title: Can modeling improve estimation of desert tortoise population densities?
abstract: The federally listed desert tortoise (Gopherus agassizii) is currently monitored using distance sampling to estimate population densities. Distance sampling, as with many other techniques for estimating population density, assumes that it is possible to quantify the proportion of animals available to be counted in any census. Because desert tortoises spend much of their life in burrows, and the proportion of tortoises in burrows at any time can be extremely variable, this assumption is difficult to meet. This proportion of animals available to be counted is used as a correction factor (g0) in distance sampling and has been estimated from daily censuses of small populations of tortoises (6-12 individuals). These censuses are costly and produce imprecise estimates of go due to small sample sizes. We used data on tortoise activity from a large (N = 150) experimental population to model activity as a function of the biophysical attributes of the environment, but these models did not improve the precision of estimates from the focal populations. Thus, to evaluate how much of the variance in tortoise activity is apparently not predictable, we assessed whether activity on any particular day can predict activity on subsequent days with essentially identical environmental conditions. Tortoise activity was only weakly correlated on consecutive days, indicating that behavior was not repeatable or consistent among days with similar physical environments.
authors.affiliation: U.S. Geological Survey, Western Ecological Research Center, Henderson, Nevada 89074, USA. knussear@usgs.gov
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Nussear
authors.forename: C Richard
authors.initials: CR
authors.lastname: Tracy
mesh_terms: D000818:Animals; D003889:Desert Climate; D017753:Ecosystem; D008954:Models, Biological; D016571:Neural Networks, Computer; D011156:Population Density; D013997:Time Factors; D014426:Turtles / Q000502:physiology*