Data from: A case for considering individual variation in diel activity patterns

• Main Authors: Hertel, Anne G., Swenson, Jon E., Bischof, Richard
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2017
• Subjects: Ursus arctos; individual tactic; hunting risk; circadian activity; kernel density estimator; 2007 - 2013
• Online Access: https://doi.org/10.5061/dryad.67fn1

There is a growing recognition of the role of individual variation in patterns emerging at higher levels of biological organization. Despite the importance of the temporal configuration of ecological processes and patterns, intraspecific individual variation in diel activity patterns is almost never accounted for in behavioral studies at the population level. We used individual-based monitoring data from 98 GPS-collared brown bears in Scandinavia to estimate diel activity patterns before the fall hunting season. We extracted 7 activity measures related to timing and regularity of activity from individual activity profiles. We then used multivariate analysis to test for the existence of distinct activity tactics and their environmental determinants, followed by generalized linear regression to estimate the extent of within-individual repeatability of activity tactics. We detected 4 distinct activity tactics, with a high degree of individual fidelity to a given tactic. Demographic factors, availability of key foraging habitat, and human disturbance were important determinants of activity tactics. Younger individuals and those with higher bear and road densities within their home range were more nocturnal and more likely to rest during the day. Good foraging habitat and increasing age led to more diurnal activity patterns and nocturnal resting periods. We did not find evidence of diel activity tactics influencing survival during the subsequent hunting season. We conclude that individual variation in activity deserves greater attention than it currently receives, as it may help account for individual heterogeneity in fitness and could facilitate within-population niche partitioning that can have population- or community-level consequences. Activity Distance based categorization of successive half hour relocation intervals of all individuals as active (1) or passive (0). dist = straight line distance between locations, Sample_ID = Disguised Bear ID, Sample Year = Disguised Sample Year (1 – 7, 2007 - 2013), ...