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Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary
Citation:
Angradi, T., M. Pearson, Dave Bolgrien, B. Bellinger, M. Starry, AND C. Reschke. Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, 39(4):536-546, (2013).
Impact/Purpose:
This research provides validated statistical model for predicting SAV occurrence in the St. Louis River Estuary. This is the only model of its kind for the Great Lakes and will be of use to regional aquatic scientists and habitat restoration specialists. Hydroacoustic survey methods were used to collect data on the distribution of submerged aquatic (SAV) in the St. Louis River Estuary (SLRE) of Lake Superior. This is the first attempt to create predictive models for SAVs in a Great Lakes estuary. Models resulting from this work are of sufficient quality for use in planning habitat restoration projects.
Description:
Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected in 2011 using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River estuary of western Lake Superior. The dominant SAV species in most areas of the estuary was American wildcelery (Vallisneria americana Michx.). Maximum depth of SAV was approximately 2.1 m. In regression tree models, the most variation in SAV cover was explained by an autoregression (lag) term, depth, and a measure of exposure. Logistic SAV occurrence models including water depth, exposure, bed slope, substrate fractal dimension, the lag term, and interactions predicted the occurrence of SAV in three areas of the St. Louis River with 78-86% accuracy based on cross validation of a holdout dataset. Reduced models excluding fractal dimension and the lag term, predicted SAV occurrence with 75-82% accuracy based on cross validation and with 68-85% accuracy for an independent SAV data set collected using a different sampling method. In one area of the estuary, the probability of SAV occurrence was related to the interaction of depth and exposure. At more exposed sites, SAV was more likely to occur in shallow areas than at less exposed sites. Our predictive models show the range of depth, exposure, and bed slope favorable for SAV in the SLRE, information useful for shallow-water habitat restoration projects.
URLs/Downloads:
Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary