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Modeling and Mapping Reptile Distributions on the Idaho National Laboratory Site

Background

This study was designed for the purpose of understanding factors affecting reptile distribution and to make predictive distribution maps for individual species across the development zone (central 259 km2) of the INL Site. This information will be used to help develop the conservation management plan for the INL Site, which will help the Department of Energy make decisions about future facility locations.

Objectives

The main objective of this project is to assist in development of a conservation management plan that will be used for future facility siting decisions: Specific objectives for the 2007 season included:

  • Develop and apply a habitat-based sampling design for reptiles
  • Determine the occurrence, distribution, and habitat relationships of reptiles on the development zone
  • Develop a habitat model for each reptile species in the development zone
  • Make and test predicted distribution maps.

Accomplishments Through 2007

Reptile data collection was completed and modeling procedures for six reptile species on the INL Site were developed. Three distribution modeling techniques were also completed: 1) Boolean Modeling, 2) Trapping and observational probability modeling, and 3) Mahalanobis Distance Modeling.

Results

  • Incidental observation was the sampling technique that provided most reptile observations. However, skinks were not detected using this method.

  • Visual Encounter Surveys produced the second highest number of reptile observations. All six species were detected.

  • Trapping detected all species but in few numbers. However, the use of traps was required to sample for night snakes, which potentially occur in the study area.

Distribution Modeling. Boolean Model. Uses all positive data to determine in which environmental types each species occurs, then map all suitable environmental type polygons

Trapping / Observational Probability Model. Uses all positive and negative trapping and visual encounter survey data to calculate a probability of trapping or observing a particular species in a particular environmental type.

Mahalanobis Distance Model. Uses all positive data to create a habitat similarity index based on the characteristics of pixels in the GIS layers for sites where a species of interest is known to occur.

Model Ranking. We used our sample sizes, statistical analyses, and knowledge of species ecology to rank each model for each species. We also used this information to determine our relative confidence for the highest ranked model for each species.

Species Richness

  • The species richness map was made by overlapping all of the boolean distribution model results.

  • The number of species within the Development Zone varied from 2 to 6.

  • The area with the highest reptile species richest is located on the southern end of the L shaped corridor where development is most likely to occur.

  • The highest species richness areas are characterized by big sagebrush and no recent burns.

Plans for Continuation

Additional modeling approaches will be tried (e.g., DOMAIN and Maximum Entropy).

Publications, Theses, Reports, etc.

Thesis and publications are in progress.


Investigators and Affiliations

David P. Hilliard, Graduate Student, Herpetology Laboratory, Department of Biological Sciences, Idaho State University, Pocatello, Idaho

Charles R. Peterson, Professor, Herpetology Laboratory, Department of Biological Sciences, Idaho State University, Pocatello, Idaho

Funding Sources

U.S. Department of Energy, Idaho Operations Office.

Idaho State University, teaching assistantship.
 


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