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Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS,Python, R,MATLAB, and C++
Authors:Jason J Roberts  Benjamin D Best  Daniel C Dunn  Eric A Treml  Patrick N Halpin
Affiliation:1. University of Lyon, CNRS-UMR 5600 Environnement - Ville - Société, ENS de Lyon, 15 Parvis René Descartes, BP 7000, 69342 Lyon Cedex 07, France;2. Direction Régionale de l''Environnement, de l''Aménagement et du Logement (Région Centre), Service Eau et Biodiversité, 5 avenue Buffon, 45064 Orléans Cedex, France;3. IRSTEA-Érosion torrentielle, neige et avalanches (ETGR)-Centre de Grenoble, National Research Institute for Environmental and Agricultural Sciences and Technologies, Domaine universitaire, 2 rue de la Papeterie-BP 76, 38402, Saint-Martin-d''Hères Cedex, France
Abstract:With the arrival of GPS, satellite remote sensing, and personal computers, the last two decades have witnessed rapid advances in the field of spatially-explicit marine ecological modeling. But with this innovation has come complexity. To keep up, ecologists must master multiple specialized software packages, such as ArcGIS for display and manipulation of geospatial data, R for statistical analysis, and MATLAB for matrix processing. This requires a costly investment of time and energy learning computer programming, a high hurdle for many ecologists. To provide easier access to advanced analytic methods, we developed Marine Geospatial Ecology Tools (MGET), an extensible collection of powerful, easy-to-use, open-source geoprocessing tools that ecologists can invoke from ArcGIS without resorting to computer programming. Internally, MGET integrates Python, R, MATLAB, and C++, bringing the power of these specialized platforms to tool developers without requiring developers to orchestrate the interoperability between them.In this paper, we describe MGET’s software architecture and the tools in the collection. Next, we present an example application: a habitat model for Atlantic spotted dolphin (Stenella frontalis) that predicts dolphin presence using a statistical model fitted with oceanographic predictor variables. We conclude by discussing the lessons we learned engineering a highly integrated tool framework.
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