Accuracy assessment of primary production models with and without photoinhibition using Ocean Colour Climate Change Initiative data in the North East Atlantic Ocean.

The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ 14C uptake measurements from the North East Atlantic Ocean (NEA). The models were run using the European Space Agency (ESA), Ocean Colour Climate Change Initiative (OC-CCI) version 3.0 data. The objectives of the study were to determine which is the most accurate PP model for the region in different provinces and seasons, what is the accuracy of the models using both high (daily) and low (eight day) temporal resolution OC-CCI data, and whether the performance of the models is improved by implementing a photoinhibition function? The Platt-Sathyendranath primary production model (PPPSM) was the most accurate over all NEA provinces and, specifically, in the Atlantic Arctic province (ARCT) and North Atlantic Drift (NADR) provinces. The implementation of a photoinhibition function in the PPPSM reduced its accuracy, especially at lower range PP. The Vertical Generalized Production Model-VGPM (PPVGPM) tended to over-estimate PP, especially in summer and in the NADR. The accuracy of PPVGPM improved with the implementation of a photoinhibition function in summer. The absorption model of primary production (PPAph), with and without photoinhibition, was the least accurate model for the NEA. Mapped images of each model showed that the PPVGPM was 150% higher in the NADR compared to PPPSM. In the North Atlantic Subtropical Gyre (NAST) province, PPAph was 355% higher than PPPSM, whereas PPVGPM was 215% higher. A sensitivity analysis indicated that chlorophyll-a (Chl a), or the absorption of phytoplankton, at 443 nm (aph (443)) caused the largest error in the estimation of PP, followed by the photosynthetic rate terms and then the irradiance functions used for each model

Hodgkin's Disease in HIV Patients: Two Clinical Case Reports

Os autores apresentam dois casos de febre prolongada em doente com infecção VIH. O seu estudo conduziu ao diagnóstico de Doença de Hodgkin

Editorial: The Atlantic Meridional Transect programme (1995-2023)

Since 1995 the Atlantic Meridional Transect (AMT) has undertaken measurements of oceanographic and atmospheric variables during 30 research cruises on a passage between the UK and destinations in the South Atlantic (Aiken and Bale, 2000; Robinson et al., 2006; Robinson et al., 2009; Rees et al., 2017). The transect spans more than 100° of latitude, samples to ocean depths of up to 1000 m and crosses a range of ecosystems from sub-polar to tropical, from eutrophic shelf seas and upwelling systems, to oligotrophic mid-ocean gyres. AMT has enabled the acquisition of repeat measurements of several Essential Ocean Variables and other ecosystem parameters and rate processes at a resolution of ~160 km (over ~13000 km). In delivering these activities AMT has facilitated long-term collaborations with NASA and ESA for the calibration and validation of satellite ocean colour sensors; with the UK Met-Office, NOC, NOAA, SOCCOM and University of Washington for ARGO and Bio-ARGO float deployment; and has maintained a long-term mooring in the South Atlantic Gyre (2009 to 2023). AMT data is archived and managed by the British Oceanographic Data Centre (BODC), whilst key data are also directed to other focus specific databases (e.g. NASA SeaBASS, ESA OC-CCI, SOCAT, CDIAC, SeaDataNet). The generation of sustained observations of ocean biogeochemical variables is invaluable in monitoring ecosystem function and health during this period of rapid climate and environmental change. Globally there are a number of initiatives which aim to make repeated observations which include ship transects such as GO-SHIP and GEOTRACES and deployment of hydrodynamical and biogeochemical sensors as part of the ARGO programme. Examples of fixed point observations in the Atlantic include: The European Station for Time-Series in the Ocean (ESTOC) which has provided observations of the eastern sub-tropical Atlantic for more than twenty five years (González-Dávila and Santana-Casiano, 2023), the Bermuda Atlantic Time Series (BATS) in the western sub�tropical Atlantic, which, since 1988 has documented increases in temperature, ocean acidification and decreasing oxygen (Bates and Johnson, 2021); In the north-east Atlantic,the Western Channel Observatory (WCO) has records dating to the early 20th century and in recent decades has further evidenced climate related shifts in plankton communities alongside increases in temperature and ocean acidification (McEvoy et al., 2023); the Estación Permanente de Estudios Ambientale (EPEA) in the western South Atlantic has evidenced increases in chlorophyll associated with an increased proportion of small celled phytoplankton (Lutz et al., 2023). The AMT offers a unique and alternative approach by making repeat measurements along a transect which incorporates the latitudinal range of all these fixed-point stations. AMT provides an inclusive platform for multi-disciplinary ocean research with cruise berths open to the international community upon request. The thirty research expeditions to date have involved 310 sea-going scientists from 81 institutes representing 31 countries, resulting in 400 refereed papers which are available here

Sensitivity of a Satellite Algorithm for Harmful Algal Bloom Discrimination to the Use of Laboratory Bio-optical Data for Training

Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical datafromthecultureandabio-opticalmodelwereusedtoconstructatrainingdatasetfor an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive ratesofdetectionofK. mikimotoi betweenusinganalgorithmtrainedwithpurelymanually selected areas on satellite images and using laboratory data as training was overall <1%. The difference was higher, <15%, when using modeled optical data rather than laboratorydata,withpotentialforimprovementiflocalaveragechlorophyllconcentrations are used. Using a laboratory-derived training dataset improved the ability of the algorithm to distinguish high turbidity from high chlorophyll concentrations. However, additional in situ observations of non-harmful high chlorophyll blooms in the area would improve testing of the ability to distinguish harmful from non-harmful high chlorophyll blooms. This approach can be expanded to use additional wavelengths, different satellite sensors and different phytoplankton genera

Uncertainty in ocean-color estimates of chlorophyll for phytoplankton groups

This is the final version. Available from Frontiers Media via the DOI in this record.Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.National Centre for Earth Observation (NCEO)European Space Agency (ESA)NERC-UK ECOMA

Uncertainty in ocean-colour estimates of chlorophyll for phytoplankton groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups

Deriving phytoplankton size classes from satellite data: Validation along a trophic gradient in the eastern Atlantic Ocean

In recent years, the global distribution of phytoplankton functional types (PFT) and phytoplankton size classes (PSC) has been determined by remote sensing. Many of these methods rely on interpretation of phytoplankton size or type from pigment data, but independent validation has been difficult due to lack of appropriate in situ data on cell size. This work uses in situ data (photosynthetic pigments concentration and cell abundances) from the north-east Atlantic, along a trophic gradient, sampled from 2005 to 2010, as well as Atlantic Meridional Transect (AMT) data for the same region, to test a previously developed conceptual model, which calculates the fractional contributions of pico-, nano- and micro-plankton to total phytoplankton chlorophyll biomass (Brewin et al., 2010). The application of the model proved to be successful, as shown by low mean absolute error between data and model fit. However, regional values obtained for the model parameters had some effect on the relative distribution of size classes as a function of chlorophyll-a, compared with the results according to the original model. The regional parameterisation yielded a dominance of micro-plankton contribution for chlorophyll-a concentrations greater than 0.5 mg m− 3, rather than from 1.3 mg m− 3 in the original model. Intracellular chlorophyll-a (Chla) per cell, for each size class, was computed from the cell enumeration results (microscope counts and flow cytometry) and the chlorophyll-a concentration for that size class given by the model. The median intracellular chlorophyll-a values computed were 0.004, 0.224 and 26.78 pg Chla cell− 1 for pico-, nano-, and micro-plankton respectively. This is generally consistent with the literature, thereby providing an indirect validation of the method based on pigments to assign size classes. Using a satellite-derived composite image of chlorophyll-a for the study area, a map of cell abundance was generated based on the computed intracellular chlorophyll-a for each size-class, thus extending the remote-sensing method for mapping size classes of phytoplankton from chlorophyll-a concentration to mapping cell numbers in each class. The map reveals the ubiquitous presence of pico-plankton, and shows that all size classes are more abundant in more productive areas

Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long Transect in the Atlantic Ocean

Phytoplankton biomass, through its proxy, Chlorophyll a, has been assessed at synoptic temporal and spatial scales with satellite remote sensing (RS) for over two decades. Also, RS algorithms to monitor relative size classes abundance are widely used; however, differentiating functional types from RS, as well as the assessment of phytoplankton structure, in terms of carbon remains a challenge. Hence, the main motivation of this work it to discuss the links between size classes and phytoplankton groups, in order to foster the capability of assessing phytoplankton community structure and phytoplankton size fractionated carbon budgets. To accomplish our goal, we used data (on nutrients, photosynthetic pigments concentration and cell numbers per taxa) collected in surface samples along a transect on the Atlantic Ocean, during the 25th Atlantic Meridional Transect cruise (AMT25) between 50◦ N and 50◦ S, from nutrient-rich high latitudes to the oligotrophic gyres. We compared phytoplankton size classes from two methodological approaches: (i) using the concentration of diagnostic photosynthetic pigments, and assessing the abundance of the three size classes, micro-, nano-, and picoplankton, and (ii) identifying and enumerating phytoplankton taxa by microscopy or by flow cytometry, converting into carbon, and dividing the community into five size classes, according to their cell carbon content. The distribution of phytoplankton community in the different oceanographic regions is presented in terms of size classes, taxonomic groups and functional types, and discussed in relation to the environmental oceanographic conditions. The distribution of seven functional types along the transect showed the dominance of picoautotrophs in the Atlantic gyres and high biomass of diatoms and autotrophic dinoflagellates (ADinos) in higher northern and southern latitudes, where larger cells constituted the major component of the biomass. Total carbon ranged from 65 to 4 mg carbon m−3 , at latitudes 45◦ S and 27◦ N, respectively. The pigment and cell carbon approaches gave good consistency for picoplankton and microplankton size classes, but nanoplankton size class was overestimated by the pigment-based approach. The limitation of enumerating methods to accurately resolve cells between 5 and 10 µm might be cause of this mismatch, and is highlighted as a knowledge gap. Finally, the three-component model of Brewin et al. was fitted tothe Chlorophyll a (Chla) data and, for the first time, to the carbon data, to extract the biomass of three size classes of phytoplankton. The general pattern of the model fitted to the carbon data was in accordance with the fits to Chla data. The ratio of the parameter representing the asymptotic maximum biomass gave reasonable values for Carbon:Chla ratios, with an overall median of 112, but with higher values for picoplankton (170) than for combined pico-nanoplankton (36). The approach may be useful for inferring size-fractionated carbon from Earth Observation

Response of coccolithophore communities to oceanographic and atmospheric processes across the North- and Equatorial Atlantic

Changes in coccolithophore productivity in response to climate-driven ocean warming are likely to have cascading biogeochemical effects that feed back to the changing climate. This paper investigates the role (and interplay) of large-scale oceanographic and atmospheric processes across the North- and Equatorial Atlantic, including Saharan dust deposition, on the distribution of coccolithophore communities. The study is based on biological and hydrological data collected across the photic zone of the ocean, and aerosol data collected from the lower atmosphere, across 50°N–1°S during the Atlantic Meridional Transect in boreal Autumn of 2018 (AMT28), in synergy with Earth Observations. Results confirm existing understanding of the distribution of coccolithophore communities which are related to major meridional hydrological gradients across the North Atlantic. Dynamic, oxygenated and microphytoplankton-enriched waters at higher-latitudes were characterized by less diverse coccolithophore populations, dominated by placolith-bearing r-selected coccolithophores. In contrast, the heavily stratified and picoplankton-enriched waters of the subtropical gyre revealed more diverse populations, dominated by umbelliform coccolithophores and holococcolithophores at the surface, and by floriform taxa in the lower photic zone. Mean concentrations of 14.4×103 cells/L present in the North Atlantic Tropical Gyre Province (30–12°N), only slightly lower compared to 17.7×103 cells/L produced in the North Atlantic Drift province (50–40°N), provide a snapshot perspective on the importance of coccolithophore production in heavily stratified gyre conditions. Higher concentrations of 19’-Hexanoyloxyfucoxanthin (HexFuco) in regions of enhanced production of r-selected placolith-bearing species suggest that this pigment should not be generalized as a proxy for the entire coccolithophore community. Enhanced abundances of fast-blooming Emiliania huxleyi and Gephyrocapsa oceanica, and of cyanobacteria (including both picoplankton and N2-fixing Trichodesmium spp.) at the surface of the region of more persistent Saharan dust deposition (at ~12-10°N) appeared to result from dust-born nutrient input. Underneath this stratified surface layer, enhanced productivity in the deep chlorophyll maximum (DCM) appeared decoupled from that on the surface, fueled by geostrophic eastward shoaling of the nutricline across the tropical North Atlantic. As this was the region of highest macronutrient concentrations measured along and below the nutricline, our data suggest that the NE tropical Atlantic may act as a permanent dust-born nutrient depocenter as previously hypothesized

Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas

The recent expansion of wild Pacific oysters already had negative repercussions on sites in Europe and has raised further concerns over their potential harmful impact on the balance of biomes within protected areas. Monitoring their colonisation, especially at early stages, has become an urgent ecological issue. Current efforts to monitor wild Pacific oysters rely on “walk-over” surveys that are highly laborious and often limited to specific areas of easy access. Remotely Piloted Aircraft Systems (RPAS), commonly known as drones, can provide an effective tool for surveying complex terrains and detect Pacific oysters. This study provides a novel workflow for automated detection, counting and mapping of individual Pacific oysters to estimate their density per square meter by using Convolutional Neural Networks (CNNs) applied to drone imagery. Drone photos were collected at low tides and altitudes of approximately 10 m across a variety of cases of rocky shore and mudflats scenarios. Using object detection, we compared how different Convolutional Neural Networks (CNNs) architectures including YOLOv5s, YOLOv5m, TPH-YOLOv5 and FR-CNN performed in the detection of Pacific oysters over the surveyed areas. We report the precision of our model at 88% with a difference in performance of 1% across the two sites. The workflow presented in this work proposes the use of grid maps to visualize the density of Pacific oysters per square meter towards ecological management and the creation of time series to identify trends