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Evaluation of riparian vegetation data and associated sampling techniques

Posted on:2006-06-07Degree:Ph.DType:Dissertation
University:Utah State UniversityCandidate:Coles-Ritchie, Marc CFull Text:PDF
GTID:1450390005993103Subject:Biology
Abstract/Summary:
Monitoring the vegetation of riparian areas has become important to land managers who need to evaluate and minimize anthropogenic impacts. Riparian vegetation is an important monitoring variable because of its role in maintaining habitat for fish and other animals and plants. This dissertation evaluated data from hundreds of riparian sites across the interior northwestern US, and evaluated tools for summarizing and monitoring the vegetation at those sites. The sites were compared, based on the cover of meta-community types in the riparian zone. Meta-community types were developed based on the similarity of community types from eight riparian vegetation classifications, which had been used to describe the site vegetation. The relationships of vegetation, management, environment, and stream characteristics were evaluated. The strongest relationship between vegetation and management was identified at the site level, where differences were detected between livestock exclosures and adjacent grazed areas. Differences related to management at larger scales were relatively small or undetectable. A wetland rating, which summarizes the abundance of obligate wetland plants, was developed to facilitate comparison among sites. This wetland rating was one of the more repeatable variables, compared to some other variables in use. The low observer agreement for many of the variables would inhibit change detection, which is a primary objective of monitoring. These findings highlight the need for improved repeatability with existing methods, or the development of alternative methods of data collection and summarization, in order to more accurately characterize and monitor changes in riparian vegetation and associated ecological functions.
Keywords/Search Tags:Vegetation, Riparian, Data
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