Coarse habitat models are often used in decision-making, yet fine-scale data frequently capture local conditions relevant for vulnerable species conservation. Across the northeastern USA, managers are restoring salt marshes to promote resiliency in the face of accelerated sea-level rise. The saltmarsh sparrow (Ammospiza caudacuta), an avian tidal marsh specialist, faces potential extinction due to declining habitat. It has become a flagship species for salt marsh conservation and is also an indicator of changes to the broader ecosystem. This study evaluates a marsh-scale management model developed to identify priority habitat for saltmarsh sparrows in New Hampshire. This model characterizes marshes as single units, potentially missing key fine-scale (local) features that affect species occurrence. To identify the best predictors of saltmarsh sparrow occurrence, covariates from the marsh-scale model were compared with local features capturing fine-scale variability, with occupancy modeled using data from surveys conducted across 86 points on New Hampshire salt marshes. Several metrics of marsh quality used in the marsh-scale model had no relationship to saltmarsh sparrow occurrence, whereas key local covariates were better predictors. Predicted saltmarsh sparrow occupancy increased from ~ 1% at points with 5% high marsh cover at 0 m from upland edges to ~ 53% at points with 75% high marsh cover at 100 m from upland edges. Local high marsh cover and distance to edge should guide where restoration is implemented to benefit saltmarsh sparrows. The approach can be applied to other systems and highlights the value of fine-scale data in refining habitat prioritization tools.
McCulloch et al. (Mon,) studied this question.