Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties.

Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m3 ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m3 ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

Selenium status and antibodies to selected pathogens in white-tailed deer (Odocoileus virginianus) in Southern Minnesota.

To determine exposure to a variety of infectious diseases potentially important for native ungulates, livestock, and humans, serum samples from 114 (94 adults, 20 fawns) female white-tailed deer (Odocoileus virginianus) were collected during January 2000-03 from multiple locations in southeast (SE) and southwest (SW) Minnesota. Antibody prevalence was determined for the following pathogens: Mycobacterium avium subsp. paratuberculosis, Leptospira interrogans (six serovars), Anaplasma marginale, Borrelia burgdorferi, Brucella abortus, epizootic hemorrhagic disease virus, and bovine viral diarrhea virus (BVDV) types 1 and 2. Samples collected in 2001 were screened for antibodies against Anaplasma phagocytophilum, and whole blood was submitted for polymerase chain reaction (PCR) testing for A. phagocytophilum and B. burgdorferi. In addition, serum selenium concentrations were evaluated for samples collected during 2001-03. Antibody prevalence and selenium concentration were compared by age-class and geographic region. Antibodies to all of the infectious agents except A. marginale and B. abortus were detected; when detected, antibody prevalence was highest in adults. Deer collected from SE Minnesota had a higher antibody prevalence to B. burgdorferi than SW deer. Blood culture and PCR results for A. phagocytophilum and B. burgdorferi were negative. Antibodies against BVDV (combined types 1 and 2) were more prevalent (chi(2) = 3.617, P< or = 0.029) in deer collected in SW (41%) than in SE (25%) Minnesota. No statistically significant differences in serum selenium concentrations were detected when data were analyzed by age-class or by geographic location.

Climate-driven effects of fire on winter habitat for caribou in the Alaskan-Yukon Arctic.

Climatic warming has direct implications for fire-dominated disturbance patterns in northern ecosystems. A transforming wildfire regime is altering plant composition and successional patterns, thus affecting the distribution and potentially the abundance of large herbivores. Caribou (Rangifer tarandus) are an important subsistence resource for communities throughout the north and a species that depends on terrestrial lichen in late-successional forests and tundra systems. Projected increases in area burned and reductions in stand ages may reduce lichen availability within caribou winter ranges. Sufficient reductions in lichen abundance could alter the capacity of these areas to support caribou populations. To assess the potential role of a changing fire regime on winter habitat for caribou, we used a simulation modeling platform, two global circulation models (GCMs), and a moderate emissions scenario to project annual fire characteristics and the resulting abundance of lichen-producing vegetation types (i.e., spruce forests and tundra >60 years old) across a modeling domain that encompassed the winter ranges of the Central Arctic and Porcupine caribou herds in the Alaskan-Yukon Arctic. Fires were less numerous and smaller in tundra compared to spruce habitats throughout the 90-year projection for both GCMs. Given the more likely climate trajectory, we projected that the Porcupine caribou herd, which winters primarily in the boreal forest, could be expected to experience a greater reduction in lichen-producing winter habitats (-21%) than the Central Arctic herd that wintered primarily in the arctic tundra (-11%). Our results suggest that caribou herds wintering in boreal forest will undergo fire-driven reductions in lichen-producing habitats that will, at a minimum, alter their distribution. Range shifts of caribou resulting from fire-driven changes to winter habitat may diminish access to caribou for rural communities that reside in fire-prone areas.

Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?

New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001-2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly versus daily) for estimating survival.