ABSTRACT

Significant declines in populations of songbirds associated with early successional habitats in the southeastern United States have been documented since the 1960’s. These declines have been attributed to changes in land cover, and to a general decrease in the spatial interspersion of croplands, fields, hardwood stands, natural pine stands, etc. Since the inception of the Conservation Reserve Program (CRP) in 1986, >305,838 ha of farmland have been converted to tree plantations (mostly pine) in Georgia and South Carolina. Biologists believe that the conversion of cropland to pine plantations has had a negative impact on plant and wildlife diversity and has decreased both the quantity of good early successional habitat and the diversity in the landscape matrix in which songbirds have been known to thrive.

In order to monitor the response of songbird nesting success to structural modifications of pine plantations enrolled in the CRP, in early 1998 we applied thinings to 13 (8 thinned and 5 control) pine plantations in the Upper and Lower Coastal Plains of Georgia and South Carolina. Thinning operations were applied to these pine plantations according to CRP recommendations. Treated and control (no thinning) stands were monitored for songbird activity and nesting success over a two year period. A geographic information system (GIS), GPS data of songbird nests and vegetation plots, and polygon coverages digitized from digital ortho-photographic quarter quadrangles (DOQQ) were used to build habitat maps in and around all monitored stands. Predictive models of nesting success were developed using these spatial data.

We used the predictive models to project the potential effect that similar modifications in similar landscapes could have on songbird populations throughout the Upper and Lower Coastal Plains of Georgia and South Carolina. Scaling up from the site-specific model to a landscape prediction required the use of land cover data from satellite observations. Satellite data were classified into relevant land cover classes using training site data from in and around half of the monitored stands. We evaluated the accuracy of the classification using vegetation data collected in and around the other monitored stands. The classified satellite data provided the regional land cover matrices that were evaluated and compared to the habitat model of the study sites in which nesting success improved. Based on similarity, the land cover matrices were reclassified as high, moderate or low priority for manipulation. Results included total area and the spatial distribution of areas classified as high priority for thinning of pine stands to improve relative songbird-nesting success and diversity.