Document Type

Article

Date of Original Version

2016

Abstract

Dense aggregations of phytoplankton in layers or patches alter the optical and physical properties of the water column and result in significant heterogeneity in trophic and demographic rates of local plankton populations. Determining the factors driving patch formation, persistence, intensity, and dissipation is key to understanding the ramifications of plankton patchiness in marine systems. Regression and multi-parametric statistical analyses were used to identify the physical and optical properties associated with 71 phytoplankton-rich layers (PRLs) identified from 158 CTD profiles collected between 2008 and 2010 in East Sound, Washington, USA. Generalized additive models (GAMs) were used to explore water column properties associated with and characterizing PRLs. Patch presence was associated with increasing water column stability represented by the Brunt-Väisälä frequency (N2), Thorpe scale (Lt), and turbulent energy dissipation rate (e). A predictive regression identified patch presence with 100% accuracy when log10(N2) = -1 and 70% of the cases when log10(e) = -3. A GAM of passively measured variables, which did not include fluorescence, was able to model patch intensity with considerable agreement (R2 = 0.58), and the fit was improved by including fluorescence (R2 = 0.69). Fluorescence alone was an insufficient predictor of PRLs, due in part to the influence of non-photochemical quenching (NPQ) in surface waters and the wide range of fluorescence intensities observed. The results show that a multi-parametric approach was necessary to characterize phytoplankton patches and that physical structure, resulting in steep gradients in bio-optical properties, hold greater predictive power than bio-optical properties alone. Integration of these analytical approaches will aid theoretical studies of phytoplankton patchiness but also improve sampling strategies in the field that utilize autonomous, in situ instrumentation

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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