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The neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model
William K Morris1, Peter A Vesk1, Michael A McCarthy1
1Quantitative and Applied Ecology Group, The School of Botany, The University of Melbourne Melbourne, Victoria, Australia.
Ecology and Evolution
|January 29, 2015
Summary
Ecologists can improve model precision using informative priors. While accuracy effects vary, appropriate data-derived priors enhance precision without consistently reducing accuracy in ecological models.
Area of Science:
- Ecology
- Ecological modeling
- Bayesian statistics
Background:
- Ecologists often avoid informative priors due to concerns about reduced model accuracy.
- Empirical evaluations of data-derived informative priors' impact on precision and accuracy in ecology are scarce.
Purpose of the Study:
- To empirically assess the effects of data-derived informative priors on the precision and accuracy of ecological mortality models.
- To investigate whether informative priors enhance or diminish model performance in ecological studies.
Main Methods:
- Evaluated tree species mortality models using data from a forest dynamics plot in Thailand.
- Compared models with vague priors against models with informative priors derived from data.
Main Results:
- Models utilizing informative priors demonstrated increased precision.
- The impact of informative priors on accuracy was variable, improving it in some instances and reducing it in others.
- On average, models with informative priors showed no systematic difference in accuracy compared to those without.
Conclusions:
- Appropriately specified informative priors can lead to greater precision in ecological models.
- Informative priors do not necessarily reduce model accuracy and can be a valuable tool when used correctly.
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