4.7 Article

On Theory in Ecology

期刊

BIOSCIENCE
卷 64, 期 8, 页码 701-710

出版社

OXFORD UNIV PRESS
DOI: 10.1093/biosci/biu098

关键词

theory unification; metabolic theory; neutral theory biodiversity; maximum entropy theory of ecology; big data

类别

资金

  1. National Center for Ecological Analysis and Synthesis
  2. National Science Foundation (NSF) [DEB-0072909]
  3. University of California, Santa Barbara
  4. Santa Fe Institute, through NSF [DEB-0628281, ICM P05-002, PFB-23, FONDAP 1501-0001]
  5. Direct For Biological Sciences
  6. Division Of Integrative Organismal Systems [1121797] Funding Source: National Science Foundation
  7. Emerging Frontiers
  8. Direct For Biological Sciences [1065861] Funding Source: National Science Foundation
  9. Emerging Frontiers
  10. Direct For Biological Sciences [1065836] Funding Source: National Science Foundation

向作者/读者索取更多资源

We argue for expanding the role of theory in ecology to accelerate scientific progress, enhance the ability to address environmental challenges, foster the development of synthesis and unification, and improve the design of experiments and large-scale environmental-monitoring programs. To achieve these goals, it is essential to foster the development of what we call efficient theories, which have several key attributes. Efficient theories are grounded in first principles, are usually expressed in the language of mathematics, make few assumptions and generate a large number of predictions per free parameter, are approximate, and entail predictions that provide well-understood standards for comparison with empirical data. We contend that the development and successive refinement of efficient theories provide a solid foundation for advancing environmental science in the era of big data.

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