Sex drives intraspecific scaling of home range size in mammals

Giroux, Aline 1 ; Valle, Denis1; Gurarie, Eliezer2; Vieira, Marcus3; Forester, James4; Börger, Luca5; Sepulvida, Raissa6; Barbour, Nicole7; Garcez da Rocha, Erika8; Oshima, Julia9; Cavalcanti de Azevedo, Fernanda10; Gemesio Lemos, Frederico10; Brandão Niebuhr, Bernardo11; de Paula, Rogerio12; da Motta Lima, Caio9; Mourão, Guilherme13; Attias, Nina14; Favarini, Marina15; Bortolotto Peters, Felipe15; Desbiez, Arnaud16; S. P. Jorge, Maria Luisa17; Ribeiro, Milton18; Keuroghlian, Alexine19; Candeias, Isis10; Zucco, Carlos Andre13; Moraes Tomas, Walfrido13; C. Cavalcanti, Sandra15; A. Vilalba, Ermeson15; V. Alberico, Vanessa15; Tortato, Marcos20; L.P. Boulhosa, Ricardo15; Fiori, Flavia15; de Farias Peres, Pedro21; Grotta-Neto, Francisco21; Piovezan, Ubiratan20; Barbanti Duarte, Jose21; Rodrigues Oliveira-Santos, Luiz Gustavo6

Published Oct 07, 2025 on Dryad. https://doi.org/10.5061/dryad.v6wwpzh8p

Abstract

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.

Dataset DOI: 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):

Effect of habitat quality on standardized home range size.
Sex effect on home range size.
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

Animal data collection

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 (Puma concolor; 5 females and 15 males), ocelot (Leopardus pardalis; 5 females and 6 males), Geoffroy’s cat (Leopardus geoffroyi; 17 females and 9 males), margay (Leopardus wiedii; 5 females and 6 males), jaguar (Panthera onca; 13 females and 8 males), crab-eating fox (Cerdocyon thous; 14 females and 19 males), hoary fox (Lycalopex vetulus; 9 females and 12 males), maned wolf (Chrysocyon brachyurus; 17 females and 20 males), coati (Nasua nasua; 10 females and 7 males), six-banded armadillo (Euphractus sexcintus; 8 females and 10 males), southern three-banded armadillo (Tolypeutes matacus; 9 females and 8 males), pampas deer (Ozotoceros bezoarticus; 19 females and 15 males), wild boar (Sus scrofa; 13 females and 13 males), giant armadillo (Priodontes maximus; 15 females and 6 males), brown brocket deer (Subulo gouazoubira; 2 females and 4 males), white-lipped peccary (Tayassu pecari; 7 females and 3 males), giant anteater (Myrmecophaga tridactyla; 8 females and 6 males), and capybara (Hydrochoerus hydrochaeris; 3 females and 3 males).

Home range estimation

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.

Habitat quality estimation

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.

Statistical model

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.