Marine Geospatial Ecology Lab

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High Seas @ Duke – Work

Spatial Ecology   The high seas is both a single thing and the sum of many parts.  It is singular because it is connected by both currents and by species.  However, it is also seamounts and hydrothermal vents and gyres, and convergence zones, and fracture zones and the “midnight zone”… and the list goes on. …

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High Seas @ Duke

What are the high seas? Deep, distant, dynamic, and diverse. Beyond borders but not beyond governance. A global commons and the common heritage of humankind. What we do  Spatial Ecology Where does biodiversity exist in the ocean? To answer this question, Duke scientists, lead by the Marine Geospatial Ecology Lab (MGEL), have endeavored to provide fellow scientists …

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Blog

Discover the latest research from MGEL Read about the latest research and updates from the Marine Geospatial Ecology Lab

Education

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Research

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Landing Page

Hi. My name is Ben Arthur. Digital marketing expert. Aliquet adipiscing in dignissim scelerisque fermentum nibh condimentum ligula eget nunc blandit interdum eros, massa ante ipsum senectus quis in. Get My Guide As Featured In: I have been more than 12 years in digital marketing industries, and have help more than 240 companies to grow …

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People

Patrick N. HalpinDirector of the Geospatial Ecology Program Professor of Marine Geospatial Ecology in the Marine Science and Conservation Division of the Nicholas School of the Environment, Duke University  Patrick Halpin is a Professor of Marine Geospatial Ecology and Director of the Geospatial Analysis Program at the Nicholas School of the Environment, Duke University Marine …

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Privacy Policy

Who we are Our website address is: https://mgel.env.duke.edu. © all rights reserved What personal data we collect and why we collect it Comments and Media We do not allow comments or uploads on our site. Site content is created by the Marine Geospatial Lab and associates. Cookies The site does not offer accounts to general …

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Random Forest Tools

As part of our recent overhaul of the predictive modeling tools, we added support for random forests as a new modeling type. Random forest (Breiman, 2001) is machine learning algorithm that fits many classification or regression tree (CART) models to random subsets of the input data and uses the combined result (the forest) for prediction. A …

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