Temporal and Procedural Influences on Activity Estimated by Time-Sampling

Abstract

We used computer simulations to evaluate the direction and extent of error of time-sampling activity during different times of day, stages of growth, or seasons. Estimated total time-in-activity (TIA) of captive white-tailed deer (Odocoileus virginianus) and duration of activity bouts were compared with actual values obtained from 24-hour continuously telemetered records of activity. In studies of suckling fawns, and time-sampling had the highest correspondence between estimated and actual values of TIA when intervals between sampling were 20 min elapsed between samples. Actual TIA was misrepresented more often during the night. In seasonal studies, TIA significantly differed from actual values only when estimated by Poorest estimates of TIA occurred at sample intervals a>20 min and declined to 70% of actual values. Use of time-sampling to estimate average duration of activity bouts often underand overestimated the continuous measure of fawns and over the annual cycle (from 0 to 10.02 and 0.67 to 5.14 times actual, respectively). Frequency distributions of bout-lengths of activity of deer were adequately modeled by the gamma-type density function. J. WILDL. MANAGE. 46(2):313-324 Accurate determinations of the time individual animals expend in activity are needed to develop theoretical models of time-energy budgets according to sex, age, and season, and to evaluate the possible effects of environmental perturbations on wildlife. Remote sensing of activity by biotelemetry has increased rapidly in the past decade as a method for studying activity-time budgets of animals in captivity and the wild (Mackay 1970; Fryer et al. 1976; Long 1977, 1979; Carneggie and Marmelstein 1978). Use of a telemetered analog of activity often provides information about intractable or elusive animals that cannot be obtained otherwise. Collecting behavioral data by telemetric monitoring for sample times that are short in comparison with the long periods between samples is a form of time-sampling (Altmann 1974, Tyler 1979). Commonly, radio-tagged animals are monitored for less than 60 seconds at intervals of 5 minutes or longer (e.g., Jackson et al. 1972, Craighead et al. 1973). Duration and frequency of the samples are related to both the number of animals studied and to the ability to identify each behavioral state of activity or inactivity from the telemetered analog. Because the behavior of the animal for most of the sample determines the state of activity or inactivity assigned, the procedure is similar to the visual sampling method of predominant activity (Hutt and Hutt 1970). With biotelemetry, however, some uncertainty is often associated with the analog monitored due to changes in signal character, strength, or pulses recorded, or because of concern that a system bias may exist that favors 1 of the behavioral states. Consequently, the sample may be assigned to activity or inactivity only if a predetermined sequence or chain of behavioral states follow in 1 or more subsequent samples (Jackson et al. 1972, Quigley et al. 1979). We call the 2 kinds of telemetric timesampling and conditional. When the behavioral state is assigned at the time of sampling, it is instantaneous time-sampling. If future samples must be inspected before the behavioral state J. Wildl. Manage. 46(2):1982 313 This content downloaded from 157.55.39.170 on Wed, 19 Oct 2016 03:57:43 UTC All use subject to http://about.jstor.org/terms 314 INFLUENCES ON TIME-SAMPLING Jacobsen and Wiggins is assigned, it is conditional time-sampling. It has been established in laboratoryoriented, short-term studies that different kinds of time-sampling can generate values differing from each other and from the actual record (Arrington 1943, Hayes et al. 1970, Powell et al. 1977, Tyler 1979). To our knowledge, the problems of sampling activity have not been investigated in ungulates monitored during different times of the day and stages of behavioral development or seasons. By referring to empirical records of 24-hour activity of captive deer in our simulations, we have evaluated the direction and extent of error that different kinds of time-sampling have in estimating timein-activity (TIA) and average duration of activity (bout-lengths). We appreciate the programming of semi-Markov processes by L. Tai and the help of S. Minta in the computer analyses. W. Armstrong, R. Reynolds, and A. Moen provided the facilities and deer used to obtain the data base; the help of others in caring for animals, assisting in various stages of data reduction, and the financial support provided are detailed in Jacobsen (1973, 1979). Preparation of this report was supported by the College of Agricultural and Environmental Sciences, University of California, Davis, and Hatch Project 3402.