Development of a macrophyte-based index of biotic integrity for Minnesota lakes.pdf

D M M a b c a A R R A K A B I L M P 1 t w t S i C t U ( ( t a 1 dEcological Indicators 10 (2010) 968–979 Contents lists available at ScienceDirect Ecological Indicators journa l homepage: www.e lsev ier .com/ locate /eco l ind evelopment of a macrophyte-based index of biotic integrity for innesota lakes arcus W. Becka,?, Lorin K. Hatcha, Bruce Vondracekb, Ray D. Valleyc Conservation Biology Graduate Program, 187 McNeal Hall, 1985 Buford Avenue, St. Paul, MN 55108, USA US Geological Survey, Minnesota Cooperative Fish and Wildlife Research Unit1, 1980 Folwell Ave, St. Paul, MN 55108, USA Minnesota Department of Natural Resources, Division of Fish and Wildlife, 1200 Warner Road, St. Paul, MN 55106, USA r t i c l e i n f o rticle history: eceived 22 November 2009 eceived in revised form 7 February 2010 ccepted 14 February 2010 eywords: quatic macrophytes iological assessment BI ake monitoring etrics oint intercept a b s t r a c t Traditional approaches for managing aquatic resources have often failed to account for effects of anthro- pogenic disturbances on biota that are not directly reflected by chemical and physical proxies of environmental condition. The index of biotic integrity (IBI) is a potentially effective assessment method to integrate ecological, functional, and structural aspects of aquatic systems. Amacrophyte-based IBI was developed forMinnesota lakes to assess the ability of aquatic plant communities to indicate environmen- tal condition. The indexwas developed using quantitative point intercept vegetation surveys for 97 lakes that represent a range of limnological and watershed characteristics. We followed an approach similar to that used in Wisconsin to develop the aquatic macrophyte community index (AMCI). Regional adap- tation of the AMCI required the identification of species representative of macrophyte communities in Minnesota. Metrics and scaling methods were also substantially modified to produce a more empirically robust index. Regression analy