Supplementary Material S1 - Description of the Study Area

General approach

Although we specifically aim to project species’ future relative and absolute abundances, our simulation experiment is the first implementation of an evolutive forest simulation platform. The spatiotemporal scope includes those related to high-level (e.g., strategic) planning, i.e., that our setup must accommodate large areas ≥ 1-10 M-ha for temporal horizons at least one tree generation long, i.e., ≥ 50-300 years. To account for the considerable and largely irreducible uncertainty at these spatiotemporal scales, we produce large ensemble simulations, meaning that we conduct multiple simulations where climate forcing, disturbance regimes, and, potentially, other input parameters vary.

We also run replicates to account for stochasticity. To provide the most accurate projections in a variety of regions, we take advantage of data products that were recently made available by the Canadian Forest Service, which were developed combining remote sensing and field observations and cover the entire Canadian forested landbase (e.g.,

Beaudoin et al. 2014; Guindon et al. 2014; Mansuy et al. 2014). We used the spatial resolution of these data products as a starting point for the rest of our simulation setup

(250 m; 6.25 ha pixel size). Supplementary material S1 - Description of the study area

Climate varies from temperate maritime characterized by mild mean annual temperatures

(+2.5 ˚C) and high annual precipitation (>1000 mm) in the easternmost Atlantic Maritime ecozone, to more continental climate (-0.5 ˚C) typified by lower precipitation (< 450 mm), colder winters and short but warm summers in westernmost quadrats. Landform and soils of the two boreal shield quadrats are typical of the Canadian boreal shield geological formations dominated by a broadly rolling mosaic of uplands and wetlands where Precambrian granitic bedrock outcrops alternate with ridged to hummocky deposits of glacial origin of a rather coarse texture (Ecological Stratification Working

Group 1996). Uplands covered by glacial till and humo-ferric podzols typical of the

Appalachians occur within the Atlantic Maritime quadrat. Flat topography, deep luvisols and chernozem on glacial moraine and lacustrine deposits are more characteristic of the

Boreal Plain quadrat (Ecological Stratification Working Group 1996). Forests in the

Atlantic Maritime quadrat are typical of the Acadian forest regions characterized by a mix of coniferous (e.g., red spruce [Picea rubens], balsam fir [Abies balsamea], white pine [Pinus strobus]), and deciduous species (e.g., red [Acer rubrum] and sugar [A. saccharum] maples, American beech [Fagus grandifolia] and yellow birch [Betula alleghaniensis]) (Rowe 1972). Rather similar diversified mixed forests characteristic of the Great Lakes-St. Lawrence forest region occur in the southern portions of the Boreal

Shield East and Boreal Shield West quadrats (Rowe 1972). Northern portions of these quadrats are more typical of the boreal forest region with increasing abundance of coniferous (e.g., balsam fir, black spruce [Picea mariana], jack pine [Pinus banksiana]) and boreal deciduous species (e.g., trembling aspen [Populus tremuloides] and white birch [Betula papyrifera]) (Rowe 1972). Less diversified forests, mainly including jack pine, black and white [P. glauca] spruces as well as trembling aspen and white birch, are typical of the Boreal Plain quadrat (Rowe 1972). Large and rather frequent stand- replacing fires mostly occur within the boreal portions of the quadrats (Boulanger et al.

2014) whereas recurrent spruce budworm outbreaks are the most important natural disturbances in the mixed forest portions (MacLean 1980). Current harvesting activities are most important within the Boreal Shield East and Atlantic Maritime regions (Guindon et al. 2014). Supplementary material S2 – Current and future climate and fire projections

Current and future projections for a) mean annual temperature (MAT) and b) total annual precipitation from the Canadian Coupled Global Climate Model CanESM2 for each of the three different forcing scenarios, i.e., RCP 2.6, RCP 4.5 and RCP 8.5 used in our simulations. Baseline (1981-2010) and future 30-year values were averaged here to better reflect the climate data that were used in LANDIS-II to parameterize tree growth

a) Mean annual temperature

b) Total annual precipitation Supplementary Material S3 – Parameterization, calibration and validation of the patch-model PICUS

See attached pdf document. Supplementary material S4 - Species establishment probability (SEP), maximum aboveground net primary productivity (maxANPP) and maximum aboveground biomass (maxAGB) computation from PICUS outputs

Computational details regarding the scaling-up of PICUS outputs to LANDIS-II inputs in our projects involving climate impact on forest landscapes are frequently updated here: https://github.com/dcyr/PicusToLandisIIBiomassSuccession

1) SEP

The SEP is the probability of a given species’ cohort to successfully establish on a given landtype during one time step, granted that seeds reach it and that light conditions are adequate. It can range from 0 to 1. This parameter must therefore be specified for every combination of species by landtype by climate scenario. It is also one that is almost impossible to ground entirely into empirical data, as the experimental conditions necessary to document such probabilities are almost impossible to obtain in the real world, at least not for the entire range of species and land types that we wish to simulate with LANDIS-II.

This parameter is also scale-sensitive, which often limits its portability from one simulation project to another. Typically, three approaches have been used to set these parameter’s values. In increasing order of subjectivity, they are:

 Expert knowledge

 Forest inventory data

 Gap models / Stand-level physiological models Even the use of lower-level physiological models implies some level of subjectivity.

First, there are many models, which can yield considerably different results. Choosing one over another is not always easy to justify and is often a matter of accessibility.

Secondly, these models usually will not provide the probabilities that are needed in the most direct way. Some additional assumptions may be required.

Luckily, we can take advantage of the stand-level simulations conducted with PICUS, even though it does not yield a SEP per se. First, and most importantly, we assume that

SEP for LANDIS-II is directly linked with the time necessary to accumulate aboveground biomass in PICUS. That interval is sensitive to climate and soil. We then take the time necessary to accumulate aboveground biomass (t) in PICUS and, for our purpose of translating that information into a SEP for LANDIS-II, consider it as the result of a random process associated with an annual probability of 1/t. We thus consider the establishment of a cohort as a Bernouilli trial conducted every year during a time step. As the time step is 10 years, we compute the probability of having more than zero success (1 or more) in 10 consecutive trials.

2) Growth parameters: maxANPP and maxAGB maxANPP is the maximum aboveground net primary productivity in grams per square- meters per year. It can only be achieved in free growth conditions, i.e. in the absence of inter- or intra-specific competition. The maximum increment in biomass from PICUS output smoothed using a 10-year moving average. maxAGB is the maximum biomass that one species can attain on a given site, in grams per square-meters. The average value after a given amount of time (typically 100 or 150 years) is computed and used as maxAGB. This method seems to allow for an almost perfect fit of LANDIS growth curves with PICUS outputs when establishment is not limited by light conditions. Supplementary Material S5 – Successional trajectories as simulated from LANDIS- II pixel-level simulation.

See attached pdf documents. Supplementary Material S6 – Comparison between initial biomass as simulated by LANDIS-II at time t = 0 and biomass as assessed from NFI forest cover maps

Frequency distribution of cell-level discrepancies between species-specific biomass after spin-up (at time t = 0) and biomass as assessed from Beaudoin et al. (2014) NFI forest cover maps. We also show mean discrepancies ± 1 SD. Comparisons were made at the 250m cell level. Spin-up mortality fraction was 0.015, 0.001, 0.001 and 0.002 for AM, BSE, BSW and BP respectively. See Table 1 for species abbreviations. Most species initial biomass showed little discrepancy with NFI forest cover maps. However, balsam fir biomass was underestimated in AM whereas jack pine and maples biomasses were overestimated in most study regions. Given that pixel-level simulations were following the generally known successional trajectories for these latter species, we decided that despite the remaining observed discrepancies with the NFI data, we were satisfied with the results and the final calibration.

*Poplar (Populus spp.) and maples (Acer spp.) were pooled at the genus level as species- level biomasses were not available nationwide using NFI forest cover maps. I) Species-specific comparisons for AM II) Species-specific comparisons for BSE

III) Species-specific comparisons for BSW IV) Species-specific comparisons for BP Supplementary material S7 – Estimation of a) maxANPP, b) maxAGB and c) SEP for all combinations of landtype, forcing scenario (baseline, RCP 2.6, RCP 4.5, RCP 8.5), time period (2011 – 2040, 2041 – 2070, 2071 - 2100) and species simulated using pure- stand PICUS simulations (see Supp. Mat. S4). Parameters results are presented as relative to those estimated using the baseline climate. Solid line represent the mean difference with baseline when averaging parameters results from all landtypes pertaining to a given region x forcing scenario x time period while shading represents the 25th and 75th percentile. See Table 1 in the manuscript for species abbreviations. a) maxANPP b) maxAGB

c) SEP

Supplementary material S8 - Projection of disturbance regimes

a) Baseline and future annual area burned as estimated from models developed by

Boulanger et al. (2014) and further updated for RCP scenarios (Gauthier et al.

2015). Values were kept constant after 2100. Values at year 2000 are those

estimated for baseline climate.

b) Total area affected by the spruce budworm at each outbreak within the three study

regions where this disturbance was included in simulations under each of the four

climate forcing scenarios. c) Total biomass harvested in the four study regions under each of the four climate

forcing scenarios. Supplementary material S9 – Mean age of the oldest cohort in the four study regions under each of the four climate forcing scenarios.

b) BSE

c) BSW

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