Pea Ridge National Military Park Natural Resource Condition Assessment Natural Resource Report NPS/HTLN/NRR?2011/426 |
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Author:
| Annis, Gust Pursell, Dyanna DeBacker, Michael Diamond, David Elliott, Lee Garringer, Aaron Hanberry, Phillip James, Kevin Lee, Ronnie Morey, Michael |
Prepared for Publication by:
| National Park Service Staff, |
ISBN: | 978-1-4927-3541-0 |
Publication Date: | Sep 2013 |
Publisher: | CreateSpace Independent Publishing Platform
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Book Format: | Paperback |
List Price: | USD $17.99 |
Book Description:
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In accordance with National Park Service requirements, staff with the Missouri Resource Assessment Partnership and the Heartland Inventory and Monitoring Network conducted a natural resource condition assessment (NRCA) for Pea Ridge National Military Park (PERI). NRCA's are intended to provide a synthesized assessment of current conditions in the park. The NCRA for PERI builds on methods developed for a similar effort for Effigy Mounds National Monument. Basic elements of the...
More DescriptionIn accordance with National Park Service requirements, staff with the Missouri Resource Assessment Partnership and the Heartland Inventory and Monitoring Network conducted a natural resource condition assessment (NRCA) for Pea Ridge National Military Park (PERI). NRCA's are intended to provide a synthesized assessment of current conditions in the park. The NCRA for PERI builds on methods developed for a similar effort for Effigy Mounds National Monument. Basic elements of the methodology include (1) reliance on a framework of essential ecological attributes provided by the Environmental Protection agency, (2) development of a list of resource types, indicators, and attributes for assessment, and (3) application of assessments by reporting unit, including park wide, major terrestrial landscapes types, and major streams and tributaries. Current condition was assigned to indicators based on contemporary data and management targets were defined based on best available information, which ranged from quantitative sampling data to expert opinion.