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Article

A Comparison of the Performance and Compatibility of Protocols Used by Seven Monitoring Groups to Measure Stream Habitat in the Pacific Northwest

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Pages 565-587 | Received 24 Apr 2009, Accepted 22 Jan 2010, Published online: 08 Jan 2011
 

Abstract

To comply with legal mandates, meet local management objectives, or both, many federal, state, and tribal organizations have monitoring groups that assess stream habitat at different scales. This myriad of groups has difficulty sharing data and scaling up stream habitat assessments to regional or national levels because of differences in their goals and data collection methods. To assess the performance of and potential for data sharing among monitoring groups, we compared measurements made by seven monitoring groups in 12 stream reaches in northeastern Oregon. We evaluated (1) the consistency (repeatability) of the measurements within each group, (2) the ability of the measurements to reveal environmental heterogeneity, (3) the compatibility of the measurements among monitoring groups, and (4) the relationships of the measurements to values determined from more intensive sampling (detailed measurements used as a standard for accuracy and precision in this study). Overall, we found that some stream attributes were consistently measured both within and among groups. Furthermore, for all but one group there was a moderate correlation (0.50) between the group measurements and the intensive values for at least 50% of the channel attributes. However, none of the monitoring groups were able to achieve high consistency for all measured stream attributes, and few of the measured attributes had the potential for being shared among all groups. Given the high cost of stream habitat monitoring, we suggest directing more effort to developing approaches that will increase the consistency and compatibility of measured stream attributes so that they will have broader utility. Ultimately, local monitoring programs should consider incorporating regional and national objectives so that data can be scaled up and the returns to limited monitoring dollars can be maximized across spatial scales.

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