Browsing by Subject "Marine science"

Now showing 1 - 3 of 3
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    A Review of Successful Practices in Environmental Education to apply in the design of a Marine Science Curriculum
    (2020-04-23) Wright, Walter
    Connecting people to natural systems has become an important way of addressing current environmental issues. The need for conservation and protection of these natural systems will have a much greater chance of success if environmental education for students is designed to help link human actions to environmental impacts. This can be a challenge for educators when environmental issues are not directly related to a student’s lifestyle or understood. How to connect environmental issues to anyone that may have little or no understanding of an issue is a challenge for environmental educators. This MP will provide a short review of environmental education - the past, current themes and methods, and ideas for the future. This review will also assist in helping the client, SAILwind (an educational organization which focuses on coastal ecology and environmental issues) by developing an understanding of how environmental education developed and what is currently happening in several selected learning institutions and organizations. Obtaining the background for environmental education, helps in providing appropriate and accurate curriculum for the educational organization SAILwind, to assist them with their goals of protection and conservation. SAILwind is a highly interactive, educational non-profit, using science, adventure, and fun to help people learn about natural systems and to connect their individual actions and habits with impacts on the natural world. This is accomplished through “play with a purpose”, a tagline of the organization. Sailing and kayak trips, stream bed exploration and coastal excursions all are part of the platform using a nature based experience to learn about an ecosystem and to see how humans affect these natural systems. SAILwind needs more curriculum designed around school aged kids to help expand their educational efforts and this MP includes three educational models/programs to be used for school kids in SAILwind’s educational and conservation efforts.
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    Applying Fishery Catch Data to Population Trends and Community Structures in Atlantic Pelagic Species
    (2017-05-06) May, Eva
    Recent studies have shown that fishing can influence marine trophic networks, as larger, higher trophic level species are preferentially targeted by fishers. In this study, we investigate US Pelagic Longline catch dynamics of twenty-nine Atlantic species from 1986 to 2008 using information provided by the fishermen’s logbook data program. Two different analyses were performed on the data for this study. Data were cleaned and monthly catch per unit effort (CPUE) for each species within each of five fishing effort regions – the Mid Atlantic Bight and Northeast Costal area, The Caribbean area, the Florida East Coast and South Atlantic Bight area, the Gulf of Mexico area, and the Northeast Distant area – were calculated to examine trends in catch rates. Annual CPUE graphs were made for all areas combined, correcting for changes in fishing dynamics to examine changes in relative abundance of species over time. A regression was run through these annual graphs of CPUE over the study period in order to estimate increases or decreases in relative population abundance for each species. The slopes from these annual abundances regressions were further regressed versus trophic level, average generation length, and resiliency to test for significant correlations between these ecological factors and trends in population size. Though trends matched our predictions (species with higher trophic level, lower resiliency, and longer generation length showed greater declines in abundance), no significant p-values were found. It seems likely that ecosystem structural shifts similar to those seen in other recent studies are occurring, but the lack of significance makes it difficult to draw strong conclusions. The lack of significance between ecological factors and population trends observed in this study may be due to trophic shifts in fishing are not occurring or may be due to issues with the data used for regressions. Future studies using different methods of obtaining demographic factors and incorporating additional years of catch data could bring more clarity to this work.
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    Drones and Machine Learning for Marine Animal Behavior Analysis
    (2023-04-28) Poling, David
    Understanding the behavior of marine animals is critical to effective management especially as they fall under increasing anthropogenic pressures. Recent advances in two technologies, drones and machine learning offer versatile, data driven, automatable solutions capable of effective collection and analysis of large datasets. In this paper I illustrate how pose estimation as an effective machine learning based solution for analyzing marine animal behavior. This study investigates pose estimations use on drone imagery due to its rising prevalence in marine science and prior combination with pose estimation in our lab. As initial work at our lab has investigated the use of pose estimation on marine mammal datasets and my goal is to expand on these efforts and build an overview of both technologies integration for researchers interested working with them. In the present study I use a collection of shark video taken by myself and other Duke researchers locally off the Rachel Carson Reserve on the North Carolina coast as demonstration and to help build a catalog of models and best practices for use of pose estimation on different taxa. This paper will provide an overview of drones and pose estimation including Social LEAP Estimates Animal Poses (SLEAP), a pose estimation framework which has proven to have good potential in marine science. SLEAP was chosen due to its accessibility, versatility and tracking algorithm which allows multiple subjects to be tracked and analyzed at the same time. The latter is a major steppingstone for pose estimation software as past projects may have been able to identify multiple individuals in one frame but not be able to keep track of who is who across thousands of frames of video. Covered topics will include: 1. Technical overview of drones and pose estimation. 2. Data collection 3. Using pose estimation a. Model types and programming 4. Data export and processing 5. Analysis 6. Conclusions on using pose estimation in marine science and future work. After data export, a novel solution will also be assessed for compensating for camera movement, in this case a moving drone, which has proven to be one of the biggest roadblocks of using SLEAP, which was developed for processing stationary video. This solution processes data in a way that is plug and play with existing analytical methods and will be open source.