Single-Turnover Variable Chlorophyll Fluorescence as a Tool for Assessing Phytoplankton Photosynthesis and Primary Productivity: Opportunities, Caveats and Recommendations

Abstract

Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.

Photosynthetic Characteristics of Smaller and Larger Cell Size-Fractioned Phytoplankton Assemblies in the Daya Bay, Northern South China Sea

“…Such a separation is operational and used extensively [ 7 , 27 , 28 ], although the shortcomings are obvious, e.g., some chain-forming species default to micro-cells and some other smaller cells are missing under vacuum. The concentrated samples were then dark-acclimated for 5 min in a 15 mL chamber of fast repetition rate fluorometer (FRRf), coupled to a FastAct base unit (Fast Ocean, Chelsea Technologies Group, Ltd., West Molesey, UK) where temperature was maintained at that of the field conditions, in order to oxidate the electron transport chains and relax non-photochemical quenching (NPQ) [ 33 ] before measuring chlorophyll fluorescence. After the dark-acclimation, the FRRf was activated with a single turnover protocol that consisted of 100 saturation flashets of 1 μs duration with 1 μs pitch, followed by 40 relaxation flashets with 60 μs pitch [ 34 ].…”

Chlorophyll fluorescence-based estimates of photosynthetic electron transport in Arctic phytoplankton assemblages

“…Baseline fluorescence, may also influence the terms used to define ETR k :ETR a . This phenomenon represents a non-variable contribution to the Chl fluorescence signal, which is understood to reflect the presence of energetically decoupled light harvesting complexes under nutrient limitation or photoinhibitory stress [31]. High baseline fluorescence decreases the amplitude of fluorescence transients, making F v / F m a useful gauge of phytoplankton physiological stress [5], [62], [63].…”

“…As a first step, resolving discrepancies between ETR a and ETR k will require consensus regarding the analysis of raw fluorescence transient data. This aim is fundamental for consistent data reporting among the growing community of FRRf and FIRe users [31]. Second, ETR a and ETR k should be validated against fluorescence-independent measures of productivity.…”

“…Each algorithm relies on the same principles of light harvesting and photosynthetic electron transport, but arrive at ETR PSII estimates using slightly different, but theoretically equivalent, combinations of photophysiological metrics. As a result, different algorithms confer different field-sampling advantages and challenges (see [31]). The so-called ‘amplitude’ based approach (abbreviated ETR a ; sometimes also referred to as the sigma-algorithm), calculates ETR PSII as the product of photosynthetically available radiation (PAR), the functional absorption cross section of PSII in the dark-acclimated state (σ PSII ), and the photochemical efficiency of PSII normalized by the dark-acclimated maximum photochemical efficiency of PSII [27].…”

“…The so-called ‘amplitude’ based approach (abbreviated ETR a ; sometimes also referred to as the sigma-algorithm), calculates ETR PSII as the product of photosynthetically available radiation (PAR), the functional absorption cross section of PSII in the dark-acclimated state (σ PSII ), and the photochemical efficiency of PSII normalized by the dark-acclimated maximum photochemical efficiency of PSII [27]. This approach reduces uncertainty in ETR PSII estimates by using σ PSII measurements made in the dark-acclimated state, which are subject to less noise than σ PSII ′ measurements made in the light [31]. As a result, ETR a is a favorable approach in low biomass waters where low signal-noise measurements represent a considerable challenge.…”