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Biodegradation is an important process for hydrocarbon weathering that influences its fate and transport, yet little is known about in situ biodegradation rates of specific hydrocarbon compounds in the deep ocean. Using data collected in the Gulf of Mexico below 700m during and after the Deepwater Horizon oil spill, we calculated first-order degradation rate constants for 49 hydrocarbons and inferred degradation rate constants for an additional 5 data-deficient hydrocarbons. Resulting calculated (not inferred) half-lives of the hydrocarbons ranged from 0.4 to 36.5days. The fastest degrading hydrocarbons were toluene (k=-1.716), methylcyclohexane (k=-1.538), benzene (k=-1.333), and C1-naphthalene (k=-1.305). The slowest degrading hydrocarbons were the large straight-chain alkanes, C-26 through C-33 (k=-0.0494 through k=-0.007). Ratios of C-18 to phytane supported the hypothesis that the primary means of degradation in the subsurface was microbial biodegradation. These degradation rate constants can be used to improve models describing the fate and transport of hydrocarbons in the event of an accidental deep ocean oil spill., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

We compare oil spill model predictions for a prototype subsea blowout with and without subsea injection of chemical dispersants in deep and shallow water, for high and low gas-oil ratio, and in weak to strong crossflows. Model results are compared for initial oil droplet size distribution, the nearfield plume, and the farfield Lagrangian particle tracking stage of hydrocarbon transport. For the conditions tested (a blowout with oil flow rate of 20,000 bbl/d, about 1/3 of the Deepwater Horizon), the models predict the volume median droplet diameter at the source to range from 0.3 to 6mm without dispersant and 0.01 to 0.8 mm with dispersant. This reduced droplet size owing to reduced interfacial tension results in a one to two order of magnitude increase in the downstream displacement of the initial oil surfacing zone and may lead to a significant fraction of the spilled oil not reaching the sea surface., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Because bivalve larvae are difficult to identify using morphology alone, the use of Raman spectra to distinguish species could aid classification of larvae collected from the field. Raman spectra from shells of bivalve larvae exhibit bands that correspond to polyene pigments. This study determined if the types of shell pigments observed in different species could be unique enough to differentiate larvae using chemotaxonomic methods and cluster analysis. We collected Raman spectra at three wavelengths from 25 samples of bivalve larvae representing 16 species and four taxonomic orders. Grouping spectra within general categories based on order/family relationships successfully classified larvae with cross-validation accuracies ≥92% for at least one wavelength or for all wavelengths combined. Classifications to species were more difficult, but cross-validation accuracies above 86% were observed for 7 out of 14 species when tested using species groups within orders/families at 785 nm. The accuracy of the approach likely depends on the composition of species in a sample and the species of interest. For example, high classification accuracies (85-98%) for distinguishing spectra from Crassostrea virginica larvae were achieved with a set of bivalve larvae occurring in the Choptank River in the Chesapeake Bay, USA, whereas as lower accuracies (70-92%) were found for a set of C. virginica larvae endemic to the Northeast, USA. In certain systems, use of Raman spectra appears to be a promising method for assessing the presence of certain bivalves in field samples and for validating high-throughput image analysis systems for larval bivalve studies.

Restoration of ecologically important marine species and habitats is restricted by funding constraints and hindered by lack of information about trade-offs among restoration goals and the effectiveness of alternative restoration strategies. Because ecosystems provide diverse human and ecological benefits, achieving one restoration benefit may take place at the expense of other benefits. This poses challenges when attempting to allocate limited resources to optimally achieve multiple benefits, and when defining measures of restoration success. We present a restoration decision-support tool that links ecosystem prediction and human use in a flexible "optimization" framework that clarifies important restoration trade-offs, makes location-specific recommendations, predicts benefits, and quantifies the associated costs (in the form of lost opportunities). The tool is illustrated by examining restoration options related to the eastern oyster, Crassostrea virginica, which supported an historically important fishery in Chesapeake Bay and provides a range of ecosystem services such as removing seston, enhancing water clarity, and creating benthic habitat. We use an optimization approach to identify the locations where oyster restoration efforts are most likely to maximize one or more benefits such as reduction in seston, increase in light penetration, spawning stock enhancement, and harvest, subject to funding constraints and other limitations. This proof-of-concept Oyster Restoration Optimization model (ORO) incorporates predictions from three-dimensional water quality (nutrients-phytoplankton zooplankton-detritus [NPZD] with oyster filtration) and larval transport models; calculates size- and salinity-dependent growth, mortality, and fecundity of oysters; and includes economic costs of restoration efforts. Model results indicate that restoration of oysters in different regions of the Chesapeake Bay would maximize different suites of benefits due to interactions between the physical characteristics of a system and nonlinear biological processes. For example, restoration locations that maximize harvest are not the same as those that would maximize spawning stock enhancement. Although preliminary, the ORO model demonstrates that our understanding of circulation patterns, single-species population dynamics and their interactions with the ecosystem can be integrated into one quantitative framework that optimizes spending allocations and provides explicit advice along with testable predictions. The ORO model has strengths and constraints as a tool to support restoration efforts and ecosystem approaches to fisheries management.