Title: Possibilities and limitations of using historic provenance tests to infer forest species growth responses to climate change
Author: Leites, Laura P.; Rehfeldt, Gerald E.; Robinson, Andrew P.; Crookston, Nicholas L.; Jaquish, Barry
Source: Natural Resource Modeling. 25(3): 409-433.
Description: 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.
Keywords: climate-change response functions, provenance tests, genotype by environment interaction, provenance transfer functions, Larix occidentalis Nutt, linear mixed-effects models
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Leites, Laura P.; Rehfeldt, Gerald E.; Robinson, Andrew P.; Crookston, Nicholas L.; Jaquish, Barry 2012. Possibilities and limitations of using historic provenance tests to infer forest species growth responses to climate change. Natural Resource Modeling. 25(3): 409-433..