You are here

Evidence of Alaskan Trumpeter Swan Population Growth Using Bayesian Hierarchical Models

TitleEvidence of Alaskan Trumpeter Swan Population Growth Using Bayesian Hierarchical Models
Publication TypeJournal Article
Year of Publication2008
AuthorsSchmidt, JH, Lindberg, MS, Johnson, DS, Conant, B, King, J
JournalThe Journal of Wildlife Management
Volume73
Pagination720–727
Abstract

Alaska (USA) contains a large proportion of the breeding population of trumpeter swans (Cygnus buccinator) in the UnitedStates. However, tracking population trends in Alaska trumpeter swans is complicated by variables such as an increase in survey effort over time,periodic surveys (1968 and every 5 yr after 1975), and missing data. We therefore constructed Bayesian hierarchical negative binomial models toaccount for nuisance variables and to estimate population size of trumpeter swans using aerial survey data from all known breeding habitats inAlaska, 1968–2005. We also performed an augmented analysis, where we entered zeroes for missing data. This approach differed from thestandard (nonaugmented) analysis where we generated estimates for missing data through simulation. We estimated that adult swanpopulations in Alaska increased at an average rate of 5.9% annually (95% credibility interval¼5.2–6.6%) and cygnet production increased at5.3% annually (95% credibility interval ¼ 2.2–8.0%). We also found evidence that cygnet production exhibited higher rates of increase athigher latitudes in later years, which may be a response to warmer spring temperatures. Augmented analyses always produced higher swanpopulation estimates than the nonaugmented estimates and likely overestimate true population abundance. Our results provide evidence thattrumpeter swan populations are increasing in Alaska, especially at northern latitudes. Changes in population size and distribution couldnegatively affect tundra swans (Cygnus columbianus) breeding in Alaska, and biologists should monitor these interactions. We recommend usingnonaugmented Bayesian hierarchical analyses to estimate wildlife populations when missing survey data occur. (JOURNAL OF WILDLIFEMANAGEMENT 73(5):720–727; 2009)

DOI10.2193/2008-262
Username Tag: