ACJV Predicted occupancy - Sharp-tailed Sparrow (unknown Ammospiza)

<div>These data estimate and predict the occupancy for unknown Sharp-tailed Sparrows (unknown Ammospiza) using a fine-scale, raster-based vegetation data layer (Correll et al. 2019) using the existing Saltmarsh Habitat and Avian Research Program (SHARP; 2011-2014) point count database spanning tidal marshes from Maine to Virginia. These data are estimates produced using “Sharp-tailed Sparrow” detections from the SHARP database. These detections occur when an observer could not tell whether the observed bird was a Saltmarsh or Nelson’s sparrow, which occurs frequently within the hybrid zone (Maine to northern Massachusetts). </div><div><br /></div><div>These data were produced with 5 other complementary layers to 1) estimate Saltmarsh and Nelson’s Sparrow occupancy and density across suitable habitat in USFWS Region 5, 2) predict sparrow occupancy and density using these established relationships in unsurveyed areas, and 3) provide an estimate of unknown Sharp-tailed Sparrows within the established hybrid zone for these two species. These data can be used to examine predicted hotspots for Saltmarsh and Nelson’s Sparrow occupancy and density to inform local, state, and regional planning efforts involving these species. A full description of methods, results, and accuracy assessment can be found <a href='https://acjv.org/documents/UD_SALS_OccuDen_predictions_FINAL_report.pdf' target='_blank' rel='nofollow ugc noopener noreferrer'>here</a>.</div><div><br /></div><div>This predictive layer is meant to be used in tandem with either the Saltmarsh or Nelson’s Sparrow layer to add information about potential hybrid populations in these areas. We strongly recommend use of the numeric values presented in these layers only as an index to identify hotspots, and not for extrapolation of population estimates or trends. For population estimates for Saltmarsh and Nelson’s sparrows please reference Wiest et al. 2016 and 2019. For trend estimates please reference Correll et al. 2017.</div>