{"id":81981,"date":"2019-01-30T12:10:53","date_gmt":"2019-01-30T17:10:53","guid":{"rendered":"https:\/\/mrnf.gouv.qc.ca\/nos-publications\/framework-decision-makers-forest-model-predictions\/"},"modified":"2019-01-30T12:10:53","modified_gmt":"2019-01-30T17:10:53","slug":"framework-decision-makers-forest-model-predictions","status":"publish","type":"cpt_publication","link":"https:\/\/mrnf.gouv.qc.ca\/en\/our-publications\/framework-decision-makers-forest-model-predictions\/","title":{"rendered":"An analytical framework to assist decision makers in the use of forest ecosystem model predictions"},"content":{"rendered":"\n<p>Published in <strong>Environmental Modelling &amp; Software 26(3): 280-288<\/strong><\/p>\n<p>The predictions from most forest ecosystem models originate from deterministic simulations. However, few evaluation exercises for model outputs are performed by either model developers or users. This issue has important consequences for decision makers using these models to develop natural resource management policies, as they cannot evaluate the extent to which predictions stemming from the simulation of alternative management scenarios may result in significant environmental or economic differences. Various numerical methods, such as sensitivity\/uncertainty analyses, or bootstrap methods, may be used to evaluate models and the errors associated with their outputs. However, the application of each of these methods carries unique challenges which decision makers do not necessarily understand; guidance is required when interpreting the output generated from each model. This paper proposes a decision flow chart in the form of an analytical framework to help decision makers apply, in an orderly fashion, different steps involved in examining the model outputs. The analytical framework is discussed with regard to the definition of problems and objectives and includes the following topics: model selection, identification of alternatives, modelling tasks and selecting alternatives for developing policy or implementing management scenarios. Its application is illustrated using an on-going exercise in developing silvicultural guidelines for a forest management enterprise in Ontario, Canada.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Published in Environmental Modelling &amp; Software 26(3): 280-288 The predictions from most forest ecosystem models originate from deterministic simulations. However, few evaluation exercises for model outputs are performed by either model developers or users. This issue has important consequences for decision makers using these models to develop natural resource management policies, as they cannot evaluate [&hellip;]<\/p>\n","protected":false},"template":"","format":"standard","cptt_secteur":[1179],"cptt_theme":[1180,1181],"cptt_categ_publi":[1149],"cptt_auteurs_ministeriels":[1097],"class_list":["post-81981","cpt_publication","type-cpt_publication","status-publish","format-standard","hentry","cptt_secteur-forests","cptt_theme-forestry-research","cptt_theme-forests","cptt_categ_publi-scientific-article","cptt_auteurs_ministeriels-mailly-daniel-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>An analytical framework to assist decision makers in the use of forest ecosystem model predictions - Minist\u00e8re des Ressources naturelles et des For\u00eats<\/title>\n<meta name=\"description\" content=\"This paper proposes a decision flow chart in the form of an analytical framework to help decision makers apply different steps involved in examining the model outputs.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mrnf.gouv.qc.ca\/en\/our-publications\/framework-decision-makers-forest-model-predictions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An analytical framework to assist decision makers in the use of forest ecosystem model predictions - 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