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Aim: Altitudinal and latitudinal treeline ecotones have not consistently responded to climate warming in the direction and/or magnitude predicted by climate alone, suggesting that non‐climatic mechanisms (e.g. biotic interactions) also mediate treeline range dynamics. Through a collaborative research approach, we assessed environmental conditions associated with pre‐dispersal insect cone granivory and how this biotic interaction may govern the reproductive potential, and therefore range dynamics, of spruce‐dominated treelines across northern Canada. Location: In all, 10 boreal forest treelines, tundra and alpine, from Yukon to Newfoundland and Labrador, Canada. Taxa: White spruce (Picea glauca [Moench] Voss), Black spruce (Picea mariana [Mill.] B.S.P.), Strobilomyia spp., Megastigmus spp. Methods: Treeline sites were assessed for presence and magnitude of pre‐dispersal seed granivory by insects along with viable seed availability. We quantified stand density metrics, organic layer depth and understorey vegetation composition at each location and, subsequently, incorporated those variables into generalized linear mixed models to establish predictors of granivory magnitude and viability of available seed. Results: Insect granivory was widespread across sites; however, site‐specific patterns of granivory were associated with increased moss cover and decreased shrub cover and stand density. While all black spruce‐dominated sites exhibited seed viability rates > 50%, the number of seeds produced per cone varied, suggesting that within‐site abiotic conditions and biotic interaction pressures limit successful colonization of novel environments in advance of seed dispersal. Main Conclusions: The modelled relationships between granivory, seed viability and environmental conditions represent an essential step towards generalizing how and when biotic interactions across subarctic treelines influence boreal tree range dynamics before seed dispersal. Connections between granivory magnitude and site‐level treeline characteristics (e.g. stand density, understorey vegetation) will provide a more comprehensive understanding of treeline range dynamics under continued climate change. [ABSTRACT FROM AUTHOR]

1. Boreal forest soils contain large stocks of soil carbon (C) that may be sensitive to changes in climate and disturbance. Destablization of boreal forest soil C through changes in C inputs, belowground C pools and/or wildfire could feedback to accelerate rising atmospheric CO2 concentration. Additionally, increasing frequency of severe fires may be changing the dominant forest types and reshaping aboveground C stocks. 2. Although controls on ecosystem C pools have received considerable attention, many studies have been limited to locations near the road system, leading to uncertainty in current and future C stocks across boreal Alaska. Here, we leveraged 545 randomly selected and spatially balanced Forest Inventory and Analysis (FIA) plots across ~13.5 million hectares in interior Alaska to examine the factors governing soil and live tree C pools. 3. Forest type mediated the effects of mean summer air temperature and the probability of near-surface permafrost on soil C. Meanwhile, forest type, stand age and aspect were the primary drivers of live tree C. Overall, plots with a known history of wildfire during the past 70 years did not have significantly different soil C stocks than plots without a known history of fire, likely due to the historical predominance of low severity fires. 4. Where wildfire likely initiated a transition to deciduous trees (19% of plots), live tree and soil C pools were reduced by 16% and 20%, respectively. Ecosystem C likely recovered over time, as maturing deciduous stands rapidly gained C in live trees. Deciduous stands without a known fire had comparatively very large live tree C stocks, suggesting a significant change in the distribution of ecosystem C following severe fire. 5. Synthesis. Our results highlight the nuanced interactions among wildfire, landscape position and forest type that will play important roles in shaping future boreal ecosystem C stocks. [ABSTRACT FROM AUTHOR]

Spruce–fir (Picea–Abies) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce (Picea rubens), black spruce (Picea mariana), and balsam fir (Abies balsamea), while white spruce (Picea glauca) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated. [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]