High-resolution climate reconstructions are needed to understand past and future climate–vegetation dynamics. However, climate reconstructions using ring width are scarce in temperate regions where climate has limited impacts on tree growth. In Témiscamingue, white pine sunken logs represent valuable archives for long-term climate reconstructions, but white pine’s response to climate variations in this temperate region is unknown. Moreover, the most suitable proxies for climate reconstruction in white pine—ring widths or stable isotopes—remain unclear. This study evaluates which proxy, or combination of proxies, best captures climate signals in white pine radial growth across contrasting soil drainage conditions. Ring widths were measured in 60 trees from Opémican National Park (30 on each of fast and moderate drainage conditions). Carbon and oxygen isotope ratios were analyzed from five trees per site over the period 1950–2020. Climatic data from ERA5 were used to calibrate response functions. June-August drought index (SPEI) as well as June-July precipitation and temperature were the dominant climatic drivers on all proxies, regardless of drainage type. Bayesian linear models revealed that combining carbon isotopes with ring-width data provided the most robust reconstruction of June–July precipitation (r = 0.43), that combining oxygen isotopes with ring widths yielded the best reconstruction of June–July temperatures (r = 0.49), and that combining carbon and oxygen isotopes produced the best reconstruction of June-August SPEI (r = 0.45). This study is among the first to demonstrate the potential of a multi-proxy approach for climate reconstruction using white pine. Our results provide insights that may guide future climate reconstructions in temperate regions, where tree species with lower climate sensitivity could also be considered.

Process-based forest models are increasingly used to guide management, but few are validated against fine-scale spatial patterns that emerge from neighborhood interactions. We tested whether the spatially explicit individual-based model SORTIE-ND, which simulates growth, mortality, and recruitment as functions of neighbourhood interactions among individual trees, can reproduce observed fine-scale structure in boreal mixedwoods. Using long-term data from the Lake Duparquet Research and Teaching Forest (Québec) station, we initialized simulations from transect plots representing younger post-fire stands and compared simulated outcomes to independent hectare plots of similar ages along a 249-year chronosequence. The spatial structure was quantified with inhomogeneous l-functions for univariate and bivariate patterns, and model performance was assessed by comparing observed curves to simulation envelopes. SORTIE-ND reproduced fine-scale patterns for balsam fir and trembling aspen, showed partial agreement for white spruce, and failed to match the observed clustering of paper birch. Cross-species patterns were captured for fir–aspen but not for pairs involving white spruce. These results indicate that SORTIE-ND can approximate fine-scale spatial patterns for dominant species in boreal mixedwoods, but limitations remain where key processes (e.g., vegetative propagation, substrate dependence) are under-represented. We discuss implications for stand- to landscape-scale management and recommend model extensions and more independent validation to improve generality.