A TRAIT-BASED DISTURBANCE FRAMEWORK FOR PHYTOPLANKTON PHENOLOGY ACROSS LAKE TROPHIC STATES

Phytoplankton are sentinels of ecosystem change that respond rapidly to disturbance and in turn influence ecosystem function. As environmental processes in freshwater systems become increasingly stochastic, understanding mechanistic drivers of phytoplankton community assembly is necessary to predict functional diversity and ecosystem feedbacks in response to current and future climate change scenarios. Here we describe a mechanistic, trait-based framework for algal taxa that are resilient to disturbance to predict algal community structure and function in response to stochastic extreme events. We identify three functional axes that confer resilience to disturbance across lake trophic state, including dormancy, morphology, and alternative carbon metabolism (mixotrophy and carbon concentrating mechanisms). We synthesize phytoplankton assemblage data spanning time (multi-annual to multi-decadal) and trophic state (oligotrophic, mesotrophic, dystrophic, eutrophic) to test the hypotheses that (1) the stability of functional traits conferring resilience to extreme events will be inversely related to the frequency of extreme events over time and (2) functional diversity of phytoplankton communities will be greatest at the interface of trophic categories. We then apply this theoretical framework to understand increases in phytoplankton blooms in unexpected conditions, including oligotrophic and cold-water lake ecosystems. 

Tutorial/Invited: Invited

Primary Presenter: Ana Morales-Williams, University of Vermont (ana.morales@uvm.edu)

Authors:

Ana Morales-Williams, University of Vermont  (ana.morales@uvm.edu)

Rachel Cray, University of Vermont  (Rachel.Cray@uvm.edu)

Katelynn Warner, University of Vermont  (Katelynn.Warner@uvm.edu)