The scent-station survey method has been widely used to estimate trends in carnivore abundance. However, statistical properties of scent-station data are poorly understood, and the relation between scentstation indices and carnivore abundance has not been adequately evaluated. We assessed properties of scentstation indices by analyzing data collected in Minnesota during 1986-93. Visits to stations separated by <2 km were correlated for all species because individual carnivores sometimes visited several stations in succession. Thus, visits to stations had an intractable statistical distribution. Dichotomizing results for lines of 10 stations (0 or ?1 visits) produced binomially distributed data that were robust to multiple visits by individuals. We

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European settlement led to extirpation of native Audubon's bighorn sheep (formerly Ovis canadensis auduboni) from North Dakota during the early 20th century. The North Dakota Game and Fish Department subsequently introduced California bighorn sheep (formerly O. c. californiana) that were indigenous to the Williams Lake region of British Columbia, Canada, and Rocky Mountain bighorn sheep (O. c. canadensis) that were indigenous to the Sun River region of Montana. Although California bighorn sheep are no longer recognized as a distinct subspecies, they are smaller and adapted to a milder climate than either the native bighorn sheep of North Dakota or introduced bighorn sheep from Montana. Because reintroductions still play a key role in the management of bighorn sheep and because local adaptation may have substantial demographic consequences, we evaluated causes of variation in recruitment of bighorn sheep reintroduced in North Dakota. During 2006–2011, Montana stock recruited 0.54 juveniles/adult female (n = 113), whereas British Columbia stock recruited 0.24 juveniles/adult female (n = 562). Our most plausible mixed‐effects logistic regression model (53% of model weight) attributed variation in recruitment to differences between source populations (odds ratio = 4.5; 90% CI = 1.5, 15.3). Greater recruitment of Montana stock (fitted mean = 0.56 juveniles/adult female; 90% CI = 0.41, 0.70) contributed to a net gain in abundance (r = 0.15), whereas abundance of British Columbia stock declined (fitted mean = 0.24 juveniles/adult female; 90% CI = 0.09, 0.41; r = − 0.04). Translocations have been the primary tool used to augment and restore populations of wild sheep but often have failed to achieve objectives. Our results show that ecotypic differences among source stocks may have long‐term implications for recruitment and demographic performance of reintroduced populations. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Accurate maps of species distributions are essential tools for wildlife research and conservation. Unfortunately, biologists often are forced to rely on maps derived from observed occurrences recorded opportunistically during observation periods of variable length. Spurious inferences are likely to result because such maps are profoundly affected by the duration and intensity of observation and by methods used to delineate distributions, especially when detection is uncertain. We conducted a systematic survey of swift fox (Vulpes velox) distribution in western Kansas, USA, and used Markov chain Monte Carlo (MCMC) image restoration to rectify these problems. During 1997–1999, we searched 355 townships (ca. 93 km) 1–3 times each for an average cost of $7,315 per year and achieved a detection rate (probability of detecting swift foxes, if present, during a single search) of = 0.69 (95% Bayesian confidence interval [BCI] = [0.60, 0.77]). Our analysis produced an estimate of the underlying distribution, rather than a map of observed occurrences, that reflected the uncertainty associated with estimates of model parameters. To evaluate our results, we analyzed simulated data with similar properties. Results of our simulations suggest negligible bias and good precision when probabilities of detection on ≥1 survey occasions (cumulative probabilities of detection) exceed 0.65. Although the use of MCMC image restoration has been limited by theoretical and computational complexities, alternatives do not possess the same advantages. Image models accommodate uncertain detection, do not require spatially independent data or a census of map units, and can be used to estimate species distributions directly from observations without relying on habitat covariates or parameters that must be estimated subjectively. These features facilitate economical surveys of large regions, the detection of temporal trends in distribution, and assessments of landscape‐level relations between species and habitats. Requirements for the use of MCMC image restoration include study areas that can be partitioned into regular grids of mapping units, spatially contagious species distributions, reliable methods for identifying target species, and cumulative probabilities of detection ≥0.65.