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Abstract Snow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods.

ABSTRACT One tool used for wildlife management is the deployment of radiocollars to gain knowledge of animal populations. Understanding the influence of individual factors (e.g., species, collar characteristics) and landscape characteristics (e.g., forested cover, shrubs, and fencing) on retention of expandable radiocollars for ungulates is important for obtaining empirical data on factors influencing ecology of young‐of‐the‐year ungulates. During 2001–2009, we captured and radiocollared 198 white‐tailed deer (Odocoileus virginianus) fawns, 142 pronghorn (Antilocapra americana) fawns, and 73 mule deer (O. hemionus) fawns in South Dakota, Minnesota, and California, USA. We documented 72 (36.4%), 8 (5.6%), and 7 (9.6%) premature (

Abstract Despite widespread use of fecal pellet‐group counts as an index of ungulate density, techniques used to convert pellet‐group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet‐group count and count of Sitka black‐tailed deer (Odocoileus hemionus sitkensis) during a 3‐year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2 of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed‐effects generalized linear model to analyze expected deer count as a function of pellet‐group count. Pellet‐group count was a significant predictor of DNA‐based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in our models was residual and unexplained. Assuming that our DNA‐based results were a more accurate and precise metric of true deer count, we do not support the use of pellet‐group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA‐based methods, for monitoring trends in ungulate populations. © 2013 The Wildlife Society.