Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++

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...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 25; no. 10; pp. 1197 - 1207
Main Authors: Roberts, Jason J., Best, Benjamin D., Dunn, Daniel C., Treml, Eric A., Halpin, Patrick N.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2010
Subjects:
Cetacea
Computer programs
Dolphins
Ecology
GIS
Global Positioning System
Habitat modeling
Informatics
Interoperability
Marine
Marine ecology
Mathematical analysis
Mathematical models
Matlab
Oceanography
Satellite navigation systems
Software integration
Spatial ecology
Stenella frontalis
A, Atlantic
GIS
Software integration
Spatial ecology
Marine ecology
Habitat modeling
Oceanography
Informatics
Interoperability
ISSN:1364-8152
Online Access:Get full text
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Summary: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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ISSN:1364-8152
DOI:10.1016/j.envsoft.2010.03.029