Results of in vitro digestibility trials indicate that big sagebrush(Artemisia tridentata) is a highly digestible browse for wintering mule deer. Subspecies tridentata (62.lYo digested dry matter) was more highly digested than subspecies vaseyana (53.2% digested dry matter) and subspecies wyomingensis (51.4% digested dry matter).
'Artemisia tridentata. 2Rose hips-sweetbrier-Rosa eglanteriaCurlleaf-curlleaf
mahogany-Cercocarpus ledifoliusMahogany-true mahogany-Cercocarpus
Highlight: Doe-fawn counts show that the mule deer herd on the LaSal Mountains of southeastern Utah produced over 38% more fawns per doe than the Henry Mountain herd over a 9-year period. Carcass weights of animals from the LaSal herd were generally greater for all age classes. Observed reproductive differences appear to be unrelated to the incidence of diseases, parasites, or predation. Furthermore, winter ranges are nearly equal in forage quantity and quality on the two ranges. Summer range vegetation on the LaSal Mountains, however, produced more forage of better quality than did similar community types on the Henry Mountains. LaSal summer ranges produced 2,149 kg/ha fresh weight of available forage while similar ranges on the Henrys produced only 1,314 kg/ha. Forbs account for 52% of the forage on LaSal summer ranges but only 12% of the forage on ranges of comparable elevation on the Henrys. The data suggest that the characteristics of the forage found on the summer range, especially the quantity and quality of forbs, exert important influences on productivity of these herds.
Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km 2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial
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