Graduation Date

Spring 2021

Document Type



Master of Science degree with a major in Natural Resources, option Wildlife

Committee Chair Name

Dr. Micaela Szykman Gunther

Committee Chair Affiliation

HSU Faculty or Staff

Second Committee Member Name

Ms. Carrington Hilson

Second Committee Member Affiliation

Community Member or Outside Professional

Third Committee Member Name

Dr. William Bean

Third Committee Member Affiliation

Community Member or Outside Professional

Fourth Committee Member Name

Dr. Daniel Barton

Fourth Committee Member Affiliation

HSU Faculty or Staff


Wildlife, Bayesian, Population, Abundance, Spatial capture-recapture, Roosevelt elk, Detection dog

Subject Categories

Wildlife Management


Determining abundance of Roosevelt elk (Cervus canadensis roosevelti) in central Humboldt County, California has presented a unique challenge to wildlife managers due to the dense forest habitat and the animals’ elusive behavior. As the elk population has increased, so has human-wildlife conflict, and wildlife agencies need efficient and repeatable methods for determining abundance to inform management decisions. Traditional monitoring methods such as helicopter surveys are ineffective due to low sighting probability and strong behavioral responses to the aircraft. They also often lead to biased sex ratios when the distribution of males and females varies across the landscape. Non-invasive genetic sampling combined with spatial capture-recapture (SCR) is an alternative approach to monitoring populations that are difficult to observe directly. This study combined a Bayesian SCR with a binomial point process modeling approach and an unstructured single survey search method to estimate elk abundance. We aimed to increase the count of males by using a detection dog to search forested areas, and searched open grassy hillsides for cow-calf groups. Additionally, GPS collar data were used to quantify cohesion of movement among elk through a spatiotemporal analysis of home ranges. Over two seasons, we genotyped 436 unique individuals (326 females, 110 males). For the SCR analysis, we used sex and survey effort as covariates in detection probability, and used a “trap”-level random effect to account for the overdispersion in the count data from the herding behavior of elk. The population estimate in the study area was 618 ± 36.34 individuals (95% BCI 551-693) with a density of 1.09 ± 0.06 elk per km2. This study demonstrated a reliable way to obtain a biological reasonable population estimate for elk in an area that is not conducive to traditional monitoring methods.

Citation Style

Journal of Wildlife Management


Thesis/Project Location


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