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Evaluation of Land Condition Trend Analysis for Birds on a Kansas Military Training Site
1U.S. Geological Survey, Biological Resources Division, Kansas Cooperative Fish and Wildlife Research Unit, Division of Biology, 204 Leasure Hall, Kansas State University, Manhattan, Kansas 66506, USA
Abstract:
/ Land condition trend analysis (LCTA) is a long-term monitoring program used on military training lands to identify ecological changes that result from training and management activities. We initiated LCTA at the Kansas Army National Guard Training Facility (KANGTF) in Saline County, Kansas, in March 1998. This paper evaluates the LCTA methodology for birds by comparing LCTA results with a modified methodology designed to place sampling transects in field-identified rather than satellite-identified land-cover types. In the satellite-identified land-cover types developed at the site, grassland habitats included a large component of woody vegetation, which resulted in poor resolution of bird assemblages associated with the different land-cover types. Using these cover classes, mixed grass prairie included five grass/forb (g/f) and 10 woody-dependent species; old-field included four g/f and four woody-dependent species; and riparian included one g/f and six woody-dependent species. LCTA sampling was too limited in the ecologically important riparian woodland habitat with the result that bird species were not adequately sampled there. In the alternate sampling strategy, we identified three land-cover classes (grassland, hedgerow, and riparian woodland) by field reconnaissance and increased sampling in the riparian woodland. Grassland included six g/f and three woody-dependent species; hedgerow included six g/f and 20 woody-dependent species, and riparian included two g/f and 19 woody-dependent species. The modifications greatly improved the resolution of bird assemblages associated with land-cover classes at the KANGTF. Use of the alternative sampling method should improve the ability to detect long-term trends in the bird communities.
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