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Climate-diameter growth relationships of black spruce and jack pine trees in boreal Ontario, Canada


Correspondence: Present address: Nirmal Subedi, Forest Economics and Tenure Branch, Forest Industry Division, Ministry of Natural Resources, 70 Foster Drive, Suite 210, Sault Ste. Marie, ON, P6A 6V5, Canada, tel. 705 945 5843, fax 705 541 5111,e-mail:


To predict the long-term effects of climate change – global warming and changes in precipitation – on the diameter (radial) growth of jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana [Mill.] B.S.P.) trees in boreal Ontario, we modified an existing diameter growth model to include climate variables. Diameter chronologies of 927 jack pine and 1173 black spruce trees, growing in the area from 47°N to 50°N and 80°W to 92°W, were used to develop diameter growth models in a nonlinear mixed-effects approach. Our results showed that the variables long-term average of mean growing season temperature, precipitation during wettest quarter, and total precipitation during growing season were significant (alpha = 0.05) in explaining variation in diameter growth of the sample trees. Model results indicated that higher temperatures during the growing season would increase the diameter growth of jack pine trees, but decrease that of black spruce trees. More precipitation during the wettest quarter would favor the diameter growth of both species. On the other hand, a wetter growing season, which may decrease radiation inputs, increase nutrient leaching, and reduce the decomposition rate, would reduce the diameter growth of both species. Moreover, our results indicated that future (2041–2070) diameter growth rate may differ from current (1971–2000) growth rates for both species, with conditions being more favorable for jack pine than black spruce trees. Expected future changes in the growth rate of boreal trees need to be considered in forest management decisions. We recommend that knowledge of climate–growth relationships, as represented by models, be combined with learning from adaptive management to reduce the risks and uncertainties associated with forest management decisions.

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