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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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Six height‐age determination methods (Graves, Lenhart, Carmean, Newberry, ratio, and ISSA) were evaluated for their accuracy and sensitivity to sample size in determining height‐age pairs using stem analysis data from plantation-grown black spruce (Picea mariana[Mill.] B.S.P.) and jack pine (Pinus banksiana Lamb.) trees from Ontario, Canada. Twenty-three disks (sections) were used from 102 jack pine and 93 black spruce trees each for evaluation. The Graves, ratio, and Newberry methods were unbiased for determining height‐age pairs forboth black spruce and jack pine across the site productivity gradient and different crown classes. However, on the basis of the magnitude of height prediction bias, reconstructed tree profiles, and the amount of information required for height‐age determination, the Graves method withat least 13 stem sections is recommended for height‐age determination.

Accuracy of a taper equation is affected by the quality of calibration data. We evaluated the effects of eight tree selection protocols, originating from two sample sizes and four tree selection criteria (randomly selected trees, trees with diameter at breast height (DBH) closest to quadratic mean diameter, dominant/ co-dominant trees, and trees randomly selected from each class of stratified basal area [BA]), on the accuracy of taper equations by Sharma and Zhang and Kozak by comparing resulting predictions of diameters inside bark and cumulative volumes of tree stems. Evaluations were performed using the data collected via stem analysis from 1098 jack pine (Pinus banksiana Lamb.), and 1122 black spruce (Picea mariana Mill. BSP) trees sampled across the boreal forest of Northern Ontario. About half of the trees were randomly selected for model calibration and the remainder was used for model evaluation. Prediction accuracy, here defined as bias, depended on the tree species, the tree selection protocol including sample size and tree selection criteria, and the model form of the taper equation. A protocol that involved selecting trees from five stratified BA classes (one randomly selected tree from each BA class) was more efficient than other protocols in representing the mean taper function of jack pine and black spruce trees for both taper equations for small sample sizes (five trees per plot). The minimum number of trees required to model taper equations without compromising model accuracy depended on tree species and the model form used to describe tree taper.