GPS loggers and time-depth recorders were used to characterize the foraging behavior of the sexually dimorphic Peruvian booby Sula variegata on 2 islands in northern Peru. We evaluated whether (1) its foraging behavior differed from tropical boobies and temperate gannets (the Peruvian boobies feed in areas of enhanced productivity and high fish density), and (2) females and males exploited different foraging habitats as a consequence of size dimorphism. Birds foraged only during daylight hours, 1 to 3 times a day, in trips of short duration (median = 1.8 h). Overall, 92% of the total foraging time was spent flying. They fed exclusively on anchovetas Engraulis ringens, which were captured in shallow dives (median = 2.5 m, max = 8.8 m) with a dive median rate of 11 dives h -1 (max = 37 dives h -1 ). The median foraging range was 25 km (max = 68 km), whereas the median total distance traveled was 69 km (max = 179 km). Foraging site fidelity was high, and the orientation of foraging flights in any given day was similar among birds that departed at the same time. There were no sex-specific differences in 13 of 15 foraging variables; however, females dived slightly deeper and spent a larger proportion of time sitting on the water. We speculate that (1) the foraging behavior of Peruvian boobies contrasts with that of their tropical and temperate relatives as a result of the proximity and predictability of food sources, elevated energetic demands of the brood (up to 4 chicks) and high prey encounter rate in the Peruvian upwelling system, and (2) the lack of spatial segregation between sexes may be related to the attraction of birds to feeding aggregations that are formed in the vicinity of the colonies. Once the foraging patches are localized, females dive deeper because of passive mechanisms associated with a heavier mass.

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Abstract:Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping.

Abstract:Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island's future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions.

BackgroundThe COMprehensive Post-Acute Stroke Services (COMPASS) pragmatic trial compared the effectiveness of comprehensive transitional care (COMPASS-TC) versus usual care among stroke and transient ischemic attack (TIA) patients discharged home from North Carolina hospitals. We evaluated implementation of COMPASS-TC in 20 hospitals randomized to the intervention using the RE-AIM framework.MethodsWe evaluated hospital-level Adoption of COMPASS-TC; patient Reach (meeting transitional care management requirements of timely telephone and face-to-face follow-up); Implementation using hospital quality measures (concurrent enrollment, two-day telephone follow-up, 14-day clinic visit scheduling); and hospital-level sustainability (Maintenance). Effectiveness compared 90-day physical function (Stroke Impact Scale-16), between patients receiving COMPASS-TC versus not. Associations between hospital and patient characteristics with Implementation and Reach measures were estimated with mixed logistic regression models.ResultsAdoption: Of 95 eligible hospitals, 41 (43%) participated in the trial. Of the 20 hospitals randomized to the intervention, 19 (95%) initiated COMPASS-TC.Reach: A total of 24% (656/2751) of patients enrolled received a billable TC intervention, ranging from 6 to 66% across hospitals.Implementation: Of eligible patients enrolled, 75.9% received two-day calls (or two attempts) and 77.5% were scheduled/offered clinic visits. Most completed visits (78% of 975) occurred within 14 days.Effectiveness: Physical function was better among patients who attended a 14-day visit versus those who did not (adjusted mean difference: 3.84, 95% CI 1.42–6.27, p = 0.002).Maintenance: Of the 19 adopting hospitals, 14 (74%) sustained COMPASS-TC.ConclusionsCOMPASS-TC implementation varied widely. The greatest challenge was reaching patients because of system difficulties maintaining consistent delivery of follow-up visits and patient preferences to pursue alternate post-acute care. Receiving COMPASS-TC was associated with better functional status.Trial registrationClinicalTrials.gov number: NCT02588664. Registered 28 October 2015.