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A multi‐scale occupancy model for the grasshopper sparrow in the Mid‐Atlantic
Author(s) -
Irvin Eric,
Duren Kenneth R.,
Buler Jeffrey J.,
Jones William,
Gonzon Anthony T.,
Williams Christopher K.
Publication year - 2013
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 - 10.1002/jwmg.609
Subject(s) - sparrow , grasshopper , occupancy , habitat , breeding bird survey , geography , ecology , wildlife , grassland , habitat fragmentation , land cover , environmental science , physical geography , land use , biology
Identifying features of breeding habitat that influence occupancy and modeling the distribution of grassland birds is needed to direct conservation efforts to reduce population declines associated with habitat loss and fragmentation. Many recent studies on grassland bird habitat use incorporate both local and landscape attributes. However, few studies have determined the appropriate spatial scales at which to measure these relationships. We conducted roadside point counts within Delaware, USA, to determine the presence of grasshopper sparrows ( Ammodramus savannarum ). We quantified both land cover composition and configuration at local and landscape scales at our survey sites using data from the National Oceanic and Atmospheric Administration Coastal Change Analysis Program. We determined the spatial scales at which grasshopper sparrow presence was most strongly related to landscape metrics and modeled grasshopper sparrow habitat occupancy at multiple scales, while accounting for variation in detection. At the site scale, occupancy was negatively related to forest and shrub composition. At the landscape scale, grasshopper sparrow occupancy was positively related to the amount of grasslands and pastures, and negatively related to mean inter‐patch distance of grasslands and amount of low‐intensity development. Our model had good predictive accuracy (area under the receiver operating characteristics curve = 0.717). We present our predictive model applied to the Delmarva Peninsula, USA. © 2013 The Wildlife Society.