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Active chlorophyll-a fluorescence was measured during five summer research cruises (2008 – 2016), spanning the Atlantic sector of the Southern Ocean. This unique data set provides information for assessing zonal, inter-annual and intra-seasonal variability (early versus late summer) of photosynthetic efficiency (Fv/Fm). The zonal variability of Fv/Fm showed a typical latitudinal decline from a maximum in the Polar Frontal Zone (PFZ) (0.24±0.03) to a minimum in the Southern Antarctic Circumpolar Current Zone (SACCZ) (0.18±0.07). The inter-annual variability in Fv/Fm (between each cruise) was the highest in the SACCZ, while the Antarctic Zone (AZ) exhibited low inter-annual variability. Intra-seasonal variability between the zones was limited to a significantly higher mean Fv/Fm in the PFZ and AZ in early summer compared to late summer. Intra-seasonal variability between the cruises was, however, inconsistent as higher mean Fv/Fm in early summer were seen during some years as opposed to others. Ancillary physical and biogeochemical parameters were also assessed to investigate potential direct and indirect drivers of co-variability with Fv/Fm through a series of statistical t-tests, where significant differences in Fv/Fm were used as focus points to interrogate the plausibility of co-variance. Inter-zonal variability of surface seawater temperature (SST) and Silicate:Phosphate (Si:P) ratios were highlighted as co-varying with Fv/Fm in all zones, whilst community structure played an indirect role in some instances. Similarly, inter-annual variability in Fv/Fm co-varied with SST, Nitrate:Phosphate (N:P) and Si:P ratios in the PFZ, AZ and SACCZ, while community structure influenced inter-annual variability in the PFZ and SACCZ. Intra-seasonal variability in Fv/Fm was linked to all the ancillary parameters, except community structure in the AZ, whilst different ancillary parameters dominated differences during each of the cruises. These results were further scrutinized with a Principal Component Analysis for a subset of co-located data points, where N:P and Si:P ratios emerged as the principal indirect drivers of Fv/Fm variability. This study highlights the scope for using Fv/Fm to reflect the net response of phytoplankton photophysiology to environmental adjustments and accentuates the complex interplay of different physical and biogeochemical parameters that act simultaneously and oftentimes antagonistically, influencing inter-zonal, inter-annual and intra-seasonal variability of Fv/Fm.

The uptake and application of single turnover chlorophyll fluorometers to the study of phytoplankton ecosystem status and microbial functions has grown considerably in the last two decades. However, standardization of measurement protocols, processing of fluorescence transients and quality control of derived photosynthetic parameters is still lacking and makes community goals of large global databases of high-quality data unrealistic. We introduce the Python package Phytoplankton Photophysiology Utilities (PPU), an adaptable and open-source interface between Fast Repetition Rate and Fluorescence Induction and Relaxation instruments and python. The PPU package includes a variety of functions for the loading, processing and quality control of single turnover fluorescence transients from many commercially available instruments. PPU provides the user with greater flexibility in the application of the Kolber-Prasil-Falkowski model; tools for plotting, quality control, correcting instrument biases and high-throughput processing with ease; and a greater appreciation for the uncertainties in derived photosynthetic parameters. Using data from three research cruises across different biogeochemical regimes, we provide example applications of PPU to fit raw active chlorophyll-a fluorescence data from three commercial instruments and demonstrate tools which help to reduce uncertainties in the final fitted parameters.

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.

Chlorophyll fluorescence, primarily used to derive phytoplankton biomass, has long been an underutilized source of information on phytoplankton physiology. Diel fluctuations in chlorophyll fluorescence are affected by both photosynthetic efficiency and non-photochemical quenching (NPQ), where NPQ is a decrease in fluorescence through the dissipation of excess energy as heat. NPQ variability is linked to iron and light availability, and has the potential to provide important diagnostic information on phytoplankton physiology. Here we establish a relationship between NPQsv (Stern-Volmer NPQ) and indices of iron limitation from nutrient addition experiments in the sub-Antarctic zone (SAZ) of the Atlantic Southern Ocean, through the derivation of NPQmax (the maximum NPQsv value) and αNPQ (the light limited slope of NPQsv). Significant differences were found for both Fv/Fm and αNPQ for iron versus control treatments, with no significant differences for NPQmax. Similar results from CTDs indicated that changes in NPQ were driven by increasing light availability from late July to December, but by both iron and light from January to February. We propose here that variability in αNPQ, which has removed the effect of light availability, can potentially be used as a proxy for iron limitation (as shown here for the Atlantic SAZ), with higher values being associated with greater iron stress. This approach was transferred to data from a buoyancy glider deployment at the same location by utilizing the degree of fluorescence quenching as a proxy for NPQGlider, which was plotted against in situ light to determine αNPQ. Seasonal increases in αNPQ are consistent with increased light availability, shoaling of the mixed layer depth (MLD) and anticipated seasonal iron limitation. The transition from winter to summer, when positive net heat flux dominates stratification, was coincident with a 24% increase in αNPQ variability and a switch in the dominant driver from incident PAR to MLD. The dominant scales of αNPQ variability are consistent with fine scale variability in MLD and a significant positive relationship was observed between these two at a ∼10 day window. The results emphasize the important role of fine scale dynamics in driving iron supply, particularly in summer when this micronutrient is limiting.

Underwater gliders have become widely used in the last decade. This has led to a proliferation of data and the concomitant development of tools to process the data. These tools are focused primarily on converting the data from its raw form to more accessible formats and often rely on proprietary programing languages. This has left a gap in the processing of glider data for academics, who often need to perform secondary quality control (QC), calibrate, correct, interpolate and visualize data. Here, we present GliderTools, an open-source Python package that addresses these needs of the glider user community. The tool is designed to change the focus from the processing to the data. GliderTools does not aim to replace existing software that converts raw data and performs automatic first-order QC. In this paper, we present a set of tools, that includes secondary cleaning and calibration, calibration procedures for bottle samples, fluorescence quenching correction, photosynthetically available radiation (PAR) corrections and data interpolation in the vertical and horizontal dimensions. Many of these processes have been described in several other studies, but do not exist in a collated package designed for underwater glider data. Importantly, we provide potential users with guidelines on how these tools are used so that they can be easily and rapidly accessible to a wide range of users that span the student to the experienced researcher. We recognize that this package may not be all-encompassing for every user and we thus welcome community contributions and promote GliderTools as a community-driven project for scientists.