A HIERARCHICAL SPATIAL COUNT MODEL WITH APPLICATION TO IMPERILED GRASSLAND 
BIRDS.
Wayne E. Thogmartin1, John R. Sauer2, and Melinda G. Knutson1
1Upper Midwest Environmental Sciences Center, U.S. Geological Survey, La Crosse, 
WI, U.S.A.; 2 USGS Patuxent Wildlife Research Center, Laurel, MD, U.S.A.
2603 Fanta Reed Road, La Crosse, WI 54603, U.S.A.

We utilized a Markov Chain Monte Carlo approach to spatially predict abundance 
of 5 rare grassland birds (Bobolink, Grasshopper Sparrow, Sedge Wren, Upland 
Sandpiper, Henslow’s Sparrow) in the upper midwestern US.  Twenty-one years of 
North American Breeding Bird Survey counts were modeled as a hierarchical 
loglinear function of explanatory variables describing habitat, spatial 
relatedness between route counts, year effects, and nuisance effects associated 
with differences in observers.  The model included a conditional autoregressive 
term representing the correlation between adjacent routes.  Explanatory habitat 
variables in the model included land cover composition and configuration, 
climate, terrain physiognomy, and human influence.  The model hierarchy was due 
to differences in route counts between observers over time.  We fitted this 
model with WinBUGS.  Preliminary evaluation of the models based on independent 
data suggested generally good agreement with model predictions.  Discrepancies 
between evaluation data and model predictions were due, in some unknown measure, 
to insertion of errors when translating the statistical model into a mapped 
model.   

Keywords: MCMC, overdispersed Poisson regression, spatially-correlated counts, 
species-habitat models