1. Potential impact of climate change on growth and wood quality in white spruce Christophe ANDALO 1,2, Jean BEAULIEU 1 & Jean BOUSQUET 2 1 Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., P.O. Box 3800, Sainte-Foy, Quebec, Canada G1V 4C7 2 CRBF, Université Laval, Sainte-Foy, Québec, Canada G1K 7P4 General circulation models (GCM) predict a rapid global change of climate. The mean annual temperature in the northern hemisphere is expected to rise and the pattern of precipitation could be modified profoundly. However, GCM’s spatial resolution is still poor and the prediction of regional climate change remains limited, especially for precipitation. In this context, our objective was to integrate several different climate variables (see Table 2) related to temperature and precipitation in order to develop models of tree response to major environmental disturbances Statistical analyses Data analysis followed a procedure outlined by Schmidtling (1994). Regression analyses were used to relate phenotypic observations to climate distance between provenance origin and planting site. The following steps were carried out: -Phenotypic data centered on site means were first regressed on climatic distances to identify the climatic variable explaining the largest proportion of the inter-provenance variation. -Local source performances were estimated for each site with a quadratic regression model using the independent variables identified in the first step (Phenotype =  climate + error;  climate = 0 for the local source); -General regression models were obtained by combining data of the three sites and using as dependent variable the deviation between the provenance tested and the local source (  phenotype(%) =  climate + error); -Validation was conducted by correlation analysis between observed and predicted deviations in the second provenance-progeny test. In order to take into account more than one climatic variable at once, a canonical correlation analysis was also used to develop linear combinations of the climatic distances as independent variables and then the same procedure described above was followed. Some dependent variables were transformed in order to obtain normal distributions. Results and discussion Wood quality For the regression models relating wood quality variables to climatic data, the minimum average temperature was the most important explanatory factor (Fig. 2). Despite a large proportion of the variation in wood quality explained by this single climate variable (Table 1), the validation procedure did not permit to confirm the predictive power of the models obtained. Growth In estimating regression models, forward selection made it possible to identify maximum temperature as the most explicative climate variable for all the growth traits (Fig. 3). Despite the validation of their predictive values (p-value < 0.05), the univariate models explained only a very small proportion of the inter-provenance variation in height or diameter (Table 1). Thus, other climate parameters should be taken into account to improve models’ fitting (see below models using canonical scores). Local adaptation Regression models did not peak near the origin, which is null climatic distance (Figs. 2 and 3). Consequently, provenances did not appear locally adapted when taking into account only one climatic variable at a time to describe the local environment. As a general trend, provenance growth seemed to be better at sites cooler than their origin [  climate(source- planting) > 0]. Models using canonical scores For wood quality and growth, based on both the proportion of inter-provenance variation explained and a positive validation (significant positive correlation between observed and predicted values), the models obtained using the canonical scores as independent variables were better than univariate or bivariate models (Table 1). Here again, local provenances did not seem to be the best adapted within the region sampled, even when taking into account simultaneously temperature and precipitation. Potential impact of global climate change Four scenarios were assessed to estimate the potential impact of climate change. In these scenarios (Table 2), we tested different combinations of temperature and precipitation variables. The range used neither exceeded the range of our actual data set nor the maximum predicted values already published for the region studied (Environment Canada 1997). Conclusion It appears that wood quality of white spruce should not be affected to a large extent within the range of climate changes tested in this study. However, with increasing temperature, a growth loss relative to growth expected from a genetically adapted source was observed. With respect to its migration capacity, white spruce will probably not be able to exploit the more favourable growth conditions in the short term. We also showed that regression models, taking simultaneously into account various climatic parameters, allow for a better understanding of their interactions. Hence, we showed that the effects of precipitation variables should not be neglected. Given the uncertainty about the precipitation changes in the future, caution must be exercised in interpreting the predictions of increasing adaptation lag in global warming scenarios based solely on temperature change. For wood quality, increase in temperature did not have a major effect. Furthermore, this effect did not seem to be dependent of changes in precipitation (Table 3, Fig. 4). For growth and especially for 22-year height, in the climate warming scenarios tested (+4  C or +1  C), the significant predictions were always negative and in the range of those previously published for this species (Table 3, Carter 1996). However, these predictions were strongly influenced by the level of change in precipitation (Fig. 5). With a decrease of precipitation (-100 mm), the predictions were modified significantly (Table 3). Density deviation from « local » source (%) Minimum temperature difference (source - planting) Figure 2 Maximum temperature difference (source - planting) Height deviation from « local » source (%) Figure 3 Density deviation from « local » source (%) Minimum temperature difference (source - planting) Summer precipitations difference (source - planting) Figure 4 Height deviation from « local » source (%) Maximum temperature difference (source - planting) Summer precipitations difference (source - planting) Figure 5 Materials A regional white spruce provenance-progeny test replicated on three sites was analyzed 22 years after planting (Fig. 1). Phenotypic traits studied were growth (height and diameter) and wood quality (density and proportion of late wood) (see Table 1). All the climatic data in relation to seed sources and study sites were obtained from simulation models using data from strategically located meteorological stations as input (Régnière 1996). The models developed were tested using data collected in a second provenance-progeny test. To do so, provenances common to both provenance-progeny tests as well as provenances tested only in the second one were used. References Carter, K.K. 1996. Provenance tests as indicators of growth response to climate change in 10 north temperate tree species. Can. J. For. Res. 26: 1089-1095. Environment Canada. 1997. The Canada country study: Climate impacts and adaptation. http://www.ec.gc.ca/climate/ccs/. Regnière, J. 1996. Generalized approach to landscape-wide seasonal forcasting with temperature-driven simulation models. Environ. Entomol. 25: 869-881. Schmidtling, R.C. 1994. Use of provenance tests to predict response to climatic change: loblolly pine and Norway spruce. Tree Physiol. 14: 805-817. Table 2. Delineation of different climatic scenarios based on climatic differences between location of origin and the planting sites. Table 3. Percentage in growth and wood quality deviations from “local” source predicted for 4 different scenarios of climate change (cf Table 2 for the details of tested scenarios). 95% confidence interval is given in parentheses. Figure 1 Provenance-progeny test in Québec 3 sites ( ) 2 blocks per site 41 provenances ( ) 4 open-pollinated families per provenance 2 trees per family plot Table 1. Comparison of different models. Validations were obtained from correlation analysis between deviations from “local” sources predicted by regression models and observed deviations in the second provenance-progeny test.