| Summary: | Informing conservation and management decisions for habitats frequented by species of high management interest often face the challenge of limited resources for conducting wildlife surveys. When surveys are focused on local areas or sparsely distributed species, it may also be difficult to obtain counts sufficient for implementing abundance models that account for imperfect detection. With replicated aerial surveys collected within a 70.25 km 2 portion of the Eastern Alaska Range, Alaska, USA during the summers of 2013–2015, we estimated daily abundance of Dall's sheep using two different estimation methods: Bayesian N -mixture models and Poisson regression models. We then compared estimates of relative abundance from both model types while paying special attention to the assumption of closure within individual survey units. With abundance estimates obtained from individual survey days, we then estimated the average number of Dall's sheep within the survey area for the period 1 July–1 October. Daily ewe abundance followed a quadratic pattern, with 10–20 ewes being within our survey area in early July and late September, and approximately 90 ewes within the survey area in mid-August. Lamb to ewe ratios averaged 0.2 from July–September, while ram to ewe ratios averaged 0.4 from July until mid-August before increasing to about 1.0 by the end of September. These results indicate that our survey area is an important habitat to local Dall's sheep populations when lambs are vulnerable to predators. Accordingly, human recreation and military training within the survey area should be minimized 1.5–3.0 months after parturition to minimize disturbance. We also found that N -mixture models displayed a pattern of abundance estimates that increased in magnitude as model complexity increased. We thus recommend an a priori approach to N -mixture model construction that balances the risk of overfitting models to modest data against the risk of fitting models that do not explain heterogeneity in abundance and detection ...
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