Download or read book Repeatability of Riparian Vegetation Sampling Methods written by and published by . This book was released on 2004 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tests were conducted to evaluate variability among observers for riparian vegetation data collection methods and data reduction techniques. The methods are used as part of a largescale monitoring program designed to detect changes in riparian resource conditions on Federal lands. Methods were evaluated using agreement matrices, the Bray-Curtis dissimilarity metric, the coefficient of variation, the percentage of total variability attributed to observers, and estimates of the number of sites needed to detect change. Community type (CT) cover data differed substantially among the six to seven observers that sampled the same sites. The mean within-site similarity in the vegetation data ranged from 40 to 65 percent. Converting CT data to ratings (bank stability, successional, and wetlands ratings) resulted in better repeatability, with coefficients of variation ranging from 6 to 13 percent and a percentage of variability attributed to observers of 16 to 44 percent. Sample size estimates for the ratings generated from CT cover data ranged from 56 to 224 sites to detect a change of 10 percent between two populations. The woody species regeneration method was imprecise. The effective ground cover method was quite precise with a coefficient of variation of two, but had so little variability among sites that statistically significant change in this attribute would not be expected. In general, reducing the CTs to ratings increased precision because of the elimination of differences among observers that were not important from the perspective of the rating. Studies that seek to detect change at a single site would need to take into account the observer variability described here. Studies that seek to detect differences between populations of sites could detect relatively large changes with these methods and ratings. Small differences among populations would be difficult to detect with a high degree of confidence, unless hundreds of sites were sampled.