Recommendations for obtaining unbiased chlorophyll estimates from in situ chlorophyll fluorometers: A global analysis of WET Labs ECO sensors

Chlorophyll fluorometers provide the largest in situ global data set for estimating phytoplankton biomass because of their ease of use, size, power consumption, and relatively low price. While in situ chlorophyll a (Chl) fluorescence is proxy for Chl a concentration, and hence phytoplankton biomass, there exist large natural variations in the relationship between in situ fluorescence and extracted Chl a concentration. Despite this large natural variability, we present here a global validation data set for the WET Labs Environmental Characterization Optics (ECO) series chlorophyll fluorometers that suggests a factor of 2 overestimation in the factory calibrated Chl a estimates for this specific manufacturer and series of sensors. We base these results on paired High Pressure Liquid Chromatography (HPLC) and in situ fluorescence match ups for which non-photochemically quenched fluorescence observations were removed. Additionally, we examined matches between the factory-calibrated in situ fluorescence and estimates of chlorophyll concentration determined from in situ radiometry, absorption line height, NASA’s standard ocean color algorithm as well as laboratory calibrations with phytoplankton monocultures spanning diverse species that support the factor of 2 bias. We therefore recommend the factor of 2 global bias correction be applied for the WET Labs ECO sensors, at the user level, to improve the global accuracy of chlorophyll concentration estimates and products derived from them. We recommend that other fluorometer makes and models should likewise undergo global analyses to identify potential bias in factory calibration

An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe

Abstract. Nearly half of the earth’s photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Obtaining phytoplankton diversity from ocean color: A scientific roadmap for future development

This is the final version. Available from Frontiers Media via the DOI in this record.To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition.ESA SEOM SY-4Sci Synergy projectNAS

Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications

Since 2012, an array of 105 Biogeochemical-Argo (BGC-Argo) floats has been deployed across the world’s oceans to assist in filling observational gaps that are required for characterizing open-ocean environments. Profiles of biogeochemical (chlorophyll and dissolved organic matter) and optical (single-wavelength particulate optical backscattering, downward irradiance at three wavelengths, and photosynthetically available radiation) variables are collected in the upper 1000m every 1 to 10 days. The database of 9837 vertical profiles collected up to January 2016 is presented and its spatial and temporal coverage is discussed. Each variable is quality controlled with specifically developed procedures and its time series is quality-assessed to identify issues related to biofouling and/or instrument drift. A second database of 5748 profile-derived products within the first optical depth (i.e., the layer of interest for satellite remote sensing) is also presented and its spatiotemporal distribution discussed. This database, devoted to field and remote ocean color applications, includes diffuse attenuation coefficients for downward irradiance at three narrow wavebands and one broad waveband (photosynthetically available radiation), calibrated chlorophyll and fluorescent dissolved organic matter concentrations, and single wavelength particulate optical backscattering. To demonstrate the applicability of these databases, data within the first optical depth are compared with previously established bio-optical models and used to validate remotely derived bio-optical products. The quality-controlled databases are publicly available from the SEANOE (SEA scieNtific Open data Edition) publisher at https://doi.org/10.17882/49388 and https://doi.org/10.17882/47142 for vertical profiles and products within the first optical depth, respectively

Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades

Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challengesinthisapproachliesintheassignmentoftheappropriatemodelparametersthatdefinethe photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climatequalitysatelliteobservationswereusedtoassessglobalprimaryproductionanditsvariability with seasons and locations as well as between years. In addition, the sensitivity of the computed primaryproductiontopotentialchangesinthephotosyntheticresponseofphytoplanktoncellsunder changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr−1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. TheassimilationnumberoftheP-Icurveshowedstrongrelationshipswithenvironmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. RelationshipsbetweentheinitialslopeoftheP-Icurveandenvironmentalvariableswere more elusive

Phytoplankton functional types from Space.

The concept of phytoplankton functional types has emerged as a useful approach to classifying phytoplankton. It finds many applications in addressing some serious contemporary issues facing science and society. Its use is not without challenges, however. As noted earlier, there is no universally-accepted set of functional types, and the types used have to be carefully selected to suit the particular problem being addressed. It is important that the sum total of all functional types matches all phytoplankton under consideration. For example, if in a biogeochemical study, we classify phytoplankton as silicifiers, calcifiers, DMS-producers and nitrogen fix- ers, then there is danger that the study may neglect phytoplankton that do not contribute in any significant way to those functions, but may nevertheless be a significant contributor to, say primary production. Such considerations often lead to the adoption of a category of “other phytoplankton” in models, with no clear defining traits assigned them, but that are nevertheless necessary to close budgets on phytoplankton processes. Since this group is a collection of all phytoplankton that defy classification according to a set of traits, it is difficult to model their physi- ological processes. Our understanding of the diverse functions of phytoplankton is still growing, and as we recognize more functions, there will be a need to balance the desire to incorporate the increasing number of functional types in models against observational challenges of identifying and mapping them adequately. Modelling approaches to dealing with increasing functional diversity have been proposed, for example, using the complex adaptive systems theory and system of infinite diversity, as in the work of Bruggemann and Kooijman (2007). But it is unlikely that remote-sensing approaches might be able to deal with anything but a few prominent functional types. As long as these challenges are explicitly addressed, the functional- type concept should continue to fill a real need to capture, in an economic fashion, the diversity in phytoplankton, and remote sensing should continue to be a useful tool to map them. Remote sensing of phytoplankton functional types is an emerging field, whose potential is not fully realised, nor its limitations clearly established. In this report, we provide an overview of progress to date, examine the advantages and limitations of various methods, and outline suggestions for further development. The overview provided in this chapter is intended to set the stage for detailed considerations of remote-sensing applications in later chapters. In the next chapter, we examine various in situ methods that exist for observing phytoplankton functional types, and how they relate to remote-sensing techniques. In the subsequent chapters, we review the theoretical and empirical bases for the existing and emerging remote-sensing approaches; assess knowledge about the limitations, assumptions, and likely accuracy or predictive skill of the approaches; provide some preliminary comparative analyses; and look towards future prospects with respect to algorithm development, validation studies, and new satellite mis- sions

Ginger Stimulates Hematopoiesis via Bmp Pathway in Zebrafish

) has been widely used in traditional medicine; however, to date there is no scientific research documenting the potential of ginger to stimulate hematopoiesis. expression in the caudal hematopoietic tissue area. We further confirmed that Bmp/Smad pathway mediates this hematopoiesis promoting effect of ginger by using the Bmp-activated Bmp type I receptor kinase inhibitors dorsomorphin, LND193189 and DMH1.Our study provides a strong foundation to further evaluate the molecular mechanism of ginger and its bioactive components during hematopoiesis and to investigate their effects in adults. Our results will provide the basis for future research into the effect of ginger during mammalian hematopoiesis to develop novel erythropoiesis promoting agents

Controls of picophytoplankton abundance and composition in a highly dynamic marine system, the Northern Alboran Sea (Western Mediterranean)

The Alboran Sea is a highly dynamic basin which exhibits a high spatio-temporal variability of hydrographic structures (e.g. fronts, gyres, coastal upwellings). This work compares the abundance and composition of picophytoplankton observed across the northern Alboran Sea among eleven cruises between 2008 and 2012 using flow cytometry. We evaluate the seasonal and longitudinal variability of picophytoplankton on the basis of the circulation regimes at a regional scale and explore the presence of cyanobacteria ecotypes in the basin. The maximal abundances obtained for Prochlorococcus, Synechococcus and picoeukaryotes (12.7 × 104, 13.9 × 104 and 8.6 × 104 cells mL− 1 respectively) were consistent with those reported for other adjacent marine areas. Seasonal changes in the abundance of the three picophytoplankton groups were highly significant although they did not match the patterns described for other coastal waters. Higher abundances of Prochlorococcus were obtained in autumn-winter while Synechococcus and picoeukaryotes exhibited a different seasonal abundance pattern depending on the sector (e.g. Synechococcus showed higher abundance in summer in the west sector and during winter in the eastern study area). Additionally, conspicuous longitudinal gradients were observed for Prochlorococcus and Synechococcus, with Prochlorococcus decreasing from west to east and Synechococcus following the opposite pattern. The analysis of environmental variables (i.e. temperature, salinity and inorganic nutrients) and cell abundances indicates that Prochlorococcus preferred high salinity and nitrate to phosphate ratio. On the contrary, temperature did not seem to play a role in Prochlorococcus distribution as it was numerically important during the whole seasonal cycle. Variability in Synechococcus abundance could not be explained by changes in any environmental variable suggesting that different ecotypes were sampled during the surveys. In particular, our data would indicate the presence of at least two ecotypes of Synechococcus: a summer ecotype widely distributed in the whole Alboran Sea and a winter ecotype adapted to lower temperature and higher nutrient concentration whose growth is favoured in the eastern sector.Versión del editor2,444