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Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods

Laubmeier, Amanda N. ; Cazelles, Bernard ; Cuddington, Kim ; Erickson, Kelley D. ; Fortin, Marie-Josée ; Ogle, Kiona ; Wikle, Christopher K. ; Zhu, Kai ; Zipkin, Elise F.

Trends in ecology & evolution (Amsterdam), 2020-12, Vol.35 (12), p.1090-1099 [Periódico revisado por pares]

England: Elsevier Ltd

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  • Título:
    Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods
  • Autor: Laubmeier, Amanda N. ; Cazelles, Bernard ; Cuddington, Kim ; Erickson, Kelley D. ; Fortin, Marie-Josée ; Ogle, Kiona ; Wikle, Christopher K. ; Zhu, Kai ; Zipkin, Elise F.
  • Assuntos: Animals ; Bayes Theorem ; dynamical analysis ; Ecosystem ; hierarchical inference ; Models, Biological ; Models, Statistical ; Population Dynamics ; Predatory Behavior ; transient behavior ; Uncertainty
  • É parte de: Trends in ecology & evolution (Amsterdam), 2020-12, Vol.35 (12), p.1090-1099
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-3
    content type line 23
    ObjectType-Review-2
  • Descrição: Understanding ecological processes and predicting long-term dynamics are ongoing challenges in ecology. To address these challenges, we suggest an approach combining mathematical analyses and Bayesian hierarchical statistical modeling with diverse data sources. Novel mathematical analysis of ecological dynamics permits a process-based understanding of conditions under which systems approach equilibrium, experience large oscillations, or persist in transient states. This understanding is improved by combining ecological models with empirical observations from a variety of sources. Bayesian hierarchical models explicitly couple process-based models and data, yielding probabilistic quantification of model parameters, system characteristics, and associated uncertainties. We outline relevant tools from dynamical analysis and hierarchical modeling and argue for their integration, demonstrating the value of this synthetic approach through a simple predator–prey example. Increasing availability of data sets from diverse sources over a range of spatial and temporal scales presents an opportunity to address important research questions, including how to efficiently use such data to understand and predict the dynamical behavior of ecological systems.Recent mathematical advances, stemming from traditional dynamical analysis, describe long-term system behavior in relation to governing ecological properties, allowing for the exploration of short-term (transient) system behaviors.Bayesian hierarchical models (BHMs) facilitate the integration of multiple data sources with theoretical models, providing great potential to improve understanding and predictions of ecological dynamics when combined with mathematical dynamical analysis.Previous work with integral projection models and integrated population models also suggests that pairing BHMs with process-based models can yield novel ecological insights.
  • Editor: England: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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