POSSIBILITIES AND LIMITATIONS OF USING HISTORIC PROVENANCE TESTS TO INFER FOREST SPECIES GROWTH RESPONSES TO CLIMATE CHANGE
Article first published online: 10 JUL 2012
©2012 Wiley Periodicals, Inc.
Natural Resource Modeling
Volume 25, Issue 3, pages 409–433, August 2012
How to Cite
LEITES, L. P., REHFELDT, G. E., ROBINSON, A. P., CROOKSTON, N. L. and JAQUISH, B. (2012), POSSIBILITIES AND LIMITATIONS OF USING HISTORIC PROVENANCE TESTS TO INFER FOREST SPECIES GROWTH RESPONSES TO CLIMATE CHANGE. Natural Resource Modeling, 25: 409–433. doi: 10.1111/j.1939-7445.2012.00129.x
- Issue published online: 1 AUG 2012
- Article first published online: 10 JUL 2012
- Received by the editors on 18th July 2011. Accepted 17th February 2012.
- Climate-change response functions;
- provenance tests;
- genotype by environment interaction;
- provenance transfer functions;
- Larix occidentalis Nutt;
- linear mixed-effects models
Abstract. Under projected changes in global climate, the growth and survival of existing forests will depend on their ability to adjust physiologically in response to environmental change. Quantifying their capacity to adjust and whether the response is species- or population-specific is important to guide forest management strategies. New analyses of historic provenance tests data are yielding relevant insights about these responses. Yet, differences between the objectives used to design the experiments and current objectives impose limitations to what can be learned from them. Our objectives are (i) to discuss the possibilities and limitations of using such data to quantify growth responses to changes in climate and (ii) to present a modeling approach that creates a species- and population-specific model. We illustrate the modeling approach for Larix occidentalis Nutt. We conclude that the reanalysis of historic provenance tests data can lead to the identification of species that have population-specific growth responses to changes in climate, provide estimates of optimum transfer distance for populations and species, and provide estimates of growth changes under different climate change scenarios. Using mixed-effects modeling techniques is a sound statistical approach to overcome some of the limitations of the data.