Canadian Forest Service Publications

Quantifying the sources of epistemic uncertainty in model predictions of insect disturbances in an uncertain climate. 2017. Gray, D.R. Annals of Forest Science 74: 48.

Year: 2017

Issued by: Atlantic Forestry Centre

Catalog ID: 39574

Language: English

Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1007/s13595-017-0645-y

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Abstract

  • Key message Natural disturbance can disrupt the anticipated delivery of forest-related ecosystem goods and services. Model predictions of natural disturbances have substantial uncertainties arising from the choices of input data and spatial scale used in the model building process, and the uncertainty of future climate conditions which are a major driver of disturbances. Quantifying the multiple contributions to uncertainty will aid decision making and guide future research needs.
  • Context Forest management planning has been able, in the past, to rely on substantial empirical evidence regarding tree growth, succession, frequency and impacts of natural disturbances to estimate the future delivery of goods and services. Uncertainty has not been thought large enough to warrant consideration. Our rapidly changing climate is casting that empirical knowledge in doubt.
  • Aims This paper describes how models of future spruce budworm outbreaks are plagued by uncertainty contributed by (among others): selection of data used in the model building process; model error; and uncertainty of the future climate and forest that will drive the future insect outbreak. The contribution of each to the total uncertainty will be quantified.
  • Methods Outbreak models are built by the multivariate technique of reduced rank regression using different datasets. Each model and an estimate of its error are then used to predict future outbreaks under different future conditions of climate and forest composition. Variation in predictions is calculated, and the variance is apportioned among the model components that contributed to the epistemic uncertainty in predictions.
  • Results Projections of future outbreaks are highly uncertain under the range of input data and future conditions examined. Uncertainty is not uniformly distributed spatially; the average 75% confidence interval for outbreak duration is 10 years. Estimates of forest inventory for model building and choice of climate scenario for projections of future climate had the greatest contributions to predictions of outbreak duration and severity.
  • Conclusion Predictions of future spruce budworm outbreaks are highly uncertain. More precise outbreak data with which to build a new outbreak model will have the biggest impact on reducing uncertainty. However, an uncertain future climate will continue to produce uncertainty in outbreak projections. Forest management strategies must, therefore, include alternatives that present a reasonable likelihood of achieving acceptable outcomes over a wide range of future conditions.

Plain Language Summary

Forest management planning has been able, in the past, to rely on substantial empirical evidence regarding tree growth, succession, frequency and impacts of natural disturbances to estimate the future delivery of goods and services. Uncertainty has not been thought large enough to warrant consideration. However, our rapidly changing climate is casting that empirical knowledge in doubt: the delivery of anticipated forest-related ecosystem goods and services may not materialize. Outbreaks of the eastern spruce budworm are among the most impactful natural disturbances. And our predictions of those outbreaks are, in fact, plagued by uncertainty that is contributed by a number of sources. This paper describes those sources of uncertainty; it estimates the total uncertainty; it quantifies the contribution that each source makes to the total uncertainty. Quantifying the uncertainty is important for two principal reasons: • When natural disturbance predictions are uncertain (as they always are) and the uncertainties are not quantified, decision makers and policy makers are unable to respond wisely to the multiple, and different, predictions. • When we quantify the contributions to prediction uncertainty we are able to develop logical research strategies to efficiently reduce the total uncertainty.

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