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Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithm to a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m−2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.

Seagrasses account for approximately 10% of the total carbon stored in the ocean, although photosynthesis of seagrasses is carbon-limited at present oceanic pH levels. Therefore, increasing atmospheric CO2 concentration, which results in ocean acidification/carbonation, is predicted to have a positive impact on seagrass productivity. Previous studies have confirmed the positive influence of increasing CO2 on photosynthesis and survival of the temperate eelgrass Zostera marina, but the acclimation of photoprotective mechanisms in this context has not been characterized. We aimed to quantify the long-term impacts of ocean acidification on photochemical control mechanisms that promote photosynthesis while simultaneously protecting eelgrass from photodamage. Eelgrass were grown in controlled outdoor aquaria at different aqueous CO2 concentrations ranging from ~50 to ~2100 µM from May 2013 to October 2014 and examined for differences in leaf optical properties. Even with daily and seasonal variations of temperature and light, CO2 enrichment consistently increased plant size, leaf thickness and chlorophyll use efficiency, and decreased pigment content and the package effect while maintaining similar light-harvesting efficiency. These acclimation responses suggest that a common photosynthetic sensory function, such as redox regulation, can be manipulated by CO2 availability, as well as light, and may serve to optimize photosynthetic carbon gain by seagrasses into the Anthropocene.

Photorespiration, commonly viewed as a loss in photosynthetic productivity of C3 plants, is expected to decline with increasing atmospheric CO

Since Susan Williams and I started our scientific careers in the mid-1970s, seagrass science has been transformed from a largely descriptive field to an increasingly quantitative and predictive endeavor that requires a mechanistic understanding of environmental influence on metabolic networks that control energy assimilation, growth, and reproduction. Although the potential impacts of environment on gene products are myriad, important phenotypic responses are often regulated by a few key points in metabolic networks where externally supplied resources or physiological substrates limit reaction kinetics. Environmental resources commonly limiting seagrass productivity, survival, and growth include light, temperature, and CO2 availability that control carbon assimilation and sucrose formation, and regulate stress responses to environmental change. Here I present a systems approach to quantify the responses of seagrasses to shifts in environmental factors that control fundamental physiological processes and whole plant performance in the context of a changing climate. This review shows that our ability to understand the past and predict the future trajectory of seagrass-based ecosystems can benefit from a mechanistic understanding of the responses of these remarkable plants to the simultaneous impacts of ocean acidification, climate warming, and eutrophication that are altering ecosystem function across the globe.

As CO2 levels in Earth’s atmosphere and oceans steadily rise, varying organismal responses may produce ecological losers and winners. Increased ocean CO2 can enhance seagrass productivity and thermal tolerance, providing some compensation for climate warming. However, the metabolic shifts driving the positive response to elevated CO2 by these important ecosystem engineers remain unknown. We analyzed whole-plant performance and metabolic profiles of two geographically distinct eelgrass (Zostera marina L.) populations in response to CO2 enrichment. In addition to enhancing overall plant size, growth and survival, CO2 enrichment increased the abundance of Calvin Cycle and nitrogen assimilation metabolites while suppressing the abundance of stress-related metabolites. Overall metabolome differences between populations suggest that some eelgrass phenotypes may be better suited than others to cope with an increasingly hot and sour sea. Our results suggest that seagrass populations will respond variably, but overall positively, to increasing CO2 concentrations, generating negative feedbacks to climate change.

Seagrass meadows are degraded globally and continue to decline in areal extent due to human pressures and climate change. This study used the bio-optical model GrassLight to explore the impact of climate change and anthropogenic stressors on seagrass extent, leaf area index (LAI) and belowground organic carbon (BGC) in St. Joseph Bay, Florida, using water quality data and remotely-sensed sea surface temperature (SST) from 2002 to 2020. Model predictions were compared with satellite-derived measurements of seagrass extent and shoot density from the Landsat images for the same period. The GrassLight-derived area of potential seagrass habitat ranged from 36.2 km

Corals are relatively stenothermic organisms and highly sensitive to thermal stress. To understand how heating rate modulates the holobiont response to thermal challenge, we compared the effects of acute heat shock and cumulative thermal exposures on Acropora cervicornis from Southeast Florida (26° 06 N, 80° 05 W). In March of 2017, maximum dark-adapted yield of photosystem II (FV/FM), rates of dark respiration (R), and rates of gross photosynthesis (Pg) were measured at temperatures spanning 25–36 °C. Thermal performance curves of each response variable were constructed as a function of temperature and thermal dose (i.e., degree heating minutes). Acute exposure (i.e., instantaneously increasing temperature by between 2.5 and 10 °C) resulted in a more marked decline in physiology despite a lower thermal dose, compared to the cumulative exposure (2.5 °C h−1). Apparent deficits in coral holobiont metabolism were observed as a function of both temperature and thermal dose, but examination of holobiont metabolism in a dose-context revealed more pronounced differences between acute and cumulative exposures. Subtle physiological differences may be more easily identified and better cross-study comparisons of cnidarian thermal tolerance may be achieved by placing temperature exposures in degree heating times. In addition, the ratio of daily Pg:R fell below 1 when temperatures exceeded 32 °C, corroborating prior observations that Pg:R may be a means of identifying physiological deficits in advance of visual signs of dysbiosis, such as bleaching.

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods.

Passive ocean color remote sensing has revolutionized our ability to quantify the horizontal distribution of phytoplankton across the ocean surface. Lidar technology can provide remotely sensed estimates of the vertical distribution of optical properties and suspended particles in natural waters, significantly improving our ability to model upper ocean biogeochemical processes. In this study, we constructed and deployed a ship-based lidar system to measure laser backscattering and linear depolarization profiles in the coastal Mid-Atlantic ranging from estuarine to oceanic conditions, and across the Gulf of Maine (GoM). The instrument identified layers with different backscattering intensity in stratified waters of the coastal Mid-Atlantic and produced system attenuation coefficients (Ksys) approximating the absorption coefficient (apg) of particulate + dissolved matter. The linear depolarization ratio was strongly related to in situ measurements of the particulate backscattering ratio (bbp/bp). Measurements of Ksys and linear depolarization made across the GoM corresponded well with simultaneous in situ observations performed aboard the M/V Nova Star and by an autonomous glider deployed along the transect. The relationship between Ksys and apg differed between sampling schemes, likely due to differences in the deployment geometries (e.g., height, nadir angle). These results support the proposition that ship-based lidar systems can provide a powerful tool for remotely measuring the vertical distributions of optical properties and geochemical constituents (e.g., particles) in the upper ocean. Continued development of compact lidar systems for deployment on ships, moorings, and autonomous platforms has the potential to greatly improve the quality and scope of a variety of oceanographic investigations.

The proximity of seagrass meadows to centers of human activity makes them vulnerable to a variety of habitat degrading insults. Physical scarring has long been recognized as an important but difficult-to-quantify source of habitat fragmentation and seagrass loss. We present a pixel-based algorithm to detect seafloor propeller seagrass scars in shallow water that promises to automate the detection and measurement of scars across the submarine landscape. 1 We applied the algorithm to multispectral and panchromatic images captured at the Deckle Beach, Florida using the WorldView-2 commercial satellite. The algorithm involves four steps using spectral and spatial information from radiometrically calibrated multispectral and panchromatic images. First, we fused multispectral and panchromatic images using a principal component analysis (PCA)-based pan-sharpening method to obtain multispectral pan-sharpened bands. In the second step, we enhanced the image contrast of the pan-sharpened bands for better scar detection. In the third step, we classified the contrast enhanced image pixels into scar and non-scar categories based on a sparse coding algorithm that produced an initial scar map in which false positive scar pixels were also present. In the fourth step, we applied post-processing techniques including a morphological filter and local orientation to reduce false positives. Our results show that the proposed method may be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters.

Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here, we construct such a budget for Eastern North America using historical data, empirical models, remote-sensing algorithms, and process-based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO2 uptake split evenly between tidal wetlands and shelf waters, and estuarine CO2 outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they respectively make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling.

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network (GAN) loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods.

As CO2 levels in Earth’s atmosphere and oceans steadily rise, varying organismal responses may produce ecological losers and winners. Increased ocean CO2 can enhance seagrass productivity and thermal tolerance, providing some compensation for climate warming. However, the metabolic shifts driving the positive response to elevated CO2 by these important ecosystem engineers remain unknown. We analyzed whole-plant performance and metabolic profiles of two geographically distinct eelgrass (Zostera marina L.) populations in response to CO2 enrichment. In addition to enhancing overall plant size, growth and survival, CO2 enrichment increased the abundance of Calvin Cycle and nitrogen assimilation metabolites while suppressing the abundance of stress-related metabolites. Overall metabolome differences between populations suggest that some eelgrass phenotypes may be better suited than others to cope with an increasingly hot and sour sea. Our results suggest that seagrass populations will respond variably, but overall positively, to increasing CO2 concentrations, generating negative feedbacks to climate change.

Variation in environmental characteristics and divergent selection pressures can drive adaptive differentiation across a species’ range. Astrangia poculata is a temperate scleractinian coral that provides unique opportunities to understand the roles of phenotypic plasticity and evolutionary adaptation in coral physiological tolerance limits. A. poculata inhabits hard bottom ecosystems from the northwestern Atlantic to the Gulf of Mexico and withstands an annual temperature range up to 20°C. Additionally, A. poculata is facultatively symbiotic and co-occurs in both symbiotic (“brown”) and aposymbiotic (“white”) states. Here, brown and white A. poculata were collected from Virginia (VA) and Rhode Island (RI), USA and exposed to heat (18-32°C) and cold (18-6°C) temperatures during which respiration (R) of the coral host along with photosynthesis (P) and photochemical efficiency (Fv/Fm) of Breviolum psygmophilum photosymbionts were measured. Thermal performance curves (TPCs) of respiration revealed a pattern of countergradient variation with RI corals exhibiting higher respiration rates overall, and specifically at 6, 15, 18, 22, and 26°C. Additionally, thermal optimum (Topt) analyses show a 3.8°C (brown) and 6.9°C (white) higher Topt in the VA population, corresponding to the warmer in situ thermal environment in VA. In contrast to respiration, no origin effect was detected in photosynthesis rates or Fv/Fm, suggesting a possible host-only signature of adaptation. This study is the first to consider A. poculata’s response to both heat and cold stress across symbiotic states and geography and provides insight into the potential evolutionary mechanisms behind the success of this species along the East Coast of the US.

The absorption of solar energy by colored dissolved organic material (CDOM) in Arctic surface waters during the springtime is increasingly recognized as having profound impacts on seasonal retreat of ice from the Arctic Ocean. This study observed the production of CDOM within first year landfast sea ice, its contribution to light absorption within the water column and its photoreactivity, at a location near Ellef Ringnes Island in the Canadian Archipelago during the spring of 2011. CDOM was the optically dominant component of non-water absorption, increasing the energy absorption and heating potential in the top 8 m by 30% over pure seawater. CDOM absorption within sea ice brine channels increased with solar-stimulated ice algae production. This ice algal mediated production of CDOM exceeded photooxidation rates, potentiating a net delivery of CDOM to the immediately underlying water column from brine channels in the ice. The rates of production and loss of CDOM in Arctic waters during the early spring quantified here will help constrain a major uncertainty in modeling light absorption by surface waters that contributes to sea ice loss in the Arctic.

Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe's WorldView-2 and Planet's RapidEye. A single scene from each platform was obtained at St. Joseph Bay in Florida, USA, corresponding to a November 2010 field campaign. A reproducible processing regime was developed to transform imagery from basic products, as delivered from each company, into analysis-ready data usable for various scientific applications. Satellite-derived surface reflectances were compared against field measurements. WorldView-2 imagery exhibited high disagreement in the coastal blue and blue spectral bands, chronically overpredicting. RapidEye exhibited better agreement than WorldView-2, but overpredicted slightly across all spectral bands. A deep convolutional neural network was used to classify imagery into deep water, land, submerged sand, seagrass, and intertidal classes. Classification results were compared to seagrass maps derived from photointerpreted aerial imagery. This study offers the first radiometric assessment of WorldView-2 and RapidEye over a coastal system, revealing inherent calibration issues in shorter wavelengths of WorldView-2. Both platforms demonstrated as much as 97% agreement with aerial estimates, despite differing resolutions. Thus, calibration issues in WorldView-2 did not appear to interfere with classification accuracy, but could be problematic if estimating biomass. The image processing routine developed here offers a reproducible workflow for WorldView-2 and RapidEye imagery, which was tested in two additional coastal systems. This approach may become platform independent as more sensors become available.

Oceanographic lidar can provide remote estimates of the vertical distribution of suspended particles in natural waters, potentially revolutionizing our ability to characterize marine ecosystems and properly represent them in models of upper ocean biogeochemistry. However, lidar signals exhibit complex dependencies on water column inherent optical properties (IOPs) and instrument characteristics, which complicate efforts to derive meaningful biogeochemical properties from lidar return signals. In this study, we used a ship-based system to measure the lidar attenuation coefficient ( α ) and linear depolarization ratio ( δ ) across a variety of optically and biogeochemically distinct water masses, including turbid coastal waters, clear oligotrophic waters, and calcite rich waters associated with a mesoscale coccolithophore bloom. Sea surface IOPs were measured continuously while underway to characterize the response of α and δ to changes in particle abundance and composition. The magnitude of α was consistent with the diffuse attenuation coefficient ( K d ), though the α versus K d relationship was nonlinear. δ was positively related to the scattering optical depth and the calcite fraction of backscattering. A statistical fit to these data suggests that the polarized scattering properties of calcified particles are distinct and contribute to measurable differences in the lidar depolarization ratio. A better understanding of the polarized scattering properties of coccolithophores and other marine particles will further our ability to interpret polarized oceanographic lidar measurements and may lead to new techniques for measuring the material properties of marine particles remotely.

Seagrass habitats are becoming extremely vulnerable due to human intrusion to seagrass meadows, which results in unbalanced marine ecosystems and extinction of marine animals. Traditionally, manual scarring has been used to identify and quantify seagrass propeller scars. However, this method requires site visitation and it is cost ineffective. In this paper, we propose deep learning method to automatically detect propeller seagrass scars in multispectral satellite images. Our proposed algorithm is more computationally efficient than our previous sparse coding detection model and can accurately detect seagrass scars. Additionally, we explored two pan-sharpening methods for obtaining high-resolution multispectral satellite images for scar detection. We evaluated our methods on four multispectral images collected in Florida and experimental results show that the proposed deep learning model combined with the Gram-Smith (GS) pan-sharpening approach achieved the best sensitivities in seagrass scar detection and this combination is also the most computational efficient method, requiring only 7 minutes for a testing image with a size of 1000x800 in testing phase.

© 2018 The Authors. Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America Ocean acidification threatens many marine organisms, especially marine calcifiers. The only global-scale solution to ocean acidification remains rapid reduction in CO2emissions. Nevertheless, interest in localized mitigation strategies has grown rapidly because of the recognized threat ocean acidification imposes on natural communities, including ones important to humans. Protection of seagrass meadows has been considered as a possible approach for localized mitigation of ocean acidification due to their large standing stocks of organic carbon and high productivity. Yet much work remains to constrain the magnitudes and timescales of potential buffering effects from seagrasses. We developed a biogeochemical box model to better understand the potential for a temperate seagrass meadow to locally mitigate the effects of ocean acidification. Then we parameterized the model using data from Tomales Bay, an inlet on the coast of California, USA which supports a major oyster farming industry. We conducted a series of month-long model simulations to characterize processes that occur during summer and winter. We found that average pH in the seagrass meadows was typically within 0.04 units of the pH of the primary source waters into the meadow, although we did find occasional periods (hours) when seagrass metabolism may modify the pH by up to ±0.2 units. Tidal phasing relative to the diel cycle modulates localized pH buffering within the seagrass meadow such that maximum buffering occurs during periods of the year with midday low tides. Our model results suggest that seagrass metabolism in Tomales Bay would not provide long-term ocean acidification mitigation. However, we emphasize that our model results may not hold in meadows where assumptions about depth-averaged net production and seawater residence time within the seagrass meadow differ from our model assumptions. Our modeling approach provides a framework that is easily adaptable to other seagrass meadows in order to evaluate the extent of their individual buffering capacities. Regardless of their ability to buffer ocean acidification, seagrass meadows maintain many critically important ecosystem goods and services that will be increasingly important as humans increasingly affect coastal ecosystems.

Variation in environmental characteristics and divergent selection pressures can drive adaptive differentiation across a species' range.

Seagrass is a highly valuable component of coastal ecosystems ecologically and economically, yet reliable mapping of seagrass density is not available due to the high cost of data processing and spatial mapping. This paper presents a deep learning approach for quantification of leaf area index (LAI) levels of seagrass in coastal water using high resolution multispectral satellite images. Specifically, a deep capsule network (DCN) is developed for simultaneous classification and quantification of seagrass based on the multispectral images. The DCN is jointly optimized for classification and regression, and is capable of performing end-to-end seagrass quantification. We separately validated the proposed method on three images taken in Florida coastal area and achieved better results with DCN when compared against a deep convolutional neural network (CNN) model and a linear regression model. In addition, transfer learning strategies are developed to transfer knowledge in a DCN trained at one location for seagrass quantification to different locations with minimum field observations, which saves a significant amount of time and resources in the mapping of seagrass LAI. Our experimental results show that the developed capsule network achieved superb performances in few-shot transfer learning as compared to direct linear regression and traditional CNN models.

Seagrass is an important factor to balance marine ecological systems, and there is a great interest in monitoring its distribution in different parts of the world. This paper presents a deep capsule network for classification of seagrass in high-resolution multispectral satellite images. We tested our method on three satellite images of the coastal areas in Florida and obtained better performances than those achieved by the traditional deep convolutional neural network (CNN) model. We also propose a few-shot deep learning strategy to transfer knowledge learned by the capsule network from one location to another for seagrass detection, in which the capsule network’s reconstruction capability is utilized to generate new artificial data for fine-tuning the model at new locations. Our experimental results show that the proposed model achieves superb performances in cross-validation on three satellite images collected in Florida as compared to support vector machine (SVM) and CNN.

Isotopic discrimination against 13 C during photosynthesis is determined by a combination of environmental conditions and physiological mechanisms that control delivery of CO2 to RUBISCO. This study investigated the effects of light, flow, dissolved inorganic carbon (DIC) concentration, and its speciation, on photosynthetic carbon assimilation of Zostera marina L. (eelgrass) using a combination of laboratory experiments and theoretical calculations leading to a mechanistic understanding of environmental conditions that influence leaf carbon uptake and determine leaf stable carbon isotope signatures (d 13 C). Photosynthesis was saturated with respect to flow at low velocity (� 3c m s 21 ), but was strongly influenced by [DIC], and particularly aqueous CO2 (CO2(aq)) under all flow conditions. The non-linear responses of light- and flow-saturated photosynthesis to [DIC] were used to quantify the maximum physiological capacity for photosynthesis, and to determine the degree of photosynthetic carbon limitation for light-saturated photosynthesis, which provided a mechanistic pathway for modeling regulation of carbon uptake and 13 C discrimination. Model predictions of d 13 C spanned the typical range of values reported for a variety of seagrass taxa, and were most sensitive to [DIC] (predominantly [CO2(aq)]) and flow, but less sensitive to DIC source [CO2(aq) vs. HCO 2 ]. These results provide a predictive understanding of the role of key environmental parameters (light, flow, and DIC availability) can have in driving d 13 C of seagrasses, which will become increasingly important for predicting the response of these ecosystem engineers to local processes that affect light availability and flow, as well as global impacts of climate warming and ocean acidification in the Anthropocene.

Although environmental requirements of seagrasses have been studied for years, reliable metrics for predicting their response to current or future conditions remain elusive. Eelgrass (Zostera marina L.) populations of the Chesapeake region lie near the southern limit of their range in the Western North Atlantic, exposing them to increasing thermal stress as the climate warms. However, CO2 stimulated photosynthesis may offset some of the negative effects of temperature stress. The combined effects of temperature, CO2, and light availability controlled by water quality and epiphytes were explored using GrassLight, a bio-optical model that provided a predictive environment for evaluating the interaction of multiple stressors on eelgrass distribution and density across the submarine landscape. Model predictions were validated against in situ measures of spectral diffuse attenuation, eelgrass density, and distribution. The potential for photosynthesis stimulated by ocean acidification to mitigate the effects of high temperature on eelgrass populations growing near the southern limit of their distribution was explored. The model accurately reproduced the submarine light environment from measured water quality parameters, and predicted their impacts on eelgrass distribution. It also reproduced the negative effects of warm summer temperatures on eelgrass distributions, and demonstrated that CO2 increases projected for the next century should stimulate photosynthesis sufficiently to offset the negative effects of thermal stress on eelgrass growing in the Chesapeake region, even in the presence of epiphytes. Thus, improved water quality should facilitate the survival of eelgrass populations in Chesapeake region, even in the face of a warming climate.

© 2015, The Oceanography Society. All rights reserved. Ocean acidification (OA) research seeks to understand how marine ecosystems and global elemental cycles will respond to changes in seawater carbonate chemistry in combination with other environmental perturbations such as warming, eutrophication, and deoxygenation. Here, we discuss the effectiveness and limitations of current research approaches used to address this goal. A diverse combination of approaches is essential to decipher the consequences of OA to marine organisms, communities, and ecosystems. Consequently, the benefits and limitations of each approach must be considered carefully. Major research challenges involve experimentally addressing the effects of OA in the context of large natural variability in seawater carbonate system parameters and other interactive variables, integrating the results from different research approaches, and scaling results across different temporal and spatial scales.

Introduction: The Chesapeake Bay was once renowned for expansive meadows of submerged aquatic vegetation (SAV). However, only 10% of the original meadows survive. Future restoration efforts will be complicated by accelerating climate change, including physiological stressors such as a predicted mean temperature increase of 2–6°C and a 50–160% increase in CO 2 concentrations. Outcomes: As the Chesapeake Bay begins to exhibit characteristics of a subtropical estuary, summer heat waves will become more frequent and severe. Warming alone would eventually eliminate eelgrass ( Zostera marina) from the region. It will favor native heat-tolerant species such as widgeon grass ( Ruppia maritima ) while facilitating colonization by non-native seagrasses (e.g., Halodule spp.). Intensifying human activity will also fuel coastal zone acidification and the resulting high CO 2 /low pH conditions may benefit SAV via a “CO 2 fertilization effect.” Discussion: Acidification is known to offset the effects of thermal stress and may have similar effects in estuaries, assuming water clarity is sufficient to support CO 2 -stimulated photosynthesis and plants are not overgrown by epiphytes. However, coastal zone acidification is variable, driven mostly by local biological processes that may or may not always counterbalance the effects of regional warming. This precarious equipoise between two forces – thermal stress and acidification – will be critically important because it may ultimately determine the fate of cool-water plants such as Zostera marina in the Chesapeake Bay. Conclusion: The combined impacts of warming, coastal zone acidification, water clarity, and overgrowth of competing algae will determine the fate of SAV communities in rapidly changing temperate estuaries.

There is a pressing need to globally inventory and assess coastal and inland aquatic habitats; extremely valuable and productive regions that are vulnerable to global anthropogenic pressures and climatic change. Basic information about sessile communities (wetlands, coral reefs, and sea grasses) includes mapping their extent and distribution, which can be gleaned from spectral surface reflectance imagery at high spatial resolution, but moderate temporal resolution. Moderate to high temporal resolution is also required for detailed observations of sessile community change (e.g., phenology, disturbance) and high temporal resolution is required for environmental changes in the surrounding water, phytoplankton concentration and composition, and concentrations of sediment or chromophoric dissolved organic matter (CDOM). Current and upcoming satellite missions and technology could meet spatial and spectral challenges. Multiple orbiting and airborne platforms, along with a network of in situ measurements, could provide a more complete picture of how these vital resources are changing. This paper provides an overview of these resources.

Predicting adaptive fitness to any environment requires mechanistic understanding of environmental influence on metabolic networks that control energy assimilation, growth, and reproduction. Although the potential impacts of environment on gene products are myriad, important phenotypic responses are often regulated by a few key points in metabolic networks where externally supplied resources or physiological reaction substrates limit reaction kinetics. Environmental resources commonly limiting seagrass productivity, survival, and growth include light and CO2 availability that control carbon assimilation and sucrose formation. Phosphate availability can also be important in oligotrophic tropical environments, particularly in the presence of carbonate sediments. Temperature and macronutrient oversupply (eutrophication) can act as confounding stressors, particularly in temperate environments. Photoacclimation can be regulated by electron transport pathways residing in the chloroplast stroma, but stress responses are often manifest by the expression of generalized stress response proteins, both of which appear to be affected by temperature and CO2 availability. A systems approach is employed to explore (1) the responses of seagrasses to the combined impacts of environmental limiting factors that control fundamental physiological processes leading to whole-plant performance; (2) sediment diagenetic processes that facilitate nutrient remineralization, carbon sequestration, and toxin neutralization; (3) interactions with other organisms induced by trophic cascades; and (4) impacts of human-induced climate change that affect system dynamics at numerous points in the network.

Seagrasses provide a number of critical ecosystem services, including habitat for numerous species, sediment stabilization, and shoreline protection. Ariel photography is a useful tool to estimate the areal extent of seagrasses, but recent innovations in radiometrically calibrated sensors and algorithm development have allowed identification of benthic types and retrieval of absolute density. This study demonstrates the quantitative ability of a high spatial resolution (1 m) airborne hyperspectral sensor (3.2 nm bandwidth) in the complex coastal waters of Saint Joseph’s Bay (SJB). Several benthic types were distinguished, including submerged and floating aquatic vegetation, benthic red algae, bare sand, and optically deep water. A total of 23.6 km2 of benthic vegetation was detected, indicating no dramatic change in vegetation area over the past 30 years. SJB supported high seagrass density at depths shallower than 2 m with an average leaf area index of 2.0 ± 0.6 m2 m−2. Annual seagrass production in the bay was 13,570 t C year−1 and represented 41 % of total marine primary production. The effects of coarser spatial resolution were investigated and found to reduce biomass retrievals, underestimate productivity, and alter patch size statistics. Although data requirements for this approach are considerable, water column optical modeling may reduce the in situ requirements and facilitate the transition of this technique to routine monitoring efforts. The ability to quantify not just areal extent but also productivity of a seagrass meadow in optically complex coastal waters can provide information on the capacity of these environments to support marine food webs.

The estuaries and continental shelf system of the United States Mid-Atlantic are subject to ocean acidification driven by atmospheric CO2, and coastal acidification caused by nearshore and land-sea interactions that include biological, chemical, and physical processes. These processes include freshwater and nutrient input from rivers and groundwater; tidally-driven outwelling of nutrients, inorganic carbon, alkalinity; high productivity and respiration; and hypoxia. Hence, these complex dynamic systems exhibit substantial daily, seasonal, and interannual variability that is not well captured by current acidification research on Mid-Atlantic organisms and ecosystems. We present recommendations for research priorities that target better understanding of the ecological impacts of acidification in the U. S. Mid-Atlantic region. Suggested priorities are: 1) Determining the impact of multiple stressors on our resource species as well as the magnitude of acidification; 2) Filling information gaps on major taxa and regionally important species in different life stages to improve understanding of their response to variable temporal scales and sources of acidification; 3) Improving experimental approaches to incorporate realistic environmental variability and gradients, include interactions with other environmental stressors, increase transferability to other systems or organisms, and evaluate community and ecosystem response; 4) Determining the capacity of important species to acclimate or adapt to changing ocean conditions; 5) Considering multi-disciplinary, ecosystem-level research that examines acidification impacts on biodiversity and biotic interactions; and 6) Connecting potential acidification-induced ecological impacts to ecosystem services and the economy. These recommendations, while developed for the Mid-Atlantic, can be applicable to other regions will help align research towards knowledge of potential larger-scale ecological and economic impacts.

Recent warming of surface waters, accompanied by reduced ice thickness and extent may have significant consequences for climate-driven changes of primary production (PP) in the Arctic Ocean (AO). However, it has been difficult to obtain a robust benchmark estimate of pan-Arctic PP necessary for evaluating change. This paper provides an estimate of pan-Arctic PP prior to significant warming from a synthetic analysis of the ARCSS-PP database of in situ measurements collected from 1954 to 2007 and estimates derived from satellite-based observations from 1998 to 2007. Vertical profiles of in situ chlorophyll a (Chl a ) and PP revealed persistent subsurface peaks in biomass and PP throughout the AO during most of the summer period. This was contradictory with the commonly assumed exponential decrease in PP with depth on which prior satellite-derived estimates were based. As remotely sensed Chl a was not a good predictor of integrated water column Chl a , accurate satellite-based modeling of vertically integrated primary production (IPP sat ), requires knowledge of the subsurface distribution of phytoplankton, coincident with the remotely sensed ocean color measurements. We developed an alternative approach to modeling PP from satellite observations by incorporating climatological information on the depths of the euphotic zone and the mixed layer that control the distribution of phytoplankton that significantly improved the fidelity of satellite derived PP to in situ observations. The annual IPP of the Arctic Ocean combining both in situ and satellite based estimates was calculated here to be a minimum of 466 ± 94 Tg C yr −1 and a maximum of 993 ± 94 Tg C yr −1 , when corrected for subsurface production. Inflow shelf seas account for 75% of annual IPP, while the central basin and Beaufort northern sea were the regions with the lowest annual integrated productivity, due to persistently stratified, oligotrophic and ice-covered conditions. Although the expansion of summertime ice retreat should stimulate photosynthesis by exposing more of the AO to solar irradiance, total PP is ultimately limited by nutrient availability. Therefore, changes in AO PP will be forced by the balance between stratification and mixing, the effects of which are not yet quantified.

Water column optical properties of Greater Florida Bay were investigated in the context of their impacts on seagrass distribution. Scattering played an important role in light attenuation throughout the shallow water system. The northwest region was characterized by an absence of seagrasses and the highest scattering by particles, mostly from resuspended carbonate sediments. Higher seagrass densities were observed in the open waters just north of the Florida Keys, where absorption coefficients were dominated by colored dissolved organic material and scattering was lower than in the northwest region. Patchy dense seagrass meadows were observed in the clear waters south of the Keys where scattering and absorption were low and contributed equally to light attenuation. In general, seagrasses were observed in areas where >7.5% of surface irradiance reached the plants and where optical properties were not dominated by scattering. Although the prevention of eutrophication and nuisance algal blooms may be necessary for preserving seagrass meadows in this system, our observations and model calculations indicate that nutrient control alone may be insufficient to permit seagrass recolonization if optical properties are dominated by particulate scattering from resuspended sediments.

The Arctic Ocean, including its regional shelf seas, is assumed to play an important role in the global carbon cycle. However, the true magnitude of annual production is unknown, as in situ data are sparse in time and space. Remote sensing technology has the potential to provide large scale estimates of phytoplankton biomass at much higher frequency and spatial coverage than shipboard observations in this remote region. Subsurface peaks in both biomass and primary production (PP), which are the characteristics of the Arctic, are shown to limit the reliability of ocean color based integrated PP (IPP) models in the Chukchi Sea. Here we report that the retrievals of IPP from remotely sensed ocean color data were accurate only when limited to 1.2 optical depths, which severely constrains the utility of ocean color remote sensing for the assessment of Arctic Ocean dynamics. Active sensors such as LIDAR, can, in combination with passive ocean color, dramatically improve our ability to estimate IPP for the Arctic. IPP retrievals were improved to within a factor of 2–3 of the measured values, when the vertical distribution of Chl a was determined to a resolution of 1 m using modeled LIDAR retrievals of the beam attenuation coefficient. This was far better than models using only passive ocean color. The instrument specifications of the current NASA spaceborne LIDAR (CALIOP) allow for the retrieval of K d at a depth resolution of 23 m. Even with this constraint, however, the accuracy of the modeled IPP was improved over passive ocean color retrievals to approximately a factor of 3. The Arctic is a perfect location to merge ocean color and LIDAR measurements as the polar orbit of CALIOP provides complete grid coverage of the area every 8 days, crossing the horizontal gradients in Chl a already known to exist from passive ocean color observations.

Development of repeatable and quantita- tive tools are necessary for determining the abundance and distribution of different types of benthic habitats, de- tecting changes to these ecosystems, and determining their role in the global carbon cycle. Here we used ocean color remote sensing techniques to map different major groups of primary producers and estimate net primary productivity (NPP) across Great Bahama Bank (GBB). Field investigations on the northern portion of the GBB in 2004 revealed 3 dominant types of benthic primary pro- ducers: seagrass, benthic macroalgae, and microalgae at- tached to sediment. Laboratory measurements of NPP ranged from barely net autotrophic for grapestone sedi- ment with thin microalgal biofilm to highly productive for dense accumulations of brown macroalgae. A logarith- mic relationship between NPP and green seafloor re- flectance described the general trend in NPP across various benthic constituents. Using a radiative transfer- based approach, satellite-derived estimates of NPP for the region totaled ~2 × 10 13 gC yr -1 across the GBB. The prevailing benthic habitat was mapped as sediment with little to no microalgal biofilm. Moderate to dense sea- grass meadows of Thalassia testudinum were the domi- nant primary producers and contributed over 80% of NPP in the region. If the vast majority of seagrass leaves decompose in the primarily carbonate sediments, car- bonate dissolution processes associated with this decom- position may result in sequestration of seagrass above- and below-ground carbon into the bicarbonate pool (2.4 × 10 13 gC yr -1 ), where it has a residence time on the order of tens of thousands of years.

Using two complimentary approaches (pore water advection/diffusion/ reaction modeling and stable isotope mass balance calculations) we show that carbonate dissolution/reprecipitation occurs on early diagenetic time scales across a broad range of sediments on the Great Bahamas Bank. The input of oxygen into the sediments, which strongly controls sediment carbonate dissolution, has two major sources—belowground input by seagrasses (that is, seagrass O 2 pumping), and permeability-driven advective pore water exchange. The relative importance of these O 2 delivery mechanisms depends on both seagrass density, and on how bottom water flow interacts with the seagrass canopy and leads to this advective exchange. Dissolution appears to involve the preferential dissolution of high-Mg calcite, and the rates of dissolution increase linearly with increasing seagrass density. Isotopic evidence of dissolution/reprecipitation is consistent with the occurrence of Ostwald ripening as the mechanism of reprecipitation, in which smaller crystals dissolve and then reprecipitate as larger crystals, with little or no change in mineralogy. Estimates of the aerially-integrated dissolution flux on the Bahamas Bank suggest that carbonate dissolution is an important loss term in the budget of shallow water carbonate sediments, and that on-bank carbonate dissolution, rather than offshore transport, may represent an important sink for gross shallow water carbonate production. Dissolution in carbonate bank and bay sediments may also be a significant alkalinity source to the surface ocean, and should be considered in global alkalinity/ carbonate budget. Finally, coupled dissolution/reprecipitation may have a major impact on the stable isotope composition of carbonate sediments that are ultimately preserved in the rock record. These processes may therefore need to be considered, for example, when using carbon isotope records to obtain information on the operation of the global carbon cycle during the Phanerozoic.

We focus on the validation of a simplified approach to bathymetry retrieval from hyperspectral imagery (HSI) in the very shallow water limit (less than 1–2 m), where many existing bathymetric LIDAR sensors perform poorly. In this depth regime, near infra-red (NIR) reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role. Our processing framework exploits two optimal regions where a simple model depending on bottom type and water depth can be applied in the very shallow limit. These two optimal spectral regions are at a local maximum in the near infra-red reflectance near 810 nm, corresponding to a local minimum in absorption, and a maximum in the first derivative of the reflectance near 720 nm. These two regions correspond to peaks in spectral correlation with bathymetry at these depths.

Regions of milky white seas or "whitings" periodically occur to the west of Andros Island along the Great Bahama Bank where the bottom sediment consists of fine-grained aragonite mud. We present comprehensive measurements of inherent optical properties within a whiting patch and discuss the potential for monitoring the frequency, extent, and quantity of suspended matter from ocean colour satellite imagery. Sea spectral reflectance measured in situ and remotely from space revealed highly reflective waters elevated across the visible spectrum (i.e., "whitened") with a peak at 490 nm. Particulate backscattering was an order of magnitude higher than that measured at other stations throughout the region. The whiting also had one of the highest backscattering ratios measured in natural waters (0.05–0.06) consistent with water dominated by aragonite particles with a high index of refraction. Regular periodicity of 40 and 212 s evident in the light attenuation coefficient over the sampling period indicated patches of fluctuating turbidity on spatial scales that could be produced from regular rows of Langmuir cells penetrating the 5-m water column. We suggest that previously described mechanisms for sediment resuspension in whitings, such as tidal bursting and fish activity, are not fully consistent with these data and propose that wind-driven Langmuir cells reaching the full-depth of the water column may represent a plausible mechanism for sediment resuspension and subsequent whiting formation. Optics and remote sensing provide important tools for quantifying the linkages between physical and biogeochemical processes in these dynamic shallow water ecosystems.

In this study we estimate sediment carbonate dissolution rates for sandy sea grass sediments on the Bahamas Bank using an inverse pore-water advection/diffusion/reaction model constrained by field observations. This model accounts for sea grass O2 input to these sediments, and also parameterizes pore-water advection through these permeable sediments as a nonlocal exchange process. The resulting rates of carbonate dissolution are positively correlated with sea grass density, and are comparable with previous rate estimates for Florida Bay sediments. In contrast, the advective uptake of O2 by these sediments decreased with increasing sea grass density. This suggests that the competing interplay between bottom-water flow, near-seabed pressure gradients, and the presence of a sea grass canopy is important in controlling this type of sediment oxygen uptake. When the carbonate dissolution rates estimated here are examined in the context of carbonate budgets for shallow-water carbonate platforms systems, they suggest that carbonate dissolution may be a significant loss term in these budgets. Sea grass-mediated carbonate dissolution may also exert a negative feedback on rising atmospheric CO2, although the magnitude of this effect remains to be quantified.