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Building Hierarchical Models of Avian Distributions for the State of Georgia
Author(s) -
HOWELL JAY E.,
PETERSON JAMES T.,
CONROY MICHAEL J.
Publication year - 2008
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.2193/2006-098
Subject(s) - geography , occupancy , ecology , habitat , sciurus , warbler , emberizidae , akaike information criterion , biology , cartography , statistics , mathematics
To predict the distributions of breeding birds in the state of Georgia, USA, we built hierarchical models consisting of 4 levels of nested mapping units of decreasing area: 90,000 ha, 3,600 ha, 144 ha, and 5.76 ha. We used the Partners in Flight database of point counts to generate presence and absence data at locations across the state of Georgia for 9 avian species: Acadian flycatcher ( Empidonax virescens ), brown‐headed nuthatch ( Sitta pusilla ), Carolina wren ( Thryothorus ludovicianus ), indigo bunting ( Passerina cyanea ), northern cardinal ( Cardinalis cardinalis ), prairie warbler ( Dendroica discolor ), yellow‐billed cuckoo ( Coccyzus americanus ), white‐eyed vireo ( Vireo griseus ), and wood thrush ( Hylocichla mustelina ). At each location, we estimated hierarchical‐level‐specific habitat measurements using the Georgia GAP Analysis18 class land cover and other Geographic Information System sources. We created candidate, species‐specific occupancy models based on previously reported relationships, and fit these using Markov chain Monte Carlo procedures implemented in OpenBugs. We then created a confidence model set for each species based on Akaike's Information Criterion. We found hierarchical habitat relationships for all species. Three‐fold cross‐validation estimates of model accuracy indicated an average overall correct classification rate of 60.5%. Comparisons with existing Georgia GAP Analysis models indicated that our models were more accurate overall. Our results provide guidance to wildlife scientists and managers seeking predict avian occurrence as a function of local and landscape‐level habitat attributes.