A northern Gulf of Mexico (GoM) cetacean unusual mortality event (UME) involving primarily bottlenose dolphins (Tursiops truncatus) in Louisiana, Mississippi, and Alabama began in February 2010 and continued into 2014. Overlapping in time and space with this UME was the Deepwater Horizon (DWH) oil spill, which was proposed as a contributing cause of adrenal disease, lung disease, and poor health in live dolphins examined during 2011 in Barataria Bay, Louisiana. To assess potential contributing factors and causes of deaths for stranded UME dolphins from June 2010 through December 2012, lung and adrenal gland tissues were histologically evaluated from 46 fresh dead non-perinatal carcasses that stranded in Louisiana (including 22 from Barataria Bay), Mississippi, and Alabama. UME dolphins were tested for evidence of biotoxicosis, morbillivirus infection, and brucellosis. Results were compared to up to 106 fresh dead stranded dolphins from outside the UME area or prior to the DWH spill. UME dolphins were more likely to have primary bacterial pneumonia (22% compared to 2% in non-UME dolphins, P = .003) and thin adrenal cortices (33% compared to 7% in non-UME dolphins, P = .003). In 70% of UME dolphins with primary bacterial pneumonia, the condition either caused or contributed significantly to death. Brucellosis and morbillivirus infections were detected in 7% and 11% of UME dolphins, respectively, and biotoxin levels were low or below the detection limit, indicating that these were not primary causes of the current UME. The rare, life-threatening, and chronic adrenal gland and lung diseases identified in stranded UME dolphins are consistent with exposure to petroleum compounds as seen in other mammals. Exposure of dolphins to elevated petroleum compounds present in coastal GoM waters during and after the DWH oil spill is proposed as a cause of adrenal and lung disease and as a contributor to increased dolphin deaths.

We developed a Kemp's ridley (Lepidochelys kempii) stock assessment model to evaluate the relative contributions of conservation efforts and other factors toward this critically endangered species' recovery. The Kemp's ridley demographic model developed by the Turtle Expert Working Group (TEWG) in 1998 and 2000 and updated for the binational recovery plan in 2011 was modified for use as our base model. The TEWG model uses indices of the annual reproductive population (number of nests) and hatchling recruitment to predict future annual numbers of nests on the basis of a series of assumptions regarding age and maturity, remigration interval, sex ratios, nests per female, juvenile mortality, and a putative ''turtle excluder device effect'' multiplier starting in 1990. This multiplier was necessary to fit the number of nests observed in 1990 and later. We added the effects of shrimping effort directly, modified by habitat weightings, as a proxy for all sources of anthropogenic mortality. Additional data included in our model were incremental growth of Kemp's ridleys marked and recaptured in the Gulf of Mexico, and the length frequency of stranded Kemp's ridleys. We also added a 2010 mortality factor that was necessary to fit the number of nests for 2010 and later (2011 and 2012). Last, we used an empirical basis for estimating natural mortality, on the basis of a Lorenzen mortality curve and growth estimates. Although our model generated reasonable estimates of annual total turtle deaths attributable to shrimp trawling, as well as additional deaths due to undetermined anthropogenic causes in 2010, we were unable to provide a clear explanation for the observed increase in the number of stranded Kemp's ridleys in recent years, and subsequent disruption of the species' exponential growth since the 2009 nesting season. Our consensus is that expanded data collection at the nesting beaches is needed and of high priority, and that 2015 be targeted for the next stock assessment to evaluate the 2010 event using more recent nesting and in-water data.

The transient species formed following excitation of fac-[Re(CO)(3)(F(2)dppz)(py)](+) (F(2)dppz = 11,12-difluorodipyrido[3,2-a:2',3'-c]phenazine) bound to double-stranded polynucleotides [poly(dA-dT)](2) or [poly(dG-dC)](2) have been studied by transient visible and infra-red spectroscopy in both the picosecond and nanosecond time domains. The latter technique has been used to monitor both the metal complex and the DNA by monitoring the regions 1900-2100 and 1500-1750 cm(-1) respectively. These data provide direct evidence for electron transfer from guanine to the excited state of the metal complex, which proceeds both on a sub-picosecond time scale and with a lifetime of 35 ps, possibly due to the involvement of two excited states. No electron transfer is found for the [poly(dA-dT)](2) complex, although characteristic changes are seen in the DNA-region TRIR consistent with changes in the binding of the bases in the intercalation site upon excitation of the dppz-complex.

ABSTRACT1. Seasonal movements and core habitat areas of immature Kemp's ridley sea turtles (Lepidochelys kempii) in the northern Gulf of Mexico were tracked via satellite telemetry. Tagged turtles were incidentally captured by recreational fishermen and rehabilitated at the Institute for Marine Mammal Studies in Gulfport, Mississippi.2. The average size of the core habitat areas (50% KDE (kernel density estimation)) was 1660.2 km 2 ± 3438.2 SD. Turtles displayed strong intra-and inter-annual site fidelity to the Mississippi Sound during the spring, summer, and autumn months. During the winter months, most turtles, probably influenced by water temperatures, migrated to nearshore waters of Louisiana on either side of the Mississippi River Delta. However, other migration strategies were also observed. 3. Overall, these data indicate that the Mississippi Sound is an important developmental habitat for this critically endangered species. In addition, their wintering grounds in Louisiana are utilized by adult Kemp's ridleys and other sea turtle species as foraging grounds and migratory corridors. The high use of these areas by sea turtle populations increases the potential for negative impacts from anthropogenic disturbances (e.g. shrimp trawling, oil production, hypoxia) that occur there.

Wildfires can be beneficial for native vegetation. However, wildfires can impact property values, human safety, and ecosystem function. Resource managers require safe, easy to use, timely, and cost-effective methods for quantifying wildfire damage and regeneration. In this work, we demonstrate an approach using an unmanned aerial system (UAS) equipped with a MicaSense RedEdge multispectral sensor to classify and estimate wildfire damage in a coastal marsh. We collected approximately 7.2 km2 of five-band multispectral imagery after a wildfire event in February 2016, which was used to create a photogrammetry-based digital surface model (DSM) and orthomosaic for object-based classification analysis. Airborne light detection and ranging data were used to validate the accuracy of the DSM. Four-band airborne imagery from pre- and post-fire were used to estimate pre-fire health, post-fire damage, and track the vegetation recovery process. Immediate and long-term post-fire classifications, area, and volume of burned regions were produced to track the revegetation progress. The UAS-based classification produced from normalized difference vegetation index and DSM was compared to the Landsat-based Burned Area Reflectance Classification. Experimental results show the potential of using UAS and the presented approach compared to satellite-based mapping in terms of classification accuracies, turnaround time, and spatial and temporal resolutions.

Global climate change has received increased attention in the Mid-Atlantic Highlands (MAH) Region of the United States in recent years. Several climate models predict increases in mean temperature of 1-5°C over the next one hundred years for the region, which has considerable implications for wetland ecosystems already encumbered by numerous anthropogenic stressors; however, historical (i.e., 1890s-current) data from the MAH presented here show increasing trends in precipitation intensity and decreasing trends in temperature. Continuation of historical trends for the next 90 years are used to predict potential impacts on regional wetland extent and function using empirical and conceptual models. Recommendations for management of climate related impacts on wetlands include analysis of historical climate trends at regional and local scales, establishment of wetland monitoring networks to quantify impacts of climate induced stress on wetland ecosystems, and integration of historical trends and research findings into empirical and conceptual models. Management strategies of this nature will facilitate early detection and mitigation of climate induced effects on wetlands in the MAH.

Spatial distribution models (SDMs) have been useful for improving management of species of concern in many areas. This study was designed to model the spatial distribution of bottlenose dolphins among seasons of the year in the Mississippi Sound within the northern Gulf of Mexico. Models were constructed by integrating presence locations of dolphins acquired from line‐transect sampling from 2011–2013 with maps of environmental conditions for the region to generate a likelihood of dolphin occurrence for winter (January–March), spring (April–June), summer (July–September), and autumn (October–December) using maximum entropy. Models were successfully generated using the program MaxEnt and had high predictive capacity for all seasons (AUC (area under curve) > 0.8). Distinct seasonal shifts in spatial distribution were evident including increased predicted occurrence in deepwater habitats during the winter, limited predicted occurrence in the western Mississippi Sound in winter and spring, widespread predicted occurrence over the entire region during summer, and a distinct westward shift of predicted occurrence in autumn. The most important environmental predictors used in SDMs were distance to shore, salinity, and nitrates, but variable importance differed considerably among seasons. Geographic shifts in predicted occurrence probably reflect both direct effects of changing environmental conditions and subsequent changes in prey availability and foraging efficiency. Overall, seasonal models helped to identify preferred habitats for dolphins among seasons of the year and can be used to inform management of this protected species in the northern Gulf of Mexico. Copyright © 2015 John Wiley & Sons, Ltd.

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