Testing a methodology for assessing plant communities in temperate inland lakes

Abstract

We outline and test an aquatic plant sampling methodology designed to track changes in and make comparisons among lake plant communities over time. The method employs a systematic grid-based point-intercept sampling design with sampling resolution adjusted based on littoral area and lake shape. We applied this method in 72 Wisconsin lakes ranging from 6.5–245 ha in size, recording species presence–absence and depth at approximately 20,000 unique sample points. To assess how reductions in sampling effort might affect data quality, we used Monte Carlo simulations (100 iterations at each of 9 levels of sampling intensity) to reduce total lake sample points by 10% through 90% using a stratified random selection approach. Species accumulation curves were fit using the Michaelis-Menten 2-parameter formula for a hyperbola, and the predicted asymptote was similar to observed species richness. In a subset of lakes, oversampling (200% effort) did not yield significant increases in species richness. However, even a modest reduction (10–20%) in sampling effort affected species richness, while frequencies of occurrence of dominant species and estimations of percent littoral area and maximum depth of plant growth were less sensitive to sampling effort. In addition, we provide results of a power analysis for detecting changes in plant communities over time. Future applications of this protocol will provide information suitable for in-lake management and for assessing patterns in aquatic plant communities state-wide related to geographic region, hydrological characteristics, land use, invasive species and climate.