Freshwater cyanobacterial harmful algal blooms (CHABs) are a well-known global public health threat. Monitoring and early detection of CHAB toxins are currently accomplished using labor-intensive sampling techniques and subsequent shore-based analyses, with results typically reported 24-48 h after sample collection. We have developed and implemented an uncrewed, autonomous mobile sampler-analytical system capable of conducting targeted in situ toxin measurements in < 2 h. A surface plasmon resonance (SPR) instrument was combined with the environmental sample processor (ESP) to fully automate detection and quantification of particle-associated cyanobacterial microcystins (pMC). This sensor-sampler system was integrated with a long-range autonomous underwater vehicle (LRAUV) and deployed in western Lake Erie for field trials in the summer of 2021. The LRAUV was remotely piloted to acquire samples at selected locations within and adjacent to a CHAB. Sixteen pMC measurements ranging from 0.09 to 0.55 mu g/L lake water were obtained over a 14-day period without recovery of the LRAUV. The SPR/ESP/LRAUV system complements existing satellite, aerial, and manual sampling CHAB survey techniques, and could be used to enhance predictive models that underpin bloom and toxicity forecasts. This system is also extensible to detection of other algal toxins in freshwater and marine environments, with its near real-time assessment of bloom toxin levels potentially offering additional socioeconomic benefits and public health protection in a variety of settings.
Characterizing marine phytoplankton community variability is crucial to designing sampling strategies and interpreting time series. Satellite remote sensing, microscopy sampling, and flow through imaging systems have widely different resolutions: from weekly or monthly with microscopy sampling to daily when no cloud cover or glint is present with polar-orbiting satellites, and hourly for autonomous imaging instruments. To improve our understanding of data robustness against sampling resolution at different taxonomic levels, we analyze 2 yr of data from an Imaging FlowCytobot with hourly resolution and resample it to daily, satellite-temporal, and weekly microscopy sampling resolution. We show that weekly and satellite-temporal resolutions are sufficient to resolve general community composition but that the randomness of satellite-temporal resolution can result in overrepresenting or underrepresenting certain categories. While the yearly phytoplankton biomass bloom is detected in late winter by all four resolutions, category-specific yearly blooms are generally consistent in timing but often underestimated or missed by the weekly and satellite-temporal resolutions, introducing a bias in year-to-year comparisons. A minimum of biweekly sampling, particularly during known bloom periods, would lower the bias in such categories. Similarly, sampling time should be considered as daily variations are category-specific. Overall, morning and low tide sampling tended to have higher biomass. We provide tables for categories detected by the IFCB in Narragansett Bay with their major bloom characteristics and recorded daily variability to inform future sampling designs. These results provide tools to interpret past and future time series, including possible detection of specific taxonomic groups with targeted satellite algorithms.
The use of camera and video technologies for conducting underwater surveys has rapidly expanded over the past several decades. However, the utility of these systems can be significantly hampered by numerous logistical factors, including limited underwater visibility, rough bottom topography, and ease of use for the operator. Video studies can be difficult to compare when methods and terminologies differ. Here, we describe the development of a cost-effective diver-propelled underwater ski-based video system for rapidly acquiring videos in challenging shallow, high-energy rocky benthic habitats for quantifying fish, macroalgae, and invertebrates in a coastal temperate system. The ski held the camera at a (relatively) fixed distance from the seafloor, we used parallel lasers to quantify our observations, and we used the standardized language of the Coastal and Marine Ecological Classification Standard to acquire consistent quantitative data to serve as an ecological baseline, also including archived images. Our results indicate that the ski proved to be an effective tool for capturing insightful data that would otherwise be very difficult and time-consuming to collect. Our baseline and repeatable methods can be used by other investigators at this or other locations for monitoring, re-evaluation, or comparisons to other sites.
Reduced light is one of the primary threats to seagrass meadows in the coming decades, with reduced light reaching the benthos due to eutrophication. We assessed a multispectral photography technique using near‐infrared photography to estimate chlorophyll content in the seagrass Zostera marina . Using near‐infrared and red wavelength cameras in the lab environment, we measured normalized difference vegetation index (NDVI) in photographs of sampled seagrass leaves. In samples taken from three different environments, we found a positive correlation between lab‐based NDVI and chlorophyll content, with variation attributable to leaf age. In samples grown under different light conditions, we found high levels of NDVI associated with lower light possibly due to seagrass photoacclimation. This method may be used in addition to existing seagrass monitoring methods to collect data on seagrass photic status and estimate chlorophyll content, and detect possible light limitation due to turbidity or high epibiota cover. The relatively low cost and time required for this method may make it useful where researchers are already collecting and imaging seagrass as part of routine monitoring.
The ocean–atmosphere exchange of carbon largely depends on the balance between carbon export of particulate organic carbon (POC) as sinking marine particles, and POC remineralization by attached microbial communities. Despite the vast spectrum of types, sources, ages, shapes, and composition of individual sinking particles, they are usually considered as a bulk together with their associated microbial communities. This limits our mechanistic understanding of the biological carbon pump (BCP) and its feedback on the global carbon cycle. We established a method to sample individual particles while preserving their shape, structure, and nucleic acids by placing a jellified RNA‐fixative at the bottom of drifting sediment traps. Coupling imaging of individual particles with associated 16S rRNA analysis reveals that active bacterial communities are highly heterogenous from one particles origin to another. In contrast to lab‐made particles, we found that complex in situ conditions lead to heterogeneity even within the same particle type. Our new method allows to associate patterns of active prokaryotic and functional diversity to particle features, enabling the detection of potential remineralization niches. This new approach will therefore improve our understanding of the BCP and numerical representation in the context of a rapidly changing ocean.
