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When tree rings go global: Challenges and opportunities for retro- and prospective insight
The demand for large-scale and long-term information on tree growth is increasing rapidly as environmental change research strives to quantify and forecast the impacts of continued warming on forest ecosystems. This demand, combined with the now quasi-global availability of tree-ring observations, has inspired researchers to compile large tree-ring networks to address continental or even global-scale research questions. However, these emergent spatial objectives contrast with paleo-oriented research ideas that have guided the development of many existing records. A series of challenges related to how, where, and when samples have been collected is complicating the transition of tree rings from a local to a global resource on the question of tree growth. Herein, we review possibilities to scale tree-ring data (A) from the sample to the whole tree, (B) from the tree to the site, and (C) from the site to larger spatial domains. Representative tree-ring sampling supported by creative statistical approaches is thereby key to robustly capture the heterogeneity of climate-growth responses across forested landscapes. We highlight the benefits of combining the temporal information embedded in tree rings with the spatial information offered by forest inventories and earth observations to quantify tree growth and its drivers. In addition, we show how the continued development of mechanistic tree-ring models can help address some of the non-linearities and feedbacks that complicate making inference from tree-ring data. By embracing scaling issues, the discipline of dendrochronology will greatly increase its contributions to assessing climate impacts on forests and support the development of adaptation strategies.
- Boston University United States
- University of Cambridge United Kingdom
- Polish Academy of Sciences Poland
- University of Arizona United States
- Polish Academy of Learning Poland
Dendrochronology, Remote sensing, 333, Scaling, Vegetation models, Forest growth, Anthropocene, Physical Sciences and Mathematics, Climate change, Data integration, Environmental Sciences, Forest inventory
Dendrochronology, Remote sensing, 333, Scaling, Vegetation models, Forest growth, Anthropocene, Physical Sciences and Mathematics, Climate change, Data integration, Environmental Sciences, Forest inventory
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