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Across North America, many ungulate species that experienced historic population declines and range contractions are now broadly distributed across their native ranges after the implementation of successful restoration programs. The use of translocation continues to serve as an important restoration tool and is often informed through habitat models used to identify potential translocation areas based on biotic and abiotic landscape characteristics. Within the context of partially migratory wildlife populations, resident and migrant population segments can select for varying habitat characteristics, yet these population segments are often pooled when building habitat models. We used a large spatial dataset collected from eight bighorn sheep (Ovis canadensis) populations to build separate winter and summer resource selection models for migrant and resident animals with the purposes of (1) characterizing differences in seasonal selection patterns between resident and migrant population segments and (2) generating broad spatial predictions of bighorn sheep habitat to inform future translocations across western Montana, USA. Lastly, we used the model to inform two potential management scenarios, first to establish a new population in an unoccupied area and second to expand the distribution of existing populations through intra‐mountain translocations. Selection patterns were generally similar among migrants and residents, especially in winter. Summer selection patterns varied between the two behaviors, with migrants selecting for higher elevations and residents selecting for lower elevations. Throughout the western Montana prediction area, bighorn sheep habitat was centered around mountain areas in all seasons. In the first management scenario, our model predicted that adequate resident and migratory bighorn sheep habitat existed in the restoration area, thus justifying the use of either resident or migrant source populations in translocation. In the second management scenario, our model predicted that there was broad potential for translocations into unoccupied areas adjacent to the current bighorn sheep distribution in western Montana. The behavior‐specific approach to predicting bighorn sheep seasonal habitat captures the specific habitat characteristics of multiple migratory behaviors and may help to inform targeted and effective translocation programs. [ABSTRACT FROM AUTHOR]

Ecological theory and empirical studies have demonstrated population‐level demographic benefits resulting from a diversity of migratory behaviors with important implications for ecology, conservation, and evolution of migratory organisms. Nevertheless, evaluation of migratory portfolios (i.e., the variation in migratory behaviors across space and time among individuals within populations) has received relatively little attention in migratory ungulates, where research has focused largely on the dichotomous behaviors (e.g., resident and migrant) of partially migratory populations. Using GPS data from 361 female bighorn sheep (Ovis canadensis) across 17 (4 restored, 6 augmented, 7 native) populations in Montana and Wyoming, USA, we (1) characterized migratory portfolios based on behavioral and spatial migratory characteristics and (2) evaluated the relative influence of landscape attributes and management histories on migratory diversity. Native populations, which had been extant on the landscape for many generations, had more diverse migratory portfolios, higher behavioral switching rates, reduced seasonal range fidelity, and broad dispersion of individuals across summer and winter ranges. In contrast, restored populations with an abbreviated history on the landscape were largely non‐migratory with a narrow portfolio of migratory behaviors, less behavioral switching, higher fidelity to seasonal ranges, and less dispersion on summer and winter ranges. Augmented populations were more variable and contained characteristics of both native and restored populations. Differences in migratory diversity among populations were associated with management histories (e.g., restored, augmented, or native). Landscape characteristics such as the duration and regularity of green‐up, human landscape alterations, topography, and snow gradients were not strongly associated with migratory diversity. We suggest a two‐pronged approach to restoring migratory portfolios in ungulates that first develops behavior‐specific habitat models and then places individuals with known migratory behaviors into unoccupied areas in an effort to bolster migratory portfolios in restored populations, potentially with synergistic benefits associated with variation among individuals and resulting portfolio effects. Management efforts to restore diverse migratory portfolios may increase the abundance, resilience, and long‐term viability of ungulate populations. [ABSTRACT FROM AUTHOR]

The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring., Competing Interests: This study was supported in part through funds that originated from a commercial funding source, Canon Inc., but were awarded by the nonprofit partner of Yellowstone National Park, Yellowstone Forever (formerly the Yellowstone Park Foundation). This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. The authors declare no other competing interests.

Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics., Competing Interests: The authors have declared that no competing interests exist.