Evaluation of diagnostic pigments to estimate phytoplankton size classes

Phytoplankton pigment dynamics in marine lake fluctuating between stratified and holomictic euxinic conditions

Biomass dynamics in the marine lake are strongly dependent on seasonal variability in vertical stratification, indicating rapid adaptation of phytoplankton to short-term changes in the water column. A small marine lake (Rogoznica Lake, Croatia), which fluctuates between stably stratified and holomictic euxinic conditions, was used as a model to study the phytoplankton responses to environmental perturbations, in particular the anoxic stress, caused by periodic holomixia. The epilimnion showed significant temporal and vertical variability with a chlorophyll a subsurface maximum with the highest biomass near the chemocline. Fucoxanthin-containing biomass (diatoms) dominated in the epilimnion in colder seasons and was first to recover after holomictic euxinic events. The shift towards the smaller groups prevailed during highly stratified water column conditions in warmer seasons. Results for the hypolimnion were more enigmatic, with high concentrations of alloxanthin, zeaxanthin, and violaxanthin indicating the presence of a viable small-size mixotrophic community under extreme conditions.

Phytoplankton package effect in oceanic waters: Influence of chlorophyll-a and cell size

In this study, the interaction between the packaging effect (Q_a^⁎) and total chlorophyll-a concentration (C_t) or total size index (SI_t) was investigated to explore the potential bio-optical mechanism in phytoplankton cells in the global oceans. In addition, the long-term spatiotemporal characteristics of these interactions were necessary for grasping their variation. Numerous in situ surface measurements (phytoplankton pigment and absorption coefficients) from the global oceans were analyzed first, and then correlation and causality analyses were performed on the satellite-deduced Q_a^⁎, C_t, and SI_t in the global oceans during 2002-2020. The results show a negative correlation between Q_a^⁎ and C_t or SI_t in the low latitudes (30°S-30°N) and a positive correlation in the middle latitudes (30°S-55°S and 30°N-55°N). The causality analysis reveals a mutual and asymmetric cause-effect relationship between Q_a^⁎ and C_t or SI_t in the low latitudes. The stabilization effect of Q_a^⁎ contributes to a 10%-50% variation in C_t and SI_t, with 40%-60% uncertainty of Q_a^⁎ caused by C_t and SI_t in the low latitudes, which is inverse in the middle latitudes. The remaining contribution to each variable mainly originates from long-term trends and noise. Combining the analysis between Q_a^⁎ and the irradiance, the balancing processes in phytoplankton cells are different in the low (phytoplankton-driving mode) and middle latitudes (irradiance-driving mode), which is related to photoacclimation and photoinhibition. The analyses provide insights into the quantitative interpretation of the relationship between Q_a^⁎ and C_t or SI_t, which contribute knowledge that has not been previously reported.

Phytoplankton Responses to Bacterially Regenerated Iron in a Southern Ocean Eddy

In the Subantarctic sector of the Southern Ocean, vertical entrainment of iron (Fe) triggers the seasonal productivity cycle but diminishing physical supply during the spring to summer transition forces microbial assemblages to rapidly acclimate. Here, we tested how phytoplankton and bacteria within an isolated eddy respond to different dissolved Fe (DFe)/ligand inputs. We used three treatments: one that mimicked the entrainment of new DFe (Fe-NEW), another in which DFe was supplied from bacterial regeneration of particles (Fe-REG), and a control with no addition of DFe (Fe-NO). After 6 days, 3.5 (Fe-NO, Fe-NEW) to 5-fold (Fe-REG) increases in Chlorophyll a were observed. These responses of the phytoplankton community were best explained by the differences between the treatments in the amount of DFe recycled during the incubation (Fe-REG, 15% recycled c.f. 40% Fe-NEW, 60% Fe-NO). This additional recycling was more likely mediated by bacteria. By day 6, bacterial production was comparable between Fe-NO and Fe-NEW but was approximately two-fold higher in Fe-REG. A preferential response of phytoplankton (haptophyte-dominated) relative to high nucleic acid (HNA) bacteria was also found in the Fe-REG treatment while the relative proportion of diatoms increased faster in the Fe-NEW and Fe-NO treatments. Comparisons between light and dark incubations further confirmed the competition between picophytoplankton and HNA for DFe. Overall, our results demonstrate great versatility by microorganisms to use different Fe sources that results in highly efficient Fe recycling within surface waters. This study also encourages future research to further investigate the interactions between functional groups of microbes (e.g. HNA and cyanobacteria) to better constraint modeling in Fe and carbon biogeochemical cycles.

Plankton Imagery Data Inform Satellite-Based Estimates of Diatom Carbon

Estimating the biomass of phytoplankton communities via remote sensing is a key requirement for understanding global ocean ecosystems. Of particular interest is the carbon associated with diatoms given their unequivocal ecological and biogeochemical roles. Satellite-based algorithms often rely on accessory pigment proxies to define diatom biomass, despite a lack of validation against independent diatom biomass measurements. We used imaging-in-flow cytometry to quantify diatom carbon in the western North Atlantic, and compared results to those obtained from accessory pigment-based approximations. Based on this analysis, we offer a new empirical formula to estimate diatom carbon concentrations from chlorophyll a. Additionally, we developed a neural network model in which we integrated chlorophyll a and environmental information to estimate diatom carbon distributions in the western North Atlantic. The potential for improving satellite-based diatom carbon estimates by integrating environmental information into a model, compared to models that are based solely on chlorophyll a, is discussed.

Machine Learning for the Study of Plankton and Marine Snow from Images

Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.

Relationships between optical backscattering, particulate organic carbon, and phytoplankton carbon in the oligotrophic South China Sea basin

The particulate backscattering coefficient (b_bp) provides effective proxies for particulate organic carbon (POC) and phytoplankton carbon (C_phy); however, their bio-optical relationships in the oligotrophic ocean are rarely reported. In this work, based on the in-situ synchronous optical and biogeochemical measurements in the oligotrophic South China Sea (SCS) basin, we refined the regional relationships between POC (and C_phy) and b_bp and investigated the impacts of phytoplankton community compositions and size classes on the b_bp variability. The observations showed that: 1) POC and C_phy exhibited good linear relationships with b_bp; 2) the relationship between C_phy and POC could also be fitted in a linear function with a positive POC intercept, and the POC contributed by phytoplankton-covarying non-algal particles was nearly two-fold of C_phy; and 3) the POC-specific b_bp (b*_bp) was positively correlated with the fraction of the phytoplankton groups haptophytes (Type 8) and diatoms to total Chla, but negatively correlated with the fraction of pico-phytoplankton to Chla (f_pico). These findings suggest that in oligotrophic waters, the variability of b*_bp was mainly determined by the variability in the relative contribution of large phytoplankton with complex structures.