, Volume 32, Issue 4, pp 725–736 | Cite as

Comparing Habitat Models Using Ground-Based and Remote Sensing Data: Saltmarsh Sparrow Presence Versus Nesting

  • Susan Meiman
  • Daniel Civco
  • Kent Holsinger
  • Chris S. Elphick


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.


Ammodramus caudacutus Bayesian hierarchical models Probability of presence Remote sensing Saltmarsh sparrow 



Funding for this project was provided through the Department of Ecology and Evolutionary Biology at the University of Connecticut, the Connecticut Department of Environmental Protection, Office of Long Island Sound Programs, and the Connecticut Sea Grant College Program. We thank K. Banick, L. Bartlett, T. Bayard, M. Borsari, J. Brubaker, C. Bunce, M. Ellis, C. Field, T. Steeves, and K. Sullivan-Wiley for their assistance collecting data on the ground, M. Hoover for use of his state-wide marsh community GIS layer, and P. Capotosto and R. Wolfe for information about tidal marsh restoration. Permission for marsh site access was granted by the Connecticut Department of Environmental Protection, multiple municipal agencies, and an extensive group of concerned private land owners and citizen-stewards associated with the local conservation groups and land trust organizations. M. Willig and the Center for Environmental Science and Engineering at the University of Connecticut provided valuable sabbatical office space for CSE.

Supplementary material

13157_2012_306_MOESM1_ESM.pdf (142 kb)
ESM 1 (PDF 141 kb)


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Copyright information

© Society of Wetland Scientists 2012

Authors and Affiliations

  • Susan Meiman
    • 1
  • Daniel Civco
    • 2
  • Kent Holsinger
    • 1
  • Chris S. Elphick
    • 1
    • 3
  1. 1.Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsUSA
  2. 2.Department of Natural Resources and the EnvironmentUniversity of ConnecticutStorrsUSA
  3. 3.Center for Conservation and BiodiversityUniversity of ConnecticutStorrsUSA

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