Distribution Of Phytoplankton Biomass Research Articles

Multi-vehicle adaptive 3D mapping for targeted ocean sampling.

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Exploring intrinsic distribution of phytoplankton relative abundance and biomass in combination with continental-scale field investigation and microcosm experiment

Phytoplankton are primary producers in aquatic ecosystems and their diversity directly affects the community stability and primary productivity. However, the commonly used diversity indices (such as Shannon and Pielou indices) were originally derived from other fields rather than ecology and did not have a direct biological explanatory function. There is still a need to incorporate biological explanatory functions into diversity evaluation methods and theories to bridge the gap between phytoplankton biodiversity and biological characteristics. This study aimed to explicate the intrinsic distribution patterns of phytoplankton relative abundance and biomass. Our study demonstrated an exponential distribution pattern of phytoplankton relative abundance and biomass ranking through field investigations of 367 phytoplankton samples in China and microcosm experiments, respectively. Microcosm experiments illustrated that the linear distribution of the specific growth rate ranking resulted in an exponential distribution of the relative phytoplankton biomass ranking due to exponential growth patterns. Through mathematical deduction, it was found that the three indices a, k and N in the exponential distribution could be considered as the critical relative abundance of extinction, competition coefficient and the environmental taxa capacity, respectively. We found that a was positively correlated with Shannon index and Pielou index, k was negatively correlated with Shannon index, Pielou index and Chao1 index. In addition, N and Chao1 index were almost exactly the same. Our study obtained these indices based on the distribution pattern of phytoplankton, enabling a comprehensive analysis of the phytoplankton community and providing novel insights for further evaluating the health of aquatic ecosystems.

Seasonal enhancement of phytoplankton biomass in the southern tropical Indian Ocean: Significance of meteorological and oceanography parameters

The present study focused on understanding the seasonality of the phytoplankton biomass (chlorophyll a) distribution in the oligotrophic, Equatorial, and Southern Tropical Indian Ocean (ESTIO; 0–30°S and 60–90°E). The long-term satellite data analyses (2003–2020) showed a strong seasonality in sea surface temperature (SST), wind, currents, mean sea level anomaly (MSLA), photosynthetically available radiation (PAR), euphotic depth (ZEU) and mixed layer depth (MLD). As a response to the hydrographical changes, the phytoplankton biomass showed noticeable seasonal variation with the highest biomass during the Austral Winter (AW; June–September; avg. 0.11 ± 0.03 mg/m³) and lowest during the Austral Summer (AS; November–February; avg. 0.07 ± 0.03 mg/m). High chlorophyll patches (>0.1 mg/m³) were found between 0°–8°S during the AS and expanded over 0°–18°S during the AW. As multi-year mean chlorophyll a was higher (>0.1 mg/m³) in the northern part of the ESTIO (north of ∼13°S; HCD: high chlorophyll a domain) than the southern side (LCD: low chlorophyll a domain), the study area was divided into two domains and all the variables were analysed. In the HCD, enhancement of chlorophyll a was positively correlated with variables such as wind speed, wind stress, Ekman pumping, stronger northward and westward winds, as well as the presence of cyclonic eddies. These features are likely to stimulate primary production by uplifting the thermocline and enhancing nutrient supply. In the LCD, mixed layer depth also showed a strong positive correlation with elevated chlorophyll a, apparently because it is deep throughout the year (thereby keeping lower biomass) and deepens more strongly in winter than in the HCD. Another contrast with the HCD is that the cyclonic eddies appear to be insufficiently abundant to influence its chlorophyll a. Pearson's multivariable correlation analysis and principle component analysis confirmed the statistical significance of the above parameters on the enhancement of chlorophyll a in the ESTIO.

Impact of critical eddy diffusivity on seasonal bloom dynamics of Phytoplankton in a global set of aquatic environments

The intensity of eddy diffusivity and the spatial average of water velocity at the depths of the water column in oceans and lakes play a fundamental role in phytoplankton production and phytoplankton and zooplankton biomass, and community composition. The critical depth and intensity of turbulent mixing within the water column profoundly affect phytoplankton biomass, which depends on the sinking characteristic of planktonic algal species. We propose an Nutrient-Phytoplankton-Zooplankton (NPZ) model in 3D space with light and nutrient-limited growth in a micro-scale ecological study. To incorporate micro-scale observation of phytoplankton intermittency in bloom mechanism in stationary as well as oceanic turbulent flows, a moment closure method has been applied in this study. Experimental observations imply that an increase in turbulence is sometimes ecologically advantageous for non-motile planktonic algae. How do we ensure whether there will be a bloom cycle or whether there can be any bloom at all when the existing phytoplankton group is buoyant, heavier, motile, or non-motile? To address these questions, we have explored the effects of critical depth, the intensity of eddy diffusivity, spatial average of water velocity, on the concentration as well as horizontal and vertical distribution of phytoplankton and zooplankton biomass using a mathematical model and moment closure technique. We quantify a critical threshold value of eddy diffusivity and the spatial average of water velocity and observe the corresponding changes in the phytoplankton bloom dynamics. Our results highlight the importance of eddy diffusivity and the spatial average of water velocity on seasonal bloom dynamics and also mimic different real-life bloom scenarios in Mikawa Bay (Japan), Tokyo Bay (Japan), Arakawa River (Japan), the Baltic Sea, the North Atlantic Ocean, Gulf Alaska, the North Arabian Sea, the Cantabrian Sea, Lake Nieuwe Meer (Netherlands) and several shallower lakes.

The influences of phenology, spatial distribution, and nitrogen form on Long Island Sound phytoplankton biomass and taxonomic composition

Long Island Sound (LIS), USA, is an urban estuary that receives excessive nitrogen (N) inputs resulting in ecosystem impairments, such as hypoxia and algal blooms. Despite established linkages between N concentrations and algal blooms worldwide, connections between N forms and phytoplankton assemblages within LIS remain less characterized, representing a critical information gap for water quality management. This study assessed the spatio-temporal distributions of phytoplankton biomass and abundances, N, and other key water quality parameters Mar-2020 to Oct-2021 in Western (WLIS), Central (CLIS), and Eastern LIS (ELIS). During both study years, mean dissolved inorganic N (DIN) concentrations were significantly higher during fall than other seasons (ANOVA: p < 0.004), coincident with diatom blooms. Diatoms were abundant throughout the study, though WLIS flagellate numbers rose during Jul and Aug both years. Smaller (<5 μm diameter) cells generally contributed most to overall chlorophyll a (Chl a) and with significantly higher summer biomass in WLIS and CLIS than other seasons (ANOVA; WLIS: p < 0.004; CLIS: p < 0.006). Across LIS, there was a clear west to east decline in levels of Chl a, DIN, and the proportional contribution of picoplankton to overall phytoplankton biomass; however, seasonal mean concentrations of Chl a, nutrients, and other water quality parameters did not significantly differ between sampling (0.5 and 2 m) depths except for ELIS summer temperature and Chl a (T-test; both p = 0.002). Given climate change challenges, such as warmer temperatures enhancing blooms particularly in systems enriched with anthropogenic N, findings indicate that N-management strategies should re-evaluate targets spanning LIS. Study results are likely applicable to other eutrophic urban estuaries worldwide.

Machine learning for prediction of daily sea surface dimethylsulfide concentration and emission flux over the North Atlantic Ocean (1998–2021)

As the most ubiquitous natural source of sulfur in the atmosphere, dimethylsulfide (DMS) promotes aerosol formation in marine environments, impacting cloud radiative forcing and precipitation, eventually influencing regional and global climate. In this study, we propose a machine learning predictive algorithm based on Gaussian process regression (GPR) to model the distribution of daily DMS concentrations in the North Atlantic waters over 24 years (1998–2021) at 0.25° × 0.25° spatial resolution. The model was built using DMS observations from cruises, combined with satellite-derived oceanographic data and Copernicus-modelled data. Further comparison was made with the previously employed machine learning methods (i.e., artificial neural network and random forest regression) and the existing empirical DMS algorithms. The proposed GPR outperforms the other methods for predicting DMS, displaying the highest coefficient of determination (R2) value of 0.71 and the least root mean square error (RMSE) of 0.21. Notably, DMS regional patterns are associated with the spatial distribution of phytoplankton biomass and the thickness of the ocean mixed layer, displaying high DMS concentrations above 50°N from June to August. The amplitude, onset, and duration of the DMS annual cycle vary significantly across different regions, as revealed by the k-means++ clustering. Based on the GPR model output, the sea-to-air flux in the North Atlantic from March to September is estimated to be 3.04 Tg S, roughly 44 % lower than the estimates based on extrapolations of in-situ data. The present study demonstrates the effectiveness of a novel method for estimating seawater DMS surface concentration at unprecedented space and time resolutions. As a result, we are able to capture high-frequency spatial and temporal patterns in DMS variability. Better predictions of DMS concentration and derived sea-to-air flux will improve the modeling of biogenic sulfur aerosol concentrations in the atmosphere and reduce aerosol-cloud interaction uncertainties in climate models.

Chlorophyll-a concentration climatology, phenology, and trends in the optically complex waters of the St. Lawrence Estuary and Gulf

The spatiotemporal distribution of phytoplankton biomass drives the marine ecosystem. Chlorophyll-a concentration (Chla) is a biomass index for microalgae in seawater that is commonly used to study phytoplankton by means of satellite remote sensing. The St. Lawrence Estuary and Gulf (SLEG), in Eastern Canada, is a highly dynamic subpolar environment characterized by complex marine optical properties that make it difficult to distinguish Chla from the background signal caused by a strong freshwater discharge. In this study, we implement an inverse model based on a set of in situ Chla processed through a principal component analysis, making it specifically designed for the local marine optical conditions. We used this model to convert a multi-mission remote sensing reflectance dataset to daily Chla between 1998 and 2019 at a 4 km spatial resolution. From the resulting Chla time series, we computed the climatology, phenology and trend over the SLEG. The Chla climatology reveals the presence of relatively high Chla in the Gaspé Current, along the Gulf's North Shore and in areas of strong tidal mixing. Important differences in the phytoplankton phenology between the various subregions are found, with a prevailing shift towards earlier spring blooms of larger intensity. Finally, we found a positive mean Chla increase of 1.1% yr −1 over the SLEG, with strong positive trends in the Magdalen Shallows and west of the Anticosti Island. This description of the surface Chla in the SLEG provides important baseline information for the marine ecosystem. • Our regional algorithm produces realistic chlorophyll-a in the St. Lawrence Estuary and Gulf. • Chlorophyll-a is higher in coastal areas with upwellings or freshwater input. • Spring blooms develop from south to north, with the estuarine portion last. • Between 1998 and 2019, chlorophyll-a increased by 1.1% yr −1 .

Physical control on the inter-annual variability of summer dissolved nutrient concentration and phytoplankton biomass in the Indian sector of the Southern Ocean

To understand the role of physical processes and their interannual variability on the dissolved nutrient concentration and phytoplankton biomass distribution, field data collected in the Indian sector of the Southern Ocean (ISSO) during the austral summer of 2009–2011 are used. In the subtropical zone, macronutrients were limited (N:P < 1, N:Si < 1, Si:P ≈ 1) and the phytoplankton biomass variability was mainly governed by the mesoscale eddy activity associated with the Agulhas Return Current. High nutrient low chlorophyll condition prevailed in the sub-Antarctic zone and further south. A South-North gradient of the upper layer dissolved SiO2 was higher than that of NO3. The sub-Antarctic zone was characterized by the highest N:Si ratio (>4) and it was associated with the enhanced draw down of silicate due to the winter/spring diatom blooms in the region. The chlorophyll-a (CHL) concentration in the Polar Frontal Zone was low (∼0.2 mg m–3) in 2009 and 2010 but it was high (0.5 mg m–3) in 2011. This increase in CHL in 2011 was due to the supply of dissolved iron from the strong winds and subsequent mixing during the winter of 2010. Further, the increased CHL values in the Antarctic zone (0.5 mg m–3) in 2011 compared to 2009 and 2010 could be due to the increased sea ice melting associated with positive Southern Annular Mode. The increased phytoplankton biomass in the summer of 2011 coincides with an increase in nitrate utilization (N:P≈13) compared to 2009 and 2010. Observations showed that ISSO frontal zones are characterized by inter-annual variability in terms of nutrient utilization and phytoplankton biomass production.

Phytoplankton Distributions in the Kuroshio-Oyashio Region of the Northwest Pacific Ocean: Implications for Marine Ecology and Carbon Cycle

The Northwest Pacific Ocean (NWPO) is a significant sink for atmospheric CO2 but a paucity of large-scale phytoplankton surveys in the upper and lower euphotic zone results in uncertainties in estimates of the efficiency of the biological carbon pump there. Here, we report the spatial distribution of lipid biomarkers from diatoms (brassicasterol/epi-brassicasterol), dinoflagellates (dinosterol), and haptophytes (C37 alkenones) as proxies of phytoplankton biomass and community structure in suspended particles from the surface and deep chlorophyll maximum (DCM) layers across low- and mid-latitude regions of the NWPO. Our observations suggest that these lipid biomarkers can be used as indicators of the vertical distributions of phytoplankton biomass, which was comparable between the surface and DCM layers. Water masses with different nutrient concentrations strongly controlled the variations of lipid biomarkers, showing high biomass and the dominance of diatoms in the eutrophic Oyashio region, whereas low biomass and high proportions of dinoflagellates and haptophytes occurred in the oligotrophic Kuroshio region. Diatoms predominated in the DCM, likely enhancing carbon sequestration in the deep ocean and in sediments. Our results quantitatively demonstrate the horizontal and vertical variations of phytoplankton biomass and community structure, leading to an improved understanding of ecosystem function and biogeochemical cycles in this important region of the NWPO.

Distinctive phytoplankton size responses to the nutrient enrichment of coastal upwelling and winter convection in the eastern Arabian Sea

Even though the seasonal pattern of phytoplankton biomass distribution in the seas around India is reasonably well understood, there is relatively little information on phytoplankton size classes. The current study shows how phytoplankton of different size classes respond to nutrient enrichment caused by vertical convective mixing during the Northeast Monsoon [(NEM) November-February] and coastal upwelling during the Southwest Monsoon [(SWM) June-September] in the Eastern Arabian Sea (EAS). In the Northeastern Arabian Sea (NEAS), phytoplankton biomass [Chlorophyll-a (Chl-a)] and primary production (PP) were moderate and comparable during both the NEM (Chl-a av. 21 mg m−2 and PP av. 601 mg C m−2 d-1) and the SWM (Chl-a av. 27 mg m−2 and PP av. 516 mg C m−2 d-1). The Southeastern Arabian Sea (SEAS) was oligotrophic (Chl-a av. 12 mg m−2 and PP av. 197 mg C m−2 d-1) during the NEM due to strong surface stratification, but due to coastal upwelling, it turned into a very productive system along the shelf waters during the SWM (Chl-a av. 45 mg m−2 and PP av. 1201 mg C m−2 d-1). Interestingly, the subsurface Chl-a maximum (SCM) was almost absent in the NEAS during both seasons. Similarly, SCM was absent in the entire coastal upwelling zone of the EAS during the SWM. In these areas and situations, Chl-a was found to accumulate in the nutrient-rich surface layers of the shallow euphotic zones.The nutrient enrichment of the coastal upwelling along the shelf waters in the EAS favoured the growth of larger micro-and meso-phytoplankton during the SWM [av. 43.8 mg m−2 (av. 65.9% total Chl-a)], whereas the entire NEAS favoured the growth of nano-phytoplankton during the NEM [av. 13.9 mg m−2 (av. 64.8% total Chl-a)]. Differences in the physical processes enabling the entrainment of nutrients (Nitrate (NO3), Phosphate (PO4) and Silicate (SiO4)] into the euphotic layer could explain the observed differences in the phytoplankton size fractions. The convective mixing in the NEAS during the NEM erodes the bottom of the mixed layer (50–120 m), resulting in only moderate enrichment of NO3 (av. 0.62 ± 0.45 µM) and SiO4 (av. 3.01 ± 0.83 µM), and low level of PO4 (av. 0.49 ± 0.13 µM) in the mixed layer (av. 60 m). This caused N -limitation in the NEAS, which favoured the dominance of nano-phytoplankton. Alternatively, coastal upwelling during the SWM drives deeper subsurface waters (75–150 m) into the shallow surface mixed layer (av. 30 m) in shelf water, causing significant enrichment of NO3 (av. 11.02 ± 3.55 µM), SiO4 (av. 18.34 ± 11.37 µM) and PO4 (av. 1.15 ± 0.21 µM). The enhanced nutrients in the coastal upwelling zones favoured the dominance of larger micro and meso-phytoplankton. However, during the SWM, the oceanic waters of the entire EAS showed low nutrient concentration compared to the shelf waters, which favoured the dominance of nanophytoplankton. In conclusion, our work reveals the large contribution of nano-phytoplankton in Chl-a biomass across the entire EAS, underlining their ecological relevance in both N-limited and N-enriched environments.

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

Abstract. Earth system models (ESMs) are useful tools for predicting and understanding past and future aspects of the climate system. However, the biological and physical parameters used in ESMs can have wide variations in their estimates. Even small changes in these parameters can yield unexpected results without a clear explanation of how a particular outcome was reached. The standard method for estimating ESM sensitivity is to compare spatiotemporal distributions of variables from different runs of a single ESM. However, a potential pitfall of this method is that ESM output could match observational patterns because of compensating errors. For example, if a model predicts overly weak upwelling and low nutrient concentrations, it might compensate for this by allowing phytoplankton to have a high sensitivity to nutrients. Recently, we demonstrated that neural network ensembles (NNEs) are capable of extracting relationships between predictor and target variables within ocean biogeochemical models. Being able to view the relationships between variables, along with spatiotemporal distributions, allows for a more mechanistically based examination of ESM outputs. Here, we investigated whether we could apply NNEs to help us determine why different ESMs produce different spatiotemporal distributions of phytoplankton biomass. We tested this using three cases. The first and second case used different runs of the same ESM, except that the physical circulations differed between them in the first case, while the biological equations differed between them in the second. Our results indicated that the NNEs were capable of extracting the relationships between variables for different runs of a single ESM, allowing us to distinguish between differences due to changes in circulation (which do not change relationships) from changes in biogeochemical formulation (which do change relationships). In the third case, we applied NNEs to two different ESMs. The results of the third case highlighted the capability of NNEs to contrast the apparent relationships of different ESMs and some of the challenges it presents. Although applied specifically to the ocean components of an ESM, our study demonstrates that Earth system modelers can use NNEs to separate the contributions of different components of ESMs. Specifically, this allows modelers to compare the apparent relationships across different ESMs and observational datasets.

Variability of hydrographic factors, biomass and structure of the phytoplankton community at the entrance to the Gulf of California (spring 2013)

This study reports observations of the horizontal and vertical variation of chlorophyll a (Chla) and the taxonomic composition of phytoplankton at the entrance to the Gulf of California (GC) and its relationship with physicochemical processes. This region is part of the northern boundary of one of the largest shallow Oxygen Minimum Zones in the world, characterized by marked physicochemical gradients. Therefore, understanding the prevailing seasonal patterns and their relationship with primary producers is considered particularly relevant. The study period, April 2013, was characterized by mesoscale eddies and fronts associated with several water masses (California Current Water - CCW, Gulf of California Water - GCW, and Subtropical Subsurface Water - StSsW), which influenced the horizontal and vertical distribution of Chla between 0 and 150 m. Superficial southward flow of CCW was observed near the west coast of Baja California, turning toward the GC flowing between 30 and 60 m depth. At the entrance to the GC, the currents circulated anticyclonically, flowing into the GC near Baja California, while GCW flowed out of the GC near the mainland coast. The vertical distribution of Chla featured a subsurface maximum layer (SCML) associated with the 25 kg m-3 isopycnal, highlighting the importance of stratification processes as drivers of the vertical distribution of phytoplankton biomass. In turn, this favored different associations of phytoplankton groups. In surface waters, the phytoplankton community was dominated by picocyanobacteria (Synechococcus and Prochlorococcus) and Prymnesiophytes, while at the SCML, Prymnesiophytes coexist mainly with Prasinophytes, Chrysophytes, and Cryptophytes. The prevalence of these flagellates (and the absence of autotrophic dinoflagellates) suggests the importance of a multivorous food web in this region, typical of environments where carbon recycling predominates in the pelagic zone. Diatoms made no significant contribution to Chla, although their abundance increased in the SCML associated with the influence of the CCW. The low N:P (8.3) and Si:N (0.86) ratios indicate that nitrates and silica are potentially limiting nutrients for the development of this group.

Phytoplankton Bloom Changes under Extreme Geophysical Conditions in the Northern Bering Sea and the Southern Chukchi Sea

The northern Bering Sea and the southern Chukchi Sea are undergoing rapid regional biophysical changes in connection with the recent extreme climate change in the Arctic. The ice concentration in 2018 was the lowest since observations began in the 1970s, due to the unusually warm southerly wind in winter, which continued in 2019. We analyzed the characteristics of spring phytoplankton biomass distribution under the extreme environmental conditions in 2018 and 2019. Our results show that higher phytoplankton biomass during late spring compared to the 18-year average was observed in the Bering Sea in both years. Their spatial distribution is closely related to the open water extent following winter-onset sea ice retreat in association with dramatic atmospheric conditions. However, despite a similar level of shortwave heat flux, the 2019 springtime biomass in the Chukchi Sea was lower than that in 2018, and was delayed to summer. We confirmed that this difference in bloom timing in the Chukchi Sea was due to changes in seawater properties, determined by a combination of northward oceanic heat flux modulation by the disturbance from more extensive sea ice in winter and higher surface net shortwave heat flux than usual.

FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth

Abstract. Coupled physical–biogeochemical models can generally reproduce large-scale patterns of primary production and biogeochemistry, but they often underestimate observed variability and gradients. This is partially caused by insufficient representation of systematic variations in the elemental composition and pigment density of phytoplankton. Although progress has been made through approaches accounting for the dynamics of phytoplankton composition with additional state variables, formidable computational challenges arise when these are applied in spatially explicit setups. The instantaneous acclimation (IA) approach addresses these challenges by assuming that Chl:C:nutrient ratios are instantly optimized locally (within each modeled grid cell, at each time step), such that they can be resolved as diagnostic variables. Here, we present the first tests of IA in an idealized 1-D setup: we implemented the IA in the Framework for Aquatic Biogeochemical Models (FABM) and coupled it with the General Ocean Turbulence Model (GOTM) to simulate the spatiotemporal dynamics in a 1-D water column. We compare the IA model against a fully dynamic, otherwise equivalently acclimative (dynamic acclimation; DA) variant with an additional state variable and a third, non-acclimative and fixed-stoichiometry (FS) variant. We find that the IA and DA variants, which require the same parameter set, behave similarly in many respects, although some differences do emerge especially during the winter–spring and autumn–winter transitions. These differences however are relatively small in comparison to the differences between the DA and FS variants, suggesting that the IA approach can be used as a cost-effective improvement over a fixed-stoichiometry approach. Our analysis provides insights into the roles of acclimative flexibilities in simulated primary production and nutrient drawdown rates, seasonal and vertical distribution of phytoplankton biomass, formation of thin chlorophyll layers and stoichiometry of detrital material.

Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake

The over-proliferation of phytoplankton has been a public concern for the last several decades. To evaluate the importance of different environmental factors on phytoplankton biomass variations with their complex response relationships in the large eutrophic Lake Okeechobee, the nonlinear methods including Generalized Additive Model (GAM) and Random Forest (RF) algorithm were employed. A long-term monitoring dataset of 8 sampling sites ranging from January 1996 to December 2010 was applied to explore the driven factors and response relationships of phytoplankton biomass from the different scales in Lake Okeechobee. Results showed a spatially heterogeneous distribution of phytoplankton biomass there, and the western and southern littoral zones occurred heavier algal blooms than the central pelagic zones. Spearman’s correlation results denoted Chlorophyll a (Chla) was negatively correlated with phosphorus in different scales. In the aspect of temporal variations, phytoplankton biomass appeared a three-peak-two-valley variation trend there, which peaked in 1997, 2003, and 2010, respectively. On the lake-wide scale, the RF model indicated that the inorganic nutrients were the primary predictors of phytoplankton biomass, while underwater light availability factors were followed to play essential roles in the prediction. On the local scale, total nitrogen (TN) was the top predictor in the western near-shore zone, while other zones were nitrate and nitrite (NO23) or phosphate (PO4). GAM results suggested that phytoplankton biomass had positive response to TN increase in both lake-wide and local scales. However, its response to water temperature (WT) appeared spatial heterogeneity. This study provided a new perspective to evaluate the primary predictors of phytoplankton biomass with their response relationships from lake-wide scale and local scale in the large shallow eutrophic lake, and the importance of taking spatial heterogeneity into account for lake water quality management was stressed.

Do Loop Current eddies stimulate productivity in the Gulf of Mexico?

Abstract. Surface chlorophyll concentrations inferred from satellite images suggest a strong influence of the mesoscale activity on biogeochemical variability within the oligotrophic regions of the Gulf of Mexico (GoM). More specifically, long-living anticyclonic Loop Current eddies (LCEs) are shed episodically from the Loop Current and propagate westward. This study addresses the biogeochemical response of the LCEs to seasonal forcing and show their role in driving phytoplankton biomass distribution in the GoM. Using an eddy resolving (1/12∘) interannual regional simulation, it is shown that the LCEs foster a large biomass increase in winter in the upper ocean. It is based on the coupled physical–biogeochemical model NEMO-PISCES (Nucleus for European Modeling of the Ocean and Pelagic Interaction Scheme for Carbon and Ecosystem Studies) that yields a realistic representation of the surface chlorophyll distribution. The primary production in the LCEs is larger than the average rate in the surrounding open waters of the GoM. This behavior cannot be directly identified from surface chlorophyll distribution alone since LCEs are associated with a negative surface chlorophyll anomaly all year long. This anomalous biomass increase in the LCEs is explained by the mixed-layer response to winter convective mixing that reaches deeper and nutrient-richer waters.

Phytoplankton mortality in a changing thermal seascape.

Predicting spatiotemporal distributions of phytoplankton biomass and community composition heavily relies on experimental studies that document how environmental conditions influence population growth rates. In unicellular phytoplankton, the net population growth rate is the difference between the cell division rate and the death rate. Along with predation and disease, phytoplankton mortality arises from abiotic stress. Although the effect of temperature on the net population growth rate is well understood, studies examining thermally induced death rates in phytoplankton are scarce. We investigated how cell division and death rates of the diatom Phaeodactylum tricornutum varied within its thermal tolerance limits (thermal niche), and at temperatures just above its upper thermal tolerance limit. We show that death rates were largely independent of temperature when P. tricornutum was grown within its thermal niche, but increased significantly at temperatures that approached or exceeded its upper thermal tolerance limit. Furthermore, the sensitivity of mortality increased with the duration of exposure to heat stress and was affected by the pre-acclimation temperature. Heat waves can be expected to significantly affect phytoplankton mortality episodically. The increasing frequency of heat waves accompanying global warming can be expected to drive changes in phytoplankton community structure due to interspecific variability of thermal niches with potential implications for food web dynamics and biogeochemical cycles.

Under Ice and Early Summer Phytoplankton Dynamics in Two Arctic Lakes with Differing DOC

AbstractWe investigated Arctic lake phytoplankton response along vertical gradients in the water column during seasonal succession from ice‐covered to open‐water conditions. Two oligotrophic lakes in West Greenland with different dissolved organic carbon (DOC) concentrations were selected. We assessed which factors: (1) promote under‐ice growth of phytoplankton, and (2) trigger shifts in the community structure. Our results suggest that DOC is an important driver of the seasonal distribution of phytoplankton biomass—high DOC exacerbates light limitation under ice resulting in low phytoplankton biomass, but supports phytoplankton growth during the open‐water period when photolytic and biological degradation of organic matter contributes to the pool of available nutrients. Under‐ice phytoplankton biomass in a clear, low DOC lake was as high as during the open‐water period, suggesting the importance of under‐ice processes for lake metabolic balance, also evidenced by persistent oxic conditions in the hypolimnion. Species composition was dynamic, reflecting rapidly changing conditions over the course of the season. Our data suggest that phytoplankton under ice provides seed populations for early spring blooms and that the largest shifts in species composition occur later during the summer, likely as a result of nutrient depletion and intensified grazing pressure. Early spring temperatures in West Greenland have risen rapidly in recent decades, triggering shifts in the timing of lake ice‐out and the onset of stratification; therefore, it is important to understand phytoplankton seasonal dynamics to disentangle the effects of climate‐driven shifts on aquatic biota.

Impact of a Mesoscale Cyclonic Eddy on the Phytoplankton Biomass of Bay of La Paz in the Southern Gulf of California

Mesoscale eddies are ubiquitous oceanic structures that play pivotal roles in planktonic ecosystems. In this paper, we present observational evidence of the impact of a mesoscale cyclonic eddy on the phytoplankton biomass, measured by chlorophyll-a concentration, in the Bay of La Paz, Gulf of California, using information gathered during a multidisciplinary research cruise in the summer (August) of 2017. A CTD rosette system was used to record high-resolution hydrographic readings and to take water samples for nutrients and spectrophotometric determination of chlorophyll-a concentrations, throughout a grid of stations covering the whole area. The results showed that a mesoscale cyclonic eddy, with a diameter of ∼20 km and a maximum velocity of 0.95 m s–1 at its periphery, had established from the surface to a depth of 100 m, inducing differences in the hydrographic properties of the water column. The phytoplankton biomass distribution was closely related to the circulation pattern, with the highest chlorophyll-a concentration (>3 mg m–3) observed in the center of the eddy, in a layer between 20 and 30 m depth, with absolute maximum values located at 20 m depth as a consequence of nutrient pumping. The presence of mesoscale eddies in the Bay of La Paz has been described previously, but their impact on the phytoplankton biomass and its three-dimensional variability has not been quantified systematically. Therefore, further studies that document the effects of these eddies on phytoplankton biomass are needed to better understand highly productive marine areas, such as the Bay of La Paz, and to propose conservation and management strategies.