Set of generalized linear mixed effect models (Restricted Estimate of Maximum Likelihood) with different random structures and different measures of elk age, either allowing individuals to change behaviour between years or not.

<p>Elk have been monitored for multiple years, and the terminology ‘true age’ implies the actual age of the elk in a given year. The term ‘age at capture’ implies the age of the elk kept constant to that recorded at the beginning of the monitored period. ‘True age’ allows models to account for behavioural adjustments with age (learning), while ‘age at capture’ does not allow depicting learning processes. The 5 <i>a priori models</i> were run to explain the variability of three different response variables (log step-length, use of terrain ruggedness, use of forest). The top ranked structure (#5) selected using AIC was the same for all response variables. Because model selection was performed on models with different random effect structures, we opted to use the number of levels of the random effects minus 1 as the punishment for added random effects when calculating the AIC.</p

Movement rate (step-length, i.e., distance in meters travelled every 2 hours, log-transformed) in female elk as a function of age (range 1–20 years old) and hunting regime (no-hunting, bow, and rifle) as predicted by the linear mixed effect model.

<p>Ninety-five percent marginal confidence intervals are shown as shaded areas [sample size: n = 49 female elk, each of them contributing with telemetry relocations collected over 2 consecutive years].</p

Comparison of three sets (1 = log step-length, 2 = use of terrain ruggedness, 3 = use of forest by female elk as response variables, respectively) of Generalized Linear Mixed Models.

<p>The structure of the fixed component of the model was constant across models (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178082#pone.0178082.t001" target="_blank">Table 1</a> footnotes) with the only exception of age (not included, included) and age interacted with human-activity proxies (time of the day, distance from road, hunting season, and time of the week). All models had a random slope for true age and a random intercept for individual elk, as well as a random intercept for year–i.e., the best random effect structure selected in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178082#pone.0178082.t001" target="_blank">Table 1</a> –and were fit with Maximum-Likelihood estimation. Models indicated by an asterisk accounted for more than 0.90 of the Akaike weights and were further inspected for model averaging (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178082#pone.0178082.s003" target="_blank">S3 Table</a>).</p

Use of terrain ruggedness (in meters) in female elk as a function of age (range 1–20 years old) and hunting regime (no-hunting, bow, and rifle) as predicted by the linear mixed effect model.

<p>Ninety-five percent marginal confidence intervals are shown as shaded areas [sample size: n = 49 female elk, each of them contributing with telemetry relocations collected over 2 to 5 consecutive years].</p

Use of terrain ruggedness (in meters) in female elk as a function of age (range 1–20 years old) and distance to road (distance higher or lower than 500 meters) as predicted by the linear mixed effect model.

<p>Ninety-five percent marginal confidence intervals are shown as shaded areas [sample size: n = 49 female elk, each of them contributing with telemetry relocations collected over 2 to 5 consecutive years].</p

Use of forest (0 = no forest, 1 = forest) in female elk as a function of age (range 1–20 years old) and distance to road (distance higher or lower than 500 meters) as predicted by the generalized linear mixed effect model.

<p>Ninety-five percent marginal confidence intervals are shown as shaded areas [sample size: n = 49 female elk, each of them contributing with telemetry relocations collected over 2 to 5 consecutive years].</p

Use of terrain ruggedness (in meters) in female elk as a function of age (range 1–20 years old) and time of the day (night, dawn, day, and dusk) as predicted by the linear mixed effect model.

<p>Ninety-five percent marginal confidence intervals are shown as shaded areas [sample size: n = 49 female elk, each of them contributing with telemetry relocations collected over 2 to 5 consecutive years].</p

Dispersal Ecology Informs Design of Large-Scale Wildlife Corridors - Fig 6

<p>Corridor-road intersections of a) 168.8 km in spring considering SSFs predictions computed with actual distance to roads, b) 355.5 km in spring assuming as there were no roads (i.e., distance to roads set to maximum value when predicting SSFs), c) 172.9 km in autumn considering roads, and d) 379.5 km in autumn assuming as there were no roads. Maps b) and d) depict road segments that would be crossed by elk if there were no roads. About half of these segments are predicted not to be crossed by elk (maps a, c) as a result of road avoidance by elk.</p

Conceptual figure illustrating the rationale behind the approach described in this study.

<p>The example refers to the identification of a wildlife corridor connecting two core areas, <i>i</i>.<i>e</i>., a hypothetical elk winter range and a new one reached after a dispersal event. Typical connectivity models (a) would depict the least cost corridor connecting the two areas, which is the most likely path given a friction map on the background. Our approach highlights the importance to implement behavioural ecology (in this case, dispersal ecology) into connectivity modelling science. A young male elk usually migrates during late spring—early summer with the mother’s group, moving from the natal winter range to the early summer range (b). During summer, the young elk may disperse to a new suitable summer home range (c), and, in autumn, eventually move to the new winter range (d). If the animal will adopt the migratory strategy, then it will periodically migrate between the new winter range and the new summer range (d). Implementing dispersal ecology into connectivity modelling means combining a sequence of migratory and dispersal movements (e, resulting from b + c + d), which suggests a very different potential wildlife corridor (e) compared to the one predicted by simply connecting the two winter ranges (a).</p