Modeling Phytoplankton Heterogeneity in a Consumer-Resource Agent-Based Model

Abstract

Increased eutrophication and climate-induced environmental change in freshwater ecosystems are exacerbating Harmful Algal Blooms (HABs) through the proliferation of dominating phytoplankton species such as Microcystis. To help mitigate HABs, it is important to have effective predictive tools, such as models, that will adequately inform management decisions. Consumer-resource models are one such application to describe phytoplankton interactions with their environment, however they typically employ population-level estimates for functional traits despite research showing intra- and inter-specific variability. To address this, I adapted phytoplankton consumer-resource models—such as the Droop equation to describe growth rates based on internal stores of limiting resources—to an Agent-Based Model (ABM) framework. With this framework, I developed 5 models to test the effects of functional trait variability on the consumption and use of an extracellular resource: no variation, variation of 3 functional traits (maximum specific uptake rate, minimum quota, maximum growth rate) concurrently, and 3 models of varying each functional trait separately. Overall the model with 3 varied functional traits had the largest population, lowest extracellular resource, and largest C:P ratio than the other models. This indicates that the more variable phytoplankton population will be better competitors, have a higher biomass, and a higher overall population than its homogenous counterparts given the same nutrient environment. These findings raise important considerations for future phytoplankton consumer resource models and ultimately for freshwater management decisions.