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dc.contributor.authorJones, Benjamin T.  Concept link
dc.coverage.spatialGulf of Maine
dc.date.accessioned2017-07-27T18:39:21Z
dc.date.available2017-07-27T18:39:21Z
dc.date.issued2017-09
dc.identifier.urihttps://hdl.handle.net/1912/9134
dc.descriptionSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution September 2017en_US
dc.description.abstractPopulation connectivity is a fundamental process that governs the spatial and temporal dynamics of marine ecosystems. For many marine species, population connectivity is driven by dispersal during a planktonic larval phase. The ability to obtain accurate, affordable, and meaningful estimates of larval dispersal patterns is therefore a key aspect of understanding marine ecosystems. Although field observations provide insight into dispersal processes, they do not provide a comprehensive assessment. Individual-based models (IBMs) that couple ocean circulation and particle-tracking models provide a unique ability to examine larval dispersal patterns with high spatial and temporal resolution. Obtaining accurate results with IBMs requires simulating a sufficient number of particles, and the sequential Bayesian procedure presented in chapter 2 identifies when the number of particles is adequate to address predefined research objectives. In addition, this method optimizes the particle release locations to minimize the requisite number of particles. Even after applying this method, the computational expense of IBM studies is still large. The model in chapter 3 seeks to increase the affordability of IBM studies by transferring some of the calculations to graphics processing units. Chapter 4 describes three algorithms that assist in interpreting IBM output by identifying coherent geographic clusters from population connectivity data. The first two algorithms have existed for nearly a decade and recently been applied separately to marine ecology, and we provide a direct comparison of the results from each. Additionally, we develop and present a new algorithm that simultaneously considers multiple species. Finally, in chapter 5, we apply these tools and a trait-based modeling framework to assess which species traits are most likely to impact dispersal success and patterns in the Gulf of Maine. We conclude that the traits influencing spawning distributions and habitat requirements for settlement are most likely to influence dispersal.en_US
dc.description.sponsorshipFinancial support was provided by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program, Woods Hole Oceanographic Institution (WHOI) via the Ocean Ventures Fund (OVF), and the National Science Foundation through grant numbers OCE-1459133, 0928442, and 1031256.en_US
dc.language.isoen_USen_US
dc.publisherMassachusetts Institute of Technology and Woods Hole Oceanographic Institutionen_US
dc.relation.ispartofseriesWHOI Thesesen_US
dc.subjectMarine ecology
dc.subjectPlankton
dc.subjectLarval phase
dc.subjectDispersal
dc.titleTrait-based modeling of larval dispersal in the Gulf of Maineen_US
dc.typeThesisen_US
dc.identifier.doi10.1575/1912/9134


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