Kyle J. Pearson

Winter warming is altering plant exposure to cold events, making it increasingly important to understand how cold hardiness dynamics respond across the dormant season. Using the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota, we measured bud cold hardiness across four dormant seasons (2021–2025). There, a boreal peatland community was exposed to continuous active whole ecosystem warming across five levels (+0.00°C to +9.00°C) combined with atmospheric and elevated CO ₂ (+500 ppm) in open‐top chambers. Cold hardiness of two overstory ( Larix laricina and Picea mariana ) and two understory ( Chamaedaphne calyculata and Rhododendron groenlandicum ) species was evaluated semi‐regularly throughout seasons. Both understory and overstory species gained cold hardiness (acclimated) at warmer temperatures in the fall than those causing deacclimation in the spring, demonstrating different temperature responsiveness phases across the dormant season. Based on cold hardiness sensitivity, warming did not alter maximum midwinter cold hardiness (sensitivity~0°C change in cold hardiness/°C of warming) but delayed fall acclimation and, most importantly, accelerated spring deacclimation (sensitivity > 0°C). These changes resulted in increased cold damage risk in late winter and spring with warming (sensitivity > 1°C/°C) for L. laricina , C. calyculata and R. groenlandicum . Among overstory species, the deciduous L. laricina was more sensitive to warming than the evergreen P. mariana , indicating warming may disadvantage acquisitive species. For understory shrubs, reduced snow cover in the warmest enclosures that removed the insulating buffer and exposed buds to temperatures colder than in control plots associated with the high sensitivity of these species resulted in observed midwinter bud mortality with warming. Rather than uniformly changing cold damage risk, warming decreases risk of cold damage in fall and early winter for all species, but increases risk in late winter and spring for most species, suggesting species‐specific cold hardiness dynamics and snow‐driven microclimate may result in boreal forest composition changes.

Winter warming is altering plant exposure to cold events, and its effects on seasonal cold hardiness dynamics remain poorly understood. Here we quantified the effect of whole-ecosystem warming (+0.00°C to +9.00°C) on bud cold hardiness of two overstory ( Larix laricina and Picea mariana ) and two understory ( Chamaedaphne calyculata and Rhododendron groenlandicum ) boreal peatland forest species across four dormant seasons. Warming reduced cold hardiness in fall and spring by delaying acclimation and advancing deacclimation, yet damage risk increased only in late winter and spring for three species. Warming also reduced snow cover, increasing temperature variability and cold damage for understory shrubs. Together, these results show that warming restructures rather than uniformly increasing cold hardiness risk, concentrating vulnerability at transitional seasons through species-specific dynamics and microclimate exposure.

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea‐Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO₂ parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.