Growth response functions improved by accounting for nonclimatic site effects

Abstract

Growth response functions (GRFs) that relate the growth of a population to the climate of the sites in which it is tested are gaining attention for their ability to predict impacts of climate change on tree growth. However, nonclimatic site to site variation introduces error into GRFs. Using data from a large lodgepole pine ( Pinus contorta Dougl. ex Loud.) provenance test in British Columbia and the Yukon, Canada, a technique is presented that accounts for the effect of nonclimatic variation in GRFs. The mean height of the “local” provenances at each test site was used to predict “site height” from site climate variables in multiple regression. Residuals from the site height equation provided an index of the nonclimatic effect for each site and were included as a covariate in quadratic GRFs that related provenance height at each test site to mean annual temperature at each test site. Inclusion of the nonclimatic index in the model resulted in a moderate or large displacement of GRFs for 25% of the provenances, while increasing mean R2values for 138 of 140 provenances and decreasing the root mean squared error for 113 of 140 provenances. These results suggest that inclusion of the nonclimatic index in GRF models could substantially affect height predictions for some provenances and reduce prediction error for most provenances.