Inhibition and success of prymnesium parvum invasion on plankton communities in Texas, USA and prymnesium parvum pigment dynamics

Prymnesium parvum Carter, a haptophyte species capable of forming harmful algal blooms (HABs), has been identified in fresh and brackish water habitats worldwide. In Texas, P. parvum blooms have diminished local community revenues from losses to tourism, fishing, and hatchery production. In this thesis, P. parvum dynamics were studied using in-situ microcosm experiments at Lake Possum Kingdom, Texas during three seasons (fall, winter, spring) in 2004-2005. Specifically, nutrient additions were used to test the hypothesis that increased nutrient levels would not enhance P. parvum's ability to invade phytoplankton communities. In addition to full nutrient additions to levels of f/2 media, other treatments included nutrient additions deficient in either nitrogen (N) or phosphorus (P). Additionally, barley straw extract was tested as a growth inhibitor to prevent P. parvum blooms. Furthermore, P. parvum initial population density was examined to test the hypothesis that increased initial populations could promote an increase in P. parvum population densities. Findings indicated that P. parvum populations in Lake Possum Kingdom would not likely gain a selective advantage over other species when inorganic nutrients (nitrogen and phosphorus) were not limiting. P. parvum did, however, gain an advantage during both N- and P-limited conditions as indicated by toxicity, cell concentrations, and bulk phytoplankton community shifts. Furthermore, P. parvum blooms in Lake Possum Kingdom would likely not be inhibited by barley straw extract application. Initial population densities affected the final population density, but only when initial populations were low. A method to quickly and accurately detect the presence of P. parvum is needed due to P. parvum's potential to cause toxic and lethal blooms. This thesis tested whether P. parvum photopigments are conservative regardless of growth conditions and could be used to quantify the relative abundance of P. parvum in mixed community samples. If biomarker pigments are conservative, then an optimized version of CHEMTAX could be employed as an alternative diagnostic tool to microscopy for enumeration of P. parvum. However, P. parvum pigments in the Texas strain were not conservative throughout the growth cycle and therefore may not be a reliable indicator of cell abundance

A chlorophyll-a algorithm for Landsat-8 based on mixture density networks

Material suplementario disponible en:Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs ). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ( N=905 ) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ∼73 % and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

Plenary: Exploring toxic cyanobacteria blooms using emerging technologies: From space to the benthos

Traditionally coastal systems have been monitored using ship-based sampling methods, which provide a discrete spatial and temporal snapshot of current conditions. These discrete sampling strategies often miss key environmental shifts that lead to high variability in bloom biomass and toxin concentrations (e.g. 2014 Toledo Water Crisis). Understanding and interpreting the complex interactions between biological, chemical, and physical variables in coastal systems require versatile monitoring approaches. To enhance our spatio-temporal resolution, NOAA is developing a monitoring network in Lake Erie to study and track cyanobacteria harmful algal blooms using traditional methods and emerging technologies. Weekly monitoring at fixed sampling sites provides baseline environmental conditions. The extent of bloom biomass and phytoplankton community composition is determined through daily satellite and weekly hyperspectral imaging. Paired with physical observations and modeling, bloom biomass and toxin concentration trajectories are forecasted up to 5 days in the future. Within the water column, real-time nutrient buoys and second generation (2G) Environmental Sample Processors (ESP) provided near real-time relevant water quality data and particulate microcystin concentrations, respectively. With colleagues at the Monterey Bay Aquarium Research Institute (MBARI) we have tested a 3rd Generation (3G) ESP, an autonomous molecular diagnostic device integrated with an uncrewed underwater vehicle, capable of collecting eDNA and conducting near real-time toxin (e.g., microcystin) analysis using an embedded Surface Plasmon Resonance (SPR) module. Finally, field-based experiments evaluate the role of resuspension events, cell buoyancy, resting cells, and community dynamics on bloom development. This collection of actionable temporal and spatial environmental data is provided to the scientific community, managers, and public stakeholders to support decision making and enhance our understanding of bloom succession

Response of the Toxic Dinoflagellate Karenia brevis to Current and Projected Environmental Conditions: Salinity and Global Climate Change

Harmful algal blooms (HABs) are increasing in frequency and duration worldwide. Karenia brevis, the major toxic dinoflagellate in the Gulf of Mexico, produces potent neurotoxins, known as brevetoxins. For K. brevis, only minor concentrations of brevetoxins are needed to induce toxicity and environmental conditions appear to have the most direct impact on the cellular content of these toxins. A better understanding of K. brevis biology is essential to understand the mechanisms underlying toxin production and the ecology of such HABs, as well as to better anticipate and respond to such blooms. Here we present findings on the effect of salinity and availability of carbon on cellular physiology and brevetoxin and brevenal production by K. brevis. When grown at salinities of 35 and 27, but otherwise identical conditions, total brevetoxin cellular concentration varied between 0 to 18.5 pg cell-1 and brevenal varied between 0 and 1 pg cell-1. In response to hypoosmotic stress brevetoxin production was triggered, as a result, brevetoxin production increased up to 53%, while growth rates remained unchanged. A significant hypoosmotic event of >11%, was needed to trigger the response in brevetoxin production. To determine if K. brevis was sensing changes in specific ions within seawater (K+, Cl- or Ca2+), we systematically removed one ion while keeping the remaining ions at equivalent molar concentration for salinity of 35. Dilution in seawater K+ concentrations triggered the production of brevetoxins, increasing production ≥44%. Ecosystem changes due to climate change have increased the production of toxins in other HAB species; here we examined the impact on K. brevis. We have shown that modification of pCO2 level and temperature did not influence brevetoxin production; however, predicted climate change scenarios (increased temperature and pCO2) did significantly increase the growth rate of K. brevis, by 60% at 25°C and 55% at 30°C. We suggest that K. brevis blooms could benefit from predicted increase in pCO2 over the next 100 years. Overall, our findings close a critical gap in knowledge regarding the function of brevetoxin in K. brevis by identifying a connection between brevetoxin production and osmoacclimation

Osmotic stress triggers toxin production by the dinoflagellate Karenia brevis

With the increase in frequency of harmful algal blooms (HABs) worldwide, a better understanding of the mechanisms that influence toxin production is needed. Karenia brevis, the major HAB dinoflagellate in the Gulf of Mexico, produces potent neurotoxins, known as brevetoxins. Human health is directly impacted by blooms of K. brevis through consumption of shellfish contaminated by accumulated brevetoxins (neurotoxic shellfish poisoning) or from aerosolized brevetoxins in sea spray (reduced respiratory function); however, the reason for brevetoxin production has remained a mystery. Here we show that brevetoxin production increased dramatically in response to osmotic stress in three of the four K. brevis clones examined. By rapidly changing salinity to simulate a shift from oceanic conditions to a decreased salinity typical of coastal conditions, brevetoxin production was triggered. As a result, brevetoxin cell quota increased by >14-fold, while growth rate remained unchanged. Live images of K. brevis cells were also examined to assess changes in cell volume. In the K. brevis Wilson clone, cells responded quickly to hypoosmotic stress by increasing their brevetoxin cell quota from ∼10 to 160 pg of brevetoxin per cell, while cell volume remained stable. In contrast, the K. brevis SP1 clone, which has a consistently low brevetoxin cell quota (<1 pg per cell), was unable to balance the hypoosmotic stress, and although brevetoxin production remained low, average cell volume increased. Our findings close a critical gap in knowledge regarding mechanisms for toxin production in K. brevis by providing an explanation for toxin production in this harmful dinoflagellate

Impacts of elevated pCO\u3csub\u3e2\u3c/sub\u3e on estuarine phytoplankton biomass and community structure in two biogeochemically distinct systems in Louisiana, USA

© 2018 Ocean acidification has the potential to impact the ocean\u27s biogeochemical cycles and food web dynamics, with phytoplankton in the distinctive position to profoundly influence both, as individual phytoplankton species play unique roles in energy flow and element cycling. Previous studies have focused on short-term exposure of monocultures to low pH, but do not reflect the competitive dynamics within natural phytoplankton communities. This study explores the use of experimental microcosms to expose phytoplankton assemblages to elevated pCO2 for an extended period of time. Phytoplankton communities were collected from two biogeochemically distinct Louisiana estuaries, Caillou Lake (CL) and Barataria Bay (BB), and cultured in lab for 16 weeks while bubbling CO2 enriched air corresponding to current (400 ppm) and future (1000 ppm) pCO2 levels. Results suggest that elevated pCO2 does not implicitly catalyze an increase in phytoplankton biomass (chlorophyll a). While pigment data showcased a parabolic trend and microscopic observations revealed a loss in species diversity within each major taxonomic class. By the end of the 16-week incubation, 10 out of the 12 cultures had a community structure analogous to that of the startup phytoplankton assemblage collected from the field. Natural phytoplankton assemblages exposed to elevated pCO2 experienced multiple transitional states over the course of a 16-week incubation, indicating that there is no deterministic successional pathway dictated by coastal acidification but community adaptation was observed