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Understanding the characteristics of high-quality avian habitat is critical for guiding salt marsh management and restoration. Existing insights into salt marsh avian habitat are often based on the composition of marsh vegetation, e.g., individual plant species cover. This study investigated whether the spatial configuration of marsh surface cover (e.g., patch number, density, size, shape complexity and compactness, degree of dissection of the landscape, variation and repetition of cover type, and the variance within these metrics) is a useful, additional indicator of avian habitat quality for the Alameda Song Sparrow (Melospiza melodia pusillula), a non-migratory California Species of Special Concern endemic to southern San Francisco Bay. M. m. pusillula density during the breeding seasons of 2002 through 2005 was estimated at 82 observation points in 10 marsh sites within the bird’s geographic range. The mean bird density index (overall mean: 5.61 birds detected per hectare of marsh) was not significantly different among marshes of different ages. We mapped the vegetation zones, open water, and upland areas within each marsh site using high resolution aerial photographs and automated classification analysis. We quantified the configuration of surface cover around each bird observation point by 31 metrics. Bird density index was best modeled by a multiple linear regression containing positive relationships with the metrics Mean Core Area Index and Patch Core Area Coefficient of Variation (R2 = 0.210, p < 0.0001). Qualitatively, this model suggested that M. m. pusillula abundance during the breeding season was greatest in marsh areas with compact patches that spanned a variety of patch sizes from moderate-tolarge, uninterrupted by other cover. We conclude that configuration-based vegetation pattern analysis could usefully complement more customary compositionbased habitat assessments to aid wetland habitat research, management, and restoration.

Alameda Song Sparrow Abundance Related to Salt Marsh Vegetation Patch Size and Shape Metrics Quantified from Remote Sensing Imagery