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Journal Article

Untangling methodological and scale considerations in growth and productivity trend estimates of Canada's forests


Hartmann,  Henrik
Tree Mortality Mechanisms, Dr. H. Hartmann, Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Marchand, W., Girardin, M. P., Gauthier, S., Hartmann, H., Bouriaud, O., Babst, F., et al. (2018). Untangling methodological and scale considerations in growth and productivity trend estimates of Canada's forests. Environmental Research Letters, 13: 093001. doi:10.1088/1748-9326/aad82a.

Cite as: http://hdl.handle.net/21.11116/0000-0001-F258-B
In view of the economic, social and ecological importance of Canada's forest ecosystems, there is a growing interest in studying the response of these ecosystems to climate change. Accurate knowledge regarding growth trajectories is needed for both policy makers and forest managers to ensure sustainability of the forest resource. However, results of previous analyses regarding the sign and magnitude of trends have often diverged. The main objective of this paper was to analyze the current state of scientific knowledge on growth and productivity trends in Canada's forests and provide some explanatory elements for contrasting observations. The three methods that are commonly used for assessments of tree growth and forest productivity (i.e. forest inventory data, tree-ring records, and satellite observations) have different underlying physiological assumptions and operate on different spatiotemporal scales, which complicates direct comparisons of trend values between studies. Within our systematic review of 44 peer-reviewed studies, half identified increasing trends for tree growth or forest productivity, while the other half showed negative trends. Biases and uncertainties associated with the three methods may explain some of the observed discrepancies. Given the complexity of interactions and feedbacks between ecosystem processes at different scales, researchers should consider the different approaches as complementary, rather than contradictory. Here, we propose the integration of these different approaches into a single framework that capitalizes on their respective advantages while limiting associated biases. Harmonization of sampling protocols and improvement of data processing and analyses would allow for more consistent trend estimations, thereby providing greater insight into climate-change related trends in forest growth and productivity. Similarly, a more open data-sharing culture should speed-up progress in this field of research.