Abstract. Inventory and Monitoring Programs in the National Park Service (NPS) provide informationneeded to support wise planning, management, and decision making. Mathematical and statistical models play a critical role in this process by integrating data from multiple sources in a way that is honest about uncertainty. We show the utility of Bayesian hierarchical models for supporting decisions on managing natural resources of national parks. These models can assimilate monitoring data to provide true forecasts, resulting in probabilistic predictions of future states of park ecosystems accompanied by rigorous estimates of uncertainty. We discuss a novel approach for communicating these forecasts to decision makers who need to evaluate the probability that NPS goals will be met given different management actions, including the null model of no action. We illustrate how this approach has been used successfully to inform decisions on the elk (Cervus elaphus nelsoni) population management in Rocky Mountain National Park based on 47 yr of monitoring data. Forecasts from a discrete time, stage-structured population model assimilated with annual census and sex and age classifications are being used annually to help park managers decide on actions needed to meet goals for elk and vegetation. In particular, park managers were able to determine the probability that the elk population would fall within a desired population range, which led to both population reduction actions and no action depending on the year of interest. Moreover, this approach allowed multiple survey methodologies from the last 47 years to be incorporated into a single model with associated estimates of uncertainty. Models like this one are especially useful for adaptive management where continuous improvement in models and data results in long-term improvement in the wisdom of policy and management.

Chronic wasting disease (CWD) is a contagious prion disease affecting four species of free-ranging and captive cervids in North America. Geographic detection and distribution of CWD notably increased after 2002, although the disease has been present in North America since at least the 1960s. CWD is characterized by a prolonged course of individual infection, lengthy epizootics that last for decades, and delayed population effects until after prevalence has reached a sufficient level. We comprehensively reviewed and synthesized the available literature on CWD to assess the current state of the science on disease dynamics and population impacts. We examine transmission dynamics and mechanisms, geographic spread, infection patterns, genetics of hosts, disease effects on demographic rates and ultimately the effects on population growth rates. The early phase of a CWD epizootic is characterized by slowly increasing prevalence and geographic spread, but these eventually accelerate and lead to declines in survival and recruitment that drive population reductions. The threshold for these population impacts depends on species-specific demography, genetics, transmission and numerous factors influencing cervid infection and mortality. As prevalence and spread continue to accelerate, management actions to mitigate CWD impacts will be challenging, costly and will likely require changes in how we manage cervid populations.

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground-based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark-recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

1. Pathogens can cause host extinction, affect population dynamics and influence natural selection. Host susceptibility to pathogens can vary by species, demographics and genetics which affect epizootic and population dynamics, ultimately determining population trends and evolution.2. Chronic wasting disease (CWD), a fatal neuro-degenerative prion disease of cervids, has varying host susceptibility conferred by polymorphisms of the prion protein gene (PRNP) at codon 96 for white-tailed deer Odocoileus virginianus.3. Deer with the homozygous Glycine allele (96GG) are most susceptible and a single Serine allele (96GS/96SS) reduces the risk of infection and mortality.4. We developed epizootiological models that demonstrate CWD infection and disease-associated mortality were higher for the more susceptible (96GG) genotype; and, infection was higher for males than females. We used population models to evaluate future shifts in genotype frequencies under alternative harvest and infection rate scenarios.5. Genetic shifts towards less susceptible genotypes were predicted as CWD prevalence increased during the course of an outbreak. This further increased CWD prevalence, and likely environmental contamination from prion shedding, due to longer incubation periods. Alternative harvest management strategies directly influenced CWD prevalence and spread, the rate of genetic selection and deer population growth.6. Synthesis and applications. We show that chronic wasting disease (CWD) transmission varied by sex, age class and PRNP genotype, and that CWD diseaseassociated mortality varied by genotype. Together, these forces lead to CWD-mediated genetic selection for a white-tailed deer population. We predict that genetic selection pressure increases when hunter harvest pressure is lowered, and conversely, increasing hunter harvest can reduce genetic selection rates of antlered deer. Our results support the control of CWD prevalence by aggressive harvest of adult males, because they have the highest infection rates.Our results have strong implications for evolution, disease ecology, geographical spread, disease mitigation and cervid population management.

Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these “partial” observations must be modified to include the missing data mechanism to avoid spurious inference. We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data. We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk (Cervus elaphus nelsoni) to demonstrate improved inference for the proportions of sex and stage classes. We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.