Marine Geospatial Ecology Tools
What is it?
Marine Geospatial Ecology Tools (MGET) is a free, open-source geoprocessing toolbox that can help you solve a wide variety of marine research, conservation, and spatial planning problems. MGET plugs into ArcGIS and can perform tasks such as:
- Accessing oceanographic data from ArcGIS
- Identifying ecologically-relevant oceanographic features in remote sensing imagery
- Building predictive species distribution models
- Modeling habitat connectivity by simulating hydrodynamic dispersal of larvae
- Detecting spatiotemporal patterns in fisheries and other time series data
Learn more!
- For a quick overview, please see our latest Overview Presentation (PPTX, PDF)
- For a detailed description of MGET’s design and examples of its usage, please see our papers and those by our users
- For a list of tools within MGET and typical use cases, please see the ArcGIS Tutorial (under construction)
- For a worked example of one popular use case, please see this downloadable species distribution modeling example
ArcGIS Tutorial
In ArcGIS, MGET appears as a geoprocessing toolbox. The toolbox contains about 300 tools. The tools may be executed individually, wired together into graphical workflows using the ArcGIS ModelBuilder, executed programmatically from geoprocessing…
Programming Tutorial
Invoking MGET tools programatically MGET is, in essence, a library of functions. The functions are implemented in various programming languages, mainly Python, R, C++, and MATLAB, but are exposed uniformly via several widely-used…
MGET Publications
If you use MGET in your project, please cite our papers in your publications. Your citations help us continue to provide MGET as free software. Thanks for your support! If you are unable…
New larval connectivity modeling tools
For additional help with these tools, please contact mget-help@nicholas.duke.edu We are pleased to announce a major update to MGET’s larval connectivity modeling tools. Prior to MGET 0.8a62, the larval connectivity modeling tools were…
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…
Predictive Modeling Workflow
In MGET 0.8a42, we introduced a simplified workflow for predictive modeling, shown here for generalized linear models (GLMs). The new workflow is the same for all model types (GLM, GAM, etc.) and consists…
About
Contributors MGET was written by Jason Roberts, Ben Best, Daniel Dunn, and Eric Treml. Pat Halpin supervises the project. MGET relies on the contributions of many others in the open source community. Sponsors
User Publications and Reports
This page lists publications and reports for studies and projects that we are aware used MGET in some way. If you know of something that is not listed here, please @ Forthcoming Lins…