Sargassum algal blooms on ocean surfaces and landings of huge Sargassum mats on beaches is an emerging global environmental challenge with wide socio-economic and environmental implications. Literature on Sargassum growth cycles, travel patterns, species and morphotypes, and quantified impacts have tended to focus on a geographic region, or a specific event. Few, if any, publications document long term continuous monitoring of Sargassum blooms in large areas such as the Pacific, or the tropical Atlantic. To address this gap, this paper systematically reviews the global evidence of Sargassum bloom monitoring beyond the Sargasso Sea, and identifies gaps in the evidence base of floating and landing influxes. This systematic review uses combinations of two key terms relating to Sargassum and monitoring, and utilises the resources in ISI Web of Knowledge, Scopus and Google Scholar. The analysis moves us past a classic literature review, and produces an unbiased assessment of empirical research on Sargassum monitoring from 1960 to 2019. We find a significant research focus on open-ocean blooms and floating mats whereas research on beach landings and their associated impacts is comparatively limited. Research is focused within specific countries or water bodies (notably, the Gulf of Mexico, the Caribbean and North Atlantic Ocean) and tends not to comprehensively assess neighbouring or regional shorelines, for example, West Africa and Central America. There was a lack of consistency in the application of methods for quantifying Sargassum biomass volume (including dry/wet weight, unit of measurement, and spatial extent of calculations). Further, in many publications Sargassum species identification was omitted. Given current attempts to understand the drivers and impacts of the exponential growth in Sargassum in some parts of the world, a consistent and replicable research approach to monitoring Sargassum could support creation of a Sargassum evidence database. To move this agenda forwards, we propose a definition for a Sargassum ‘event’: a continuous bloom of any Sargassum in open oceans, or, an aggregation of landed Sargassum, with the potential to disrupt social, economic or ecosystem functioning, or to impact human health. This review highlights the importance of standardising Sargassum monitoring methods to facilitate improved documentation of temporal and spatial patterns of Sargassum blooms and beach landings.

In recent years, pelagic sargassum (S. fluitans and S. natans - henceforth sargassum) macroalgal blooms have become more frequent and larger with higher biomass in the Tropical Atlantic region. They have environmental and socio-economic impacts, particularly on coastal ecosystems, tourism, fisheries and aquaculture industries, and on public health. Despite these challenges, sargassum biomass has the potential to offer commercial opportunities in the blue economy, although, it is reliant on key chemical and physical characteristics of the sargassum for specific use. In this study, we aim to utilise remotely sensed spectral profiles to determine species/morphotypes at different decomposition stages and their biochemical composition to support monitoring and valorisation of sargassum. For this, we undertook dedicated field campaigns in Barbados and Ghana to collect, for the first time, in situ spectral measurements between 350 and 2500 nm using a Spectra Vista Corp (SVC) HR-1024i field spectrometer of pelagic sargassum stranded biomass. The spectral measurements were complemented by uncrewed aerial system surveys using a DJI Phantom 4 drone and a DJI P4 multispectral instrument. Using the ground and airborne datasets this research developed an operational framework for remote detection of beached sargassum; and created spectral profiles of species/morphotypes and decomposition maps to infer biochemical composition. We were able to identify some key spectral regions, including a consistent absorption feature (920-1080 nm) found in all of the sargassum morphotype spectral profiles; we also observed distinction between fresh and recently beached sargassum particularly around 900-1000 nm. This work can support pelagic sargassum management and contribute to effective utilisation of the sargassum biomass to ultimately alleviate some of the socio-economic impacts associated with this emerging environmental challenge.

Since 2011, pelagic sargassum blooms (S. fluitans and S. natans) have impacted coastal communities, aquaculture, tourism, and biodiversity across the Tropical Atlantic region. Whilst the initial event is generally attributed to an anomalous North Atlantic Oscillation (2009–2010), the drivers of sargassum movement and proliferation remain unclear. This research gap is particularly evident in West Africa, where annual and seasonal sargassum variability is under-researched, and a lack of consensus exists on seasonal and annual trends. This paper addresses these gaps by (1) providing a first attempt at characterising the seasonal and annual trends of sargassum biomass in the Eastern Tropical Atlantic, through using satellite imagery to create a time-series for 2011–2022; and (2) exploring the hypothetical drivers of movement and proliferation of sargassum for this area, through assessing its co-variation with potential drivers including atmospheric, oceanic, and policy, establishing a historical timeline of events. The time-series analysis reveals an annual biomass peak in September and a second peak between March and May. The exploration of potential drivers reveals that alongside sea surface temperature there are multiple factors that could be influencing sargassum biomass, and that further research is necessary to clarify primary and secondary drivers. The results contribute to understanding drivers, impacts, and predictions of sargassum blooms in the Eastern Tropical Atlantic. We anticipate that our findings will enable sargassum-affected areas to better anticipate the size and timing of sargassum events in West Africa and offer researchers a new perspective on possible drivers of proliferation within the wider Tropical Atlantic region.

Pelagic sargassum (S fluitans and S natans) algal blooms and beach landings have become a regular occurrence in the Tropical Atlantic Basin since 2011; they have a variety of impacts on the marine ecosystem and blue economy. To reduce the impacts and enable effective management, forecasting and monitoring of the blooms are essential. Challenges associated with use of satellite imagery for sargassum detection in the Tropical Atlantic are spatial resolution and cloud cover, which is particularly dense in this region due to the inter-tropical convergence zone, tropical storms and hurricanes. Successful models of forecasting and prediction of pelagic sargassum are hindered by unreliable satellite data, uncertainty around windage and as well as growth and mortality. In the longer term, we aim to improve the forecast models of pelagic sargassum mat movements in open oceans by introducing evidence of the speed of travel, changing mat morphology, and size and health status of sargassum mats. To achieve this, we deployed eight trackers on floating sargassum mats in the Western Tropical Atlantic. In addition, we explore the coincidence of surface currents, wind stress and sea surface temperature as a parameter for growth on the tracker pathways. When used in conjunction with both remote sensing methods and climate data (wind, current and sea temperature), we find that GPS tracker data can facilitate more reliable monitoring of sargassum transport pathways, helps to overcome satellite-based challenges as well as model based uncertainties, and may improve the accuracy and general utility of sargassum early warning systems.

Marine primary production, converting approximately 50 Gt of inorganic carbon into organic carbon per year, is an important component of the global carbon cycle, and a major determinant of past, present and future climate. Large-scale, long-term estimates of marine primary production rely primarily on two types of models: satellite-based models that make extensive use of remote-sensing data, and ecosystem models providing numerical simulation of ecological processes embedded in general ocean circulation models. Intercomparison exercises of model outputs (both within and across the two model types) have consistently revealed high discrepancies between estimated global ocean primary production, including divergent magnitudes and even opposite trends. Model-observation comparisons are also complex, because paucity of data, differences in measurement techniques, and evolving methodologies could all lead to difficulties with the interpretation of results. These uncertainties limit the applications of primary production models (both satellite-based and ecosystem), especially in the climate context, where an important question is whether climate change will drive significant future changes in regional or global primary production. Both satellite-based and ecosystem models rely on a range of fixed model parameters, whose values need to be carefully estimated and tested. In this paper, we suggest that such model parameters represent an underappreciated but important source of inter-model differences. With the proliferation of both satellite and in situ observations of relevant variables at global scales, and the availability of powerful statistical tools such as data assimilation and machine learning, we argue that time is right to systematically examine model parameters, gaining both better insights into parameter values and how those values might vary in space and time. We argue that such spatio-temporal parameter variability can be theoretically justified for ecosystem models with complexity similar to those commonly used within Earth System Models (ESMs) in climate studies. The spatially and temporally varying parameter values could serve to unify models that are structurally different. An important aspect of this unification could be the ability to infer the spatio-temporal variability of parameters in the less complex models from the emergent behaviour of the more complex ones. This could include ecosystem model simulations of nutrients, temperature, phytoplankton classes, or vertical distributions informing satellite-based models. We conclude that better understanding of model parameter roles and integration (or inter-calibration) of different types of models could reduce discrepancies among the primary production models and improve the reliability of marine primary production projections.

Marine primary production, converting approximately 50 GtC per year, is an important component of the global carbon cycle, and a major determinant of past, present and future climate. Large-scale, long-term estimates of marine primary production rely primarily on two types of models: satellite-based models that make extensive use of remote-sensing data, and ecosystem models providing numerical simulation of ecological processes embedded in general ocean circulation models. Intercomparison exercises of model outputs (both within and across the two model types) have consistently revealed high discrepancies between estimated global ocean primary production, including divergent magnitudes and even opposite trends. Comparisons of model results with in-situ observations have also revealed large uncertainties in marine primary production estimates. These uncertainties limit the applications of these models, especially in the climate context, where an important question is whether climate change will drive significant future changes in regional or global primary production. Both satellite-based and ecosystem model equations rely on a range of fixed parameters, whose values need to be carefully estimated and tested. In this paper, we suggest that such model parameters represent an underappreciated but important source of inter-model differences. With the proliferation of both satellite and in situ observations of relevant variables at global scales and the availability of powerful statistical tools in data assimilation and machine learning, we argue that time is right to systematically examine model parameters and gain insights into how they may vary spatially and temporally. We emphasize that such spatio-temporal variability can be easily theoretically justified for the models with complexity similar to the satellite models, or the ecosystem models commonly used within Earth System Models (ESMs) in climate studies. We argue that the spatially and temporally varying parameter values provide a strong reason to anticipate unification of models, which would otherwise appear structurally different. A better understanding of model parameter roles could therefore reduce discrepancies among the primary production models and improve the reliability of marine primary production projections.