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The Bay of Quinte, a Z-shaped embayment at the northeastern end of Lake Ontario, has a long history of eutrophication problems primarily manifested as spatially extensive algal blooms and predominance of toxic cyanobacteria. The purpose of this study was to identify the structural changes of the phytoplankton community induced by two environmental alterations: point-source phosphorus (P) loading reduction in the late 1970s and establishment of dreissenid mussels in the mid-1990s. A combination of statistical techniques was used to draw inference about compositional shifts of the phytoplankton assemblage, the consistency of the seasonal succession patterns along with the mechanisms underlying the algal biovolume variability in the Bay of Quinte over the past three decades. Based on a number of diversity and similarity indices, the algal assemblages in the upper and middle segments of the Bay are distinctly different from those typically residing in the outer segments. Our analysis also identified significant differences among the phytoplankton communities, representing the pre- and post-P control as well as the pre- and post-dreissenid invasion periods. Recent shifts in phytoplankton community composition were mainly associated with increased frequency of occurrence of toxin-producing Microcystis outbreaks and reduced biovolume of N 2 fixers, such as Aphanizomenon and Anabaena. Bayesian hierarchical models were developed to elucidate the importance of different abiotic factors (light attenuation, water temperature, phosphorus, and ammonium) on total cyanobacteria, Microcystis, Aphanizomenon, and Anabaena relative biovolume. Our modelling exercise suggests that there is significant spatial heterogeneity with respect to the role of the factors examined, and thus total phosphorus alone cannot always explain the year-to-year variability of cyanobacteria succession patterns in the system. The lessons learned from the present analysis will be helpful to the water quality criteria setting process and could influence the management decisions in order to delist the system as an Area of Concern., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Our evolving understanding of ecosystem functioning along with the advent of computational power have paved the way for the development of complex mathematical models that explicitly represent the functional diversity of biotic communities and multiple biogeochemical cycles. The ever-growing demand for more complex models underscores the importance of robust sensitivity analysis (SA) to elucidate the impact of the uncertainty on model inputs and to untangle the parameter covariance patterns that ultimately lead to the emergence of equifinality problems. In this study, we propose a novel multi-pronged SA framework that integrates advanced statistical and machine learning (ML) techniques. Principal component analysis (PCA) is first applied to dissect the wide array of predictive outputs and identify modes of variability in time and/or space. Classification and Regression Tree (CART) analysis is then used to identify a set of splitting decisions connecting threshold values of key state variables and model parameters with different ranges of predictive outputs with management interest. Self-Organizing Maps (SOM) are implemented as a final step to unravel any non-linear associations between model parameters and responses. As a proof-of-concept, we used a complex aquatic biogeochemical model developed for the Bay of Quinte, a eutrophic embayment in Lake Ontario, to examine competition patterns and structural shifts among multiple functional phytoplankton (diatoms, N-fixing cyanobacteria, and Microcystis) and zooplankton (herbivores and omnivores) groups. Our sensitivity analysis framework showed that the parameters representing the dependence of growth and metabolic processes on temperature are particularly influential to recreate plankton community dynamics during the cold period of the year, whereas the interplay among the interspecific resource competition, strength of the prey-predator interactions, and phosphorus availability mainly regulate their dynamics during the growing season. The growth strategies of diatoms, their nutritional quality that determines the assimilation efficiency by zooplankton, along with the ambient nutrient availability determine our capacity to reproduce patterns of cyanobacteria dominance and faithfully depict the severity of harmful algal blooms. Finally, our study discusses the benefits of a broader use of the ML-based SA framework to unravel influential parametric interactions in modulating the behaviors of complex mathematical models. • A novel multi-pronged sensitivity analysis framework is introduced • PCA is applied to identify seasonal modes of variability • CART analysis is used to identify splitting rules connecting model inputs and outputs • SOM is implemented to unravel any non-linear input-output associations • As a proof-of-concept, we used an aquatic biogeochemical model for a eutrophic system [ABSTRACT FROM AUTHOR]

In the context of aquatic biogeochemical modelling, there is an increasing pressure to explicitly treat multiple biogeochemical cycles and to increase the functional diversity of biotic communities. In this study, we evaluate the capacity of 124 aquatic biogeochemical models to reproduce the dynamics of phytoplankton functional groups. Our analysis reinforces earlier findings that aquatic ecosystem modellers do not seem to consistently apply conventional methodological steps during the development of their models. Although there is an improvement relative to earlier critiques, significant portion of published studies did not properly assess model sensitivity to input vectors; aquatic ecosystem modellers are still reluctant to embrace optimization techniques during model calibration; and assess the ability of their models to support predictions in the extrapolation domain. We also found significant variability with respect to the mathematical representation of key physiological processes (e.g., growth strategies, nutrient kinetics, settling velocities) as well as group-specific characterizations typically considered in the pertinent literature. Cyanobacteria blooms are a major concern for water industries as they represent high risk for human health and economic costs for drinking water treatment, and thus one of the outstanding challenges is to offer credible modelling tools that can serve as early warning systems to assist with the operational control of cyanobacteria blooms. Our study suggests that the derivation of distinct functional groups from fairly heterogeneous planktonic assemblages poses challenging problems. Because of the still poorly understood ecology, we do not have robust group-specific parameterizations that can support predictions in a wide array of spatiotemporal domains. In this context, we argue that the most prudent strategies are the gradual incorporation of complexity, where possible and relevant, along with an open dialogue on how we can mathematically depict the interconnections among different phytoplankton subunits or even how we can frame the suitable data collection efforts. [ABSTRACT FROM AUTHOR]

Elucidating patterns and mechanisms that shape phytoplankton assemblages is a popular area of research for empirical and theoretical ecologists. Despite the daunting complexity of phytoplankton dynamics, much of our current understanding has been based on simple models describing food-web interactions with few differential equations. Skeptical views in the literature raise concerns about the increasing model complexity and advice to seek parsimony rather than simplicity. To address this controversy (simple versus complex models), we propose the introduction of an extra layer of causality into plankton models by connecting algal processes (maximum growth rates, nutrient kinetics, settling velocities, metabolic rates) with species-specific morphological features (cell volume, surface-to-volume ratio, shape). In this study, we demonstrate the capacity of a size-based plankton model to reproduce observed water quality patterns (phosphate, total phosphorus, nitrate, total ammonia, total nitrogen, chlorophyll a , and total zooplankton biomass) in the Hamilton Harbour, Ontario. Consistent with empirical evidence, our modeling analysis showed that small algal species have a distinct competitive advantage in summer epilimnetic environments across the range of cell volume and nutrient loading conditions examined; especially, when they are characterized by higher optimal temperature for growth. Strong top-down pressure mediated by high zooplankton abundance effectively controls the standing biomass of phytoplankton species that can otherwise realize high growth rates under the conditions typically prevailing in the end-of-summer epilimnetic environments (e.g., higher temperature optima, higher tolerance in low water clarity). Under high zooplankton control, the secondary variations of phytoplankton are modulated by the ambient phosphorus levels and the size-based strategies for resources procurement, such as the regulation of nutrient transport kinetics. By contrast, when the summer algal assemblage is released by the zooplankton grazing, the exceedance of critical phytoplankton biomass levels and the likelihood of harmful algal blooms are determined by the multitude of factors that shape inter-specific competition patterns (e.g., relative abundance of competing species, nutrient uptake kinetics). Our study evaluates the strengths and weaknesses of this approach and identifies future directions that would provide operational models founded upon concepts of allometry. [ABSTRACT FROM AUTHOR]