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Fig. 5 | Movement Ecology

Fig. 5

From: An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore

Fig. 5

Stationary probabilities (mean and 95% CI) of behavioural state occupancy as a function of the environmental covariates included in the final HMMs for the a summer and b-f winter season. According to BIC model selection, the final summer model included light, landcover type, terrain ruggedness and Julian day as covariates; the final winter model included Julian day, time of day, landcover type, terrain ruggedness, snow depth, light, ambient temperature, year, distance to coast and wind speed. Probabilities were calculated for each covariate and state by fixing the values of the remaining continuous environmental covariates at their respective seasonal mean. Continuous temporal covariates were set to Julian Day 213 (i.e. August 1st) and 91 (i.e. April 1st) for summer and winter, respectively, and to12 o’clock for time of day. Categorical covariates were set to their corresponding reference categories, i.e. to bare ground (landcover type), daylight, and, for the winter model, winter 2013–2014 (year). Monte Carlo simulation from the estimator’s approximate multivariate normal distribution was used to obtain pointwise 95% CIs. Coefficients of the multinomial logistic regression underlying this figure, as well as figures for probabilities of behavioural state occupancy for different categories (e.g. sparse/dense vegetation, darkness), are provided in the supplementary materials (Additional file 1: Tables S2-S3, Figs. S12-S14)

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