Showing papers by "National Ocean Service published in 2020"

Atmospheric correction for satellite-derived bathymetry in the Caribbean waters: from a single image to multi-temporal approaches using Sentinel-2A/B.

Abstract: Different atmospheric correction (AC) procedures for Sentinel-2 satellites are evaluated for their effectiveness in retrieving consistent satellite-derived bathymetry (SDB) over two islands in the Caribbean (Buck and Culebra). The log-ratio method for SDB, which allows use of minimal calibration information from lidar surveys (25 points in this study), is applied to several Sentinel-2A/B scenes at 10 m spatial resolution. The overall performance during a one-year study period depends on the image quality and AC. Three AC processors were evaluated: ACOLITE Exponential model (EXP), ACOLITE Dark Spectrum Fitting model (DSF), and C2RCC model. ACOLITE EXP and ACOLITE DSF produce greater consistency and repeatability with accurate results in a scene-by-scene analysis (mean errors ∼1.1 m) for depths up to 23 m (limit of lidar surveys). In contrast, C2RCC produces lower accuracy and noisier results with generally higher (>50%) errors (mean errors ∼2.2 m), but it is able to retrieve depth for scenes in Buck Island that have moderately severe sunglint. Furthermore, we demonstrate that a multi-temporal compositing model for SDB mapping, using ACOLITE for the input scenes, could achieve overall median errors <1 m for depths ranging 0-23 m. The simple and effective compositing model can considerably enhance coastal SDB estimates with high reliability and no missing data, outperforming the traditional single image approaches and thus eliminating the need to evaluate individual scenes. The consistency in the output from the AC correction indicates the potential for automated application of the multi-scene compositing technique, which can apply the open and free Sentinel-2 data set for the benefit of operational and scientific investigations.

Sustainable Oyster Aquaculture, Water Quality Improvement, and Ecosystem Service Value Potential in Maryland Chesapeake Bay

Abstract: The United States has a $16 billion seafood deficit that the U.S. Department of Commerce and states are attempting to close by legislative policies, encouraging expansion of aquaculture in the United States. One of these policies, the 2011 National Shellfish Initiative, recognizes the benefits to water quality of cultivation of bivalve shellfish aquaculture in addition to the provision of seafood product. More recently, research addressing these policies has resulted in approval of the use of harvested oysters as a nutrient best management practice in the Chesapeake Bay region. Also discussed, but not yet fully implemented, is the inclusion of oyster growers in nutrient credit trading programs where economic compensation is provided to oyster growers for the nutrient removal ecosystem service that their oysters provide. This study used field sampling and a local-scale oyster production model to compare water quality, oyster production, and oyster associated nitrogen removal at two bottom and four water-column Maryland Chesapeake Bay oyster farms. Objectives were to highlight differences in water quality (i.e., oyster food), resultant differences in oyster production, and differences in estimated oyster-associated nutrient removal among farms. An avoided, or replacement, cost economic valuation analysis was performed to also compare the potential payment to the oyster growers for the nutrient removal service if they were included in a fully developed nutrient credit trading program. Production at the six sites varied from 1.78 to 25 metric tons of harvestable oysters acre–1 y–1. Oyster filtration–related N removal was estimated to be at a range of 28–457 kg N acre–1 y–1. The potential economic value of the total N removed by a farm was estimated to be at a range of $0.56 × 103–$12,446 × 103 y–1 among farm sites, depending on the alternative management measure used to assign the value.

Predictive modeling of suitable habitat for deep-sea corals offshore the Northeast United States

Abstract: Deep-sea corals (DSCs) are important living marine resources, forming both oases of biodiversity and three-dimensional habitat structure for fishes and invertebrates. However, because of logistical difficulties and expense of deep-sea exploration, much less is known about the distribution of DSCs than is known for their shallow-water counterparts. Predictive modeling, therefore, is essential for estimating the extent of DSC habitat in areas that are unexplored in order to support conservation efforts, to provide information for effective management of offshore activities affecting the seafloor, and for future exploration and research. In support of research and management efforts in the U.S. Northeast (Cape Hatteras, NC north to the Canadian border), we developed a comprehensive set of habitat suitability models covering this entire geographic region for nine taxonomic groups of DSCs (Alcyonacea, gorgonian corals, non-gorgonian corals, Scleractinia, Caryophylliidae, Flabellidae, Pennatulacea, Sessiliflorae, and Subselliflorae). Maximum entropy (MaxEnt) models were fit to DSC presence records and spatially-explicit environmental predictors depicting depth and seafloor topography, surficial sediment characteristics, and oceanography. A stepwise model selection procedure was then implemented to identify the set of predictor variables that maximized predictive performance for each taxonomic group. To allow for comparisons across taxonomic groups, the standard MaxEnt logistic predictions were converted into calibrated classes of habitat suitability. Overall, model performance was high for all taxonomic groups. Model fit was best for Caryophylliidae, Sessiliflorae, and Flabellidae, whereas model stability was greatest for the three taxonomic groups of Alcyonacea. Model results reported here corroborate known distributions of corals in the region. For example, large structure-forming taxa are predicted to occur mainly in canyon environments, particularly in areas of steep slope (>30°); sea pens in softer sediments of the continental shelf and slope. Additionally, the models successfully predicted DSC locations during field testing. Despite the limitations of presence-only data, several novel extensions to the traditional MaxEnt analysis workflow improved model selection, accuracy assessment, and comparability of results across taxonomic groups. This approach, when integrated with management processes, could be a powerful tool for science-based conservation, management, and spatial planning for these marine resources.

Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study

Abstract: Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (eg susceptible, exposed, infected, recovered) However, data defining these states often fall along a spectrum rather than into clear categories Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system We create a "host-pathogen space" by mapping multiple biomarkers of infection (eg serum antibodies, pathogen DNA) and disease state (eg serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (eg model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale

Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

Abstract: The Gulf of Alaska (GOA) was divided into 5 offshore and 2 inshore regions (see Fig. 1 in manuscript). Offshore waters past the 500 m isobath were defined as the “slope”, while offshore regions over the GOA shelf in waters less than 500 m bottom depth were defined by longitude: EGOA, eastern shelf (134° to 140° W); CGOA, central shelf (140° to 147° W); NGOA, northern shelf (147° to 154° W); WGOA, western shelf (154° to 165° W). The two inshore regions included: SEAK, southeast Alaska inner waters (e.g. Glacier Bay, Cross Sound, Sitka Sound), Yakutat Bay, and Icy Bay; SCAK, southcentral Alaska (Cook Inlet, coastal embayments along the Kenai Peninsula, and Prince William Sound).

Macroalgal defense phenotype correlates with herbivore abundance

Abstract: A primary goal in the study of producer–herbivore interactions is to characterize the tradeoffs between primary producer growth and defense. Across the Aleutian Island Archipelago, the widespread decline in sea otters has resulted in reduced predation on sea urchins, which has led to increases in urchin populations, the formation of urchin barrens, and ultimately to overgrazing of much of the region’s kelp forests. The occurrence of both kelp forests and urchin barrens on islands, along with among island variation in the time period that urchin barrens have formed, presents a unique opportunity to characterize the extent to which exposure to intense herbivory and increased light may alter marine macroalgal growth and defense tradeoffs. To address this, we used a field caging experiment with Codium ritteri, a common perennial green macroalga in the Aleutian Archipelago, to test whether urchin barren macroalgae exhibit increased defenses and reduced growth relative to kelp forest individuals. Our results suggest that urchin barren C. ritteri had greater defense than growth relative to kelp forest individuals. In the laboratory, we found little evidence for urchin barren C. ritteri growth under low light or altered defenses at high light. Grazing rates on C. ritteri were correlated with urchin biomass in the field suggesting higher herbivory intensity may shift primary producer energy allocation from growth to defense. Together, our data suggest that macroalgae occurring within kelp forests grow faster but are more palatable than macroalgae occurring in urchin barrens, which may increase urchin deforestation potential.

Climate Explorer: Improved Access to Local Climate Projections

Abstract: The goal of the U.S. Climate Resilience Toolkit’s (CRT) Climate Explorer (CE) is to provide information at appropriate spatial and temporal scales to help practitioners gain insights into t...

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Abstract: Since the early 2000s, Lake Erie has been experiencing annual cyanobacterial blooms that often cover large portions of the western basin and even reach into the central basin. These blooms have affected several ecosystem services provided by Lake Erie to surrounding communities (notably drinking water quality). Several modeling efforts have identified the springtime total bioavailable phosphorus (TBP) load as a major driver of maximum cyanobacterial biomass in western Lake Erie, and on this basis, international water management bodies have set a phosphorus (P) reduction goal. This P reduction goal is intended to reduce maximum cyanobacterial biomass, but there has been very limited effort to identify the specific locations within the western basin of Lake Erie that will likely experience the most benefits. Here, we used pixel-specific linear regression to identify where annual variation in spring TBP loads is most strongly associated with cyanobacterial abundance, as inferred from satellite imagery. Using this approach, we find that annual TBP loads are most strongly associated with cyanobacterial abundance in the central and southern areas of the western basin. At the location of the Toledo water intake, the association between TBP load and cyanobacterial abundance is moderate, and in Maumee Bay (near Toledo, Ohio), the association between TBP and cyanobacterial abundance is no better than a null model. Both of these locations are important for the delivery of specific ecosystem services, but this analysis indicates that P load reductions would not be expected to substantially improve maximum annual cyanobacterial abundance in these locations. These results are preliminary in the sense that only a limited set of models were tested in this analysis, but these results illustrate the importance of identifying whether the spatial distribution of management benefits (in this case P load reduction) matches the spatial distribution of management goals (reducing the effects of cyanobacteria on important ecosystem services).

Identifying Habitat Associations of Sea Turtles within an Area of Offshore Sub-Tropical Reefs (NW Atlantic)

Abstract: We observed sea turtles with time-lapse video cameras (deployed for studies of fish behavior during June 2017) at “live-bottom” reefs in depths of 18–20 m within Gray's Reef National Marine Sanctuary off the coast of Georgia, USA (NW Atlantic). These reefs, sandstone ledges emerging from surrounding sand seafloor, were deeply undercut and apparently served as resting habitat for turtles to wedge themselves between sand seafloor and hard rock overhead. We observed 22 distinct individuals over 27 occurrences including 10 Caretta caretta (L.) (Loggerhead), 3 Chelonia mydas (L.) (Green Sea Turtle), and 9 unidentified to species based on individual markings. We documented resting periods up to 144 minutes (mean = 37.2 min, SD = 39.1). Notable was that most observations (67%) occurred during twilight and night periods. To put these video observations in perspective, we analyzed diver observations of 34 turtles encountered at the surface prior to and during visual fish census surveys (2010–2017) at 18 ledges. Those ledges had significantly taller and deeper undercuts than 18 other ledges with no turtles (ANOSIM P = 0.043 and SIMPER comparisons). These limited observations indicate time-lapse video of seafloor habitats along with diver surveys may yield new insights into sea turtles' habitat requirements, patterns of site fidelity, and ecological role as ecosystem engineers, as well as effects on sea turtles of coincident human uses such as fishing, vessel use, and recreational diving.

Physical and elemental analysis of Middle East sands from recent combat zones

Abstract: Objective: The lungs are uniquely exposed to the external environment. Sand and dust exposures in desert regions are common among deployed soldiers. A significant number of Veterans deployed to the Middle East report development of respiratory disorders and diseases.Materials and methods: Sand collected from Fallujah, Iraq and Kandahar, Afghanistan combat zones was analyzed and compared to a sand sample collected from an historic United States (U.S.) battle region (Fort Johnson, James Island, SC, Civil War battle site). Sand samples were analyzed to determine the physical and elemental characteristics that may have the potential to contribute to development of respiratory disease.Results: Using complementary scanning electron microscopy (SEM) imaging and analysis, and inductively coupled plasma mass spectrometry (ICP-MS), it was determined that Iraq sand contained elevated levels of calcium and first row transition metals versus Afghanistan and U.S. sand. Iraq sand particle texture was smooth and round, and particles were considerably smaller than Afghanistan sand. Afghanistan sand was elevated in rare earth metals versus Iraq or U.S. sands and had sharp edge features and larger particle size than Iraq sand.Conclusions: These data demonstrate significant differences in Iraq and Afghanistan sand particle size and characteristics. Middle East sands contained elevated levels of elements that have been associated with respiratory disease versus control site sand, suggesting the potential of sand/dust storm exposure to promote adverse respiratory symptoms. Data also demonstrate the potential for variation based on geographical region or site of exposure. The data generated provide baseline information that will be valuable in designing future exposure studies.