Caribou movement and habitat selection data

Data on the movement and habitat selection of woodland caribou for fitting step selection functions. Data are from 23 adult female caribou, collected during winter in the Côte-Nord region of Québec, Canada, between 2005 and 2012

HarveyFortinDataset S1

Field dat

Coefficients and standard errors for the top-ranking mixed effects linear model predicting the plant biomass consumed (g/m²) in a foraging crater in winter.

<p>Independent variables included snow water equivalent, missed opportunity costs of foraging in that meadow and not elsewhere in the landscape (), index of wolf presence (absence  = 0, presence  = 1) and log-transformed meadow area. A total of 255 quadrats of plant biomass were assessed in individual craters comprised in 23 meadows in Prince Albert National Park (Saskatchewan, Canada) during the winters of 1998 and 2011. Pseudo R² = 0.66. </p

Coefficients and standard errors of a linear mixed effects model predicting the area (ha) of foraging crater in individual meadows in winter.

<p>Independent variables included snow water equivalent, missed opportunity costs of foraging in that meadow and not elsewhere in the landscape (), index of wolf presence (absence  = 0, presence  = 1) and log-transformed meadow area. A total of 144 foraging craters were recorded in 26 meadows in Prince Albert National Park (Saskatchewan, Canada) during the winters of 1997, 1998 and 2011. Pseudo R² = 0.31.</p

Coefficients and standard errors for a mixed-effects logistic regression model predicting the probability that bison foraged in a given meadow in winter.

<p>Independent variables included snow water equivalent, missed opportunity costs of foraging in that meadow and not elsewhere in the landscape (), index of wolf presence (absence  = 0, presence  = 1) and log-transformed meadow area. N = 221 surveys in 26 meadows in Prince Albert National Park (Saskatchewan, Canada) during the winters of 1997, 1998 and 2011.</p

Relative level of support by competing models explaining plant biomass consumed in foraging craters by plains bison in winter.

<p><b>Note:</b> E: Plant profitability (kJ/min) in the landscape, meadow or patch, SWE: Snow water equivalent (cm), MOC: Missed opportunity costs (kJ/min), Wolf: Index of wolf presence, ln(MS): log-transformed meadow size (ha), ΔAIC: difference in Akaike information criterion between the current model and the lowest AIC.</p

Statistics of multiple comparisons (Tukey contrats) on the Ln(NDR) between groups from the linear-mixed effects model 9d (see Table 2).

<p>The general linear hypothesis is variable 1 –variable 2 = 0. Significant differences are in bold.</p

Robust Inference from Conditional Logistic Regression Applied to Movement and Habitat Selection Analysis

<div><p>Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters.</p><p>The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14–450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.</p></div

Features of fission events.

<p>(a) Example of fission event showing the time-scale (from 1 to 5) at which variation of mean group size and net squared displacement were studied and (b) mean group size (± SE) of initial and post-fission groups according to group composition.</p

AICc-based model selection to investigate the effect of group composition on ln-transformed mean group speed (MGS) and net displacement rate (NDR) using linear mixed-effects models.

<p>Models included fission ID as a random effect between initial <i>vs</i>. post-fission groups (models including Origin) and between the smallest vs. largest post-fission groups (models including Post-fission group size). Group composition is indicated by either sex segregation (male-only, female-only vs. mixed sex groups) or the proportion of adult males or females.</p