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Uncertainty quantification for ecological models with random parameters.

Jody R Reimer1,2, Frederick R Adler1,2, Kenneth M Golden1

  • 1Department of Mathematics, University of Utah, Salt Lake City, Utah, USA.

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Summary
This summary is machine-generated.

Ecological models can better predict outcomes by treating uncertain parameters as random variables. Polynomial chaos methods offer an efficient way to analyze these uncertainties, improving ecological model predictions.

Keywords:
Jensen's inequalityaleatory uncertaintybloom dynamicsepistemic uncertaintyglobal sensitivitypolynomial chaosrandom parameterssea ice algaeuncertainty quantification

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Area of Science:

  • Ecological modeling
  • Uncertainty quantification
  • Computational mathematics

Background:

  • Ecological models often contain uncertain parameters, impacting prediction accuracy.
  • Traditional methods treat parameters as fixed, limiting the assessment of uncertainty.
  • Parametric uncertainty is crucial for understanding ecological system dynamics.

Purpose of the Study:

  • To introduce and compare polynomial chaos (PC) methods with Monte Carlo (MC) simulations for ecological model uncertainty analysis.
  • To investigate the impact of random parameters on sea ice algal bloom dynamics.
  • To assess the efficiency and applicability of PC methods in ecological modeling.

Main Methods:

  • Utilized polynomial chaos expansion for uncertainty quantification.
  • Developed an algal bloom model with key parameters treated as random variables.
  • Compared computational efficiency and results of PC methods against traditional Monte Carlo simulations.

Main Results:

  • Modeling parameters as random variables significantly altered the predicted timing, intensity, and productivity of algal blooms.
  • Polynomial chaos methods demonstrated greater computational efficiency compared to Monte Carlo simulations for this model.
  • The study highlights the substantial influence of parametric uncertainty on ecological model outcomes.

Conclusions:

  • Polynomial chaos methods provide an efficient and powerful approach for incorporating parametric uncertainty into ecological models.
  • Accounting for random parameters leads to more realistic and robust ecological model predictions.
  • This work facilitates better synthesis between ecological models and empirical data through improved uncertainty handling.