Tree species persistence under warming conditions: A key driver of forest response to climate change

Forest Ecology and Management 442 (2019) 96–104

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Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Tree species persistence under warming conditions: A key driver of forest response to climate change

T

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Mathieu Boucharda, , Núria Aquiluéb, Catherine Périéa, Marie-Claude Lamberta a b

Direction de la Recherche Forestière, Ministère des Forêts, de la Faune et des Parcs du Québec, 2700 rue Einstein, Québec, QC G1P 3W8, Canada InForest JRU, Forest Sciences Centre of Catalonia (CTFC-CEMFOR), Ctra. vella de Sant Llorenç de Morunys km 2, 25280 Solsona, Lleida, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: Climate change Forest modelling Forest dynamics Forest disturbances Fire Harvesting Boreal forests Temperate forests

From a forest management stand point, it is crucial to know which ecological processes are most likely to drive changes in tree species distributions and abundance under warming climate conditions. In this study, we simulated forest dynamics in a 703,580 km2 territory that straddles the boreal and temperate broadleaved forest biomes in the province of Québec (Canada), under a RCP 8.5 climate change scenario. The objective was to evaluate how future forest composition is sensitive to variation in four potential drivers: fire regimes, harvesting regimes, the capacity of tree species to persist under warmer climate conditions, and species capabilities for longdistance colonization. The results indicate that forest composition in 2100 is most sensitive to variation in the parameters controlling species persistence when conditions become warmer or dryer than the conditions found in their current range. Concretely, this points to avenues of research to improve the accuracy of our predictions regarding the impacts of climate change on forest composition. For instance, we should further investigate the underlying ecological (competition) or physiological (drought stresses) processes that influence tree species persistence at the receding edge of their current distributions.

1. Introduction The effects of global warming on forest composition can already be observed in some regions (Boisvert-Marsh et al., 2014, Lindner et al., 2014), and are likely to increase in the future. These impacts concern not only the ecosystems themselves, but also the ecological services they provide, such as timber supply, carbon storage or biodiversity conservation. The speed and magnitude of these impacts will depend on multiple factors interacting with climate change. For example, ecosystem-level processes such as natural or anthropogenic disturbance regimes, or tree-level processes such as dispersal capabilities and drought tolerance, are all potentially important (Chmura et al., 2011). In a forest modelling context, species climatic tolerances are generally inferred from correlations between their contemporary geographic distributions and climate conditions (Pearson and Dawson, 2003). It is understood that climate envelopes that are defined from such correlations do not necessarily tell which demographic processes are most determinant for species presence or abundance. For example, seed production, seed germination, seedling survival or mature tree death are demographic processes that could be affected differently by climate variation. Moreover, different demographic processes could be limiting in different parts of a species’ range (Kearney and Porter,

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2009). A tree species could also be absent from a given location because it could not disperse successfully to the site (Urban et al., 2013, Aubin et al., 2018), because it is outcompeted (Meier et al., 2012, Clark et al., 2016), or due to incompatibilities with soil type or other non-climatic factors (Lafleur et al., 2010). In fact, there is still considerable uncertainty about the actual ecological, biological or physiological processes that will limit future tree distributions in a climate change context (Thuiller et al., 2008). In many parts of the world, disturbance regimes are another important factor explaining the current distribution of many species, trees included (Dale et al., 2001, Pausas and Keeley, 2009). For example, landscapes where fire is important tend to be occupied by fire-adapted tree species with traits such as thick bark, serotinous cones or small seeds enabling long-distance colonization. In eastern Canada, for example, poorly adated species such as balsam fir (Abies balsamea (Linnaeus) Miller) or sugar maple (Acer saccharum Marsh.) tend to be less abundant where fire frequencies are high, whereas better adapted species such as black spruce (Picea mariana (Miller) Britton, Sterns & Poggenburgh) or trembling aspen (Populus tremuloides Michaux) tend to be more abundant (Bergeron et al., 2004, Graignic et al., 2014). Forest harvesting is another common disturbance in the same region. Harvesting tends to favor tree species that are well adapted to colonize

Corresponding author. E-mail address: mathieu.bouchard@mffp.gouv.qc.ca (M. Bouchard).

https://doi.org/10.1016/j.foreco.2019.03.040 Received 25 October 2018; Received in revised form 8 March 2019; Accepted 19 March 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.

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conditions such as yellow birch (Betula alleghaniensis Britton) and red maple (Acer rubrum Linnaeus). Finally, the temperate ecozone presents the highest tree species richness, with sugar maple being the dominant species. The temperate ecozone also features a relatively minor but significant presence of conifers such as white pine (Pinus strobus Linnaeus) or hemlock (Tsuga canadensis (Endler) Carrière).

disturbed areas, or shade-tolerant species that are abundant in the preharvest understories. In eastern Canada, timber harvesting thus facilitated the expansion of species such as sugar maple in mixed forests (Boucher et al., 2009), or trembling aspen in boreal forests (Bouchard and Pothier, 2011). Disturbances such as fires and harvesting must therefore be taken into consideration when trying to understand and predict the effects of climate change on tree species distributions (Vanderwel and Purves, 2014, Serra-Diaz et al., 2015, Thom et al., 2017). In North-America, the boreal and temperate broadleaved biomes (sensu Olson et al., 2001) are very distinct in terms of species composition, natural disturbance regimes, land-use patterns and level of economic activity. Being able to evaluate the response of these biomes to climate change and shifting disturbance regimes is a major forest ecosystem management concern (Price et al., 2013, Evans and Brown, 2017). Several recent modelling approaches have shown that the temperate broadleaved biome is expected to move north in North-America (Périé and de Blois, 2016, Boulanger et al., 2017, Talluto et al., 2017), but the speed at which this process will take place is still uncertain. Our main objective was to use a landscape modelling approach to evaluate how four different tree- or landscape-level processes could influence tree species distributions in a climate change context. These four potential processes are fire, harvesting, species tolerance to warmer conditions, and species capabilities for long-distance colonization. Hypotheses were formulated regarding each of these drivers, and sensitivity of simulation results to these hypotheses was examined.

2.2. Model overview Our landscape dynamic model simulates forest dynamics and the main natural and anthropogenic disturbances shaping the study area. It allows the simulation of fire, harvesting activities, forest dynamics and species migration in a climate change context. Sequentially, for each time step during simulations, disturbances were scheduled first (fires and harvesting, in that order) while vegetation changes were simulated at the end. The model is raster-based, uses a cell resolution of 4 km2 (representing 175 895 cells for the study area) and works at a 5-year time step. The simulations extend over a 90-year period, from 2011 to 2100 (both inclusive). The modelling platform was the R environment (R Core Team, 2018). The main processes included in the model and the main sources of information used for model initialization are presented below, but detailed descriptions of all sub-models, R code, and all initial parameter values are available in the supplementary material (SM 1). 2.3. Landscape disturbance processes 2.3.1. Fire Four large areas with distinct fire regimes were defined across the territory (Fig. 2a). These areas were delineated based on a literature review of fire studies, essentially covering the 1800–2000 period (c.f. Bouchard et al., 2015). Fires were simulated so as to mimic the patterns observed during the 1800–2000 reference. Specifically, during each 5year simulation period, a random number of fire ignitions was picked from a predefined Poisson distribution in each fire area. Spatial location of ignition cells was randomly selected inside each area, and the fires were left to expand across the territory (no matter in which zone they started) using a random walk algorithm contained in the SpaDES Rpackage (Chubaty and McIntire, 2018). We assumed that fires burned indifferently all types of vegetation, and that the contagious expansion process stopped when individual fires reached a pre-defined size, which

2. Methods 2.1. Study area The 703,580 km2 study area includes three ecozones which correspond roughly to the temperate and boreal biomes identified by Olson et al. (2001), and an intermediate, “mixed” ecozone in between (Fig. 1). The boreal ecozone is mostly dominated by conifers, chiefly black spruce but also balsam fir and jack pine (Pinus banksiana Lambert), and cold-tolerant broadleaved species such as white birch (Betula papyrifera Marshall) and trembling aspen. The mixed ecozone comprises the same tree species, but with a higher abundance of balsam fir relative to black spruce, and a few additional species owing to the generally warmer

Fig. 1. Map of the study area with the localization of the three ecozones that were used to report the simulation results. The red line indicates the northern limit of current forest management units. 97

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Fig. 2. Initial conditions in terms of (a) mean fire return interval (years), (b) administrative units used for timber supply calculations, (c) mean annual temperature (°C) and (d) total annual precipitation (mm).

was itself picked randomly from an area-specific empirical fire size distribution. Since future climate is widely expected to be more conducive to fire compared with contemporary conditions in Canada (Bergeron et al., 2011, Wang et al., 2017, Wotton et al., 2017), we increased the number of fires by 1% per 5-year period from the 1800–2000 baseline level. This represents an increase of 18% in the area burned at the end of the 90-year simulation horizon. This rate of increase could be considered intermediate between relatively optimistic (Rijal et al., 2018) and pessimistic (Bergeron et al., 2017) fire regime shift hypotheses used in recent studies.

2.4. Vegetation dynamics modelling

2.3.2. Harvesting Forest harvesting has gradually become more important than natural disturbances in terms of area affected across the province in the 20th century (Bouchard and Pothier, 2011, Boucher et al., 2009). In Québec, clearcutting (< 10% retention at the stand level) is primarily used in stands dominated by conifers (black spruce, balsam fir) and intolerant hardwoods (aspen), with rotations that vary between 70 and 100 years. Stands dominated by maple, yellow birch and other deciduous species tend to be treated with partial cuttings (50–70% retention at the stand level), with cutting cycles that vary between 20 and 40 years. Most of the clearcuts and partial cuts conducted in Québec are left to regenerate naturally. In this study, we assumed that natural regeneration will continue to be the primary mode of regeneration in the future, thus requiring a probabilistic regeneration module in the model (see Section 2.4). Harvesting rates were calculated individually for each forest management unit (FMU) of the province on public (65 FMUs) and private (15 units) lands (Fig. 2b). Harvest levels were also re-calculated during each 5-year time step (Savage et al., 2010, Rijal et al., 2018), to make sure that they were gradually adjusted to the changes in forest characteristics brought about by natural disturbances (Rijal et al., 2018) and climate change. The following factors were also considered when calculating harvest rates: (1) only mature stands (> 80 years) managed under a clearcutting regime were considered harvestable, (2) a sustained yield constraint was added to make sure that clearcutting rates did not reach a point where they cause a rapid liquidation of mature stands and abrupt fluctuations in timber supply, and (3) a minimal proportion of mature forests (20%) was maintained in all FMUs in order to satisfy biodiversity conservation objectives.

2.4.1. Initialization of state variables Initial tree species composition and stand age (time since last standreplacing disturbance) were derived from forest maps available for the study area. These maps were established between 2005 and 2015 depending on locations, and are part of the Quebec provincial forest inventory system (https://geoegl.msp.gouv.qc.ca/igo/mffpecofor). Dominant tree species composition (stand type) was assessed for each 2 × 2 km grid cell by identifying the stand at the center of the cell. We considered five dominant tree species: sugar maple, yellow birch, trembling aspen, balsam fir and black spruce. Under initial conditions, stands dominated by these species represented 74.8% of the overall forested area. Forest stands that were not dominated by any of these species were categorized as being dominated by other species; for simplification, this ‘others’ category was considered as a species in our modeling framework. Surficial deposits were used as a proxy for soil texture and drainage, which are major drivers of forest dynamics in the region (Saucier et al., 2009). Five broad categories of surficial deposits were used: medium textured (mainly glacial tills, > 25 cm deep), coarse textured sandy deposits (mainly fluvio-glacial > 25 cm deep), fine textured clay deposits (mainly glacio-lacustrine deposits, > 25 cm deep), rock outcrops or very thin deposits (depth < 25 cm) and organic deposits (organic matter > 40 cm). Information about surficial deposits was retrieved from the same forest maps that were used to determine forest composition and forest age. Distribution of the six tree species according to soil type and ecozone are shown in Table 1. Contemporary mean annual temperature and total annual precipitation were defined for each cell, using the data from WorldClim2 (Fick and Hijmans, 2017) (http://worldclim.org/version2) for the

This section first presents the initialization of the five main state variables of the model: species composition, stand age, surficial deposit, mean annual temperature and total annual precipitation. Second, we present how climate and soil suitability classes were defined for each tree species. Third, we explain how maximal colonization distances were defined. Lastly, we present the initialization of transition probabilities among species composition classes in response to environmental factors (climate and soils) and disturbance occurrence.

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Table 1 Spatial distribution of mature stands dominated by the six species in the different forest zones and on different soil types in the dataset at initial conditions. Number of cells (counts) where each species dominates are shown. Temperate ecozone

Balsam fir Black spruce Sugar maple Trembling aspen Yellow birch Others

Mixed ecozone

Boreal ecozone

Clay

Org

Rock

Sand

Till

Clay

Org

Rock

Sand

Till

Clay

Org

Rock

Sand

Till

30 2 48 60 2 118

35 145

55 8 270 63 23 460

413 49 295 492 102 1477

1419 93 4781 1506 1205 3214

173 732

38 1218

6485 5134 1279 3892 1814 9628

6 1744

262 2005

201 1870

2634 15,121

8

502 1599 6 626 55 1737

104 1592

784 3 582

224 746 7 213 29 566

326

2

12

46

347

355

23

133

462

1931

5 263

333

available information on historical migration rates (Prasad et al., 2016) and species-specific dispersal kernels mostly derived from seed characteristics (Scheller and Mladenoff, 2008). Maximal colonization distances of 75 km for aspen, 60 km for yellow birch, and 50 km for balsam fir, black spruce and sugar maple were considered. The presence of seed-producing source populations (established for > 50 years) within these maximal colonization distances was considered necessary to allow potential colonization by the species. Hence, effective maximal migration distances were 1.5 km/year, 1.2 km/year and 1 km/year, respectively. These relatively optimistic migration rates also take into account that our initial conditions dataset only reflects species dominance on forest maps, and does not fully consider the potential presence of individual trees outside the main distribution of the species. More pessimistic dispersal hypotheses were used in some scenarios (see Section 2.7).

1970–2000 period. Mean annual temperature for the reference period range between −4.5 °C and +7 °C in the study area, and total annual precipitation ranges between 750 mm and 1700 mm (Fig. 2c and d). 2.4.2. Defining climatic and soil suitability classes for each species Defining species limitations with respect to environmental variables such as climate and soil type is crucial in any modelling effort aiming at forecasting future tree species distributions in response to climate change. Species responses to a limited number of coarse but important predictors were examined: Two climatic (total annual precipitation and mean annual temperature) and one soil-related (surficial deposits) variables were considered. Climatic envelopes were inferred from contemporary species distributions using a species distribution model (SDM) approach. This approach has well known limitations (Pearson and Dawson, 2003) but remains the most practical to obtain species-level parameters for many tree species with a uniform method. This explains why SDMs still underlie, in an explicit or implicit manner, most forest dynamic modelling approaches in a climate change context (Guisan et al., 2013). Outputs of SDMs developed by Périé et al. (2014) and Périé and de Blois (2016) were used to determine climatic envelopes for the five dominant tree species in the study area. For each species, the consensus distributions along gradients of mean annual temperature and annual precipitation were used to define highly suitable, moderate, and unsuitable climatic conditions. Highly suitable conditions corresponded to temperature or precipitation values falling between the 25th and 75th percentiles of the consensus species distribution, moderate suitable conditions to values between the 5th–25th and 75th–95th percentiles, and unsuitable conditions otherwise. When a climatic variable had a low explanatory power in the models for a given species (i.e. R2 < 0.1), the variable was not considered to exert a significant influence. Soil predictors did not turn out to be significant in the SDM modelling results, probably in part because the 20 km cells used by Périé et al. (2014) were too large to detect these effects (a problem of scale mismatch; Pearson and Dawson (2003)). In eastern Canada, local tree distributions are strongly conditioned by fine-scale site characteristics, particularly the surficial deposits inherited from the last glaciation (Saucier et al., 2009). To help determine suitability classes for each species on each type of surficial deposit at the appropriate scale, we performed glm analyses on the observational data (counts) shown in Table 1, assuming a Poisson distribution of the predicted variable. The odds ratio obtained from these analyses provided initial estimates of surficial deposit suitability for the different species. The resulting climate and soil suitability classes represent a total of 5 species × 3 suitability classes × 3 environmental variables = 45 parameters.

2.4.4. Species transition probabilities Transition probabilities between dominant species were defined according to local forest dynamics studies carried out in different parts of the study area (compiled in Saucier et al. (2009)) and expert judgments. This included proposing hypothetical probabilities for transitions between tree species whose spatial distributions barely overlap under current conditions, but which could overlap under warming conditions. These probabilities are thus cell-level integrations of numerous fine-scale ecological processes that were not directly modelled, such as species interactions (competition), growth, and death. In this study, we preferred using simple but explicit transition probabilities that could be easily modified or adapted in future studies, rather than using more complex trait-based approaches that are less transparent and are subject to their own limitations (c.f. Daniel et al. (2018) for a similar approach). Distinct transition probabilities were defined for three disturbance contexts: stands affected by harvesting, stands affected by fire, and ageing stands where natural succession occurred in the absence of severe stand-replacing disturbances. These three situations are known to offer different conditions for the colonization and early growth of the different tree species, depending on their regeneration and dispersal traits (Johnstone et al., 2016). Transition probabilities were defined for the five tree species and for the “others” category, thus leading to the specification of 6 species × 6 species × 3 disturbance contexts = 108 transition probabilities. These transition probabilities were defined for environmental conditions (climate, surficial deposits) that were highly suitable for the target species. For each 5-year simulation period and in each cell, the final transition probabilities were calculated according to the following equation:

Pfb,c = Pha,b,d ·min(SP b,c,t,STb,c,t ,SSb,c)·Cb,c,t 2.4.3. Defining colonization distances for each species In a global warming context, tree species ranges are expected to expand towards the poles or higher elevations. Considering the dispersal capabilities of each species is critical for an accurate modelling of species range expansion (McLachlan et al., 2005). In our study, maximal colonization distances were defined for each species based on

(1)

where: Pfb,c = Final transition probability to species b in cell c. Continuous variable, ranging between 0 and 1. Pha,b,d = Theoretical transition probability between species a and b 99

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fire or harvesting in the future is not realistic, this allowed us to isolate and compare their potential influence on future tree species distributions. Two hypotheses regarding long-distance colonization were tested. According to the first hypothesis, maximal species-specific dispersal distance was between 50 and 75 km depending on species, corresponding to potential migration speeds that vary between 1 and 1.5 km/year. As mentioned above (Section 2.4.3), this hypothesis is mainly based on seed characteristics, and could be considered optimistic. Indeed, several studies suggest that natural migration distances could turn out to be much shorter (McLachlan et al., 2005, Corlett and Westcott, 2013). Hence, according to a second hypothesis, maximal distances were divided by 5, thus corresponding to maximal migration speeds that vary between 0.2 and 0.3 km/year depending on species. If the results are sensitive to this factor, this would indicate that future research should concentrate on better understanding long-distance colonization processes in order to be able to predict the effects of climate change on species distribution with more accuracy. Finally, two alternate hypotheses regarding the biological processes that are susceptible to drive species persistence under warmer climate conditions were tested (Aitken et al., 2008; Sexton et al., 2009). The first hypothesis states that a gradual increase in competition from species coming from warmer locations is the main factor explaining species decline. To emulate this situation, we assumed that the probability of self-replacement for a species facing unsuitable climate conditions (as defined in 2.4.2) in a given cell declined, but never became null, rather reaching an absolute minimum corresponding to the probability observed in moderately suitable conditions. Because climatic conditions in this same cell gradually become more suitable for species that are better adapted to warmer conditions, species replacement will eventually take place, but this could take a long time. In the second (alternate) hypothesis, we assumed that contraction in species’ ranges were mainly caused by physiological limitations. According to this second hypothesis, the probability of self-replacement became null when climatic conditions were unsuitable because trees simply decline or die when conditions become too warm. In practice, the impact of climate change on species distributions was therefore expected to be more rapid under the second hypothesis (direct and drastic physiological response) compared with the first (indirect competitive response). Ten iterations were run for each of the 16 potential factor combinations (24 = 16 scenarios). Linear models (ANOVAs) were used to examine which factors had the most influence on the speed and magnitude of changes observed in area covered by each dominant species at the end of the 90-year simulations. We did not examine interactions, and the importance of the different factors was interpreted by looking at the standardized coefficients and significance of each variable.

under highly suitable climatic and soil conditions, depending on disturbance context d (fire, harvesting or late-succession). Continuous variable, ranging between 0 and 1. SPb,c,t = Suitability of cell c for species b with respect to annual precipitation during simulation period t. Continuous variable, ranging between 0 and 1. STb,c,p = Suitability of cell c for species b with respect to mean annual temperature during simulation period t. Continuous variable, ranging between 0 and 1. SSb,c = Suitability of cell c for species b with respect to soil conditions. Continuous variable, ranging between 0 and 1. Cb,c,t = Presence of source populations of species b around cell c at period t. Binary variable, 0 (absent) or 1 (present). During simulations, final transition probabilities Pfb,c were calculated for each species in a given cell, and a random draw (with a uniform distribution) was used to decide which species took dominance of the cell. 2.5. Trial runs Before running the model to test our hypotheses, we performed several trial runs to make sure that the parameter values determining the transition probabilities (Ph, SP, ST and SS from Eq. (1)) allowed to maintain relatively constant proportions of the different forest types at the landscape level when climate was held constant (corresponding to initial values). In particular, it was necessary to refine and optimize the parameters for the ‘others’ species category, for which climatic suitability classes could not be determined with SDMs. Some parameter values had to be modified to minimize unrealistic departure from initial conditions. Final values for all parameters are presented in Supplementary Material S1. 2.6. Climate change scenario Future climate projections were derived from the Canadian Coupled Global Climate Model, CanESM2 GCM, which represents the Canadian contribution to the IPCC Fifth Assessment Report (AR5), forced by a RCP (Representative Concentration Pathway) of 8.5. According to this RCP, radiative forcing continuously rises to reach 8.5 W·m−2 in 2100. Monthly surfaces of total precipitation, maximum and minimum temperatures for the period 2011–2100 covering the study area were provided by Ouranos (Consortium on Regional Climatology and Adaptation to Climate Change; https://www.ouranos.ca/en). Raster maps were at a spatial resolution of 0.083 decimal degrees (≈10 km). Total annual precipitation and mean annual temperature were obtained for each 5-yr period starting from present to 2096–2100. The “change field” approach (IPCC, 2001) was used to downscale these changes (available at he 10 km grid) to the initial climate values present in the dataset (at a 2 km resolution). For each variable, this was done by calculating changes to the present for each 5-year period. These differences were then added to the baseline values in each overlapping 2 km cell during each time step in the simulations.

3. Results The results indicate that overall, the most limiting factor for changes in species abundance under a RCP 8.5 climate change scenario was the ability of the different species to persist under abnormal climate conditions (Table 2). Specifically, changes in species dominance at the ecozone level were much more rapid and extensive when it was assumed that species disappear as soon as they were exposed to unsuitable climatic conditions (due to physiological limitations), rather than being gradually excluded by superior competitors. This rapid decline under the physiological hypothesis was most dramatic for black spruce, which covers extensive areas in the boreal and mixed ecozones, and for which climatic conditions quickly became unsuitable in most of this area according to the relatively pessimistic climate change scenario that was used in this study (Fig. 3). Lower persistence of species that were located at the southern edge of their distributions had cascading effects on other species, by increasing the availability of microsites that can be potentially colonized. The "low persistence" hypothesis resulted in a much faster progression

2.7. Alternative hypotheses and simulation scenarios We evaluated a series of scenarios to compare the relative influence of four factors on the speed and extent of forest change over a 90-year period (2011–2100). The factors were fire occurrence, forest harvesting occurrence, species capabilities for long-distance colonization, and persistence of local tree populations in the face of warmer climate conditions. Alternate hypotheses were formulated for each of these variables. The influence of fire and harvesting on tree species distribution was evaluated by building scenarios in which each of these disturbances were either present or absent. Even though a complete absence of either 100

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Table 2 Effect of the four variables: fire, harvesting, colonization distance, and species persistence on the area covered by each species at the end of the simulation (2100). Each line represents a distinct statistical model. Predictor and response variables were standardized before each analysis to simplify interpretation. Significance codes: ***p < 0.001, **p < 0.01, *p < 0.05. Standardized coefficient values are in bold for the most important predictor in each statistical model. Ecozone

Boreal

Mixed

Temperate

Species

Balsam fir Black spruce Sugar maple Trembling aspen Yellow birch Others Balsam fir Black spruce Sugar maple Trembling aspen Yellow birch Others Balsam fir Black spruce Sugar maple Trembling aspen Yellow birch Others

Intercept

Fire

Harvest

Colonization

Persistence

Coeff

p

Coeff

p

Coeff

p

Coeff

p

Coeff

p

1.8 −1.0 0.8 0.5 0.7 0.6 −0.4 −0.9 1.4 −0.7 −0.6 0.5 −0.5 −0.9 1.0 −1.5 −0.9 0.4

*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***

−0.8 0.0 −0.3 0.2 −0.3 0.2 −0.3 0.0 −0.3 0.8 −0.6 0.1 0.0 0.0 −0.7 1.1 −0.2 0.8

*** – * *** *** *** *** – *** *** *** ** – – *** *** *** ***

−0.8 −0.1 0.4 1.0 0.6 −0.1 −0.7 −0.2 0.1 1.2 0.4 −0.1 −0.8 −0.2 0.5 0.6 0.0 0.0

*** *** ** *** *** – *** *** * *** *** * *** *** *** *** – –

−0.6 0.2 −0.8 −0.9 −1.7 0.5 0.1 0.0 −1.4 0.3 −0.5 0.8 0.0 0.0 −0.1 0.0 0.0 0.2

*** *** *** *** *** *** – * *** *** *** *** – – *** – – ***

−1.4 2.0 −1.0 −1.3 −0.1 −1.8 1.7 2.0 −1.2 −1.1 1.8 −1.8 1.7 2.0 −1.8 1.3 2.0 −1.8

*** *** *** *** – *** *** *** *** *** *** *** *** *** *** *** *** ***

Fig. 3. Projected changes in the area dominated by each tree species (×10 000 km2) during the 2011–2100 period in the three ecozones. Each line corresponds to a unique combination of the four potential drivers of forest dynamics that were tested: fire, harvesting (Harv), tree colonization distances (Colo) and tree persistence in the face of unsuitable climatic conditions (Pers). 101

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Fig. 4. Distribution of stands dominated by the five species and 'other species' category at the end of the 90-years simulations, according to the 16 scenarios.

of southern species such as yellow birch or sugar maple in the boreal ecozone (Fig. 3). Species whose current southern and northern range limits are both situated within the study area (e.g. balsam fir and yellow birch) tend to disappear quickly from the southern part of their range and increase quickly in the northern part when a lower persistence corresponding to a low physiological tolerance for warmer conditions was assumed (Table 2; Figs. 3 and 4). The other three drivers considered in this study (fire, harvesting and long-distance colonization) also caused major variation in species abundance in some scenarios (Table 2; Fig. 3). For instance, fire tended to facilitate the expansion of trembling aspen in the three ecozones, but led to a reduction in the abundance of sugar maple and yellow birch. Trembling aspen also benefited from clearcutting, as did yellow birch, but black spruce and balsam fir declined in abundance in scenarios with clearcutting compared with scenarios where this disturbance was absent (Table 2; Fig. 3). Finally, sugar maple and yellow birch were both highly sensitive to variation in long-distance colonization parameters in the boreal ecozone (Table 2; Fig. 3).

empirical knowledge, rather than more complex demographic- or traitbased approaches that are used in other landscape-modelling contexts such as Landis II (Scheller et al., 2007) or iLand (Seidl et al., 2012). In theory, this empirical approach limits our capacity to predict the emergence of novel ecosystems, which could for example be dominated by tree species that are not dominant under current environmental conditions. In our results, the increased occurrence of “other” dominant species in many scenarios (Fig. 3) probably reflects this type of outcome. However, we suggest that a poor understanding of the processes driving species persistence is probably a weakness that underlies most landscape modelling approaches, whether it is acknowledged explicitly or not (Evans and Brown, 2017). Indeed, the response of tree populations that are exposed to unusually warm or dry conditions is still relatively poorly known for most species (Aubin et al., 2018). Our study suggests that focalizing research efforts to better understand these critical ecological and physiological processes, with metrics that are common to all species, would contribute to improve the accuracy of forest landscape modelling of any kind.

4. Discussion

4.2. Other limiting factors: disturbances and colonization distances

4.1. Species persistence as a key driver of forest change

The results indicate that a better knowledge of future fire regimes, future harvesting rates and long-distance colonization capabilities would also contribute to a better prediction of future tree distributions. Even if these factors had less weight than species persistence in simulation outcomes, they still had a significant effect for most species and in most ecozones (Table 2). For example, the expansion of yellow birch and sugar maple at the northern (leading) edge of their distributions (in the boreal) was significantly more pronounced under long-distance colonization hypotheses. Even under the most optimistic long-distance colonization hypotheses however, these two species were far from tracking the northward displacement of their climatic optima in the boreal zone. The results are therefore consistent with the hypothesis that the long-distance colonization capabilities of many tree species could be insufficient for a successful migration under pessimistic

The study area includes the northern part of the temperate forest biome, the southern part of the boreal forest biome, as well as a “mixed” ecotone in between. Forecasting the impacts of climate change and disturbances in and around this ecotone is particularly critical for all kinds of ecological, economic and social reasons (Evans and Brown, 2017). Our results indicate that among the four factors examined, uncertainty relative to the biological processes explaining tree population persistence under warming conditions was more critical than the other factors (fire, harvesting and colonization distances) for an accurate forecasting of climate change impacts on future species distribution. Our simulations were limited in some respects. We used relatively simple transition matrices to replicate forest succession based on 102

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climatic scenarios (McLachlan et al., 2005, Aitken et al., 2008). The simulations allowed to clearly isolate the effect of clearcutting and fire on forest composition under a warming climate. Even though the effect of disturbance on forest composition is classically viewed as a key element in the ecology of boreal and sub-boreal forest ecosystems (Angelstam, 1998, Bergeron et al., 1998, Johnstone et al., 2010), this seems to become less obvious under drastic climate change scenarios. Still, the limited effect of disturbance in simulation outcomes was probably partly influenced by choices that were made when simulating the effects of these disturbances on local forest characteristics. For example, post-disturbance transition probabilities were parameterized based on observational data and knowledge about species characteristics (e.g. fire-related traits). However, these probabilities did not deal explicitly with the potential occurrence of more intense fires in the future (Brown and Johnstone, 2012, Buma et al., 2013), which could generate unexpected regeneration trajectories in some parts of the study area. Moreover, the use of plantation or reforestation measures to compensate or mitigate the impacts of climate change, often advocated in the scientific literature (McLachlan et al., 2007), was not integrated in our study.

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4.3. Management implications Our study confirms that landscape dynamic simulation models are an important tool to better visualize climate change impacts on forests, and particularly the speed at which these changes could take place. The rapidity of the simulated changes was strongly sensitive to underlying assumptions regarding species persistence under warmer climatic conditions. A better understanding of the ecological and biological processes that are susceptible to affect species persistence in these conditions is therefore particularly crucial to improve the accuracy of modelling efforts, a basic reality that probably transcends any landscape modelling approach. Such knowledge will be important to better calibrate how silvicultural measures such as plantation, assisted migration, thinnings or the use of natural regeneration could be used in a cost-efficient manner to help mitigate the impacts of climate change on forest structure, composition and functions. In the mean time, forest dynamics modelling should help managers appreciate the full range of uncertainties to which managed forests are exposed. Acknowledgements Funding for this study came from the Québec Government, and from the National Science and Engineering Research Council of Canada (NSERC) CREATE program in Forest Complexity Modelling. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2019.03.040. References Aitken, S.N., Yeaman, S., Holliday, J.A., Wang, T., Curtis-McLane, S., 2008. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111. Angelstam, P.K., 1998. Maintaining and restoring biodiversity in European boreal forests by developing natural disturbance regimes. J. Veg. Sci. 9, 593–602. Aubin, I., Boisvert-Marsh, L., Kebli, H., McKenney, D., Pedlar, J., Lawrence, K., Hogg, E., Boulanger, Y., Gauthier, S., Ste-Marie, C., 2018. Tree vulnerability to climate change: improving exposure-based assessments using traits as indicators of sensitivity. Ecosphere 9. Bergeron, Y., Cyr, D., Girardin, M.P., Carcaillet, C., 2011. Will climate change drive 21st century burn rates in Canadian boreal forest outside of its natural variability: collating global climate model experiments with sedimentary charcoal data. Int. J. Wildl. Fire 19, 1127–1139. Bergeron, Y., Engelmark, O., Harvey, B., Morin, H., Sirois, L., 1998. Key issues in disturbance dynamics in boreal forests: Introduction. J. Veg. Sci. 9, 464–468. Bergeron, Y., Gauthier, S., Flannigan, M., Kafka, V., 2004. Fire regimes at the transition

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