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As northern latitudes experience rapid winter warming, there is an urgent need to assess the effect of varying winter conditions on tree growth and forest carbon sequestration potential. We examined tree growth responses to variability in cold-season (November–- April) frequency of freeze days (FFD) over 1951 to 2018 using tree-ring data from 35,217 trees and 57 species at 4,375 sites distributed across Canada. We found that annual radial growth responses to FFD varied by species, with some commonalities across genera and clades. The growth of gymnosperms with late spring leaf-out strategies was negatively related to FFD; years with high FFD were most detrimental to the annual growth of Pinus banksiana, Pinus contorta, Larix lyalli, Abies amabilis, and Abies lasiocarpa. In contrast, the growth of angiosperms with early leaf-out strategies, namely, Populus tremuloides and Betula papyrifera, was better in the coldest years, and gymnosperms with intermediate leaf-out timing, such as widespread Picea mariana and Picea glauca, had no consistent relationship to FFD. Tree growth responses to FFD were further modulated by tree size, tree age, regional climate (i.e., mean cold-season temperature), and local site conditions. Overall, our results suggest that moderately warming winters may temporarily improve the growth of widespread pines and some high-elevation conifers in western Canada, whereas warming winters may be detrimental to the growth of widespread boreal angiosperms. Our findings also highlight the value of using species-specific climate-growth relationships to refine predictions of forest carbon dynamics. [ABSTRACT FROM AUTHOR]

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree‐ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced—UVAFME. Through this process, we provide preliminary ecological inference about the long‐term competitive dynamics between slow‐growing P. mariana and relatively faster‐growing P. glauca. Incorporating tree‐ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster‐growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting. [ABSTRACT FROM AUTHOR]

An index of tree growth based on the mean growth curve of black spruce shows that tree growth in the boreal forest of central Canada decreases linearly as the forest-tundra boundary is approached. A theoretical tree line is defined by black spruce growth data. The distribution of tree growth and the theoretical tree line indicate that the forest-tundra boundary is the mean summer position of the arctic front. The growth of white spruce is briefly considered, but because of a small sample size, no conclusions are drawn. [ABSTRACT FROM AUTHOR]