Integrating Tribal Stream Monitoring Data into Regional Multi-Metric Frameworks

The Little River Band of Ottawa Indians (LRBOI) aquatics program has collected aquatic habitat condition assessment data on their Reservation since 2002.  Stream restoration collaborations among Tribal, local, and regional partners have occurred and highlighted the need for a consistent framework for assessment data and a mechanism for comparisons.  To assess water resources it is valuable to compare local condition relative to that of similar water bodies within the Region, therefore, a framework aligning Tribal data sets with other regional data would be ideal.  With numerous options for analysis of bioassessment data we evaluated existing frameworks for both routine monitoring and data collected as part of restoration activities.  The objectives of this study were to: 1) identify available frameworks for evaluation of stream condition, 2) determine frameworks most suited towards integration of current Tribal data sets, and 3) utilize applicable frameworks for evaluation of historic, current and future assessments. Available frameworks have been identified for the region and include various macroinvertebrate and fish community metrics, indices of biological integrity, a biological condition gradient and multivariate approaches.  This study evaluated macroinvertebrate and fish metrics from study sites within the Big Manistee River watershed with regional datasets and frameworks. Fisheries datasets from 13 monitoring stations have been evaluated for concordance with available frameworks.  Within the watershed we have compared LRBOI macroinvertebrate data from six long-term monitoring sites with data from 30 sites assessed by the State of Michigan. LRBOI and State assessments overlapped at two additional sites.  Preliminary results for macroinvertebrate and fisheries data sets demonstrate consistency among agencies at the regional scale and at the overlap sites as indicated by ordination plots, overall diversity, and community composition metrics. By evaluating smaller local data sets within the broader regional scale more informed management decisions can be made for future protection and restoration.  Use of a consistent framework to interpret data will also improve collaboration and allow for better dissemination of results.