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1 September 2007 Habitat Modeling for Amaranthus pumilus: An Application of Light Detection and Ranging (LIDAR) Data
Jon D. Sellars, Claudia L. Jolls
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Anthropogenic management of dynamic ecosystems has led to decline of species dependent on processes that maintain suitable habitat, particularly on barrier islands. By evaluating environmental variables over large geographic areas, remote-sensing data and geographic information systems (GIS) hold increasing promise for management of these unique habitats and their species associates. We used light detection and ranging (LIDAR) data to extract habitat variables for Amaranthus pumilus, a federally threatened flowering annual of the Atlantic barrier islands. We asked: (1) can habitat variables for A. pumilus be extracted from remote-sensing data, and (2) can these variables be used to model suitable habitat?

We extracted topographic habitat variables for naturally occurring plants and evaluated habitat using multiple statistical techniques and other published model performance measures. We found that elevation was the most limiting topographic variable controlling the occurrence of Amaranthus pumilus. The most occurrences fell with in a 1.23 m range relative to local mean high water. Additionally, we used digital imagery collected concurrently with the LIDAR data to assess the role of vegetation cover in A. pumilus distribution. The occurrence of seabeach amaranth in previous years also was factored into the models. The models performed well, predicting 46%–100% of the plant occurrences using as little as 2% of the habitat.

Amaranthus pumilus can potentially serve as a conservation “umbrella” for coastal biodiversity. The methods presented here for identification of A. pumilus habitat, using GIS and model construction of potential habitat, can be applied to other species of concern, including nesting shorebirds and sea turtles.

Jon D. Sellars and Claudia L. Jolls "Habitat Modeling for Amaranthus pumilus: An Application of Light Detection and Ranging (LIDAR) Data," Journal of Coastal Research 23(5), 1193-1202, (1 September 2007).
Received: 26 August 2004; Accepted: 4 August 2005; Published: 1 September 2007
Geographic Information Systems (GIS)
global positioning system (GPS)
habitat assessment
habitat model
logistic regression
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