Global climate change-induced warming of the Artic seas is predicted to shift the phytoplankton community towards dominance of smaller-sized species due to global warming. Yet, little is known about their viral mortality agents despite the ecological importance of viruses regulating phytoplankton host dynamics and diversity. Here we report the isolation and basic characterization of four prasinoviruses infectious to the common Arctic picophytoplankter Micromonas. We furthermore assessed how temperature influenced viral infectivity and production. Phylogenetic analysis indicated that the putative double-stranded DNA (dsDNA) Micromonas polaris viruses (MpoVs) are prasinoviruses (Phycodnaviridae) of approximately 120 nm in particle size. One MpoV showed intrinsic differences to the other three viruses, i.e., larger genome size (205 ± 2 vs. 191 ± 3 Kb), broader host range, and longer latent period (39 vs. 18 h). Temperature increase shortened the latent periods (up to 50%), increased the burst size (up to 40%), and affected viral infectivity. However, the variability in response to temperature was high for the different viruses and host strains assessed, likely affecting the Arctic picoeukaryote community structure both in the short term (seasonal cycles) and long term (global warming).

Prokaryotes play a central role in aquatic ecosystems by consuming approximately half of the organic matter produced by aquatic primary production, of which a fraction is used for growth. Accurately measuring this prokaryotic biomass production is key to understanding aquatic carbon and nutrient cycles, since it is instrumental in driving biogeochemical processes that control parameters such as atmospheric carbon content. Aquatic prokaryotic biomass production is typically estimated from incorporation rates of the amino acid leucine during radiotracer experiments—a method widely used since the 1980s. Here we evaluate the underlying assumptions of the method with a focus on the associated conversion factors and review them in the context of empirical data. We demonstrate that the commonly used theoretical conversion factors fail to account for leucine's use as precursor for de novo protein synthesis and its respiration. As a consequence, prokaryotic biomass production is likely considerably overestimated when applying the standard conversion factors. Most severely affected are open‐ocean, mesopelagic and benthic environments, where 25% of the estimates are likely to be overestimated by at least a factor of 6.1, 4.9, and 6.5, respectively. We propose a refined carbon‐to‐leucine conversion factor and make recommendations for improving and selecting appropriate experimental protocols.

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R² = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R² = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km² of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m² resolution to underpin evidence-based management and conservation of seagrass.

Accurate knowledge of the spatial extent of seagrass habitats is essential for monitoring and management purposes given their ecological and economic significance. Extent data are typically presented in binary (presence/absence) or arbitrary, semi-quantitative density bands derived from low-resolution satellite imagery, which cannot resolve fine-scale features and intra-habitat variability. Recent advances in consumer-grade unoccupied aerial vehicles (UAVs) have advanced our ability to survey large areas at higher resolution and at lower cost. This has improved the accessibility of mapping technologies to developing coastal nations, where a large proportion of the world’s seagrass habitats are found. Here, we present the application of UAV-gathered imagery to determine seagrass habitat extent and percent of canopy cover. Four contrasting sites were surveyed in the Turneffe Atoll Marine Reserve, Belize, and seagrass canopy cover was ground truthed from in situ quadrats. Orthomosaic images were created for each site from the UAV-gathered imagery. Three modelling techniques were tested to extrapolate the findings from quadrats to spatial information, producing binary (random forest) and canopy cover (random forest regression and beta regression) habitat maps. The most robust model (random forest regression) had an average absolute error of 6.8–11.9% (SE of 8.2–14), building upon previous attempts at mapping seagrass density from satellite imagery, which achieved errors between 15–20% approximately. The resulting maps exhibited great intra-habitat heterogeneity and different levels of patchiness, which were attributed to site energetics and, possibly, species composition. The extra information in the canopy cover maps provides greater detail and information for key management decisions and provides the basis for future spatial studies and monitoring programmes.

The interface between land and sea is a key environment for biogeochemical carbon cycling, yet these dynamic environments are traditionally under sampled. Logistical limitations have historically precluded a comprehensive understanding of coastal zone processes, including ocean acidification. Using sensors on autonomous platforms is a promising approach to enhance data collection in these environments. Here, we evaluate the use of an autonomous surface vehicle (ASV), the C-Worker 4 (CW4), equipped with pH and pCO₂ sensors and with the capacity to mount additional sensors for up to 10 other parameters, for the collection of high-resolution data in shallow coastal environments. We deployed the CW4 on two occasions in Belizean coastal waters for 2.5 and 4 days, demonstrating its capability for high-resolution spatial mapping of surface coastal biogeochemistry. This enabled the characterisation of small-scale variability and the identification of sources of low pH/high pCO₂ waters as well as identifying potential controls on coastal pH. We demonstrated the capabilities of the CW4 in both pre-planned “autonomous” mission mode and remote “manually” operated mode. After documenting platform behaviour, we provide recommendations for further usage, such as the ideal mode of operation for better quality pH data, e.g., using constant speed. The CW4 has a high power supply capacity, which permits the deployment of multiple sensors sampling concurrently, a shallow draught, and is highly controllable and manoeuvrable. This makes it a highly suitable tool for observing and characterising the carbonate system alongside identifying potential drivers and controls in shallow coastal regions.

Seagrass meadows are one of the world's most diverse ecosystems offering habitats for an extensive array of species, as well as serving as protectors of coral reefs and vital carbon sinks. Furthermore, they modify hydrodynamics by diminishing water flow velocities and enhancing sediment deposition, indicating the potential for microplastic accumulation in their sediments. The build-up of microplastics could potentially have ecological impacts threatening to ecosystems, however little is known about microplastic abundance and controlling factors in seagrass sediments. Here we investigated microplastic characteristics and abundances within sediments underlying four seagrass meadow sites on the Turneffe Atoll, Belize. Sediment cores were collected and sub-sampled to include a range of replicate surface sediments (0-4 cm) and depth cores (sediment depths 0-2, 2-5, 5-10, 10-20 and 20-30 cm). These were analysed using 25 μm resolution μFTIR, with spectral maps processed using siMPle software. Microplastics were prevalent across the sites with an abundance range (limit of detection (LOD) blank-corrected) of < LOD to 17137 microplastics kg^-1 dw found on the east side of the atoll. However, their abundances varied greatly between the replicate samples. Polyethylene and polypropylene were the most commonly detected polymers overall, although the dominant polymer type varied between sites. There were no differences in the abundance of microplastics between sites, nor could abundance distributions be explained by seagrass cover. However, abundances of microplastics were highest in sediments with lower proportions of fine grained particles (clay, <4 μm) suggesting that hydrodynamics override seagrass effects. Additionally, no patterns were seen between microplastic abundance and depth of sediment. This suggests that microplastic abundance and distribution in seagrass meadows may vary significantly depending on the specific geographical locations within those meadows, and that more complex hydrodynamic factors influence spatial variability at a localised scale.