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Woods Hole Coastal and Marine Science Center > Sea Level Rise Hazards and Decision Support > Beach-dependent Shorebirds>Predicting piping plover habitat

Beach-dependent Shorebirds

Predicting piping plover habitat

Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. The iPlover dataset was used to develop a model that predicts the probability that a combination of landcover characteristics at a given location will be associated with piping plover nesting habitat. Landcover characteristics considered include Geomorphic Setting, Substrate Type, Vegetation Type, Vegetation Density, Distance to Foraging, Distance to Ocean, Beach Width, and Elevation.

Using elevation datasets (e.g., lidar) and orthoimagery, these landcover characteristics are mapped for an entire area of interest, such as a barrier island. The Piping Plover Habitat model can then evaluate combinations of landcover characteristics to map the probability that each map cell supports piping plover nesting habitat. These maps are currently being created for 21 beaches and barrier islands, referred to as ‘Deep Dive Sites’, along the U.S. Atlantic coast.

iplover location map

Locations of 21 ‘deep dive’ sites where piping plover habitat will be modeled and forecasted given sea-level rise as part of this project. Also shown is an example of the habitat likelihood map produced as part of the modeling approach. This example shows piping plover nesting habitat likelihood on the Rockaway Peninsula in 2014 given characteristics for Geomorphic Setting, Substrate Type, Vegetation Type, and Vegetation Density. Credit: Sara Zeigler, USGS

Related Publicaitons

Zeigler, S.L., Thieler, E.R., Gutierrez, B.T., Plant, N.G., Hines, M. Fraser, J.D., Catlin, D.H., Karpanty, S.M., 2017. Smartphone technologies and Bayesian networks to assess shorebird habitat selection. Wildlife Society Bulletin. DOI: 10.1002/wsb.820.

Gieder, K.D., Karpanty, S.M., Fraser, J.D., Catlin, D.H., Gutierrez, B.T., Plant, N.G., AM Turecek, A.M., Thieler, E.R., 2014. A Bayesian network approach to predict nest presence of the federally threatened piping plover (Charadrius melodus) using barrier island features. Ecological Modelling. 276: 38-50.


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