Phytoplankton Types Research Articles

Examining the size-specific photosynthesis-irradiance parameters and relationship with phytoplankton types in a subtropical marginal sea

Through photosynthesis, phytoplankton initiate material cycling and the flow of energy through marine ecosystems. The structure of marine ecosystems is sensitive to the size of primary producers, and size-specific photosynthesis-irradiance (P-I) parameters are required for modeling the size-specific primary production. We defined a phytoplankton type to be a group of phytoplankton communities with similar compositions and hypothesized that a function of phytoplankton types might be used to quantify systematic differences of P-I parameters between size classes (structural effects) and variations of the magnitudes of those parameters if the overlap between habitats was small (environmental effects). To test this hypothesis, we conducted a study to measure the size-specific P-I parameters of two size classes (<2 μm and >2 μm) and pigment-based phytoplankton community structure in a marginal sea, the Taiwan Strait. Three phytoplankton types were classified, and type-based and size-specific P-I parameters were derived. The results demonstrated the potential of a type-based model to estimate P-I parameters for two phytoplankton size classes as well as different habitats.

Komposisi dan Kemelimpahan Jenis Fitoplankton di Perairan Pelabuhan Carik Kecamatan Bayan Kabupaten Lombok Utara

This study aims to determine the composition and abundance of phytoplankton in the waters of Carik Harbor, North Lombok Regency. Research activities include: determining sampling points, taking samples, and measuring water quality (including temperature, pH, salinity, and brightness) at each sampling point and identifying types of phytoplankton. Data were collected by purposive sampling, as many as 7 points in the coastal area of ​​Carik Harbor. Based on the identification results, calculations were made on the composition of the number of species. The results of laboratory observations, found 62 types of phytoplankton from Carik harbor waters from 2 orders, namely: Bacillariophyceae (Diatomae) and Dynophyceae. Species from the order Bacillariophyceae are the most dominating, with 58 species, while from other orders, namely Dynophyceae, there are 4 species. The highest abundance was at station 6 (143080 cells/L) and the lowest was at station 5 (54880 cells/L).

HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters

Biomass estimation of multiple phytoplankton groups from remote sensing reflectance spectra requires inversion models that go beyond the traditional band-ratio techniques. To achieve this objective retrieval models are needed that are rooted in radiative transfer (RT) theory and exploit the full spectral information for the inversion. HydroLight numerical solutions of the radiative transfer equation are well suited to support this inversion. We present a fast and flexible Python framework for forward and inverse modelling of multi- and hyperspectral observations, by further extending the formerly developed HydroLight Optimization (HYDROPT) algorithm. Computation time of the inversion is greatly reduced using polynomial interpolation of the radiative transfer solutions, while at the same time maintaining high accuracy. Additional features of HYDROPT are specification of sensor viewing geometries, solar zenith angle and multiple optical components with distinct inherent optical properties (IOP). Uncertainty estimates and goodness-of-fit metrics are simultaneously derived for the inversion routines. The pursuit to retrieve multiple phytoplankton groups from remotely sensed observations illustrates the need for such flexible retrieval algorithms that allow for the configuration of IOP models characteristic for the region of interest. The updated HYDROPT framework allows for more than three components to be fitted, such as multiple phytoplankton types with distinct absorption and backscatter characteristics. We showcase our model by evaluating the performance of retrievals from simulated Rrs spectra to obtain estimates of 3 phytoplankton size classes in addition to CDOM and detrital matter. Moreover, we demonstrate HYDROPTs capability for the inter-comparison of retrievals using different sensor band settings including coupling to full spectral coverage, as would be needed for NASA’s PACE mission. The HYDROPT framework is now made available as an open-source Python package.

A bioenergetics model for seasonal growth of Indian oil sardine (Sardinella longiceps) in the Indian west coast

The Indian oil sardine (Sardinella longiceps) fishery was observed with wide stock fluctuation. Ecological parameters, mainly temperature and prey density, had a profound influence on the growth of S. longiceps and hence the production. In this study, a fish bioenergetics model, coupled with a lower trophic level model, was developed to reproduce the seasonality in the growth of S. longiceps. For this, we have used an Indian Ocean adaptation of an intermediate complex ecosystem model called North Pacific Ecological Modeling for Understanding Regional Oceanography (NEMURO) for marine productivity simulations. The model has 11-component ecosystem variables such as two types of phytoplankton (small and large including flagellates and diatoms), three types of zoo-planktons (small, large, and predatory, which includes ciliates, copepods, and euphausiids), particulate and dissolved organic matter, opal, cycling of nitrate, ammonia, and silicate. The prey densities derived from the NEMURO were input to the sardine bioenergetics model. The coupled model reproduced the appropriate growth rate and wet-weight of S. longiceps in its seasonal cycle in four major fishery regions such as Kochi, Goa, Ratnagiri, and Mumbai as verifiable from the available observation. In Kochi, the mean wet weight was 72.0 ± 12.8 g (June to September), 65.4 ± 5.3 g (October to November), 82.4 ± 2.7 g (December to February), and 66.7 ± 3.8 g (March to May). Goa and Ratnagiri have moderate weights with mean wet weight as 73.6 ± 10.6 g (June to September), 87.4 ± 3.2 g (October to November), 95.5 ± 4.3 g (December to February), and 76.2 ± 6.8 g (March to May). In the Mumbai region, maximum weight is simulated with mean wet weight as 97.4 ± 13.3 g (June to September), 102.1 ± 1.6 g (October to November), 104.8 ± 1.3 g (December to February), and 101.6 ± 1.2 g (March to May). Sensitivity analysis revealed the importance of temperature and consumption in the growth of sardine. Detailed model validation with available observations is presented.

PLANKTON ANALYSIS IN THE PONDS OF CATFISH (Clarias sp) AND NILE TILAPIA (Oreochromis niloticus)

Clarias and Nile tilapia ponds contain high organic matter levels which will be decomposed by bacteria. It also produces carbon dioxide, which can be utilized by phytoplankton for photosynthesis process. This research aimed to compare the types of phytoplankton and zooplankton in Clarias and Nila tilapia ponds for eutrophication and water quality control. The research was conducted in August-September 2020. The water samples were taken using plankton net to become 35 ml from Clarias and Nile tilapia ponds. Analysis of plankton abudance, diversity, Evenness and Dominance Index was carried out in the Laboratorium Hidrobiologi. The analysis result of the average abundance of phytoplankton in the Clarias ponds was 71.026 cell/mL (11 genera), and zooplankton was 93 ind/mL (4 genera). Meanwhile in Nile tilapia ponds was 1.503 cell/mL (9 genera), and zooplankton was 91 ind/mL (1 genus). The average of zooplankton in Nile tilapia pond was higher than in catfish ponds; since Clarias are carnivores while Tilapia are omnivores. The plankton diversity index of catfish pond was average 0.817, while in Nile Tilapia was average 0.553. The evenness ndex of catfish pond was average 0.5, while in Nile Tilapia was average 0,4. The dominance index of catfish pond was average 0.59, while in Nile Tilapia was average 0.77. The conclusion obtained is the type of phytoplankton in Catfish pond consisted of 11 genera (99.62%) and zooplankton of 4 genera (0.38%), while in Nile Tilapia pond the type of phytoplankton consisted of 9 genera (97.85%) and zooplankton of 1 genera (2.15%).

Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning

Observing the vertical dynamic of phytoplankton in the water column is essential to understand the evolution of the ocean primary productivity under climate change and the efficiency of the CO2 biological pump. This is usually made through in-situ measurements. In this paper, we propose a machine learning methodology to infer the vertical distribution of phytoplankton pigments from surface satellite observations, allowing their global estimation with a high spatial and temporal resolution. After imputing missing values through iterative completion Self-Organizing Maps, smoothing and reducing the vertical distributions through principal component analysis, we used a Self-Organizing Map to cluster the reduced profiles with satellite observations. These referent vector clusters were then used to invert the vertical profiles of phytoplankton pigments. The methodology was trained and validated on the MAREDAT dataset and tested on the Tara Oceans dataset. The different regression coefficients R2 between observed and estimated vertical profiles of pigment concentration are, on average, greater than 0.7. We could expect to monitor the vertical distribution of phytoplankton types in the global ocean.

Study on zooplankton diversity aquatic ecobiology of Batang Toru River, North Sumatera, Indonesia

The Batang Toru River is a fairly long river, flowing from the North Tapanuli region and emptying into the Indian Ocean (South Tapanuli Regency). Increased deforestation in the Batang Toru River area has increased the potential for decreasing water quality of the Batang Toru River. The purpose of this study was to determine the condition of the water quality of the Batang Toru River. This research was conducted for 3 months (April, July, October), in the Batang Toru River and its surroundings (14 observation stations scattered from the area near the upstream to the coast). The data analysis used in this research is the analysis of the abundance and composition of the types of phytoplankton, the Diversity Index (H’), the Evennes Index (E), and the Dominance Index (C). Based on the research results, zooplankton in these waters consist of Six Phylum of zooplankton, such as Ciliophora (4 genera), Amoebozoa (2 genera), Cercozoa (1 genera), Rotifera (9 genera), Arthopoda (3 genera), and Annelida (2 genera). Diversity, Evenness, and Dominance Indices were around 0.349-1.847, 0.503-0.994 and 0.240-0.802. Based on the index value, it can be concluded that the Batang Toru River is classified as experiencing moderate ecological pressure.

Plankton Diversity at Rice Field-Pond in Glagah Village, Lamongan

This research aimed to describe plankton diversity at rice field-pond in Glagah Village, Lamongan that can be used for fish culture. Sampling was done by purposive sampling with 5 sampling points at each end of the pond and in the middle, the place of rice field-pond around of the school. Each sampling point is divided into 2 depth variations, surface (0 cm) and base (70 cm). The results showed in the first point found 1 type of phytoplankton and 1 type of zooplankton, the second point found 9 types of phytoplankton and 2 type of zooplankton, the third point found 10 types of phytoplankton and 3 types of zooplankton, in the fourth point found 2 types of zooplankton while in the fifth point found 1 type of phytoplankton and 1 type of zooplankton. From the observation concluded that phytoplankton diversity of H ‘2.27 (medium) and zooplankton of H’ 1.543 (medium). So it can be concluded that the diversity of phytoplankton in rice field-pond is greater than zooplankton. Physical and chemical characteristics at rice field-pond water affect the plankton diversity so that it must be maintained.

Factors controlling the competition between &amp;lt;i&amp;gt;Phaeocystis&amp;lt;/i&amp;gt; and diatoms in the Southern Ocean and implications for carbon export fluxes

Abstract. The high-latitude Southern Ocean phytoplankton community is shaped by the competition between Phaeocystis and silicifying diatoms, with the relative abundance of these two groups controlling primary and export production, the production of dimethylsulfide, the ratio of silicic acid and nitrate available in the water column, and the structure of the food web. Here, we investigate this competition using a regional physical–biogeochemical–ecological model (ROMS-BEC) configured at eddy-permitting resolution for the Southern Ocean south of 35∘ S. We improved ROMS-BEC by adding an explicit parameterization of Phaeocystis colonies so that the model, together with the previous addition of an explicit coccolithophore type, now includes all biogeochemically relevant Southern Ocean phytoplankton types. We find that Phaeocystis contribute 46±21 % (1σ in space) and 40±20 % to annual net primary production (NPP) and particulate organic carbon (POC) export south of 60∘ S, respectively, making them an important contributor to high-latitude carbon cycling. In our simulation, the relative importance of Phaeocystis and diatoms is mainly controlled by spatiotemporal variability in temperature and iron availability. In addition, in more coastal areas, such as the Ross Sea, the higher light sensitivity of Phaeocystis at low irradiances promotes the succession from Phaeocystis to diatoms. Differences in the biomass loss rates, such as aggregation or grazing by zooplankton, need to be considered to explain the simulated seasonal biomass evolution and carbon export fluxes.

Exploring biogeochemical and ecological redundancy in phytoplankton communities in the global ocean.

Climate‐change‐induced alterations of oceanic conditions will lead to the ecological niches of some marine phytoplankton species disappearing, at least regionally. How will such losses affect the ecosystem and the coupled biogeochemical cycles? Here, we couch this question in terms of ecological redundancy (will other species be able to fill the ecological roles of the extinct species) and biogeochemical redundancy (can other species replace their biogeochemical roles). Prior laboratory and field studies point to a spectrum in the degree of redundancy. We use a global three‐dimensional computer model with diverse planktonic communities to explore these questions further. The model includes 35 phytoplankton types that differ in size, biogeochemical function and trophic strategy. We run two series of experiments in which single phytoplankton types are either partially or fully eliminated. The niches of the targeted types were not completely reoccupied, often with a reduction in the transfer of matter from autotrophs to heterotrophs. Primary production was often decreased, but sometimes increased due to reduction in grazing pressure. Complex trophic interactions (such as a decrease in the stocks of a predator's grazer) led to unexpected reshuffling of the community structure. Alterations in resource utilization may cause impacts beyond the regions where the type went extinct. Our results suggest a lack of redundancy, especially in the ‘knock on’ effects on higher trophic levels. Redundancy appeared lowest for types on the edges of trait space (e.g. smallest) or with unique competitive strategies. Though highly idealized, our modelling findings suggest that the results from laboratory or field studies often do not adequately capture the ramifications of functional redundancy. The modelled, often counterintuitive, responses—via complex food web interactions and bottom‐up versus top‐down controls—indicate that changes in planktonic community will be key determinants of future ocean global change ecology and biogeochemistry.

LIMNOLOGICAL PROCESSES OF FACULTATIVE STABILIZATION LAGOONS IN THE TREATMENT OF LIQUID EFFLUENTS

Objective: To evaluate some of the main limnological aspects and discuss the relevance of effluent treatment efficacy by the FLs treatment system and provide fundamental information for technicians and environmental managers to make decisions about this type of treatment. Methods: The literature review was used as a methodology, specifically through scientific publications from 2016 to June 2020 on FL and limnology, applied in tropical climates. We selected articles written in Portuguese and English, and we chose to search in free terms, thus promoting a greater number of references on FLs and limnology. Results: The research showed the potential of FLs for the treatment of effluents and their contribution to sustainability. To this end, we show in this article the relevance of the environmental dynamics of FLs, contemplating characteristics related to their aerobic, anerobic, facultative processes, in addition to operational and limnological variables, such as the types of phytoplankton, effectiveness in removing nutrients and contributing to the landscape harmony and biodiversity conservation. We also emphasize that this is fundamental information for the operation and management of FLs. Conclusions: FLs, owing to their operational and limnological characteristics, are a type of favorable effluent treatment system in tropical climates as occurs in Brazil. Investment in this type of system can be favorable to the sustainability and harmony of the landscape, in addition to the fact that the limnological behavior of these ecosystems is favorable to the removal of nutrients, which in turn contributes to environmental conservation and pollution reduction.

Spatial changes in the summer diatom community of the northern Bering Sea in 2017 and 2018

In recent years, the northern Bering Sea has experienced changes in the timing of sea-ice retreat and in hydrographic conditions during the summer. The influence of these environmental changes on the diatom community has not been examined. In this study, we investigated the spatial changes in the diatom community of the northern Bering Sea during the summers of 2017 and 2018, and evaluated the effects of environmental variability on these communities. We found that the diatom cell density and diversity varied with water masses. A cluster analysis based on cell density revealed that the diatom communities were separated into four groups, and that the distributions of three of these groups were different spatially between 2017 and 2018. In the Bering Strait and the Chirikov Basin regions, the diatom communities differed between 2017 and 2018. In 2017, these diatom communities were dominated by cold-water species such as Chaetoceros gelidus and Chaetoceros spp. (subgenus Hyalochaetae), while in 2018, the community was dominated by cosmopolitan species such as Thalassionema nitzschioides and Chaetoceros spp. (subgenus Phaeoceros). NMDS and multiple regression analysis indicated that the timing of the sea-ice retreat was the most important contributor to the differences in the diatom community. In contrast, there was no year-to-year difference south of St. Lawrence Island, possibly because nutrients were depleted and phytoplankton types other than diatoms were dominant.

Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective

Monitoring phytoplankton community composition from space is an important challenge in ocean remote sensing. Researchers have proposed several algorithms for this purpose. However, the in-situ data used to train and validate such algorithms at the global scale are often clustered along ship cruise tracks and in some well-studied locations, whereas many large marine regions have no in-situ data at all. Furthermore, oceanographic variables are typically spatially auto-correlated. In this situation, the common practice of validating algorithms with randomly chosen held-out observations can underestimate errors. Based on a global database of in-situ HPLC data, we applied supervised learning methods to train and test empirical algorithms predicting the relative concentrations of eight diagnostic pigments that serve as biomarkers for different phytoplankton types. For each pigment, we trained three types of satellite algorithms distinguished by their input data: abundance-based (using only chlorophyll-a as input), spectral (using remote sensing reflectance), and ecological algorithms (combining reflectance and environmental variables). The algorithms were implemented as statistical models (smoothing splines, polynomials, random forests and boosted regression trees). To address clustering of data and spatial auto-correlation, we tested the algorithms by means of spatial block cross-validation. This provided a less confident picture of the potential for global mapping of diagnostic pigments and hence the associated phytoplankton types using existing satellite data than suggested by some previous research and a 5-fold cross-validation conducted for comparison. Of the eight diagnostic pigments, only two (fucoxanthin and zeaxanthin) could be predicted in marine regions that the algorithms were not trained in with considerably lower errors than a constant null model. Thus, global-scale algorithms based on existing, multi-spectral satellite data and commonly available environmental variables can estimate relative diagnostic pigment concentrations and hence distinguish phytoplankton types in some broad classes, but are likely inaccurate for some classes and in some marine regions. Overall, the ecological algorithms had the lowest prediction errors. Finally, our results suggest that more discussion of the best approaches for training and validating empirical satellite algorithms is needed if the in-situ data are unevenly distributed in the study region and spatially clustered.

Mesozooplankton Selective Feeding on Phytoplankton in a Semi-Enclosed Bay as Revealed by HPLC Pigment Analysis

Mesozooplankton have been known to be important consumers of phytoplankton, and the community plays an important role in removing the primary production in the marine ecosystem. In the present study, mesozooplankton grazing on phytoplankton were studied in situ at two sampling stations (TM4 and TM8) in Tolo Harbour. HPLC analysis showed that diatoms were the dominant phytoplankton in the two stations throughout the year, and contributed on average to over 40% of total phytoplankton biomass. Dinoflagellates were the second most abundant group of phytoplankton in the two monitoring stations, while the contribution of haptophytes, green algae, cyanobacteria, and cryptophytes was negligible. Feeding experiments, combined with HPLC pigment analysis, were conducted to measure mesozooplankton selective feeding on phytoplankton. The results demonstrated that mesozooplankton displayed a clear feeding selectivity for phytoplankton in Tolo Harbour. Firstly, mesozooplankton showed strong preference for the phytoplankton with the size of 20–200 μm, which suggested that the grazing selectivity and grazing rates of mesozooplankton were affected by the size of the food particles. On the other hand, mesozooplankton assemblages in Tolo Harbour displayed significant feeding selectivity for diatoms, dinoflagellates, and cryptophytes over other types of phytoplankton. The three algae groups are all the major phototrophic components in marine planktonic communities, and they often cause red tides in the marine environment. These results, taken together, suggested that mesozooplankton should play an important role in the regulation of red tides.

Carbon Export Buffering and CO2 Drawdown by Flexible Phytoplankton C:N:P Under Glacial Conditions

AbstractModern observations indicate that variations in marine phytoplankton stoichiometry correlate with the boundaries of major surface waters. For example, phytoplankton in the oligotrophic subtropical gyres typically have much higher C:N:P ratios (i.e., higher C:P and higher N:P ratios) than those in eutrophic upwelling regions and polar regions. Such a spatial pattern points to nutrient availability as a key environmental driver of stochiometric flexibility. Environmental dependence of phytoplankton C:N:P opens unexplored possibilities for modifying the strength of the biological pump under different climate conditions. Here we present a power law formulation of C:N:P flexibility that is driven by nutrients, temperature, and light. We embed the formulation in a global ocean carbon cycle model with multiple phytoplankton types and explore biogeochemical implications under glacial conditions. We find three key controls on export C:N:P ratio: phytoplankton physiology and community structure as well as the balance in regional production at the global level. Glacial inputs of iron and sea ice expansion are important modifiers of these three controls. We also find that global export C:N:P increases substantially under glacial conditions, and this strongly buffers global carbon export against decrease and draws down approximately 20 μatm of atmospheric CO2. These results point to the importance of including phytoplankton C:N:P flexibility in a mix of mechanisms that drive atmospheric CO2 over glacial‐interglacial time scale. Finally, our simulations indicate decoupling of nutrients, which may provide a resolution to the longstanding disagreement regarding nutrient utilization in the glacial Southern Ocean derived from different nutrient proxies.

Towards modeling growth rates of cyanobacteria in the Baltic Sea

Potential influences of various environmental factors (light conditions, mixed layer depth, water temperature) on growth rates of different cyanobacteria in the Baltic Sea (BS) are discussed. Growth rates were modeled as a function of quanta absorbed by phytoplankton. This in turn depends on phytoplankton exposition to light, which we simulated to represent realistic conditions encountered in the Baltic Sea in summer. We considered eight realistic scenarios (two cases of water clarity and four depths of the mixed layer). According to our calculations, exposition of phytoplankton to photosynthetically available radiation (PAR) in the BS can change about five times in case of the same solar surface insolation and water turbidity, solely due to changes in the mixed layer depth from 2 to 20 m. When we additionally accounted for differences in water turbidity, phytoplankton PAR exposition changed by one order of magnitude and this resulted in one order of magnitude differences in absorbed quanta for the same species of phytoplankton. Knowledge of single-species absorption characteristics allowed us to show that under the same water column conditions, the number of absorbed quanta varies significantly between different species of cyanobacteria, because of differences in their absorptive properties. The effectiveness of light absorption translated to different growth rates estimated for each phytoplankton species. Our research shows that further progress in phytoplankton modeling requires incorporating in the models improved information about optical properties, growth rates, and temperature and nutrient dependencies for different phytoplankton types.

Planktonic Study at Nyari-1 Reservoir of Rajkot (Gujarat) in India

People on globe are under tremendous threat due to undesired changes in the physical, chemical and biological charactristic of air, water and soil. Plankton is the most sensitive floating community which is being the first target of water pollution. Thus, any undesirable change in aquatic ecosystem affects diversity as well as biomass of this community. Plakton provides a large source of food to many aquatic organisms. The diversity of various types of zooplankton and phytoplankton were studied at Nyari-1 reservoir of Rajkot (Gujarat). The zooplankton were found and classified into Ciliata, Cladocera, Copepoda and Rotifera. Phytoplankton were found and classified into Chlorophyceae, Cyanophyceae, Baccilariophyceae and Euglenophyceae. In the present study Chlorophyceae, Cyanophyceae, Bacillariophyceae and Euglenophyceae were recorded maximum (count/l) in the month of October and December and minimum (count/l) in the month of April and June. While Ciliate, Cladocera, Copepod and Rotifer were recorded maximum (count/l) in the month of December and minimum (count/l) in the month of April. The study of plankton was carried out to understand its relation with water quality parameters. The physico-chemical analysis was carried out during study and it was concluded that the water is not so much polluted and was within the permissible limit.

Satellite Ocean Color Based Harmful Algal Bloom Indicators for Aquaculture Decision Support in the Southern Benguela

The aquaculture industry of southern Africa faces environmental threats from harmful algal blooms (HABs), which have the potential to cause devastating economic losses (Pitcher et al 2019). Satellite earth observation offers a systematic and cost effective method for operational monitoring of HABs over large areas. Whilst the chlorophyll-a concentration ([Chl-a]) product, often used as a proxy for phytoplankton biomass, can be used to indicate high biomass blooms (elevated biomass against a background signal of 5 - 10 mgChl m$^{-3}$), there is a clear need for value-added products that not only alert on bloom presence, but also on the bloom type and persistence. This study demonstrates the identification of different phytoplankton communities that can feasibly be identified in bloom concentrations from space, relevant to the aquaculture industry of South Africa. In terms of water-leaving reflectance, 76 % of the variance in the red and NIR spectral region is significantly positively correlated to phytoplankton abundance, [Chl-a], and the maximum line height (MLH) (defined as the height of the maximum reflectance peak above a baseline between 665 and 753 nm). The MLH is related to dominant phytoplankton types derived from phytoplankton count data, in order to identify thresholds which represent blooms that pose a high hypoxia and/or toxicity risk; whilst 0.00160.003 has a strong likelihood of indicating high biomass dinoflagellate or Pseudo-nitzschia blooms. These techniques are routinely used by the aquaculture industry in South Africa for decision support and risk mitigation. The high biomass nature of the South African regional waters provide strong assemblage-related spectral variability, which can be exploited with the spectral bands of OLCI and MERIS. Application to these sensors not only ensures future monitoring capability, but also allows the creation of a historical risk climatology that can guide the site selection of industries sensitive to the presence of HABs, such as aquaculture facilities and desalination plants.

Conservative management of Lahor Reservoir based on eutrophication conditions due to fish farming and agriculture waste

The purpose of this study is to determine the condition of water quality, trophic status, abundance and type of phytoplankton, and pollution load, as well as to formulate a conservative effort for reservoir management based on Lahor Reservoir conditions in 2017. The data needed for water quality analysis are total P, NO3-N, NO2-N, NH3-N, BOD, DO, TSS, and pH; hydrological and reservoir data such as rainfall data, the volume of the reservoir, reservoir catchment area, and Lahor reservoir outflow; as well as data on abundance and types of phytoplankton. The results of the water quality analysis of the Lahor Reservoir showed that it did not meet Class II water quality standards and trophic status, being eutrophic to hypereutrophic, which means the Lahor Reservoir has become polluted. The abundance of phytoplankton that occurs in the Lahor Reservoir exceeds the limit of the phytoplankton abundance that has been determined and is dominated by Microcystis blue-green algae. Therefore, the recommended efforts are bioremediation techniques, regulation of reservoir operation patterns, application of the Trophic Level-Based Aquaculture (TLBA) system, and introduction of rotifer zooplankton.

Factors controlling the competition between Phaeocystis and diatoms in the Southern Ocean

Abstract. The high-latitude Southern Ocean phytoplankton community is shaped by the competition between Phaeocystis and silicifying diatoms, with the relative abundance of these two groups controlling primary and export production, the production of dimethylsulfide, the ratio of silicic acid and nitrate available in the water column, and the structure of the food web. Here, we investigate this competition using a regional physical-biogeochemical-ecological model (ROMS-BEC) configured at eddy-permitting resolution for the Southern Ocean south of 35° S. We extended ROMS-BEC by an explicit parameterization of Phaeocystis colonies, so that the model, together with the previous addition of an explicit coccolithophore type, now includes all biogeochemically relevant Southern Ocean phytoplankton types. We find that Phaeocystis contribute 46 % and 40 % to annual NPP and POC export south of 60° S, respectively, making them an important contributor to high-latitude carbon cycling. In our simulation, the relative importance of Phaeocystis and diatoms is mainly controlled by the temporal variability in temperature and iron availability. The higher light sensitivity of Phaeocystis at low irradiances promotes the succession from Phaeocystis to diatoms in more coastal areas, such as the Ross Sea. Still, differences in the biomass loss rates, such as aggregation or grazing by zooplankton, need to be considered to explain the simulated seasonal biomass evolution.