Finite block kriging with detection functions: Development of a new model for estimating the abundance of moose in Alaska by building on the Geospatial Population Estimator

The US Fish and Wildlife Service, Region 7 intends to award a single source financial assistance agreement as authorized by 505 DM 2.14 (B) to Oregon State University. This notice is not a request for proposals and the Government does not intend to accept proposals. This financial assistance opportunity is being issued under the Cooperative Ecosystem Studies Unit (CESU) Network: (http://www.cesu.psu.edu/materials/partners.htm). The CESU network provides research, technical assistance, and education to federal land management, environmental, and research agencies and their partners. The partners serve the biological, physical, social, cultural, and engineering disciplines needed to address natural and cultural resource management issues at multiple scales and in an ecosystem context. Description of Funding Opportunity Project Title: Finite Block Kriging with Detection Functions: Development of a new model for estimating the abundance of moose in Alaska by building on the Geospatial Population Estimator. The Geospatial Population Estimator (GSPE), also known as finite block kriging for plot-based sampling (Ver Hoef 2008), has been widely used in Alaska to estimate the abundance of moose. The method provided relatively unbiased and precise estimates of abundance when data were collected during recommended conditions, e.g., adequate snow cover (Kellie and Delong 2006). During the past five years, however, adequate snowfall has been unreliable throughout most of Alaska. The probability of detecting a moose is known to vary with snow conditions. As such, data are currently being collected by the USFWS, using radio-collared animals, to estimate the probability of detecting a moose for a given sample unit using a logistic model. The model could be used to reduce bias thought to occur when estimating moose in less than ideal conditions. To correctly estimate the precision of our moose population estimates, however, a model must be derived that correctly estimates the variance of the GSPE with a detection function. The objective of this funding opportunity is to develop a model that incorporates a detection function into the GSPE. The model will be used to predict moose abundance under a range of conditions known to affect the count of moose in western Alaska.