Fitting statistical distributions to seaduck count data: Implications for survey design and abundance estimation

Zipkin, Elise F.
Leirness, Jeffery B.
Kinlan, Brian P.
O’Connell, Allan F.
Silverman,Emily D.
USGS Patuxent Wildlife Research Center, 12100 Beech Forest Rd.,Laurel MD,20708 U.S.
USFWS Division of Migratory Bird Management,11510 American Holly Dr.,Laurel MD,20708, U.S.
Department of Entomology and Wildlife Ecology ,University of Delaware,Newark,DE19716,US
NOAA National Ocean Service, National Centers for Coastal Ocean Science,Center for Coastal Monitoring and Assessment,
Biogeography Branch,SSMC-4,N/SCI-1,1305 East-West Hwy.,Silver Spring,MD 20910-3281,US
Consolidated Safety Services,Inc.10301 Democracy Lane,Suite300,Fairfax,VA 22030,US
USGS Patuxent Wildlife Research Center, BARC East,Bldg.308,10300 Baltimore Ave.,Beltsville,MD 20705,US
Publication Date: 

Determining appropriate statistical distributions for modeling
animal count data is important for accurate estimation of abun-
dance, distribution, and trends. In the case of sea ducks along the
U.S. Atlantic coast, managers want to estimate local and regional
abundance to detect and track population declines, to define
areas of high and low use, and to predict the impact of future
habitat change on populations. In this paper, we used a modified
marked point process to model survey data that recorded flock
sizes of Common eiders, Long-tailed ducks, and Black, Surf, and
survey, conducted by the United States Fish & Wildlife Service
(USFWS) Division of Migratory Bird Management, during which
east-west transects were flown along the Atlantic Coast from
Maine to Florida during the winters of 2009–2011. To model the
number of flocks per transect (the points), we compared the fit
recorded for each transect flown. To model the flock sizes (the
marks), we compared the fit of flock size data for each species
binomial, positive geometric, logarithmic, discretized lognormal,
zeta and Yule–Simon. Akaike’s Information Criterion and Vuong’s

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