Predicting Avian Abundance Within and Across Tidal Marshes Using Fine-Scale Vegetation and Geomorphic Metrics

Abstract

Tidal marsh monitoring and restoration can benefit from the union of fine-scale remote sensing products and field-based survey data via spatial predictive models. As part of an interdisciplinary wetland monitoring project in San Francisco Bay, we developed a suite of 1-m pixel-level spatial metrics describing patterns in marsh vegetation and geomorphology for six sites across a large salinity gradient. These metrics, based on multi-spectral aerial imagery and derived vegetation maps, provided a basis for fine-scale spatial modeling of avian habitat potential. Using common yellowthroat (Geothlypis trichas), song sparrow (Melospiza melodia), and black rail (Later- allus jamaicensis) abundance data, we developed statistical models with relatively high explanatory power. In each case, models were improved by including vegetation-map variables, but variables directly extracted from aerial imagery were more reliable indicators of avian abundance. Although results varied by species, our models achieved reasonable within-site predictive success. When predicting to sites not used in the training set, however, validation results were inconsistent and often poor, suggesting that these models should be used with caution outside of the original study sites. As remotely sensed data become more readily available, our methods may be applied to a diverse range of sites, resulting in improved model generality and applicability.