The future is big - And small: Remote sensing enables cross- scale comparisons of microbiome dynamics and ecological consequences

Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. These data-rich, interdisciplinary methods allow us to better understand interactions between microbial communities and their environments and, in turn, their impact on ecosystem structure and function. Here, we highlight current and novel examples of applying remote sensing, machine learning, spatial statistics, and omics data approaches to marine, aquatic, and terrestrial systems. We emphasize the importance of integrating biochemical and spatiotemporal environmental data to move toward a predictive framework of microbiome interactions and their ecosystemlevel effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork

Plant species determine tidal wetland methane response to sea level rise

Blue carbon (C) ecosystems are among the most effective C sinks of the biosphere, but methane (CH4) emissions can offset their climate cooling effect. Drivers of CH4 emissions from blue C ecosystems and effects of global change are poorly understood. Here we test for the effects of sea level rise (SLR) and its interactions with elevated atmospheric CO2, eutrophication, and plant community composition on CH4 emissions from an estuarine tidal wetland. Changes in CH4 emissions with SLR are primarily mediated by shifts in plant community composition and associated plant traits that determine both the direction and magnitude of SLR effects on CH4 emissions. We furthermore show strong stimulation of CH4 emissions by elevated atmospheric CO2, whereas effects of eutrophication are not significant. Overall, our findings demonstrate a high sensitivity of CH4 emissions to global change with important implications for modeling greenhouse-gas dynamics of blue C ecosystems

Plant species determine tidal wetland methane response to sea level rise

Blue carbon (C) ecosystems are among the most effective C sinks of the biosphere, but methane (CH4) emissions can offset their climate cooling effect. Drivers of CH4 emissions from blue C ecosystems and effects of global change are poorly understood. Here we test for the effects of sea level rise (SLR) and its interactions with elevated atmospheric CO2, eutrophication, and plant community composition on CH4 emissions from an estuarine tidal wetland. Changes in CH4 emissions with SLR are primarily mediated by shifts in plant community composition and associated plant traits that determine both the direction and magnitude of SLR effects on CH4 emissions. We furthermore show strong stimulation of CH4 emissions by elevated atmospheric CO2, whereas effects of eutrophication are not significant. Overall, our findings demonstrate a high sensitivity of CH4 emissions to global change with important implications for modeling greenhouse-gas dynamics of blue C ecosystems

Seagrass Recovery Following Marine Heat Wave Influences Sediment Carbon Stocks

Worldwide, seagrass meadows accumulate significant stocks of organic carbon (C),known as “blue” carbon, which can remain buried for decades to centuries. However,when seagrass meadows are disturbed, these C stocks may be remineralized, leading to significant CO2 emissions. Increasing ocean temperatures, and increasing frequency and severity of heat waves, threaten seagrass meadows and their sediment blue C. To date, no study has directly measured the impact of seagrass declines from high temperatures on sediment C stocks. Here, we use a long-term record of sediment C stocks from a 7-km2, restored eelgrass (Zostera marina) meadow to show that seagrass dieback following a single marine heat wave (MHW) led to significant losses of sediment C. Patterns of sediment C loss and re-accumulation lagged patterns of seagrass recovery. Sediment C losses were concentrated within the central area of the meadow,where sites experienced extreme shoot density declines of 90% during the MHW and net losses of 20% of sediment C over the following 3 years. However, this effect was not uniform; outer meadow sites showed little evidence of shoot declines during the MHW and had net increases of 60% of sediment C over the following 3 years. Overall, sites with higher seagrass recovery maintained 1.7x as much C compared to sites with lower recovery. Our study demonstrates that while seagrass blue C is vulnerable to MHWs,localization of seagrass loss can prevent meadow-wide C losses. Long-term (decadal and beyond) stability of seagrass blue C depends on seagrass resilience to short-term disturbance events

Restoration of seagrass habitat leads to rapid recovery of coastal ecosystem services

There have been increasing attempts to reverse habitat degradation through active restoration, but few largescale successes are reported to guide these efforts. Here, we report outcomes from a unique and very successful seagrass restoration project: Since 1999, over 70 million seeds of a marine angiosperm, eelgrass (Zostera marina), have been broadcast into mid-western Atlantic coastal lagoons, leading to recovery of 3612 ha of seagrass. Well-developed meadows now foster productive and diverse animal communities, sequester substantial stocks of carbon and nitrogen, and have prompted a parallel restoration for bay scallops (Argopecten irradians). Restored ecosystem services are approaching historic levels, but we also note that managers value services differently today than they did nine decades ago, emphasizing regulating in addition to provisioning services. Thus, this study serves as a blueprint for restoring and maintaining healthy ecosystems to safeguard multiple benefits, including co-benefits that may emerge as management priorities over time

Preparing aquatic research for an extreme future: call for improved definitions and responsive, multidisciplinary approaches

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Aoki, L. R., Brisbin, M. M., Hounshell, A. G., Kincaid, D. W., Larson, E., Sansom, B. J., Shogren, A. J., Smith, R. S., & Sullivan-Stack, J. Preparing aquatic research for an extreme future: call for improved definitions and responsive, multidisciplinary approaches. Bioscience, 72(6), (2022): 508-520, https://doi.org/10.1093/biosci/biac020.Extreme events have increased in frequency globally, with a simultaneous surge in scientific interest about their ecological responses, particularly in sensitive freshwater, coastal, and marine ecosystems. We synthesized observational studies of extreme events in these aquatic ecosystems, finding that many studies do not use consistent definitions of extreme events. Furthermore, many studies do not capture ecological responses across the full spatial scale of the events. In contrast, sampling often extends across longer temporal scales than the event itself, highlighting the usefulness of long-term monitoring. Many ecological studies of extreme events measure biological responses but exclude chemical and physical responses, underscoring the need for integrative and multidisciplinary approaches. To advance extreme event research, we suggest prioritizing pre- and postevent data collection, including leveraging long-term monitoring; making intersite and cross-scale comparisons; adopting novel empirical and statistical approaches; and developing funding streams to support flexible and responsive data collection

Low-Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California

Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low-altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low-altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions

Disease surveillance by artificial intelligence links eelgrass wasting disease to ocean warming across latitudes

Ocean warming endangers coastal ecosystems through increased risk of infectious disease, yet detection, surveillance, and forecasting of marine diseases remain limited. Eelgrass (Zostera marina) meadows provide essential coastal habitat and are vulnerable to a temperature-sensitive wasting disease caused by the protist Labyrinthula zosterae. We assessed wasting disease sensitivity to warming temperatures across a 3500 km study range by combining long-term satellite remote sensing of ocean temperature with field surveys from 32 meadows along the Pacific coast of North America in 2019. Between 11% and 99% of plants were infected in individual meadows, with up to 35% of plant tissue damaged. Disease prevalence was 3× higher in locations with warm temperature anomalies in summer, indicating that the risk of wasting disease will increase with climate warming throughout the geographic range for eelgrass. Large-scale surveys were made possible for the first time by the Eelgrass Lesion Image Segmentation Application, an artificial intelligence (AI) system that quantifies eelgrass wasting disease 5000× faster and with comparable accuracy to a human expert. This study highlights the value of AI in marine biological observing specifically for detecting widespread climate-driven disease outbreaks.This work was supported by the National Science Foundation (awards OCE-1829921, OCE-1829922, OCE-1829992, OCE-1829890). This is contribution 104 from the Smithsonian's MarineGEO and Tennenbaum Marine Observatories Network.Peer reviewe