An application of an integrated population model: estimating population size of the Fortymile caribou herd using limited data

• Main Author: Inokuma, Megumi
• Other Authors: Short, Margaret, Barry, Ron, Goddard, Scott
• Format: Other/Unknown Material
• Language: English
• Published: 2017
• Subjects: Caribou; Fortymile River Watershed (Alaska and Yukon); Population; Fairbanks; Fortymile River; Yukon; Alaska
• Online Access: http://hdl.handle.net/11122/7966

Master's Project (M.S.) University of Alaska Fairbanks, 2017 An Integrated Population Model (IPM) was employed to estimate the population size of the Fortymile Caribou herd (FCH), utilizing multiple types of biological data. Current population size estimates of the FCH are made by the Alaska Department of Fish and Game (ADF&G) using an aerial photo census technique. Taking aerial photos for the counts requires certain environmental conditions, such as the existence of swarms of mosquitoes that drive the majority of caribou to wide open spaces, as well as favorable weather conditions, which allow low-altitude flying in mid-June. These conditions have not been met in recent years so there is no count estimate for those years. IPMs are considered as alternative methods to estimate a population size. IPMs contain three components: a stochastic component that explains the relationship between biological information and population size; demographic models that derive parameters from independently conducted surveys; and a link between IPM estimates and observed-count estimates. In this paper, we combine census count data, parturition data, calf and female adults survival data, and sex composition data, all of which were collected by ADF&G between 1990 and 2016. During this time period, there were 13 years - including two five-consecutive-year periods - for which no photo census count estimates were available. We estimate the missing counts and the associated uncertainty using a Bayesian IPM. Our case study shows that IPMs are capable of estimating a population size for years with missing count data when we have other biological data. We suggest that sensitivity analyses be done to learn the relationship between amount of data and the accuracy of the estimates.