DOI: 10.5061/dryad.67fn1
title: Data from: A case for considering individual variation in diel activity patterns

description: 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.
description: ActivityDistance 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), Sample_Bearyear = Identifier for a given bear being sampled in a given year. To protect ongoing publication efforts x/y coordinates and actual BearIdentifications have been removed.ExtractedActivityDensCoefEstimates of activity density as a function of time of day (timeRad) for each Sample Bearyear.TimeRad = time in radians; ActivityDensity = estimated activity denisty extracted from denisty curves constructed using the function "densityPlot" from the overlap package (Meredeith & Ridout 2014); AverageCoef = Average Activity Density; Coef_Dev = Deviation from the average density coefficient. Negative numbers indicate that the individual was more often inactive than active at the given time, positive values that the individual was more often active than inactive. Sample_ID = Disguised Bear ID, Sample Year = Disguised Sample Year (1 – 7, 2007 - 2013), Sample_Bearyear = Identifier for a given bear being sampled in a given year. To protect ongoing publication efforts actual BearIdentifications have been removed.ForPCASeven activity measures extracted from the activity density curve (TimeMinAct, TimeMaxAct, Variability, AUCRisk, PropRiskAct, TimeActive), age sex, fate and environmental covariates measured within the home range of an individual (bear.density, proportion berry habitat, road density)ForTacticRepeatFor individuals which were observed in multiple years we determined the focal strategy (the strategy most used by an individual). For each year we then categorized the observed strategy (“cluster”) as focal or not (“in.focal” = TRUE/FALSE). We further simulated 10 random assignments (real = FALSE) into either of the four “clusters” which were then also categorized as belonging to the focal strategy or not (“in focal”).