Dietary Exposure to Environmentally Relevant Levels of Chemical Contaminants Reduces Growth and Survival in Juvenile Chinook Salmon.

Chemical pollution can degrade aquatic ecosystems. Chinook salmon in contaminated habitats are vulnerable to health impacts from toxic exposures. Few studies have been conducted on adverse health outcomes associated with current levels and mixtures of contaminants. Fewer still address effects specific to the juvenile life-stage of salmonids. The present study evaluated contaminant-related effects from dietary exposure to environmentally relevant concentrations and mixture profiles in juvenile Chinook salmon from industrialized waterways in the U.S. Pacific Northwest using two end points: growth assessment and disease susceptibility. The dose and chemical proportions were reconstituted based on environmental sampling and analysis using the stomach contents of juvenile Chinook salmon recently collected from contaminated, industrialized waterways. Groups of fish were fed a mixture with fixed proportions of 10 polychlorinated biphenyls (PCBs), 3 dichlorodiphenyltrichloroethanes (DDTs), and 13 polycyclic aromatic hydrocarbons (PAHs) at five concentrations for 35 days. These contaminant compounds were selected because of elevated concentrations and the widespread presence in sediments throughout industrialized waterways. Fork length and otolith microstructural growth indicators were significantly reduced in fish fed environmentally relevant concentrations of these contaminants. In addition, contaminant-exposed Chinook salmon were more susceptible to disease during controlled challenges with the pathogen Aeromonas salmonicida. Our results indicate that dietary exposure to contaminants impairs growth and immune function in juvenile Chinook salmon, thereby highlighting that current environmental exposure to chemicals of potential management concern threatens the viability of exposed salmon.

Image-Based Hidden Damage Detection Method: Combining Stereo Digital Image Correlation and Finite Element Model Updating.

Maintenance and damage detection of structures are crucial for ensuring their safe usage and longevity. However, damage hidden beneath the surface can easily go unnoticed during inspection and assessment processes. This study proposes a detection method based on image techniques to detect and assess internal structural damage, breaking the limitation of traditional image methods that only analyze the structure's surface. The proposed method combines full-field response on the structure's surface with finite element model updating to reconstruct the structural model, using the reconstructed model to detect and assess hidden structural damage. Initially, numerical experiments are conducted to generate known damaged areas and parameter distributions. Data from these experiments are used to update the finite element model, establish and validate the proposed model updating method, and assess its accuracy in evaluating hidden damage, achieving an accuracy rate of 90%. Furthermore, discussions on more complex damage scenarios are carried out through numerical experiments to demonstrate the feasibility and applicability of the proposed method in reconstructing different forms of damage. Ultimately, this study utilizes stereoscopic digital imaging techniques to acquire full-field information on surfaces, and applies the proposed method to reconstruct the structure, enabling the detection and assessment of hidden damage with an accuracy rate of 86%.

Posthurricane damage assessment using satellite imagery and geolocation features.

Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed-data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare-earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The data set curated in this work is made openly available (DOI: 10.17603/ds2-3cca-f398).

Spectral Features Differentiate Aging-Induced Changes in Parchment-A Combined Approach of UV/VIS, µ-ATR/FTIR and µ-Raman Spectroscopy with Multivariate Data Analysis.

From the moment of production, artworks are constantly exposed to changing environmental factors potentially inducing degradation. Therefore, detailed knowledge of natural degradation phenomena is essential for proper damage assessment and preservation. With special focus on written cultural heritage, we present a study on the degradation of sheep parchment employing accelerated aging with light (295-3000 nm) for one month, 30/50/80% relative humidity (RH) and 50 ppm sulfur dioxide with 30/50/80%RH for one week. UV/VIS spectroscopy detected changes in the sample surface appearance, showing browning after light-aging and increased brightness after SO2-aging. Band deconvolution of ATR/FTIR and Raman spectra and factor analysis of mixed data (FAMD) revealed characteristic changes of the main parchment components. Spectral features for degradation-induced structural changes of collagen and lipids turned out to be different for the employed aging parameters. All aging conditions induced denaturation (of different degrees) indicated by changes in the secondary structure of collagen. Light treatment resulted in the most pronounced changes for collagen fibrils in addition to backbone cleavage and side chain oxidations. Additional increased disorder for lipids was observed. Despite shorter exposure times, SO2-aging led to a weakening of protein structures induced by transitions of stabilizing disulfide bonds and side chain oxidations.

[The cost of social consequences of narcotic substances using in Russian and European studies: publications review].

The article analyzes the practice of estimating social economic losses of society from drug consumption implemented in Russia and European countries from 2002 to the present time. Purpose of the study is to identify objective indicators and advantages of various calculation methods applied to analyze of foreign and national practice of estimating social and economic losses of society from drug consumption. The analytical method was applied to analyze various approaches to estimating social economic losses of society because of drug consumption in various countries. The sampling of articles was implemented in accordance with the PRISMA guidelines in the PubMed, Google Scholar and eLibrary databases. It is established that in various studies assessing value of social cost of drug consumption, different methodological approaches are applied, which affects the results of assessment. The magnitude of social cost of drug addiction in the studies ranged from 0.00023% to 4.7% of the Gross Domestic (National) Product (GNP). The large part of social cost of drug abuse in GNP is mostly conditioned by estimating number of hidden drug users during the study, as well as by optimal approach in calculating expenditure categories. The assessment of amount of economic losses of society because of drug traffic is needed to make correct management decisions within the framework of implementation of state drug policy at various levels. This approach can help to better use of the public financial resources.

Meta-analysis of salt marsh vegetation impacts and recovery: a synthesis following the Deepwater Horizon oil spill.

Marine oil spills continue to be a global issue, heightened by spill events such as the 2010 Deepwater Horizon spill in the Gulf of Mexico, the largest marine oil spill in US waters and among the largest worldwide, affecting over 1,000 km of sensitive wetland shorelines, primarily salt marshes supporting numerous ecosystem functions. To synthesize the effects of the oil spill on foundational vegetation species in the salt marsh ecosystem, Spartina alterniflora and Juncus roemerianus, we performed a meta-analysis using data from 10 studies and 255 sampling sites over seven years post-spill. We examined the hypotheses that the oil spill reduced plant cover, stem density, vegetation height, aboveground biomass, and belowground biomass, and tracked the degree of effects temporally to estimate recovery time frames. All plant metrics indicated impacts from oiling, with 20-100% maximum reductions depending on oiling level and marsh zone. Peak reductions of ~70-90% in total plant cover, total aboveground biomass, and belowground biomass were observed for heavily oiled sites at the marsh edge. Both Spartina and Juncus were impacted, with Juncus affected to a greater degree. Most plant metrics had recovery time frames of three years or longer, including multiple metrics with incomplete recovery over the duration of our data, at least seven years post-spill. Belowground biomass was particularly concerning, because it declined over time in contrast with recovery trends in most aboveground metrics, serving as a strong indicator of ongoing impact, limited recovery, and impaired resilience. We conclude that the Deepwater Horizon spill had multiyear impacts on salt marsh vegetation, with full recovery likely to exceed 10 years, particularly in heavily oiled marshes, where erosion may preclude full recovery. Vegetation impacts and delayed recovery is likely to have exerted substantial influences on ecosystem processes and associated species, especially along heavily oiled shorelines. Our synthesis affords a greater understanding of ecosystem impacts and recovery following the Deepwater Horizon oil spill, and informs environmental impact analysis, contingency planning, emergency response, damage assessment, and restoration efforts related to oil spills.

Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data.

The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes or tsunamis. However, research focusing on the level of damage or its distribution in rural areas is lacking. This study presents a methodology for mapping, characterizing, and assessing the damage in rural environments following natural disasters, both in built-up and vegetation areas, by combining synthetic-aperture radar (SAR) and optical remote sensing data. As a case study, we applied the methodology to characterize the rural areas affected by the Sulawesi earthquake and the subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images obtained pre- and post-event, alongside Sentinel-2 images, were used as inputs. This study's results emphasize that remote sensing data from rural areas must be treated differently from that of urban areas following a disaster. Additionally, the analysis must include the surrounding features, not only the damaged structures. Furthermore, the results highlight the applicability of the methodology for a variety of disaster events, as well as multiple hazards, and can be adapted using a combination of different optical and SAR sensors.

An Efficient and Uncertainty-Aware Decision Support System for Disaster Response Using Aerial Imagery.

Efficient and robust search and rescue actions are always required when natural or technical disasters occur. Empowered by remote sensing techniques, building damage assessment can be achieved by fusing aerial images of pre- and post-disaster environments through computational models. Existing methods pay over-attention to assessment accuracy without considering model efficiency and uncertainty quantification in such a life-critical application. Thus, this article proposes an efficient and uncertain-aware decision support system (EUDSS) that evolves the recent computational models into an efficient decision support system, realizing the uncertainty during building damage assessment (BDA). Specifically, a new efficient and uncertain-aware BDA integrates the recent advances in computational models such as Fourier attention and Monte Carlo Dropout for uncertainty quantification efficiently. Meanwhile, a robust operation (RO) procedure is designed to invite experts for manual reviews if the uncertainty is high due to external factors such as cloud clutter and poor illumination. This procedure can prevent rescue teams from missing damaged houses during operations. The effectiveness of the proposed system is demonstrated on a public dataset from both quantitative and qualitative perspectives. The solution won the first place award in International Overhead Imagery Hackathon.

Distributed Fiber-Optic Strain Sensing of an Innovative Reinforced Concrete Beam-Column Connection.

Distributed fiber-optic sensing (DFOS) technologies have been used for decades to detect damage in infrastructure. One recent DFOS technology, Optical Frequency Domain Reflectometry (OFDR), has attracted attention from the structural engineering community because its high spatial resolution and refined accuracy could enable new monitoring possibilities and new insight regarding the behavior of reinforced concrete (RC) structures. The current research project explores the ability and potential of OFDR to measure distributed strain in RC structures through laboratory tests on an innovative beam-column connection, in which a partial slot joint was introduced between the beam and the column to control damage. In the test specimen, fiber-optic cables were embedded in both the steel reinforcement and concrete. The specimen was tested under quasi-static cyclic loading with increasing displacement demand at the structural laboratory of the Pacific Earthquake Engineering Research (PEER) Center of UC Berkeley. Different types of fiber-optic cables were embedded both in the concrete and the rebar. The influence of the cable coating and cable position are discussed. The DFOS results are compared with traditional measurements (DIC and LVDT). The high resolution of DFOS at small deformations provides new insights regarding the mechanical behavior of the slotted RC beam-column connection, including direct measurement of beam curvature, rebar deformation, and slot opening and closing. A major contribution of this work is the quantification of the performance and limitations of the DFOS system under large cyclic strains. Performance is quantified in terms of non-valid points (which occur in large strains when the DFOS analyzer does not return a strain value), maximum strain that can be reliably measured, crack width that causes cable rupture, and the effect of the cable coating on the measurements. Structural damage indices are also proposed based on the DFOS results. These damage indices correlate reasonably well with the maximum sustained drift, indicating the potential of using DFOS for RC structural damage assessment. The experimental data set is made publicly available.

Euclidean Graphs as Crack Pattern Descriptors for Automated Crack Analysis in Digital Images.

Typical crack detection processes in digital images produce a binary-segmented image that constitutes the basis for all of the following analyses. Binary images are, however, an unsatisfactory data format for advanced crack analysis algorithms due to their sparse nature and lack of significant data structuring. Therefore, this work instead proposes a new approach based on Euclidean graphs as functional crack pattern descriptors for all post-detection analyses. Conveying both geometrical and topological information in an integrated representation, Euclidean graphs are an ideal structure for efficient crack path description, as they precisely locate the cracks on the original image and capture salient crack skeleton features. Several Euclidean graph-based algorithms for autonomous crack refining, correlation and analysis are described, with significant advantages in both their capabilities and implementation convenience over the traditional, binary image-based approach. Moreover, Euclidean graphs allow the autonomous selection of specific cracks or crack parts based on objective criteria. Well-known performance metrics, namely precision, recall, intersection over union and F1-score, have been adapted for use with Euclidean graphs. The automated generation of Euclidean graphs from binary-segmented images is also reported, enabling the application of this technique to most existing detection methods (e.g., threshold-based or neural network-based) for cracks and other curvilinear features in digital images.

Ecosystem-Service Scaling Techniques to Evaluate the Benefits of Marine Debris Removal.

While knowledge of the ecological impacts of marine debris is continually advancing, methods to evaluate the comparative scale of these impacts are less well developed. In the case of costly environmental restoration in marine and coastal environments, quantifying and comparing the ecological impacts of diverse forms of ecosystem injuries can facilitate a more efficient selection of restoration projects. This article proposes evaluating marine debris removal projects in an ecological service equivalency analysis framework that can be used to compare marine debris removal to other types of environmental restoration. Drawing on existing spatial and temporal data with respect to marine debris impacts on habitats and resources, we demonstrate how resource managers and organizations involved in marine debris removal can quantify the ecological service benefits of a removal project and use it to comparatively select between projects based on present value ecological benefits. This valuation can be useful in natural resource damage assessment restoration selection, and for directing limited funds to marine debris removal projects which produce the greatest gains in ecological services. This ecological scaling framework is applied to a seagrass injury case study to demonstrate its application for scaling marine debris removal as compensatory restoration.

Flood mapping and damage assessment due to the super cyclone Yaas using Google Earth Engine in Purba Medinipur, West Bengal, India.

This study maps flood inundation and estimates the damage caused by super cyclone Yaas in Purba Medinipur, India. We used Google Earth Engine (GEE) to create a flood inundation map of the research area using pre and post-cyclone Sentinel-1 SAR data. Using ESRI 2020 land cover data, flood damage was analysed. The flood affected 5% (239.69 km2) of the land of Purba Medinipur. The northern and southern regions were affected the most. 95% and 3% of the total flooded area are comprised of agricultural and vegetation, respectively. Kolaghat (24 km2) and Nandigram-II (1 km2) sustained the greatest damage to both agriculture and vegetation. The areas below 18 m were impacted by flooding, with the worst damage occurring below 5 m. The GEE platform was cost-effective, efficient, and faster at calculating with enhanced precision. The outcomes of this study will aid in the management of cyclone-induced hazards. We advocate planting native and salt-tolerant crops to reduce flood damage.

Outcome Scores in Pediatric Rheumatology.

PURPOSE OF REVIEW: Providing a summary of the latest research on outcome measures in juvenile idiopathic arthritis, childhood -onset systemic lupus erythematosus, and juvenile dermatomyositis. RECENT FINDINGS: A rational management of patients with pediatric rheumatic diseases requires the regular assessment of the level of disease activity and damage, as well as the evaluation of therapeutic response through validated and standardized outcome measures. Ideally, such tools should be simple, feasible, and easily applicable in routine care. Recently, there has been a great deal of effort to refine existing tools and devise novel outcome measures, aiming to address the various aspects of disease impact and to improve the reliability of research studies and clinical trials. The newest outcome tools in pediatric rheumatology have markedly enlarged the spectrum of health domains assessable in a standardized way, thus increasing the reliability of evaluation of clinical response and fostering future clinical trials.

Towards integrated modeling of the long-term impacts of oil spills.

Although great progress has been made to advance the scientific understanding of oil spills, tools for integrated assessment modeling of the long-term impacts on ecosystems, socioeconomics and human health are lacking. The objective of this study was to develop a conceptual framework that could be used to answer stakeholder questions about oil spill impacts and to identify knowledge gaps and future integration priorities. The framework was initially separated into four knowledge domains (ocean environment, biological ecosystems, socioeconomics, and human health) whose interactions were explored by gathering stakeholder questions through public engagement, assimilating expert input about existing models, and consolidating information through a system dynamics approach. This synthesis resulted in a causal loop diagram from which the interconnectivity of the system could be visualized. Results of this analysis indicate that the system naturally separates into two tiers, ocean environment and biological ecosystems versus socioeconomics and human health. As a result, ocean environment and ecosystem models could be used to provide input to explore human health and socioeconomic variables in hypothetical scenarios. At decadal-plus time scales, the analysis emphasized that human domains influence the natural domains through changes in oil-spill related laws and regulations. Although data gaps were identified in all four model domains, the socioeconomics and human health domains are the least established. Considerable future work is needed to address research gaps and to create fully coupled quantitative integrative assessment models that can be used in strategic decision-making that will optimize recoveries from future large oil spills.

A Novel Infrared Thermography Sensing Approach for Rapid, Quantitative Assessment of Damage in Aircraft Composites.

The current necessity of the scientific and industrial community, for reduction of aircraft maintenance cost and duration, prioritizes the need for development of innovative nondestructive techniques enabling fast and reliable defect detection on aircraft fuselage and wing skin parts. Herein, a new low-cost thermographic strategy, termed Pulsed Phase-Informed Lock-in Thermography, operating on the synergy of two independent, active infrared thermography techniques, is reported for the fast and quantitative assessment of superficial and subsurface damage in aircraft-grade composite materials. The two-step approach relies on the fast, initial qualitative assessment, by Pulsed Phase Thermography, of defect location and the identification of the optimal material-intrinsic frequency, over which lock-in thermography is subsequently applied for the quantification of the damage's dilatational characteristics. A state-of-the-art ultra-compact infrared thermography module envisioned to form part of a fully-automated autonomous nondestructive testing inspection solution for aircraft was conceived, developed, and tested on aircraft-grade composite specimens with impact damages induced at variable energy levels and on a full-scale aircraft fuselage skin composite panel. The latter task was performed in semi-automated mode with the infrared thermography module mounted on the prototype autonomous vortex robot platform. The timescale requirement for a full assessment of damage(s) within the sensor's field of view is of the order of 60 s which, in combination with the high precision of the methodology, unfolds unprecedented potential towards the reduction in duration and costs of tactical aircraft maintenance, optimization of efficiency and minimization of accidents.

An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment.

For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.

Automated and Model-Free Bridge Damage Indicators with Simultaneous Multiparameter Modal Anomaly Detection.

This paper pursues a simultaneous modal parameter anomaly detection paradigm to structural damage identification inferred from vibration-based structural health monitoring (SHM) sensors, e.g., accelerometers. System Realization Using Information Matrix (SRIM) method is performed in short duration sweeping time windows for identification of state matrices, and then, modal parameters with enhanced automation. Stable modal poles collected from stability diagrams are clustered and fed into the Gaussian distribution-based anomaly detection platform. Different anomaly thresholds are examined both on frequency and damping ratio terms taking two testbed bridge structures as application means, and simplistic Boolean Operators are performed to merge univariate anomalies. The first bridge is a reinforced concrete bridge subjected to incremental damage through a series of seismic shake table experiments conducted at the University of Nevada, Reno. The second bridge is a steel arch structure at Columbia University Morningside Campus, which reflects no damage throughout the measurements, unlike the first one. Two large-scale implementations indicate the realistic performance of automated modal analysis and anomaly recognition with minimal human intervention in terms of parameter extraction and learning supervision. Anomaly detection performance, presented in this paper, shows variation according to the designated thresholds, and hence, the information retrieval metrics being considered. The methodology is well-fitted to SHM problems which require sole data-driven, scalable, and fully autonomous perspectives.

Restoration Scaling Approaches to Addressing Ecological Injury: The Habitat-Based Resource Equivalency Method.

Natural resource trustee agencies must determine how much, and what type of environmental restoration will compensate for injuries to natural resources that result from releases of hazardous substances or oil spills. To fulfill this need, trustees, and other natural resource damage assessment (NRDA) practitioners have relied on a variety of approaches, including habitat equivalency analysis (HEA) and resource equivalency analysis (REA). The purpose of this paper is to introduce the Habitat-Based Resource Equivalency Method (HaBREM), which integrates REA's reproducible injury metrics and population modeling with HEA's comprehensive habitat approach to restoration. HaBREM is intended to evaluate injury and restoration using organisms that use the habitat to represent ecological habitat functions. This paper seeks to expand and refine the use of organism-based metrics (biomass-based REA), providing an opportunity to integrate sublethal injuries to multiple species, as well as the potential to include error rates for injury and restoration parameters. Applied by NRDA practitioners in the appropriate context, this methodology can establish the relationship between benefits of compensatory restoration projects and injuries to plant or animal species within an affected habitat. HaBREM may be most effective where there are appropriate data supporting the linkage between habitat and species gains (particularly regionally specific habitat information), as well as species-specific monitoring data and predictions on the growth, density, productivity (i.e., rate of generation of biomass or individuals), and age distributions of indicator species.

Avian injury quantification using the Shoreline Deposition Model and model sensitivities.

Deposition models, such as the Shoreline Deposition Model (SDM) used to quantify nearshore avian injuries resulting from the 2010 Deepwater Horizon (DWH) oil spill, were developed to improve the estimates of nearshore avian mortality resulting from the release of oil into coastal and marine environments. Unlike earlier approaches to injury quantification, such as simple counts of carcasses on the shoreline, a modeling approach allows trustees to evaluate the precision of their estimate (i.e., to develop a confidence interval) and can inform decision-making and the likely utility of additional primary data collection activities through sensitivity analyses. In this paper, we rely on published literature, actual DWH data, and a deposition model simulation to evaluate how different model inputs and assumptions can affect the accuracy and precision of model results. We find that the precision of deposition models is strongly affected by the length of time between subsequent shoreline searches, the underlying magnitude of carcass deposition, carcass persistence probabilities, and carcass detection probabilities. In addition, the accuracy of deposition model results may be affected by natural fluctuations in deposition rates. Given these findings, we recommend that natural resource trustees include an evaluation of future model uncertainty as part of their initial data collection efforts. This will allow them to deploy resources in a way that maximizes the utility of future deposition model results. We also identify several factors that do not need to be assessed immediately following a spill event, thereby potentially freeing resources for more time critical data collection efforts.

Pelagic seabird density and vulnerability in the Gulf of Mexico to oiling from the Deepwater Horizon/MC-252 spill.

Using ship-based surveys, the Natural Resource Damage Assessment (NRDA) Trustees assessed the external oiling of offshore and pelagic marine birds inhabiting the northern Gulf of Mexico (Gulf) in the year following the Deepwater Horizon oil spill (DWH spill). Study objectives were to (1) collect data on pelagic seabirds that were visibly oiled, (2) collect data to estimate abundance of seabirds in offshore and pelagic waters, and 3) document the location and condition of any bird carcasses encountered. Methods employed included strip line transects and station point counts. Surveys were conducted within a study area bound by the Texas-Mexico border and the Dry Tortugas of Florida to the south, and the nearshore coastal waters of the northern Gulf of Mexico. A total of 5665 strip line transects and 386 station point-counts of variable duration were collected during the study. More than 23,000 individual seabirds comprising 45 estuarine, coastal, offshore, and pelagic species were tallied. Average daily abundance of seabirds detected varied from a low of approximately 7 birds/day in November 2010 along regions of the mid- and outer continental shelf to a high of more than 580 birds/day in June 2011 within the near-shore, coastal waters of the northern Gulf.