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Between 1925 and 1930, 11 or 12 non‐native mountain goats (Oreamnos americanus) were translocated from Alaska and British Columbia to the foothills of the Olympic Range. By 1970, descendants of these goats had colonized the entire range and concerns about the management of this introduced species developed as damage to alpine soil and vegetation occurred. A series of removals reduced the population from 1175 in 1983 to 389 by 1990, eventually growing to 584 in 2016. We used demographic and genetic data to parameterize a population genetics individual‐based simulation model of the Olympic Range mountain goats. We calibrated the model to replicate the population trajectory for Olympic mountain goats from establishment in the 1920s through the 1983 first census. As expected, modeled population dynamics from 1928 to 1983 mimicked parameter initialization from expanding populations. However, simulated heterozygosity did not align with observations, suggesting a process not accounted for within the simulation model, such as a bottleneck or founder effect. Sensitivity analyses showed changes in annual reproductive rate, juvenile mortality, and adult female mortality influencing population trajectories, but variation in male mortality revealed no changes. Evaluating the population dynamics of the model after removals showed that approximately 80% of the total animals removed during the 1980s needed to be female in order for the observed population estimates to occur. This model has the potential to be used more widely with established or introduced mountain goat populations, as well as to provide an approach for studying other introduced species and their population dynamics.
Between 1925 and 1930, 11 or 12 non‐native mountain goats (Oreamnos americanus) were translocated from Alaska and British Columbia to the foothills of the Olympic Range. By 1970, descendants of these goats had colonized the entire range and concerns about the management of this introduced species developed as damage to alpine soil and vegetation occurred. A series of removals reduced the population from 1175 in 1983 to 389 by 1990, eventually growing to 584 in 2016. We used demographic and genetic data to parameterize a population genetics individual‐based simulation model of the Olympic Range mountain goats. We calibrated the model to replicate the population trajectory for Olympic mountain goats from establishment in the 1920s through the 1983 first census. As expected, modeled population dynamics from 1928 to 1983 mimicked parameter initialization from expanding populations. However, simulated heterozygosity did not align with observations, suggesting a process not accounted for within the simulation model, such as a bottleneck or founder effect. Sensitivity analyses showed changes in annual reproductive rate, juvenile mortality, and adult female mortality influencing population trajectories, but variation in male mortality revealed no changes. Evaluating the population dynamics of the model after removals showed that approximately 80% of the total animals removed during the 1980s needed to be female in order for the observed population estimates to occur. This model has the potential to be used more widely with established or introduced mountain goat populations, as well as to provide an approach for studying other introduced species and their population dynamics.
Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
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