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mMWeb - An Online Platform for Employing Multiple Ecological Niche Modeling Algorithms (mMWeb - A Platform for Ecological Niche Models)

Qiao, Huijie ; Lin, Congtian ; Ji, Liqiang ; Jiang, Zhigang; Sueur, Cédric (Editor)

2012, Vol.7(8), p.e43327 [Tạp chí có phản biện]

E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0043327

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  • Nhan đề:
    mMWeb - An Online Platform for Employing Multiple Ecological Niche Modeling Algorithms (mMWeb - A Platform for Ecological Niche Models)
  • Tác giả: Qiao, Huijie ; Lin, Congtian ; Ji, Liqiang ; Jiang, Zhigang
  • Sueur, Cédric (Editor)
  • Chủ đề: Research Article ; Biology ; Computer Science ; Earth Sciences ; Computational Biology ; Computer Science
  • Là 1 phần của: 2012, Vol.7(8), p.e43327
  • Mô tả: Predicting the ecological niche and potential habitat distribution of a given organism is one of the central domains of ecological and biogeographical research. A wide variety of modeling techniques have been developed for this purpose. In order to implement these models, the users must prepare a specific runtime environment for each model, learn how to use multiple model platforms, and prepare data in a different format each time. Additionally, often model results are difficult to interpret, and a standardized method for comparing model results across platforms does not exist. We developed a free and open source online platform, the multi-models web-based (mMWeb) platform, to address each of these problems, providing a novel environment in which the user can implement and compare multiple ecological niche model (ENM) algorithms. ; mMWeb combines 18 existing ENMs and their corresponding algorithms and provides a uniform procedure for modeling the potential habitat niche of a species via a common web browser. mMWeb uses Java Native Interface (JNI), Java R Interface to combine the different ENMs and executes multiple tasks in parallel on a super computer. The cross-platform, user-friendly interface of mMWeb simplifies the process of building ENMs, providing an accessible and efficient environment from which to explore and compare different model algorithms.
  • Ngôn ngữ: English
  • Số nhận dạng: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0043327

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