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 across management zones makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. For large mobile species, aerial surveys are the gold standard used to obtain estimates of abundance, particularly in geographic regions with harsh terrain. However, aerial 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. We build upon existing multi-state mark-recapture methods using a hierarchical Bayesian N-mixture model with multiple sources of commonly collected data, to estimate the abundance of a mobile population of large animals that use conservation areas with fixed boundaries. We used a state-space approach to model animal movements using telemetry data 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 the advantage of combining multiple sources of data to substantially improve abundance estimates of ground based surveys compared to existing methods. We corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. This work provides an innovative way to estimate abundance of ungulate species in remote areas using ground based survey data. 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 game species that use remote conservation areas. We notably improve inference from pre-existing methods to aid in management decisions of an important game species.