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Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

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2023

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Trends in Ecology & Evolution. Elsevier. 2023, 38(8), S. 760-772. ISSN 0169-5347. eISSN 1872-8383. Verfügbar unter: doi: 10.1016/j.tree.2023.03.011

Zusammenfassung

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

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Fachgebiet (DDC)
570 Biowissenschaften, Biologie

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hypothesis testing, biodiversity, Bayesian statistics, eco-evolutionary dynamics, mechanistic models

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ISO 690PANTEL, Jelena H., Lutz BECKS, 2023. Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics. In: Trends in Ecology & Evolution. Elsevier. 2023, 38(8), S. 760-772. ISSN 0169-5347. eISSN 1872-8383. Verfügbar unter: doi: 10.1016/j.tree.2023.03.011
BibTex
@article{Pantel2023-08Stati-71720,
  year={2023},
  doi={10.1016/j.tree.2023.03.011},
  title={Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics},
  number={8},
  volume={38},
  issn={0169-5347},
  journal={Trends in Ecology & Evolution},
  pages={760--772},
  author={Pantel, Jelena H. and Becks, Lutz}
}
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