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With rapid, sector-specific climatic changes impacting the Southern Ocean, we need circumpolar-scale biomass data of its plankton taxa to improve food web models, blue carbon budgets and resource management. Here, we provide a new dataset on mesozooplankton biomass with 2909 records spanning the last 90 yr, and describe, in comparable carbon units, their circumpolar distribution alongside those of phytoplankton, Antarctic krill, and salps. With our datasets, we estimate total summer carbon biomasses for phytoplankton (36 MT), mesozooplankton (67 MT), krill (30 MT), and salps (1.7 MT). The mesozooplankton value is much higher than previously reported and, added to that of krill and salps, points to an enormous overall biomass of zooplankton in the Southern Ocean. This means that the pyramids of biomass are often inverted, with higher biomass of zooplankton than of phytoplankton. Such high biomasses suggest key roles of grazers in nutrient cycling and we estimate an export of ~ 50 Mt C yr−1, solely from mortality of overwintering zooplankton that typically reside at depth. Deep lipid respiration (the lipid pump), for example, would increase this export even further. While inverted biomass pyramids prevailed at mid latitudes (50°–70°S), the balance of taxa differed regionally: for example, with biomass dominance by phytoplankton (highest latitudes and Pacific sector), mesozooplankton (Kerguelen Plateau), krill (north and east Scotia Sea), and salps (Crozet area). In light of contrasting climate change impacts between these sectors, we provide data that will underpin biogeochemical and food web models, blue carbon budgets, and the planning of marine protected areas.

Global biogeochemical ocean models are invaluable tools to examine how physical, chemical, and biological processes interact in the ocean. Satellite-derived ocean-color properties, on the other hand, provide observations of the surface ocean with unprecedented coverage and resolution. Advances in our understanding of marine ecosystems and biogeochemistry are strengthened by the combined use of these resources, together with sparse in situ data. Recent modeling advances allow simulation of the spectral properties of phytoplankton and remote-sensing reflectances, bringing model outputs closer to the kind of data that ocean-color satellites can provide. However, comparisons between model outputs and analogous satellite products (e.g. chlorophyll-a) remain problematic: Most evaluations are based on point-by-point comparisons in space and time where spuriously large errors can occur from small spatial and temporal mismatches, whereas global statistics provide no information on how well a model resolves processes at regional scales. Here, we employ a unique suite of methodologies, Probability Density Functions to Evaluate Models (PDFEM), which generate a robust comparison of these resources. The probability density functions of physical and biological properties of Longhurst's provinces are compared, to evaluate how well a model resolves related processes. Differences in the distributions of chlorophyll-a concentration [mg m-3] provide information on matches and mismatches between models and observations. In particular, mismatches help isolate regional sources of discrepancy, which can lead to improving both simulations and satellite algorithms. Furthermore, the use of radiative transfer in the model to mimic remotely-sensed products facilitate model-observation comparisons of optical properties of the ocean.

It is likely that the introduction of the brown macroalga Undaria pinnatfida from the Pacific into the North Atlantic will impact competitively on the native species Saccorhiza polyschides; both are large kelps occupying the same subtidal zone with very similar life histories. The present study examines their tolerance to changes in salinity, under laboratory conditions, in order to provide a better understanding of their respective competitiveness in an estuarine environment. Experiments were carried out over a full range of salinity values, from 35 to 0, with respect to zoospore settlement and attachment, germination, post-germination progression to form gametophytes, gametophyte sex ratio, sporophyte production and blade length. Undaria zoospores settled and attached over the salinity range from 35 to 14 and germinated between 35 and 3.5. Post-germination progression occurred over the range from 35 to 14 whilst only small differences in the male/female ratio were recorded. Sporophyte blade production and development occurred over the range from 35 to 17.5 and peak production and longest blade length was recorded at 21. Saccorhiza zoospores settled and attached at and above 24.5 and germinated between 35 and 21. Sporophyte production and blade development occurred over the range from 35 to 24.5. In general, Undaria was shown to be much more tolerant of reductions in salinity compared to Saccorhiza and is more likely to penetrate further into estuarine environments.

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. Wasserstein distance complements commonly used point-wise difference methods such as the root mean squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case we consider Chlorophyll (a key indicator of phytoplankton biomass) in the North-East Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: 1) Comparing model predictions with satellite observations, and 2) temporal evolution of Chlorophyll both seasonally and over longer time frames. Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite Chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean., Comment: 6 figures

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010&ndash, 2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model&rsquo, s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model&rsquo, s transferability potential and applicative value for cholera forecasting systems.

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

The timing of phytoplankton growth (phenology) in tropical oceans is a crucial factor influencing the survival rates of higher trophic levels, food web structure and the functioning of coral reef ecosystems. Phytoplankton phenology is thus categorised as an ‘ecosystem indicator’, which can be utilised to assess ecosystem health in response to environmental and climatic perturbations. Ocean-colour remote sensing is currently the only technique providing global, long-term, synoptic estimates of phenology. However, due to limited available in situ datasets, studies dedicated to the validation of satellite-derived phenology metrics are sparse. The recent development of autonomous oceanographic observation platforms provides an opportunity to bridge this gap. Here, we use satellite-derived surface chlorophyll-a (Chl-a) observations, in conjunction with a Biogeochemical-Argo dataset, to assess the capability of remote sensing to estimate phytoplankton phenology metrics in the northern Red Sea – a typical tropical marine ecosystem. We find that phenology metrics derived from both contemporary platforms match with a high degree of precision (within the same 5-day period). The remotely-sensed surface signatures reflect the overall water column dynamics and successfully capture Chl-a variability related to convective mixing. Our findings offer important insights into the capability of remote sensing for monitoring food availability in tropical marine ecosystems, and support the use of satellite-derived phenology as an ecosystem indicator for marine management strategies in regions with limited data availability.

This study demonstrates the importance of accurate extreme discharge input in hydrological and oceanographic combined modeling by introducing two extreme typhoon events. We investigated the effects of extreme freshwater outflow events from river mouths on sea surface salinity distribution (SSS) in the coastal zone of the north-eastern Japan. Previous studies have used observed discharge at the river mouth, as well as seasonally averaged inter-annual, annual, monthly or daily simulated data. Here, we reproduced the hourly peak discharge during two typhoon events for a targeted set of nine rivers and compared their impact on SSS in the coastal zone based on observed, climatological and simulated freshwater outflows in conjunction with verification of the results using satellite remote-sensing data. We created a set of hourly simulated freshwater outflow data from nine first-class Japanese river basins flowing to the western Pacific Ocean for the two targeted typhoon events (Chataan and Roke) and used it with the integrated hydrological (CDRMV3.1.1) and oceanographic (JCOPE-T) model, to compare the case using climatological mean monthly discharges as freshwater input from rivers with the case using our hydrological model simulated discharges. By using the CDRMV model optimized with the SCE-UA method, we successfully reproduced hindcasts for peak discharges of extreme typhoon events at the river mouths and could consider multiple river basin locations. Modeled SSS results were verified by comparison with Chlorophyll-a distribution, observed by satellite remote sensing. The projection of SSS in the coastal zone became more realistic than without including extreme freshwater outflow. These results suggest that our hydrological models with optimized model parameters calibrated to the Typhoon Roke and Chataan cases can be successfully used to predict runoff values from other extreme precipitation events with similar physical characteristics. Proper simulation of extreme typhoon events provides more realistic coastal SSS and may allow a different scenario analysis with various precipitation inputs for developing a nowcasting analysis in the future.

Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker, SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997–2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948–2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.

We live on a blue planet, and Earth’s waters benefit many sectors of society. The future of our blue planet is increasingly reliant on the services delivered by marine, coastal and inland waters and on the advancement of effective, evidence-based decisions on sustainable development. ‘Oceans and Society: Blue Planet’ is an initiative of the Group on Earth Observations (GEO) that aims to ensure the sustained development and use of ocean and coastal observations for the benefit of society. The initiative works to advance and exploit synergies among the many observational programmes devoted to ocean and coastal waters; to improve engagement with a variety of stakeholders for enhancing the timeliness, quality and range of information delivered; and to raise awareness of the societal benefits of ocean observations at the public and policy levels. This paper summarises the role of the initiative, current activities and considerations for future directions.

Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between \textit{in-situ} information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more towards phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR) survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. {Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples}. Six diatom assemblages were identified \textit{in-situ}, \textcolor{blue}{each having indicators taxa or species}. Once this first step was completed, the \textit{in-situ} analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated \textcolor{blue}{with} specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current \textit{in-situ} ecological knowledge.

Phytoplankton total chlorophyll concentration (TCHLa) and phytoplankton size structure are two important ecological indicators in biological oceanography. Using high performance liquid chromatography (HPLC) pigment data, collected from surface waters along the Atlantic Meridional Transect (AMT), we examine temporal changes in TCHLa and phytoplankton size class (PSC: micro-, nano- and pico-phytoplankton) between 2003 and 2010 (September to November cruises only), in three ecological provinces of the Atlantic Ocean. The HPLC data indicate no significant change in TCHLa in northern and equatorial provinces, and an increase in the southern province. These changes were not significantly different to changes in TCHLa derived using satellite ocean-colour data over the same study period. Despite no change in AMT TCHLa in northern and equatorial provinces, significant differences in PSC were observed, related to changes in key diagnostic pigments (fucoxanthin, peridinin, 19′-hexanoyloxyfucoxanthin and zeaxanthin), with an increase in small cells (nano- and pico-phytoplankton) and a decrease in larger cells (micro-phytoplankton). When fitting a three-component model of phytoplankton size structure — designed to quantify the relationship between PSC and TCHLa to each AMT cruise, model parameters varied over the study period. Changes in the relationship between PSC and TCHLa have wide implications in ecology and marine biogeochemistry, and provide key information for the development and use of empirical ocean-colour algorithms. Results illustrate the importance of maintaining a time-series of in-situ observations in remote regions of the ocean, such as that acquired in the AMT programme.

The Red Sea is a semi-enclosed tropical marine ecosystem that stretches from the Gulf of Suez and Gulf of Aqaba in the north, to the Gulf of Aden in the south. Despite its ecological and economic importance, its biological environment is relatively unexplored. Satellite ocean-colour estimates of chlorophyll concentration (an index of phytoplankton biomass) offer an observational platform to monitor the health of the Red Sea. However, little is known about the optical properties of the region. In this paper, we investigate the optical properties of the Red Sea in the context of satellite ocean-colour estimates of chlorophyll concentration. Making use of a new merged ocean-colour product, from the European Space Agency (ESA) Climate Change Initiative, and in situ data in the region, we test the performance of a series of ocean-colour chlorophyll algorithms. We find that standard algorithms systematically overestimate chlorophyll when compared with the in situ data. To investigate this bias we develop an ocean-colour model for the Red Sea, parameterised to data collected during the Tara Oceans expedition, that estimates remote-sensing reflectance as a function of chlorophyll concentration. We used the Red Sea model to tune the standard chlorophyll algorithms and the overestimation in chlorophyll originally observed was corrected. Results suggest that the overestimation was likely due to an excess of CDOM absorption per unit chlorophyll in the Red Sea when compared with average global conditions. However, we recognise that additional information is required to test the influence of other potential sources of the overestimation, such as aeolian dust, and we discuss uncertainties in the datasets used. We present a series of regional chlorophyll algorithms for the Red Sea, designed for a suite of ocean-colour sensors, that may be used for further testing.

In the Southern Ocean, there is increasing evidence that seasonal to subseasonal temporal scales, and meso- to submesoscales play an important role in understanding the sensitivity of ocean primary productivity to climate change. This drives the need for a high-resolution approach to resolving biogeochemical processes. In this study, 5.5 months of continuous, high-resolution (3 h, 2 km horizontal resolution) glider data from spring to summer in the Atlantic Subantarctic Zone is used to investigate: (i) the mechanisms that drive bloom initiation and high growth rates in the region and (ii) the seasonal evolution of water column production and respiration. Bloom initiation dates were analysed in the context of upper ocean boundary layer physics highlighting sensitivities of different bloom detection methods to different environmental processes. Model results show that in early spring (September to mid-November) increased rates of net community production (NCP) are strongly affected by meso- to submesoscale features. In late spring/early summer (late-November to mid-December) seasonal shoaling of the mixed layer drives a more spatially homogenous bloom with maximum rates of NCP and chlorophyll biomass. A comparison of biomass accumulation rates with a study in the North Atlantic highlights the sensitivity of phytoplankton growth to fine-scale dynamics and emphasizes the need to sample the ocean at high resolution to accurately resolve phytoplankton phenology and improve our ability to estimate the sensitivity of the biological carbon pump to climate change.

Phytoplankton, at the base of the marine food web, represent a fundamental food source in coral reef ecosystems. The timing (phenology) and magnitude of the phytoplankton biomass are major determinants of trophic interactions. The Red Sea is one of the warmest and most saline basins in the world, characterized by an arid tropical climate regulated by the monsoon. These extreme conditions are particularly challenging for marine life. Phytoplankton phenological indices provide objective and quantitative metrics to characterize phytoplankton seasonality. The indices i.e. timings of initiation, peak, termination and duration are estimated here using 15years (1997–2012) of remote sensing ocean-color data from the European Space Agency (ESA) Climate Change Initiative project (OC-CCI) in the entire Red Sea basin. The OC-CCI product, comprising merged and bias-corrected observations from three independent ocean-color sensors (SeaWiFS, MODIS and MERIS), and processed using the POLYMER algorithm (MERIS period), shows a significant increase in chlorophyll data coverage, especially in the southern Red Sea during the months of summer NW monsoon. In open and reef-bound coastal waters, the performance of OC-CCI chlorophyll data is shown to be comparable with the performance of other standard chlorophyll products for the global oceans. These features have permitted us to investigate phytoplankton phenology in the entire Red Sea basin, and during both winter SE monsoon and summer NW monsoon periods. The phenological indices are estimated in the four open water provinces of the basin, and further examined at six coral reef complexes of particular socio-economic importance in the Red Sea, including Siyal Islands, Sharm El Sheikh, Al Wajh bank, Thuwal reefs, Al Lith reefs and Farasan Islands. Most of the open and deeper waters of the basin show an apparent higher chlorophyll concentration and longer duration of phytoplankton growth during the winter period (relative to the summer phytoplankton growth period). In contrast, most of the reef-bound coastal waters display equal or higher peak chlorophyll concentrations and equal or longer duration of phytoplankton growth during the summer period (relative to the winter phytoplankton growth period). The ecological and biological significance of the phytoplankton seasonal characteristics are discussed in context of ecosystem state assessment, and particularly to support further understanding of the structure and functioning of coral reef ecosystems in the Red Sea.

Ocean biogeochemistry (OBGC) models span a wide range of complexities from highly simplified, nutrient-restoring schemes, through nutrient-phytoplankton-zooplankton-detritus (NPZD) models that crudely represent the marine biota, through to models that represent a broader trophic structure by grouping organisms as plankton functional types (PFT) based on their biogeochemical role (Dynamic Green Ocean Models; DGOM) and ecosystem models which group organisms by ecological function and trait. OBGC models are now integral components of Earth System Models (ESMs), but they compete for computing resources with higher resolution dynamical setups and with other components such as atmospheric chemistry and terrestrial vegetation schemes. As such, the choice of OBGC in ESMs needs to balance model complexity and realism alongside relative computing cost. Here, we present an inter-comparison of six OBGC models that were candidates for implementation within the next UK Earth System Model (UKESM1). The models cover a large range of biological complexity (from 7 to 57 tracers) but all include representations of at least the nitrogen, carbon, alkalinity and oxygen cycles. Each OBGC model was coupled to the Nucleus for the European Modelling of the Ocean (NEMO) ocean general circulation model (GCM), and results from physically identical hindcast simulations were compared. Model skill was evaluated for biogeochemical metrics of global-scale bulk properties using conventional statistical techniques. The computing cost of each model was also measured in standardised tests run at two resource levels. No model is shown to consistently outperform or underperform all other models across all metrics. Nonetheless, the simpler models that are easier to tune are broadly closer to observations across a number of fields, and thus offer a high-efficiency option for ESMs that prioritise high resolution climate dynamics. However, simpler models provide limited insight into more complex marine biogeochemical processes and ecosystem pathways, and a parallel approach of low resolution climate dynamics and high complexity biogeochemistry is desirable in order to provide additional insights into biogeochemistry–climate interactions.

The Gulf of Aden, situated in the northwest Arabian Sea and linked to the Red Sea, is a relatively unexplored ecosystem. Understanding of large-scale biological dynamics is limited by the lack of adequate datasets. In this study, we analyse 15 years of remotely-sensed chlorophyll-a data (Chl- a , an index of phytoplankton biomass) acquired from the Ocean Colour Climate Change Initiative (OC-CCI) of the European Space Agency (ESA). The improved spatial coverage of OC-CCI data in the Gulf of Aden allows, for the first time, an investigation into the full seasonal succession of phytoplankton biomass. Analysis of indices of phytoplankton phenology (bloom timing) reveals distinct phytoplankton growth periods in different parts of the gulf: a large peak during August (mid-summer) in the western part of the gulf, and a smaller peak during November (mid-autumn) in the lower central gulf and along the southern coastline. The summer bloom develops rapidly at the beginning of July, and its peak is approximately three times higher than that of the autumnal bloom. Remotely-sensed sea-surface temperature (SST), wind-stress curl, vertical nutrient profiles and geostrophic currents inferred from the sea-level anomaly, were analysed to examine the underlying physical mechanisms that control phytoplankton growth. During summer, the prevailing southwesterlies cause upwelling along the northern coastline of the gulf (Yemen), leading to an increase in nutrient availability and enhancing phytoplankton growth along the coastline and in the western part of the gulf. In contrast, in the central region of the gulf, lowest concentrations of Chl- a are observed during summer, due to strong downwelling caused by a mesoscale anticyclonic eddy. During autumn, the prevailing northeasterlies enable upwelling along the southern coastline (Somalia) causing local nutrient enrichment in the euphotic zone, leading to higher levels of phytoplankton biomass along the coastline and in the lower central gulf. The monsoon wind reversal is shown to play a key role in controlling phytoplankton growth in different regions of the Gulf of Aden.

Racault, Marie-Fanny LP/0000-0002-7584-2515; Agirbas, Ertugrul/0000-0001-7987-9668; Martinez-Vicente, Victor/0000-0003-3492-583X; Brewin, Robert/0000-0001-5134-8291 WOS: 000336490000003 Ecological indicators are used extensively as tools to manage environmental resources. in the oceans, indicators of plankton can be measured using a variety of observing systems including: mooring stations, ships, autonomous floats and ocean colour remote sensing. Given the broad range of temporal and spatial sampling resolutions of these different observing systems, as well as discrepancies in measurements obtained from different sensors, the estimation and interpretation of plankton indicators can present significant challenges. To provide support to the assessment of the state of the marine ecosystem, we propose a suite of plankton indicators and subsequently classify them in an ecological framework that characterizes key attributes of the ecosystem. We present two case studies dealing with plankton indicators of biomass, size structure and phenology, estimated using the most spatially extensive and longest in situ and remote-sensing observations. Discussion of these studies illustrates how some of the challenges in estimating and interpreting plankton indicators may be addressed by using for example relative measurement thresholds, interpolation procedures and delineation of biogeochemical provinces. We demonstrate that one of the benefits attained, when analyzing a suite of plankton indicators classified in an ecological framework, is the elucidation of non-trivial changes in composition, structure and functioning of the marine ecosystem. EC FP7 GreenSeas project [265294 FP7-ENV-2010]; Natural Environment Research CouncilNERC Natural Environment Research Council [pml010008] Funding Source: researchfish; Directorate For GeosciencesNational Science Foundation (NSF)NSF - Directorate for Geosciences (GEO) [1154661] Funding Source: National Science Foundation; Division of Ocean SciencesNational Science Foundation (NSF)NSF - Directorate for Geosciences (GEO) [1154661] Funding Source: National Science Foundation The work was funded by the EC FP7 GreenSeas project (265294 FP7-ENV-2010).

Ocean-colour remote sensing provides high-resolution and global-coverage of chlorophyll concentration, which can be used to estimate ecological indicators and to study inter-annual and long-term trends in the state of the marine ecosystem. To date, the record of ocean-colour observations is a rich one, including data from a number of sensors spanning more than three decades. The ESA Ocean-Colour Climate Change Initiative has advanced seamless merging of ocean-colour observations from missions during the period 1990s to 2010s. However, comparison of these more recent observations with records from 1970s to 1980s remains a complex undertaking, particularly for absolute values of chlorophyll concentration, primarily due to differences in the sensors. A further impediment to the analysis of the past records is the non-uniform distribution of gaps in the observations, in both time and space dimensions, when data from two or more sensors are compared. Here, we use the CZCS gap distribution from the Coastal Zone Color Scanner (CZCS, 1978–1986) as a mask to evaluate the impact that missing data may have on the estimation of six ecological indicators, when using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data set. Specifically, we evaluate the precision and accuracy of indicators by computing the root-mean-square-error (RMSE) and the bias arising purely from missing data. We develop an original resampling method allowing comparison of indicator estimates between SeaWiFS reference time-series and SeaWiFS time-series with CZCS-like gaps. We reduce some of the sampling gaps by applying a linear interpolation procedure, and compute multi-year averages of the indicators for every one-by-one degree pixel where sufficient data are available. Indicators from SeaWiFS reference and SeaWiFS with CZCS-like gaps are compared. Lowest uncertainty arising from missing data is observed in the indicators of annual mean and median chlorophyll concentration (global mean RMSE of 8% and |bias| ≤ 1%), whilst higher uncertainty is recorded for the peak chlorophyll values and the duration of the phytoplankton growing period (global mean RMSE of 33 and 47% respectively and |bias| ≤ 20%). Timing of initiation of the increasing phase of chlorophyll concentration in the seasonal cycle and timing of peak chlorophyll are subject to a global mean RMSE of nearly two months and a bias of two weeks or less. The present quantitative evaluation of uncertainty due to missing data demonstrates that, when pooled to create a nine-year climatology at 8-day temporal resolution, the coverage of CZCS is adequate for many climate-related studies on the marine ecosystem. Phytoplankton annual mean biomass can be estimated with low error in approximately 95% of the global oceans (i.e. regions where the indicators can be estimated with RMSE values of less than 30% and bias within ± 10%), and the phenological patterns can be estimated with low error in approximately 25% of the global oceans.

Although the terms 'health' and 'healthy' are often applied to marine ecosystems and communicate information about holistic condition (e.g. as required by the Ecosystem Approach), their meaning is unclear. Ecosystems have been understood in various ways, from non-interacting populations of species to complex integrated systems. Health has been seen as a metaphor, an indicator that aggregates over system components, or a non-localized emergent system property. After a review, we define good ecosystem health as: 'the condition of a system that is self-maintaining, vigorous, resilient to externally imposed pressures, and able to sustain services to humans. It contains healthy organisms and populations, and adequate functional diversity and functional response diversity. All expected trophic levels are present and well interconnected, and there is good spatial connectivity amongst subsystems.' We equate this condition with good ecological or environmental status, e.g. as referred to by recent EU Directives. Resilience is central to health, but difficult to measure directly. Ecosystems under anthropogenic pressure are at risk of losing resilience, and thus of suffering regime shifts and loss of services. For monitoring whole ecosystems, we propose an approach based on 'trajectories in ecosystem state space', illustrated with time-series from the northwestern North Sea. Change is visualized as Euclidian distance from an arbitrary reference state. Variability about a trend in distance is used as a proxy for inverse resilience. We identify the need for institutional support for long time-series to underpin this approach, and for research to establish state space co-ordinates for systems in good health. Changes in the northern North Sea, 1958-2008, plotted in a state space defined by the breeding success of kittiwakes, abundance of copepods Calanus spp., and simulated annual primary production.© Inter-Research 2013. www.int-res.com.

International audience; The composition of marine ecosystems is determined by spatial and temporal patterns of global biogeochemical cycles. Shifts in marine ecosystem composition driven by changes in climate can in turn affect biogeochemical cycles, especially through their impact on air-sea fluxes of CO2 and trace gas concentrations in the atmosphere. However, the response of marine ecosystems to climate is difficult to assess at global scale due to the scarcity of large-scale in-situ biological monitoring programmes. Here, we combine and analyse remote-sensing observations, in-situ observations, and a global ocean biogeochemistry model to gain insight into interactions between marine ecosystem composition and climate variability over the last decade, in the North Atlantic and in the Southern Ocean. Our results show large-scale shifts in the dominance of diatoms, with mean anomalies 63.3% higher during extreme positive phases of North Atlantic Oscillation index compared to its extreme negative phases and with mean anomalies 134% higher during extreme positive phases of Southern Annular Mode in the Southern Ocean over the period 1998-2008. Significant changes in the frequency of diatom dominance are detected concurrently in the three data sources studied. Model outputs indicate that this increase is driven by nutrient supply from deep waters during windier and more turbulent climate conditions.

The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean.

In recent years, phytoplankton phenology has been proposed as an indicator to monitor systematically the state of the pelagic ecosystem and to detect changes triggered by perturbation of the environmental conditions. Here we describe the phenology of phytoplankton growth for the world ocean using remotesensing ocean colour data, and analyse its variability between 1998 and 2007. Generally, the tropics and subtropics present long growing period (≈15–20 weeks) of low amplitude (

In brown algae (Phaeophyceae), only a few taxa are reported to have plastids containing pyrenoids and this character has often been used for systematic delineation. Members of the Ectocarpales display large, stalked and exserted pyrenoids in their plastids, whereas the Scytothamnales, as well as the genera Asterocladon, Asteronema and Bachelotia, exhibit plastids arranged in a stellate configuration with pyrenoids embedded in the plastid stroma. The evolution of plastid characters has remained difficult to establish, especially because of a persistent lack of resolution in brown algal molecular phylogenies. Here, we generated a seven-locus dataset for a species-rich taxon sampling, to reassess phylogenetic relationships of pyrenoid-bearing brown algal taxa. Within the Ectocarpales, the relationships among families are clarified. The minute chordariacean genus Herponema is assigned to the family Acinetosporaceae. The systematic positions of two enigmatic genera are revised: Chordariopsis is assigned to the...

The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producing essential living marine resources for the populations of Western Africa. In this region, the Ivorian continental shelf is under pressure from various anthropogenic sources, which have put the regional fish stocks, especially Sardinella aurita, the dominant pelagic species in Ivorian industrial fishery landings, under threat from overfishing. Here, we combine in situ observations of Sardinella aurita catch, temperature, and nutrient profiles, with remote-sensing ocean-color observations, and reanalysis data of wind and sea surface temperature, to investigate relationships between Sardinella aurita catch and oceanic primary producers (including biomass and phenology of phytoplankton), and between Sardinella aurita catch and environmental conditions (including upwelling index, and turbulent mixing). We show that variations in Sardinella aurita catch in the following year may be predicted, with a confidence of 78%, based on a bilinear model using only physical variables, and with a confidence of 40% when using only biological variables. However, the physics-based model alone is not sufficient to explain the mechanism driving the year-to-year variations in Sardinella aurita catch. Based on the analysis of the relationships between biological variables, we demonstrate that in the Ivorian continental shelf, during the study period 1998–2014, population dynamics of Sardinella aurita, and oceanic primary producers, may be controlled, mainly by top-down trophic interactions. Finally, based on the predictive models constructed here, we discuss how they can provide powerful tools to support evaluation and monitoring of fishing activity, which may help towards the development of a Fisheries Information and Management System.

Tropical ocean ecosystems are predicted to become warmer, more saline, and less fertile in a future Earth. The Red Sea, one of the warmest and most saline environments in the world, may afford insights into the function of the tropical ocean ecosystem in a changing planet. We show that the concentration of chlorophyll and the duration of the phytoplankton growing season in the Red Sea are controlled by the strength of the winter Arabian monsoon (through horizontal advection of fertile waters from the Indian Ocean). Furthermore, and contrary to expectation, in the last decade (1998–2010) the winter Red Sea phytoplankton biomass has increased by 75% during prolonged positive phases of the Multivariate El Nino–Southern Oscillation Index. A new mechanism is reported, revealing the synergy of monsoon and climate in regulating Red Sea greenness.