR
 and BM by dividing these variables by their species-specific mean, and
 stores a resulting table that contains the following columns (lines
 represent individuals):  species: species' scientific names  sex:
 individuals' sex represented by M (males) and F (females) sex.cat:
 individuals' sex represented by 1 (males) and 0 (females) habitat:
 habitat quality proportion inside individuals' HR (from 0 to 1)
 lw.std: log of standardized BM lhr.std: log of standardized HR lhr.std.sd:
 standard deviation of the log of standardized HR spp.id: individuals'
 species identified by numbers (1 to 18) #### File:
 Code_02_Visualize_Data.R **Description:** This code allows data
 visualization (mean and variance) by species and sex. Because greater
 variance in home range and body mass can facilitate the detection of
 scaling relationships, we checked whether, within species, one sex
 consistently exhibited higher variance in these traits than the other.
 Readers can use this code to generate Figures S1 - S3. #### File:
 Code_03_Get_Phylogenetic_Distance.R **Description:** This code uses
 the mammal super tree to calculate phylogenetic distances between species
 and store the results in an autocorrelation matrix.  #### File:
 Code_04_Run_Model.R **Description:** Fits the Bayesian hierarchical model
 in JAGS using individuals' home range estimations, phylogenetic
 correlation structure, and covariates (sex, body mass, habitat). Saves the
 fitted model and posterior samples for later analysis and visualization.
 #### File: Code_05_Aux_Model.R **Description:** Defines the hierarchical
 Bayesian model in JAGS. It models home range size as a function of sex,
 body mass, their interaction, and habitat, with species-level random
 effects informed by a phylogenetic correlation matrix. Includes
 hyper-priors for regression parameters and variance components.File:
 Code_08_Visualize_Results_3.R #### File: Code_06_Visualize_Results_1.R
 **Description:** Generates three plots summarizing model results (Figure
 3A): 1. Effect of habitat quality on standardized home range size. 2. Sex
 effect on home range size. 3. Sex-specific allometric scaling of home
 range with body mass. #### File: Code_07_Visualize_Results_2.R
 **Description:** Generates species-level density plots of posterior
 estimates for habitat effects, sex effects, and allometric scaling of home
 range for males and females (Figure 3C). #### File:
 Code_08_Visualize_Results_3.R **Description:** Plots species-level
 posterior density estimates of the interaction between sex and body mass
 on home range scaling (Figure 4). #### File: Code_09_Visualize_Results_4.R
 **Description:** Plots posterior density estimates of home range
 allometric scaling for males and females, and comparative interspecific
 values. Highlights intraspecific vs. interspecific scaling patterns
 (Figure 5).  #### File: Code_10_Visualize_Results_5.R
 **Description:** Plots posterior density estimates of home range
 allometric scaling for females and males across dietary guilds
 (carnivores, omnivores, herbivores), comparing their scaling distributions
 (Figure S4). ## Code/software R version 4.4.1