Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models
Author:
Publisher
Springer Science and Business Media LLC
Subject
Nature and Landscape Conservation,Ecology,Ecology, Evolution, Behavior and Systematics,Forestry
Link
http://link.springer.com/article/10.1007/s40725-017-0069-9/fulltext.html
Reference54 articles.
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2. Hickler T, Rammig A, Werner C. Modelling CO2 impacts on forest productivity. Current Forestry Reports. 2015;1:69–80.
3. • Reyer C. Forest productivity under environmental change—a review of stand-scale modeling studies. Current Forestry Reports. 2015;1:53–68. This paper is useful for forest modellers aiming to provide global assessments of the impacts of environmental change: it identifies those forest types and parts of the globe for which data are at present lacking.
4. Fontes L, Bontemps J-D, Bugmann H, Van Oijen M, Gracia C, Kramer K, et al. Models for supporting forest management in a changing environment. Forest Systems. 2010;3:8–29.
5. Hartig F, Dyke J, Hickler T, Higgins SI, O’Hara RB, Scheiter S, et al. Connecting dynamic vegetation models to data—an inverse perspective. J Biogeogr. 2012;39:2240–52.
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