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To better understand the future spread of chronic wasting disease, we conducted a genetic assessment of mule deer Odocoileus hemionuspopulation structure across the state of Montana, USA. Individual based analyses were used to test for population structure in the absence of a prioridesignations of population membership across the sampling area. Samples from the states of Wyoming, Colorado and Utah were also included in the analysis to provide a geographic context to the levels of population structure observed within Montana. Results showed that mule deer across our entire study region were characterized by weak isolation by distance and a lack of spatial autocorrelation at distances > 10 km. We found evidence for contemporary male bias in dispersal, with female mule deer exhibiting higher mean individual pairwise genetic distance than males. We tested for potential homogenizing effects of past translocations within Montana, but were unable to detect a genetic signature of these events. Our results indicate high levels of connectivity among mule deer populations in Montana and suggest few, if any, detectable barriers to mule deer gene flow or chronic wasting disease transmission.

Ungulate ecological studies often include components of reproduction because of its demographic importance and the ecological factors affecting it. Pregnancy status, in particular, is key because it represents a starting point for succeeding measurements of vital rates. Here, we present a case study using wild bison (Bison bison), in which we developed a non‐invasive method for assessing pregnancy in unmarked, non‐handled animals that improves upon existing approaches for wild ungulates. Specifically, we employed a model‐based binary logistic‐regression approach to estimate the probability of pregnancy predicted by fecal progestagen concentrations quantified from a single, late‐gestation scat sample. For 155 observations of 42 marked bison from the Jackson herd in northwest Wyoming, USA during 1997–2005, we used combinations of transrectal uterine palpation and calf status as independent measures of pregnancy to reduce the potential for error inherent in using either measure alone. We evaluated predictive success by calculating mis‐prediction rates from leave‐one‐out cross‐validation, and by calculating the percentage of 95% confidence intervals that crossed a pregnant–not‐pregnant threshold. Correct predictions, with high confidence, were obtained from a model using year‐centered, natural‐log‐transformed progestagen concentrations, resulting in an overall successful cross‐validation pregnancy prediction rate of 93.5%. Our approach will allow practitioners to consider the uncertainty associated with each prediction, thereby improving prediction interpretations. The approach should appeal to practitioners because fecal samples are easily collected and preserved, laboratory procedures are well‐documented, and logistic‐regression statistical software is readily available. Furthermore, samples can be obtained non‐invasively, which reduces cost and potential bias and increases animal safety, human safety, and social acceptability. © 2012 The Wildlife Society.