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Introduction: Global climate change and associated stressors threaten forest ecosystems due to the rapid pace of climate change, which could exceed the natural migration rate of some tree species. In response, there is growing interest to research and implement forest assisted migration (FAM). Here, we used a species-independent indicator based on climate analogy, according to the sigma (dis)similarity (σd) index, to match planting sites across the eastern US with (future) climatically-compatible seedlots (CCS). Methods: We developed CCS for a grid composed of 1 × 1° of latitude and longitude. CCS were based on future climate analogs with ≤2σd analogy to ensure CCS were representative of future climate change. CCS were located for three time periods, 2030's, 2050's, and 2090's and three emissions scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) from the Coupled Model Intercomparison Project phase 6 database, using 12 climate variables. Results: CCS were identified for the majority of 1 × 1° grids based on the SSP3-7.0 scenario. Approximately 28% of 1 × 1° grid's 2090's projections included future climate novelty. The 2030's, 2050's, and 2090's CCS were located on average 222, 358, and 662 km or 1, 2, and 3 eastern seed zones away from the 1 × 1° grids, respectively. CCS were also located further south-southwest (188–197°). In addition, the average forest cover of CCS was approximately 2%, 5%, and 10% less than that of the 1 × 1° grids. Discussion: Our development and synthesis of CCS emphasized four key results: (i) average distances to 2030's and 2050's CCS were similar to seed-transfer guidelines for some tree species, but 2090's CCS exceeded current recommendations; (ii) south-southwesterly locations of CCS aligned with tree species habitat distribution dynamics; (iii) future climate novelty potentially challenges the conceptual basis of FAM if tree species are not adapted to climate change; and (iv) variation in forest cover among CCS presents potential opportunities and challenges due the presence or absence of forestland to source seed. Ultimately, our goal was to locate and synthesize CCS that could enable FAM decision support. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]