title: Delineation of the role of nutrient variability and dreissenids (Mollusca, Bivalvia) on phytoplankton dynamics in the Bay of Quinte, Ontario, Canada.
abstract: 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 N2fixers, 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.
authors.affiliation: Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, Toronto, ON M1C 1A4, Canada.
authors.forename: Yuko
authors.initials: Y
authors.lastname: Shimoda
authors.affiliation: Environment Canada, Canada Centre for Inland Waters, Burlington, ON L7R 4A6, Canada.
authors.forename: Sue B
authors.initials: SB
authors.lastname: Watson
authors.affiliation: Ontario Ministry of the Environment& Climate Change, Environmental Monitoring and Reporting Branch, Toronto, ON M9P 3V6, Canada.
authors.forename: Michelle E
authors.initials: ME
authors.lastname: Palmer
authors.affiliation: Department of Fisheries & Oceans, Burlington, ON L7S 1A1, Canada.
authors.forename: Marten A
authors.initials: MA
authors.lastname: Koops
authors.affiliation: Lower Trent Conservation, Trenton, ON K8 V 5P4, Canada.
authors.forename: Shan
authors.initials: S
authors.lastname: Mugalingam
authors.affiliation: Kingston Regional Office, Ontario Ministry of the Environment& Climate Change, Kingston, ON, K7 M 8S5, Canada.
authors.forename: Andrew
authors.initials: A
authors.lastname: Morley
authors.affiliation: Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, Toronto, ON M1C 1A4, Canada. Electronic address: georgea@utsc.utoronto.ca.
authors.forename: George B
authors.initials: GB
authors.lastname: Arhonditsis
mesh_terms: D000818:Animals; D061006:Bays; D049872:Bivalvia / Q000502:physiology*; D000458:Cyanobacteria / Q000502:physiology; D017753:Ecosystem; D008954:Models, Biological; D009584:Nitrogen / Q000378:metabolism; D009864:Ontario; D010758:Phosphorus / Q000378:metabolism; D010839:Phytoplankton / Q000502:physiology*
chemical_list: D010758:Phosphorus; D009584:Nitrogen
keywords: Areas of concern; Bayesian hierarchical modelling; Dreissenid mussels; Phosphorus loading; Phytoplankton patterns