Comparing Habitat Models Using Ground-Based and Remote Sensing Data: Saltmarsh Sparrow Presence Versus Nesting
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Remote sensing data can represent various habitat characteristics, and thus can substitute for detailed ground sampling when constructing habitat models. To predict saltmarsh sparrow (Ammodramus caudacutus) distribution and nesting activity, we compared Bayesian hierarchical models in which variables were generated from field or remote sensing data, at a scale of 1-ha plots and at the landscape scale. Field data consisted of plant structure and plant composition variables. Data derived from remote sensing included high and low marsh classifications, LiDAR elevation data, and a classification derived from spectral characteristics specifically associated with saltmarsh sparrow habitat use. The best sparrow presence model used a variable derived from spectral reflectance values associated with plots where sparrows did not occur, indicating that the remote sensing data included additional information about conditions associated with saltmarsh sparrow occurrence than was provided by plant composition, structure, or community classes. In contrast, nest presence was modeled best using vegetation structure variables that required data collection on the ground, although the best remote-sensing model was almost as good. These results reinforce the value of remote-sensing data in habitat modeling, and highlight the need to distinguish between sites that contribute to reproduction and sites where a species is merely present.