Computational protocol: Common drivers of seasonal movements on the migration – residency behavior continuum in a large herbivore

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Protocol publication

[…] We tested the influence of population, season and migratory status (migrant vs resident) on (1) log-transformed seasonal range surface area (estimated using 95% kernel) using linear mixed effects models (LME) with individual identity as a random effect on the intercept to control for repeated observations per individual, in particular for individuals monitored more than one year. Similarly, we tested the influence of population and migratory status on the ratio between seasonal movement distance and seasonal range size.H1: To test the influence of local- and large-scale heterogeneity of environmental conditions on the probability to migrate (noted ‘1’) or to be resident (noted ‘0’), we used Generalized Linear Mixed Models (GLMM) with a binomial family and a logit link function and with individual identity as a random effect on the intercept to control for repeated observations per individual, as we had several individual-years per populations. Independently for each season, we then tested the influence of each environmental variable and different spatial scaling: greenuplocal, greenuplarge, greenuppredi, snowmeltlocal, snowmeltlarge, and snowmeltpredi for spring seasonal movements; senescencelocal, senescencelarge, senescencepredi, snowfalllocal, snowfalllarge, and snowfallpredi for autumn seasonal movements. As environmental variables took only five different values (five populations), the risk of over-parameterization was high (supporting artificially the more complex models). The candidate models thus included one variable only, leading to six candidate models in addition with a null model.H1: To test differences in local-scale environmental conditions (snow cover duration and plant phenology within seasonal home-range) between seasons and populations, we used LME with individual identity as a random effect on the intercept to control for repeated observations per individual. We included a two-way interaction term between season and population to account for a possible differential effect of season among populations.H2: To test for potential common drivers on seasonal movements between migrants and residents, we then separated the data into two classes. We used LME to test the influence of local- and large-scale environmental conditions on log-distance of migration for migrant individuals or on log-distance of seasonal range shifts for resident individuals. Similarly as for H1, each analysis (log-distance of migration and log-distance of seasonal range shifts) was performed separately for spring and autumn seasonal movements with candidate models including only one variable.For all the analyses, we used the Akaike Information Criterion adjusted for small sample size (AICc) as recommended by Burnham and Anderson to select the best model. We retained the model with the lowest AICc value reflecting the best trade-off between complexity and precision. When the difference in AICc between two models (ΔAICc) was less than 2, models were considered as equivalents. We provided marginal and conditional R² for models with ΔAICc < 2 to indicate model fit quality. We reported the correlation coefficient between explanatory variables in Supplementary Material (Tables  and Table ). Analyses were performed using the package “adehabitatHR”, “sp” and “nlme” from R software. […]

Pipeline specifications

Software tools lme4, nlme
Application Mathematical modeling
Organisms Cervus elaphus