Seascape Genomics Reveals Metapopulation Connectivity Network of Paramuricea biscaya in the Northern Gulf of Mexico

The degree of connectivity among populations influences their ability to respond to natural and anthropogenic stressors. In marine systems, determining the scale, rate, and directionality of larval dispersal is therefore, central to understanding how coral metapopulations are interconnected and the degree of resiliency in the event of a localized disturbance. Understanding these source-sink dynamics is essential to guide restoration efforts and for the study of ecology and evolution in the ocean. The patterns and mechanisms of connectivity in the deep-sea (>200 m deep) are largely understudied. In this study, we investigated the spatial diversity patterns and metapopulation connectivity of the octocoral Paramuricea biscaya throughout the northern Gulf of Mexico (GoM). Paramuricea biscaya is one of the most abundant corals on the lower continental slope (between 1,200 and 2,500 m) in the GoM. The 2010 Deepwater Horizon oil spill (DWH) directly impacted populations of this species and thus are considered primary targets for restoration. We used a combination of seascape genomic analyses, high-resolution ocean circulation modeling, and larval dispersal simulations to quantify the degree of population structuring and connectivity among P. biscaya populations. Evidence supports the hypotheses that the genetic diversity of P. biscaya is structured by depth, and that larval dispersal among connected populations is asymmetric due to dominant ocean circulation patterns. Our results suggest that there are intermediate unsampled populations in the central GoM that serve as stepping stones for dispersal. The data suggest that the DeSoto Canyon area, and possibly the West Florida Escarpment, critically act as sources of larvae for areas impacted by the DWH oil spill in the Mississippi Canyon. This work illustrates that the management of deep-sea marine protected areas should incorporate knowledge of connectivity networks and depth-dependent processes throughout the water column.

Seascape genomics reveals metapopulation connectivity network of Paramuricea biscaya in the northern Gulf of Mexico

The degree of connectivity among populations influences their ability to respond to natural and anthropogenic stressors. In marine systems, determining the scale, rate, and directionality of larval dispersal is therefore central to understanding how coral metapopulations are interconnected and the degree of resiliency in the event of a localized disturbance. Understanding these source-sink dynamics is essential to guide restoration efforts and for the study of ecology and evolution in the ocean. The patterns and mechanisms of connectivity in the deep-sea (> 200 meters deep) are largely understudied. In this study, we investigated the spatial diversity patterns and metapopulation connectivity of the octocoral Paramuricea biscaya throughout the northern Gulf of Mexico (GoM). Paramuricea biscaya is one of the most abundant corals on the lower continental slope (between 1200 and 2500 m) in the GoM. The 2010 Deepwater Horizon oil spill (DWH) directly impacted populations of this species and thus are considered primary targets for restoration. We used a combination of seascape genomic analyses, high-resolution ocean circulation modeling, and larval dispersal simulations to quantify the degree of population structuring and connectivity among P. biscaya populations. Evidence supports the hypotheses that the genetic diversity of P. biscaya is predominantly structured by depth, and that larval dispersal among connected populations is asymmetric due to dominant ocean circulation patterns. Our results suggest that there are intermediate unsampled populations in the central GoM that serve as stepping stones for dispersal. The data suggest that the DeSoto Canyon area, and possibly the West Florida Escarpment, critically act as sources of larvae for areas impacted by the DWH oil spill in the Mississippi Canyon. This work illustrates that the management of deep-sea marine protected areas should incorporate knowledge of connectivity networks and depth-dependent processes throughout the water column.

SpiDeR –Spill Detection and Recognition system for ROV operations

ABSTRACT The paper presents the outline of the Spill Detection and Recognition system – SpiDeR and its application to underwater oil and gas detection, classification and source characterization demonstrated in the remote-sensing survey of Mississippi Canyon area in the Gulf of Mexico founded by BSEE in 2017. The main objective of the operation was to deploy sensor package from a remotely-operated vehicle (ROV) to survey, detect, and map the location(s) of hydrocarbon emissions that are responsible for the surface oil spill and sheen footprint in the Mississippi Canyon Area. The objectives have been accomplished by conducting a multi-day, three-part survey mapping the area of interest, generation of georeferenced charts and 3D visualizations with detected oil active spills, all supported by a ROV intervention outfitted with oil spill detection and recognition system SpiDeR. SpiDeR is a modular sensor suite capable of detecting, recognizing the source and classifying the hydrocarbon underwater leaks. The sensor suit with selectable configuration can be installed on any type of ROV vehicle and interfaces to the ROV with a single cable conducting the power and data. The presented here and used during the mission complete sensor suite consist of two 3D, broad band, electronically scanning multibeam sonar systems NORBIT WBMS STX, one Forward Looking Sonar NORBIT WBMS FLS, fluorescent oil classifier LIF – Laser Induced Fluorescence detection unit and the video camera with lights. The most useful capability of the SpiDeR is the ability to generate 3D imagery (georeferenced bathymetry) even when the ROV is not moving. That combined with time gives 4D observable capabilities of the oil spill. The 4D capabilities have been proven useful during the u-bathymetry part in Phase 2 and forward-looking 3D in Phase 3 of this mission. The system has been deployed from the ROV in the area where it has been known for the last decade that the leak of hydrocarbons is coming from. The real task at hand was to recognize the leak source and that source contain hydrocarbons and accurately document the source location and provide measurable documentation of its character.

Offset-extended sparse Radon transform: application to multiple suppression in the presence of AVO

The conventional Radon transform suffers from a lack of resolution when data kinematics and amplitudes differ from those of the Radon basis functions. Also, a limited aperture of data, missing traces, aliasing, a finite number of scanned ray parameters, noise, residual statics, and amplitude variations with offset (AVO) reduce the de-correlation power of the Radon basis functions. Posing Radon transform estimation as an inverse problem by searching for a sparse model that fits the data improves the performance of the algorithm. However, due to averaging along the offset axis, the conventional Radon transform cannot preserve AVO. Accordingly, we modify the Radon basis functions by extending the model domain along the offset direction. Extending the model space helps in fitting data; however, computing the offset-extended Radon transform is an under-determined and ill-posed problem. To alleviate this shortcoming, we add model domain sparsity and smoothing constraints to yield a stable solution. We develop an algorithm using offset-extended Radon basis functions with sparsity promoting in offset-stacked Radon images in conjunction with a smoothing restriction along the offset axis. As the inverted model is sparse and fits the data, muting common-offset Radon panels based on ray-parameter/curvature is sufficient for separating primaries from multiples. We successfully apply the algorithm to suppress multiples in the presence of strong AVO on synthetic data and a real data example from the Gulf of Mexico, Mississippi Canyon. The results show that extending the Radon model space is necessary for improving the separation and suppression of the multiples in the presence of strong AVO.