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Abstract: A novel method for parameterizing the morphology of seafloor ripples with fingerprint analysis numerical techniques is presented. This fully automated analysis tool identifies rippled areas in two-dimensional imagery of the seafloor, and returns ripple orientation and wavelength as well as a new morphological parameter, the spatial density of ripple defects. In contrast to widely used manual and spectral parameterization methods, this new technique yields a unique probability distribution for each derived parameter, which describes its spatial variability across the sampled domain. Here we apply this new analysis technique to synthetic and field collected side-scan sonar seafloor images in order to assess the methods capacity to define bed geometry across a wide range of simulated and observed morphological conditions. The resulting orientation and wavelength values compare favorably with those of the existing manual and spectral parameterization methods, and are superior under environmental conditions characterized by low signal to noise ratios as well as high planform ripple sinuosity. Furthermore, the resulting ripple defect density values demonstrate correlation with ripple orientation, wave direction, and the Shields parameter, which is consistent with recent investigations that have theoretically linked this parameter to hydrodynamic forcing conditions. The presented fingerprint analysis method surpasses the capacity of existing methods for ripple parameterization and promises to yield greater insight into theoretical and applied problems associated with the temporal and spatial variability of ripple morphology across a wide spectrum of marine environments. [Copyright &y& Elsevier]

The northern Gulf of Mexico is facing high rates of wetland loss due to subsidence and sea level–rise, which has encouraged the application of various wetland restoration techniques. Marsh terracing is a restoration technique that has been implemented since the early 1990 s in Texas and Louisiana, yet few studies have been conducted to evaluate its effectiveness. Marsh terraces are segmented berms of soil built in coastal ponds that were once vegetated marshes. Marsh terracing is hypothesized to dissipate wind waves, encourage marsh expansion, and reduce shoreline erosion. This study (1) assessed the effectiveness of the most common terrace shapes (linear, chevron, and square) and spacing (100, 110, and 120 m) at reducing significant wave height (Hs), (2) assessed the effectiveness of alternative terrace designs for reducing Hs during different wind conditions, and 3) estimated the construction costs of alternative terrace designs. The Simulating WAves Nearshore (SWAN) model was used to simulate wind–driven waves in ponds with real and hypothetical terrace designs. Results revealed that: (1) The chevron shape provided the greatest reduction in Hs during all wind conditions, reducing Hs by up to 54%. (2) Hs reduction was not affected by terrace spacings. (3) Based on wave attenuation, the chevron design with a 120 m terrace spacing provided the optimal outcome with an estimated construction cost/ha of $6332 in a 250,000 m2 site compared to the terrace shapes and spacings evaluated in this study. This study will help coastal managers design marsh terraces to address wetland erosion in the Gulf of Mexico and other coastal areas facing similar environmental problems. [Display omitted] • This study investigates the optimal design for marsh terraces at reducing waves. • Chevron terraces provided the greatest reduction in significant wave height (Hs). • The chevron terrace shape reduced Hs up to 54%. • Chevron terraces with a 120 m terrace spacing provided the most optimal outcome. • The estimated cost of a chevron terrace with 120 m spacing was $6332/ha. [ABSTRACT FROM AUTHOR]

Ripple bedform response to near bed forcing has been found to be asynchronous with rapidly changing hydrodynamic conditions. Recent models have attempted to account for this time variance through the introduction of a time offset between hydrodynamic forcing and seabed response with varying success. While focusing on temporal ripple evolution, spatial ripple variation has been partly neglected. With the fingerprint algorithm ripple bedform parameterization technique, spatial variation can be quickly and precisely characterized, and as such, this method is particularly useful for evaluation of ripple model spatio-temporal validity. Using time-series hydrodynamic data and synoptic acoustic imagery collected at an inner continental shelf site, this study compares an adapted time-varying ripple geometric model to observed field observations in light of the fingerprint algorithm results. Multiple equilibrium ripple predictors are tested within the time-varying model, with the algorithm results serving as the baseline geometric values. Results indicate that ripple bedforms, in the presence of rapidly changing high-energy conditions, reorganize at a slower rate than predicted by the models. Relict ripples were found to be near peak-forcing wavelengths after rapidly decaying storm events, and still present after months of sub-critical flow conditions. [ABSTRACT FROM AUTHOR]

Marsh terracing is a coastal restoration technique that has been implemented in Louisiana and Texas for almost 30 years. Marsh terraces are segmented ridges of soil built in inland coastal ponds for the objectives of creating new marsh, disrupting fetch, and dissipating wind-driven wave energy, which causes marsh erosion. Despite widespread implementation, no numerical modeling studies have been conducted to understand the effect of terraces on wave climate in marsh environments. The objectives of this study were to: 1) simulate wave climates in marsh terrace fields using the Simulating WAves Nearshore (SWAN) model and evaluate the agreement between modeled and observed wave conditions and 2) assess the effectiveness of marsh terraces at reducing significant wave height by comparing wave climates in terraced sites with hypothetical unterraced sites. The SWAN model was used to simulate wind-waves at two terrace fields in coastal Louisiana. Model validation was performed using data collected with in situ wave instruments and wind data measured by NOAA stations located 25 km to the east and 62 km to the northeast of the study area. Results from this study modeled and measured small (0.03–0.14 m) and high–frequency waves (0.80–1.29 s) in the marsh terrace fields. Model validation revealed agreement between modeled and observed data, particularly for significant wave height and direction. Additionally, a comparison of wave climates in terraced and unterraced marsh environments indicated an average significant wave height reduction of 45% (min:18%, max: 84%) when terraces were present. Overall, the model was able to evaluate wave climates in marsh terrace fields and assess the effectiveness of this restoration technique in reducing significant wave height. The model's performance in this low energy and complex environment (shallow water, nonlinear shoreline, marsh fragmentated) is encouraging given the lack of research assessing wave parameters in marsh terrace environments. • Marsh terraces are effective at reducing wave energy. • Terraces reduced significant wave height (Hs) up to 84%. • Small (0.03 to 0.14 m) and high-frequency (0.80 to 1.29 s) waves were measured. • SWAN may be appropriate for use in low energy and complex systems. [ABSTRACT FROM AUTHOR]

Identifying critical habitat for highly mobile species such as sharks is difficult, but essential for effective management and conservation. In regions where baseline data are lacking, non-traditional data sources have the potential to increase observational capacity for species distribution and habitat studies. In this study, a research and education organization conducted a 5-year (2013-2018) survey of shark populations in the coastal waters of west-central Florida, an area where a diverse shark assemblage has been observed but no formal population analyses have been conducted. The objectives of this study were to use boosted regression tree (BRT) modeling to quantify environmental factors impacting the distribution of the shark assemblage, create species distribution maps from the model outputs, and identify spatially explicit hot spots of high shark abundance. A total of 1036 sharks were captured, encompassing eleven species. Abundance hot spots for four species and for immature sharks (collectively) were most often located in areas designated as "No Internal Combustion Engine" zones and seagrass bottom cover, suggesting these environments may be fostering more diverse and abundant populations. The BRT models were fitted for immature sharks and five species where n > 100: the nurse shark (Ginglymostoma cirratum), blacktip shark (Carcharhinus limbatus), blacknose shark (C. acronotus), Atlantic sharpnose shark (Rhizoprionodon terraenovae), and bonnethead (Sphyrna tiburo). Capture data were paired with environmental variables: depth (m), sea surface temperature (°C), surface, middle, and bottom salinity (psu), dissolved oxygen (mg/L), and bottom type (seagrass, artificial reef, or sand). Depth, temperature, and bottom type were most frequently identified as predictors with the greatest marginal effect on shark distribution, underscoring the importance of nearshore seagrass and barrier island habitats to the shark assemblage in this region. This approach demonstrates the potential contribution of unconventional science to effective management and conservation of coastal sharks. [ABSTRACT FROM AUTHOR]

The thickness normal to deposition (isopachs) and vertical thickness (isochores) of geological units is important for assessing various geologic processes. We present the first marine global sediment isochore estimates for five geological periods dating from middle Miocene (15.97 Ma) to present. We use sparsely distributed sediment depth vs. age observations from the Deep Sea Drilling Project and global maps of biological, oceanographic, geographic, and geological variables as training features in a k‐nearest neighbor regressor to estimate isochores. Results are compared to isochore estimates generated by applying a constant depositional rate from recent estimates of global total sediment thicknesses. Both models of isochore thickness exhibit consistent error. Results from a machine learning approach show major advantages, including results that are quantitative, easily updatable, and accompanied with uncertainty estimation. Final predictions can provide first‐order constraints on sediment deposition with geologic time, which is of timely importance for assessing past climate variability. Plain Language Summary: Maps displaying the thickness of sedimentary units are useful for a variety of reasons such as assessing how Earth's climate and depositional systems (e.g., river deltas) have changed through geological time. In this study, we use a machine learning approach to estimate how thick time bound (present day to 15.97 million years) geological units are. To produce these global thickness estimates, we used depth‐age observations from an international deep‐sea drilling collaboration, the Deep Sea Drilling Project. Our machine learning approach uses these observations and complementary data sets, such as water depth and latitude, to estimate unit thicknesses where previous observation has not been made. We then compare the machine learning approach to an independently empirically based method. The model skill calculated by comparing observed and predicted values for each model is approximately the same; however, there are major advantages of using a machine learning approach. Major advantages of using a machine learning approach include the ability to easily update final predictions with new data and calculated uncertainty estimates in the final prediction. Predictions of unit thicknesses can help to indicate where sediment is more likely to accumulate and be preserved in geologic time. Since sediment is capable of sequestering large amounts of carbon from the atmosphere, understanding the regions of preservation is important for studying past and future climate change. Key Points: We use a machine learning approach to produce the first global estimates of isochore thicknesses for the present to middle MioceneGiven minimal training data, we produce global and temporally variable isochores with lower error than modeled isochoresResults provide first order constraints on deposition with geologic time, which is of importance for assessing climate variability [ABSTRACT FROM AUTHOR]

The continental margin off the northeastern United States (NEUS) contains numerous, topographically complex features that increase habitat heterogeneity across the region. However, the majority of these rugged features have never been surveyed, particularly using direct observations. During summer 2013, 31 Remotely-Operated Vehicle (ROV) dives were conducted from 494 to 3271 m depth across a variety of seafloor features to document communities and to infer geological processes that produced such features. The ROV surveyed six broad-scale habitat features, consisting of shelf-breaching canyons, slope-sourced canyons, inter-canyon areas, open-slope/landslide-scar areas, hydrocarbon seeps, and Mytilus Seamount. Four previously unknown chemosynthetic communities dominated by Bathymodiolus mussels were documented. Seafloor methane hydrate was observed at two seep sites. Multivariate analyses indicated that depth and broad-scale habitat significantly influenced megafaunal coral (58 taxa), demersal fish (69 taxa), and decapod crustacean (34 taxa) assemblages. Species richness of fishes and crustaceans significantly declined with depth, while there was no relationship between coral richness and depth. Turnover in assemblage structure occurred on the middle to lower slope at the approximate boundaries of water masses found previously in the region. Coral species richness was also an important variable explaining variation in fish and crustacean assemblages. Coral diversity may serve as an indicator of habitat suitability and variation in available niche diversity for these taxonomic groups. Our surveys added 24 putative coral species and three fishes to the known regional fauna, including the black coral Telopathes magna, the octocoral Metallogorgia melanotrichos and the fishes Gaidropsarus argentatus, Guttigadus latifrons, and Lepidion guentheri. Marine litter was observed on 81% of the dives, with at least 12 coral colonies entangled in debris. While initial exploration revealed the NEUS region to be both geologically dynamic and biologically diverse, further research into the abiotic conditions and the biotic interactions that influence species abundance and distribution is needed. [ABSTRACT FROM AUTHOR]

This study is focused on mineralogical and chemical characterization of an authigenic carbonate rock (crust) collected at a recently discovered cold seep on the US North Atlantic continental margin. X-ray diffraction (XRD) and scanning electron microscopy (SEM) indicate that the carbonate rock is composed of microcrystalline aragonite cement, white acicular aragonite crystals (AcAr), equant quartz crystals, small microcrystalline aluminosilicates, and trace amounts of iron sulfide microcrystals. Element/calcium ratios were measured with laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) using a calcite standard, which was prepared by annealing USGS certified carbonate powder (MACS-3). The occurrence of microscopic, non-carbonate inclusions precluded evaluation of trace elements in the aragonite cement, but allowed for in situ analysis of AcAr crystals. Carbon and oxygen isotopes were analyzed via isotope ratio mass spectrometry (IRMS) and expressed as δ13C and δ18O. Low δ13C values suggest that aragonite grew as a result of anaerobic oxidation of methane and observed δ18O values indicate that the temperature of aragonite crystallization was 1.7–1.9 °C. [ABSTRACT FROM AUTHOR]

The seafloor and sub-seafloor exhibit a wide variety of physical, chemical, geological, and hydrological conditions that alter the propagation of sound. The creation of a predictive geoacoustic model therefore requires some knowledge or estimate of these environmental conditions. The most direct sub-seafloor observations are typically core and/or log data from ocean drilling wells. However, drilling is expensive and necessarily sparse. A comprehensive geoacoustic model requires some way of estimating sub-seafloor environmental conditions where they have not been measured. Past efforts for estimating the sub-seafloor assume spatial and/or empirical relationships. Recent advances in machine learning have provided means to predict properties, with uncertainties, on the seafloor (Lee et al., Global Biogeochem Cycles, 2018). Despite the global coverage of this technique, it is limited to geospatial prediction only – no quantities are estimated with depth. Here, we extend our geospatial prediction to the vertical dimension to predict variation as a function of depth as well as position. We use data from ocean drilling programs as training data (i.e. observed data) within NRLs Global Predictive Seabed Model (GPSM) to predict sub-seafloor sediment property profiles where no observations exist. We focus first on the Northern Mid-Atlantic Ridge, because of the relatively high density of samples, and consistent underlying geological processes. [ABSTRACT FROM AUTHOR]