Bats are important reservoirs and vectors in the transmission of emerging infectious diseases. Many highly pathogenic viruses such as SARS-CoV and rabies-related lyssaviruses have crossed species barriers to infect humans and other animals. In this study we monitored the major roost sites of bats in Singapore, and performed surveillance for zoonotic pathogens in these bats. Screening of guano samples collected during the survey uncovered a bat coronavirus (Betacoronavirus) in Cynopterus brachyotis, commonly known as the lesser dog-faced fruit bat. Using a capture-enrichment sequencing platform, the full-length genome of the bat CoV was sequenced and found to be closely related to the bat coronavirus HKU9 species found in Leschenault’s rousette discovered in the Guangdong and Yunnan provinces.

The seagrass Posidonia oceanica is the main habitat-forming species of the coastal Mediterranean, providing millennial-scale ecosystem services including habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. Meadows of this endemic seagrass species represent the largest carbon storage among seagrasses around the world, largely contributing to global blue carbon stocks. Yet, the slow growth of this temperate species and the extreme projected temperature and sea-level rise due to climate change increase the risk of reduction and loss of these services. Currently, there are knowledge gaps in its basin-wide spatially explicit extent and relevant accounting, therefore accurate and efficient mapping of its distribution and trajectories of change is needed. Here, we leveraged contemporary advances in Earth Observation—cloud computing, open satellite data, and machine learning—with field observations through a cloud-native geoprocessing framework to account the spatially explicit ecosystem extent of P. oceanica seagrass across its full bioregional scale. Employing 279,186 Sentinel-2 satellite images between 2015 and 2019, and a human-labeled training dataset of 62,928 pixels, we mapped 19,020 km2 of P. oceanica meadows up to 25 m of depth in 22 Mediterranean countries, across a total seabed area of 56,783 km2. Using 2,480 independent, field-based points, we observe an overall accuracy of 72%. We include and discuss global and region-specific seagrass blue carbon stocks using our bioregional seagrass extent estimate. As reference data collections, remote sensing technology and biophysical modelling improve and coalesce, such spatial ecosystem extent accounts could further support physical and monetary accounting of seagrass condition and ecosystem services, like blue carbon and coastal biodiversity. We envisage that effective policy uptake of these holistic seagrass accounts in national climate strategies and financing could accelerate transparent natural climate solutions and coastal resilience, far beyond the physical location of seagrass beds.

Seagrass ecosystems are globally significant hot spots of blue carbon storage, coastal biodiversity and coastal protection, rendering them a so-called natural climate solution. Their potential as a natural climate solution has been largely overlooked in national and international climate strategies and financing. This stems mainly from the lack of standardized, spatially explicit mapping and region-specific carbon inventories. Here, we introduce a novel seagrass ecosystem accounting framework that harnesses machine learning, big satellite data analytics and open region-specific reference data within the Google Earth Engine cloud computing platform. Leveraging a biennial percentile composite, assembled from 16 453 Sentinel-2 surface reflectance image tiles at 10-m spatial resolution, and 20 820 reference data points, we applied the cloud-native framework to produce the first national inventories of seagrass extent and total seagrass carbon stocks in Kenya, Tanzania, Mozambique and Madagascar. We estimated 4316 km 2 of regional seagrass extent (mean F1-score of 59.3% and overall accuracy of 84.3%) up to 23 m of depth. Pairing country-specific in situ carbon data and our spatially explicit seagrass extents, we calculated total regional seagrass blue carbon stocks between 11.2-40.2 million MgC, with the largest national carbon pool in Kenya (8-29.2 million MgC). We envisage that improvements in the remote sensing components of the framework guided by a necessary influx of region-specific data on seagrass stocks and fluxes could reduce uncertainties in our current spatially explicit ecosystem extent and carbon accounts, enhancing the incorporation of seagrasses into Multilateral Environmental Agreements for future resilient ecosystems, societies and economies.

Seagrasses are among the world’s most productive ecosystems due to their vast ‘blue’ carbon sequestration rates and stocks, yet have a largely untapped potential for climate change mitigation and national climate agendas like the Nationally Determined Contributions of the Paris Agreement. To account for the value of seagrasses for these agendas, spatially explicit high-confidence seagrass ecosystem assessments guided by nationally aggregated data are necessary. Modern Earth Observation advances could provide a scalable technological solution to assess the national extent and blue carbon service of seagrass ecosystems. Here, we developed and applied a scalable Earth Observation framework within the Google Earth Engine cloud computing platform to account the national extent, blue carbon stock and sequestration rate of seagrass ecosystems across the shallow waters of The Bahamas—113,037 km2. Our geospatial ecosystem extent accounting was based on big multi-temporal data analytics of over 18,000 10-m Sentinel-2 images acquired between 2017-2021, and deep feature engineering of multi-temporal spectral, color, object-based and textural metrics with Random Forests machine learning classification. The extent accounting was trained and validated using a nationwide reference data synthesis based on human-guided image annotation, recent space-borne benthic habitat maps, and field data collections. Bahamian seagrass carbon stocks and sequestration rates were quantified using region-specific in-situ seagrass blue carbon data. The mapped Bahamian seagrass extent covers an area up to 46,792 km2, translating into a carbon storage of 723 Mg C, and a sequestration rate of 123 Mt CO2 annually. This equals up to 68 times the amount of CO2 emitted by The Bahamas in 2018, potentially rendering the country carbon-neutral. The developed accounts fill a vast mapping blank in the global seagrass map—29% of the global seagrass extent—highlighting the necessity of including their blue carbon fluxes into national climate agendas and showcasing the need for more cost-effective conservation and restoration efforts for their meadows. We envisage that the synergy between our scalable Earth Observation technology and near-future nation-specific in-situ observations can and will support spatially-explicit seagrass and ocean ecosystem accounting, accelerating effective policy-making, blue carbon crediting, and relevant financial investments in and beyond The Bahamas.

The lack of clarity in turbid coastal waters interferes with light attenuation and hinders remotely sensed studies in aquatic ecology such as benthic habitat mapping and bathymetry estimation. Although turbid water column corrections can be applied on regions with seasonal turbidity by performing multi-temporal analysis, different approaches are needed in regions where the water is constantly turbid or only exhibits subtle turbidity variations through time. This study aims to detect these turbid zones (TZs) in optically shallow coastal waters using multi-temporal Sentinel-2 surface reflectance datasets to improve the aforementioned studies. The herein framework can be paired with other aquatic ecology remote sensing studies to establish the clear water focus area and can also be used by decision makers to identify rehabilitation areas. We selected the coastlines of Guinea-Bissau, Tunisia, and west Madagascar as our case studies which feature wide-ranging turbidity intensities across tropical, subtropical, and Mediterranean waters and applied three different methods for the TZ detection: Otsu’s method for bimodal thresholding, linear spectral unmixing, and Random Forest (RF) machine learning method on Google Earth Engine as an end-to-end process. Based on our experiments, the RF method yields good results in all study regions with overall accuracies ranging between 88 and 96% and F1-scores between 0.87 and 0.96. TZ detection is highly site-specific due to the inter-class variability that is mainly affected by the nature of the suspended materials and the environmental characteristics of the site.