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Related Experiment Video

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Applying various algorithms for species distribution modelling.

Xinhai Li1, Yuan Wang

  • 1Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China. lixh@ioz.ac.cn

Integrative Zoology
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PubMed
Summary

This review compares 11 species distribution models (SDMs) for researchers. It offers guidance on selecting and applying the best SDM for specific data and objectives in ecology and conservation.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Climate Change Biology

Background:

  • Species distribution models (SDMs) are crucial tools in ecology, conservation, and climate change research.
  • Despite advancements, selecting appropriate SDMs remains challenging for researchers.
  • A need exists for clear guidance on SDM selection and application.

Purpose of the Study:

  • To provide an overview of prevalent species distribution models for new researchers.
  • To compare the strengths, weaknesses, and characteristics of 11 popular SDMs.
  • To offer guidelines for effective SDM application, including selection, formulation, and parameter estimation.

Main Methods:

  • Comparative review of 11 species distribution models.
  • Categorization of models into regression, classification, and complex approaches.
  • Analysis of model suitability based on data type and species-environment relationships.

Main Results:

  • Detailed comparison of generalized linear models, generalized additive models, multivariate adaptive regression splines, hierarchical modeling, mixture discriminant analysis, generalized boosting models, classification and regression trees, artificial neural networks, random forests, genetic algorithms, and maximum entropy approaches.
  • Identification of specific use cases for each model based on their performance and data requirements.
  • Framework for model selection, formulation, and parameter estimation provided.

Conclusions:

  • Understanding the nuances of different SDMs is essential for accurate ecological predictions.
  • The review equips researchers with the knowledge to choose and implement SDMs effectively.
  • Guidelines facilitate informed decision-making in species distribution modeling.