Multi-model projections of tree species performance in Quebec, Canada under future climate change.

Many modelling approaches have been developed to project climate change impacts on forests. By analysing 'comparable' yet distinct variables (e.g. productivity, growth, dominance, biomass, etc.) through different structures, parameterizations and assumptions, models can yield different outcomes to rather similar initial questions. This variability can lead to some confusion for forest managers when developing strategies to adapt forest management to climate change. In this study, we standardized results from seven different models (Habitat suitability, trGam, StandLEAP, Quebec Landscape Dynamics, PICUS, LANDIS-II and LPJ-LMfire) to provide a simple and comprehensive assessment of the uncertainty and consensus in future performance (decline, status quo, improvement) for six tree species in Quebec under two radiative forcing scenarios (RCP 4.5 and RCP 8.5). Despite a large diversity of model types, we found a high level of agreement (73.1%) in projected species' performance across species, regions, scenarios and time periods. Low agreements in model outcomes resulted from small dissensions among models. Model agreement was much higher for cold-tolerant species (up to 99.9%), especially in southernmost forest regions and under RCP 8.5, indicating that these species are especially sensitive to increased climate forcing in the southern part of their distribution range. Lower agreement was found for thermophilous species (sugar maple, yellow birch) in boreal regions under RCP 8.5 mostly as a result of the way the different models are handling natural disturbances (e.g. wildfires) and lags in the response of populations (forest inertia or migration capability) to climate change. Agreement was slightly higher under high anthropogenic climate forcing, suggesting that important thresholds in species-specific performance might be crossed if radiative forcing reach values as high as those projected under RCP 8.5. We expect that strong agreement among models despite their different assumptions, predictors and structure should inspire the development of forest management strategies to be better adapted to climate change.