Quantifying multiple effects on Lake Michigan zooplankton from field time series data

Session: Great Lakes Lower Trophic Level Community Dynamics (2)

John Marino, Bradley University, jmarino@fsmail.bradley.edu
Henry Vanderploeg, NOAA GLERL, Henry.Vanderploeg@noaa.gov
Steve Pothoven, NOAA-GLERL, steve.pothoven@noaa.gov
Ashley Elgin, NOAA Great Lakes Environmental Research Laboratory, ashley.elgin@noaa.gov
Edward Ionides, University of Michigan, ionides@umich.edu
James Bence, Michigan State University, Dept. of Fisheries & Wildlife, bence@msu.edu
Scott Peacor, Michigan St. University, Dept. of Fish & Wildlife, peacor@msu.edu

Abstract

Zooplankton community dynamics, productivity, and composition depend on abiotic and biotic factors, such as temperature, resource availability, and predator density. Field time series data contain information about the contribution of these factors and underlying mechanisms. However, complexities intrinsic to ecological systems and data (e.g., multicausality, nonlinearities, dynamic stochasticity, and measurement error) pose challenges to extracting that information. We addressed these challenges for common zooplankton in Lake Michigan by applying a combination of statistical tools (generalized additive models, GAMs, and state-space models, SSMs) to long-term, offshore time series data (1994-2016). The GAMs provided estimates for effects of temperature, seasonality, population density, and the predatory zooplankter, Bythotrephes longimanus, on estimated zooplankton population growth rates (e.g., 17-30% reductions in cladoceran and cyclopoid population growth rates by B. longimanus). The SSMs allowed for more explicit consideration of the mechanisms underlying these effects, including how vital rates depended on time of year, temperature, intraspecific density, and on the contribution of consumptive and nonconsumptive B. longimanus effects. Such effects likely alter the production and composition of zooplankton, with consequences for planktivorous fish species. Our results thus provide field-based evidence for ongoing impacts of these factors in Lake Michigan and have implications for ecosystem-scale processes.