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Quantifying the sources of epistemic uncertainty in model predictions of insect disturbances in an uncertain climate / David R. Gray in Annals of Forest Science, vol 74 n° 3 (September 2017)  

Public [article]  inAnnals of Forest Science > vol 74 n° 3 (September 2017) Titre : Quantifying the sources of epistemic uncertainty in model predictions of insect disturbances in an uncertain climate Type de document : Article/Communication Auteurs : David R. Gray, Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] insecte nuisible [Termes IGN] modèle conceptuel de données [Termes IGN] modèle d'incertitude [Termes IGN] prédiction [Termes IGN] variation saisonnière [Vedettes matières IGN] Ecologie forestière Résumé : (Auteur) 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. Numéro de notice : A2017-356 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-017-0645-y Date de publication en ligne : 21/06/2017 En ligne : https://doi.org/10.1007/s13595-017-0645-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85723 [article]