Beyond the proportional frailty model: Bayesian estimation of individual heterogeneity on mortality parameters

Recent Trends in Survival and Mortality of Wolves in Minnesota, United States

Survival is a key determinant of population growth and persistence; computation and understanding of this metric is key to successful population management, especially for recovering populations of large carnivores such as wolves. Using a Bayesian frailty analytical approach, we evaluated information from 150 radio-tagged wolves over a 16-year time period to determine temporal trends and age- and sex-specific survival rates of wolves in Minnesota, United States. Based on our analyses, overall annual survival of wolves during the study was 0.67, with no clear evidence for age- or sex-specific differences in the population. Our model demonstrated statistical support for a temporal trend in annual survival; the highest survival was predicted at the beginning of the time series (0.87), with lowest survival (0.55) during 2018. We did not observe evidence that survival was markedly reduced during years when a regulated hunting and trapping season was implemented for wolves (years 2012–2014). However, cause-specific mortality analysis indicated that most mortality was human-caused. While the estimate for increasing human-caused mortality over time was positive, the evidence was not statistically significant. Anthropogenic causes resulted in ∼66% of known mortalities, including legal and illegal killing, and vehicular collisions. Trends in wolf survival in Minnesota may reflect an expanding distribution; wolf range has spread to areas with more human development during the study, presumably leading to increased hazard and reduced survival. Our results provide foundational information for evaluating and guiding future policy decisions pertaining to the Great Lakes wolf population.