Lake Management (includes both talks below)

(0:00) Aquatic plant management and regulatory compliance. Peter Filpansick, LakePro, Inc.

Managing aquatic plants with herbicides is regulated by several agencies and numerous rules. All that red tape can be downright confusing even for professional applicators. In this presentation, we will review the different agencies and their roles in regulating aquatic plant management. This includes the EPA that registers herbicides, MDARD that licenses applicators, and EGLE that issues permits for treatments. We will examine some of the main restrictions related to aquatic plant management treatments. Finally, we will discuss how you can work with your contractor to ensure the work done on your lake follows all applicable rules and regulations.

(58:48) Some useful sampling and statistical methods for assessing potential abundance changes in aquatic plant surveys. James N. McNair, Annis Water Resources Institute - Grand Valley State University, and Ryan A. Thum, Montana State University

Adaptive management of invasive aquatic plants requires objective and rigorous methods for assessing management outcomes. In particular, it is important that evidence regarding potential reductions in abundance or cover following management treatments, or changes in the relative frequencies of different ecotypes or genotypes, be based on valid statistical analysis at a specified and appropriate level of confidence. Assessments of management outcomes have two main components: field sampling and statistical analysis. In this presentation, we review several practical methods for sampling invasive aquatic plants (e.g., transect sampling, point-intercept sampling, determining fates of marked plants), then discuss some useful nonparametric statistical methods that permit rigorous statistical analysis of the resulting data (e.g., chi-squared test, McNemar’s test, various tests for proportions). The underlying assumptions on which the statistical methods we discuss depend are less restrictive than those for parametric methods like t tests and analysis of variance, but there nevertheless are assumptions that must be verified. We briefly discuss these assumptions, how they are related to the sampling method, and how one can determine whether they are tenable for a given data set. We illustrate both sampling and statistical methods with data from herbicide treatments in Michigan inland lakes, and we suggest appropriate statistical functions in the R programming language and computing environment that can be used to carry out the various statistical tests we review.

Presented during the 2020 Michigan Inland Lakes Convention.