2.1. Simulated area

Our simulated area is a forest landscape extending over more than 4 million hectares in the Mauricie region (Quebec, Canada; Fig 1). It consists of a forest management unit surrounded by a 50 km buffer zone, which has been simulated with LANDIS-II in a previous study [50]. As of 2020, the landscape comprised 339 117 ha of protected forests or around 9% of the total forest surface of the simulated area. These protected forests were divided into approximately 400 areas, five of them being relatively large (> 20 000 ha) and most of them relatively small (< 300 ha). The southern area is mainly composed of mixedwood forests dominated by balsam fir (Abies balsamea), yellow birch (Betula alleghaniensis) and trembling aspen (Populus tremuloides). In contrast, the northern area is a boreal coniferous forest dominated by balsam fir, white birch (Betula papyrifera), trembling aspen, black spruce (Picea mariana) and jack pine (Pinus banksiana). Forest fires are an important disturbance in the north, while spruce budworm (Choristoneura fumiferana) outbreaks are present in the south [53,54]. Being located at the transition from the temperate to the boreal forest, this study area thus presents a clear dichotomy of forest composition and natural disturbances, making it a good choice for exploring the effects of functional enrichment and zoning in two contrasting ecological contexts.

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Fig 1. Map showing the location and extent of our study area located in the Mauricie region of Quebec, Canada.

Satellite data from the Sentinel satellite, and edited by the Ministry of Forests and Natural Resources of Quebec under a CC-BY 4.0 license (https://www.donneesquebec.ca/recherche/dataset/mosaique-satellites).

https://doi.org/10.1371/journal.pone.0326627.g001

2.2. Experimental design

To explore the effects of functional enrichment and zoning on forest resilience, we simulated forest dynamics over 200 years using different scenarios that varied three distinct factors: the forest management strategy used, the intensity of climate change, and the occurrence of a catastrophic disturbance event imposed at year 100 of the simulation (Fig 2). The precise implementation of each of these factors within LANDIS-II is detailed in the following sections.

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Fig 2. Visual representation of the three categories of scenario simulated with LANDIS-II: forest management, climate, and catastrophic event.

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We designed four forest management strategies, each defined by the presence or absence of functional enrichment through planting (hereinafter referred to as “functional planting”) and of TRIAD zoning (Fig 2a). The first two strategies were a “Business as Usual” (BAU) scenario with (BAU-PlantFunct) and without (BAU-NoPlant) functional planting. In these scenarios, no TRIAD zoning took place: forest harvesting of different intensities could be carried out anywhere in the landscape except in the current protected areas. All simulated prescriptions are detailed in “Harvesting”. In essence, the BAU-NoPlant scenario acted as a control scenario for our study area. The other two strategies were TRIAD + , with functional planting, and the “normal” TRIAD, without functional planting. In these scenarios, the landscape was divided into extensive, intensive, and protected areas, with functional planting taking place in the extensive areas of TRIAD+ and intensive areas located on the most productive forests of the landscape (see “Harvesting”). The existing protected areas were expanded using a 5000 m buffer compared to the BAU scenarios, increasing the total percentage from around 9% to 24% of the forested area. This allotment was devised to match the philosophy of TRIAD zoning where intensive areas are made as productive as possible to spare more forest from exploitation [42,44]. Importantly, all four management scenarios (BAU-PlantFunct, BAU-NoPlant, TRIAD, TRIAD+), had to harvest the same amount of biomass at every 10-year time step. This biomass target was based on the current harvest levels in the simulated area during the period 2018–2023, as indicated in documents produced by the Ministère des Forêts, de la Faune et des Parcs du Québec [50,55].

The climate varied according to three different Representative Concentration Pathway (RCP) scenarios [56]: Baseline (no change in climate as compared to 2020) which served as a control, RCP 4.5, and RCP 8.5 (Fig 2b). Three catastrophic disturbances were chosen to represent potential, and quite unpredictable, future disturbance events that could take place in our study area, triggered by the effects of global change. Our disturbance scenarios consisted of a large forest fire covering 70% of our simulated area; an intense drought; and an outbreak from an insect not yet present in the area (Fig 2c). We also simulated scenarios without the occurrence of these catastrophic disturbances to serve as a control. Fire and drought were chosen as they are both expected to increase in frequency and severity with climate change, making the occurrence of extreme cases more probable [11,57]. In Quebec, a single fire of more than 1.2 million hectares was indeed observed in 2023 [58] during a fire season that resulted in more than 5 million hectares of forest burned at the provincial scale [59]. This fire season was caused, in part, by drought [60], which Global Climate Models predict will become more frequent and intense in the future [61]. Such large fires could become even larger in the future, according to observed trends in Canada [62]. For the insect disturbance, we selected the Mountain Pine Beetle (Dendroctonus ponderosae; MPB), a wood-boring species of bark beetles whose ongoing outbreak in western Canada is causing excessive damage by attacking a wide range of pine species. Previous studies have shown the possible future expansion of the MPB into the forest of eastern Canada, helped by a changing climate and by the presence of host species [63–65]. Therefore, we defined a scenario where a large outbreak of MPB would impact the pine species of our landscape, to which forest managers would be initially unprepared. Simulating this disturbance allowed us to study a potential undesired effect of functional plantations, as increasing pine trees in the landscape could increase its sensitivity to MPB outbreaks.

We simulated one scenario for each unique combination of these three factors (management, climate, and catastrophic disturbances), resulting in 48 distinct scenarios. We accounted for stochasticity associated with wildfires, seed dispersal and regeneration by running five replicates for each scenario, resulting in an ensemble of 240 individual simulations. In each simulation, we measured two variables of interest at the landscape scale (total mature biomass and mean functional response diversity) and we assessed the variations between these measures according to the three different factors (management, climate, catastrophic disturbances). We also measured three indicators of resilience to the catastrophic disturbances (see “Data analysis”) to evaluate whether the different forest management strategies were associated with increased or decreased forest resilience values. Moreover, we assessed whether an increased resilience implied a trade-off with another variable of interest (e.g., mature biomass).

2.3. LANDIS-II model

LANDIS-II is a spatially explicit Forest Landscape Model (FLM) that simulates forest dynamics via two main processes: forest succession (growth, mortality, recruitment, etc.) and natural or human-induced forest disturbances (harvesting, forest fire, insect outbreaks, etc.) [52]. The processes are individually simulated by extensions that are activated sequentially during each time step. These extensions are chosen by the user and can simulate the dynamics of different ecological variables (e.g., biomass, carbon stocks, etc.). In LANDIS-II the simulated landscape is composed of square cells representing a forested or non-forested area (e.g., water, urban area, etc.). All forested cells are assigned to different ecoregions to integrate the effect of climate and soil into the simulated processes.

2.3.E. Harvesting.

We developed a new harvesting extension for LANDIS-II named “Magic Harvest” that works in tandem with the existing harvest extensions of LANDIS-II [83]. This extension allowed the implementation of more complex harvest prescriptions at the stand level. Using this new extension, we implemented six different types of stand-level harvest prescriptions (Table 1). CC-PlantIntens consisted of a complete clearcut followed by the establishment of “intensive” plantations. These plantations combined a fast-growing hybrid tree species with a marketable tree species (e.g., black spruce) to maximize wood production. The model parameters used for characterizing the growth of these hybrid species were defined to emulate the rapid growth of hybrid poplar and hybrid larch recently developed in North America. As such, the main physiological parameters of these hybrid species (e.g., shade and fire tolerance) were similar to their non-hybrid alternatives (the trembling aspen and the tamarack; see [84] and [85]), but their maximum Annual Net Primary Productivity (ANPP) was doubled, and their maximum biomass was increased by 15% in every ecoregion. Moreover, the longevity of these hybrid species was divided by two, leading to the faster mortality. We based these parameter changes on expert opinion and on data from the Quebec Sylvicultural Guide [86]. CC-PlantFunct, CC-NormalPlant and CC-NoPlant, all consisted of a clearcut with protection of advanced regeneration; in CC-PlantFunct the clearcut was followed by the establishment of functionally enriched plantations, in CC-NormalPlant the clearcut was followed by tree planting of the dominant species prior to harvesting, and in CC-NoPlant the clearcut was not followed by any kind of plantations. SC consisted of a selection cutting, harvesting 30% of the stand biomass of all age cohorts older than 30 years, every 30 years during a 90-year period. Finally, CT consisted of a commercial thinning, removing 60–80% of the biomass of younger tree cohorts but only 5–40% of the biomass of older cohorts and was repeated twice in a 50-year interval in TRIAD intensive areas. Stands restricted for harvesting with SC and CT became available to any other prescription after the 90- (SC) or the 50-year (CT in intensive TRIAD areas) period. The details of each prescription are given in S1 Appendix B in S1 File. All prescriptions targeted the stands with highest biomass available for harvest in the landscape or in their area of application (see below), except for CC-PlantFunct which targeted the stands with the lowest functional response diversity (see “Data analysis”). Prescriptions were applied in an arbitrary order, harvesting their biomass target one after the other until all prescriptions for the given scenario were considered.

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Table 1. Description of the harvest prescriptions simulated.

https://doi.org/10.1371/journal.pone.0326627.t001

Furthermore, in the enriched plantations of CC-PlantFunct, species were selected based on the functional groups present in the targeted stand. The functional groups in this study were identified through a clustering analysis of the 17 simulated species using nine different functional response traits related to our three catastrophic disturbances (see “Catastrophic disturbance events”): maximum height, seed dry mass, wood density, leaf nitrogen content per leaf dry mass, specific leaf area (SLA), bark thickness, fire tolerance, drought tolerance and shade tolerance (S1 Appendix D in S1 File). The clustering method resulted in five functional groups: three gymnosperm groups with different tolerances to shade, drought, and fire; and two angiosperm groups with a clear distinction between pioneer and mid to late-succession species. When species from one or several functional groups were not present in the targeted stand, we planted one new age cohort of one species for each missing functional group. The selected species for each missing group was chosen randomly among those with the highest probability of establishment, which varied by ecoregion and could thus differ with time due to climate change. If all functional groups were already present in the stand, we selected a species from the group with the smallest abundance (as estimated by their biomass). This methodology ensured a local increase in functional response diversity.

The four forest management strategies harvested the same biomass target in different ways (Table 2). In the two types of BAU scenarios, all unprotected forests were available for harvesting by SC, CT, and CC-NormalPlant. In addition, CC-PlantFunct was available in BAU-PlantFunct and CC-NoPlant in BAU-NoPlant. In contrast, the TRIAD+ and normal TRIAD scenarios restricted the use of SC, CC-PlantFunct (in TRIAD+) and CC-NoPlant (in Normal TRIAD) to their extensive zones. In the intensive zones, forests were instead harvested with CT and CC-PlantIntens to maximize wood production using hybrid species and commercial thinning. As such, thinning (CT) in the intensive areas of TRIAD scenarios was repeated twice during the 50-year period following a first thinning to simulate an intensive commercial thinning (Table 1). The intensive zones were fixed and corresponded to 16% of the forest surface with the highest Annual Net Primary Productivity (ANPP) during a calibration run made with no natural disturbances. The gain in productivity in the intensive zones allowed us to increase the size of the unharvested areas. By creating a 5000 m buffer around the largest current protected areas (as of 2020), conservation reached 24% of the forested area. Only the size of the 10% largest protected areas was increased. Most existing small, protected areas were created by the provincial government to act as biological refugia dispersed across the landscape [87,88]. In agreement with this original goal, the size of small, protected areas was not increased. Thus, the TRIAD zoning ratio consisted of 16% of the forested surface dedicated to intensive management, 60% to extensive management, and 24% to conservation. This ratio is similar to the one recommended by Blattert et al. [45] in their estimation of an optimal TRIAD zoning in Finnish landscapes.

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Table 2. Percentage of the biomass target harvested with the different harvest prescriptions in the four forest management scenarios.

https://doi.org/10.1371/journal.pone.0326627.t002

2.3.F. Catastrophic disturbance events.

We simulated catastrophic disturbance events in LANDIS-II using the Biomass Harvest extension where mortality is represented by the loss of biomass in impacted forest stands. While originally designed to simulate harvesting, this extension can be employed to simulate any disturbance by removing tree biomass according to different severity and spatial distribution patterns. The severity of these events at the stand scale, i.e., the amount of removed biomass of a given species age cohort, was determined based on the functional response traits of that species as well as those of the other species in the community. The severity of disturbances also varies according to small-scale factors such as topography, soil, and microclimate. For simplicity, we did not include these factors and rather focused on the influence of species composition on disturbance severity.

Large fire:

We defined the large fire as a disturbance extending across the entire landscape but creating numerous unburned forest patches, i.e., fire refugia (Fig 2c). The total area covered by the refugia was fixed at 30% of the landscape, a proportion intermediate to the minimum (20%) and maximum (57%) values determined by Walker et al. [89] when measuring fire refugia from satellite imagery in coniferous and mixed forest landscapes. The size of individual refugium was sampled from a power-law distribution varying between one and 100 ha to make large refugia uncommon [89,90] (S1 Appendix C in S1 File). Each refugium was created by first randomly choosing a forest stand in the landscape to be at the center of the refugia, and then increasing its size from stand to stand until the sampled size was reached. For simplicity, the stand at the center of the refugia was selected randomly since modelling the influence of fine scale factors (e.g., slope, topographic wetness, etc.) on the creation of refugia was beyond the scope of our study [91]. Refugia were added one by one in the landscape until 30% of the landscape’s surface was reached.

Within burned stands, we assumed that tree biomass was consumed by fire according to a species-level process and a stand-level process. At the species level, we used the fire tolerance trait to determine the proportion of biomass loss of an age cohort. This proportion varied from a 60% loss at high tolerance (i.e., trait value between 4 and 5) to 100% at low tolerance (trait value less than 1) [78] (Table 3). At the stand level, we computed the Community Weighted Mean (CWM) of the fire tolerance trait over all species present (Table 3 and Fig 2c). This stand-level tolerance measure served to determine a protection effect from the community which in turn modulated the loss of biomass of each age cohort within the stand (based on [92]). The protection effect varied from 0 (no protection), when the stand CMW fire tolerance was less than 1, to 40% when it was highest (value between 4 and 5) (Table 3). The resulting biomass loss of an age cohort due to the large fire was calculated by multiplying the stand-level protection effect with the species-level proportion of biomass loss. These species and stand-level processes were parameterized based on our expert knowledge of forest fires and fire tolerance since the existing literature could not provide direct parameter values (Table 3).

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Table 3. Proportion of biomass loss for each catastrophic event at the species and stand level. The total proportion of biomass loss for a given species in a given stand is computed by multiplying both species age cohort and stand level effects.

https://doi.org/10.1371/journal.pone.0326627.t003

Although biomass loss was a function of fire tolerance during this single catastrophic fire, periodic fires generated by the Base Fire extension consumed all biomass irrespective of species tolerance to fire. Indeed, the Base Fire extension was used to simulate smaller but more intense fires, whereas the Biomass Harvest extension was used to simulate an extremely large and long fire event varying in intensity according to forest composition. In addition, following the large fire, we did not simulate the regeneration of serotinous species, as their fire tolerance implied that their age cohorts would never entirely disappear from a burned cell.

Severe drought:

In contrast to the refugia created during the large fire disturbance event, the severe drought affected the entire landscape. The loss of biomass resulting from drought mortality was also determined through a species-level and a stand-level process. For each species’ age cohort, biomass loss depended on the species’ drought tolerance trait [93]. At the stand level, higher values of functional response diversity (see “Data analysis” for its measurement) increase the drought tolerance for all species present. This stand-level effect was based on studies suggesting that functional response diversity was more important than species diversity in improving drought tolerance through resource partitioning and facilitation (S1 Appendix C in S1 File; [94], [95]). As for the large fire, we parameterized this process based on expert estimation of the biomass lost for species of different drought tolerance and according to the stand functional diversity.

Mountain pine beatle outbreak:

The MPB outbreak affected all forest stands containing any of the potential host species: white pine (Pinus strobus), red pine (Pinus resinosa), and jack pine (Pinus banksiana) (Table 3). We assumed that age cohorts of these species lost 80% of their biomass. We based this proportion on the study of Long and Lawrence [96] which reported a pine tree mortality exceeding 80% in MPB-infected landscapes of western Montana (USA). At the stand level, we hypothesized that a dilution effect from the presence of non-host species would reduce host mortality caused by MPB [97]. Therefore, we assigned a protection effect that increased as a function of the abundance of non-host species present in the stand. We used the study of Jactel et al. [98] measuring the effect of stand diversity on damages caused by borer insects to estimate this stand-level protection effect (see S1 Appendix C in S1 File, Table 3). Again, the resulting loss in biomass for a given age cohort was calculated by multiplying the proportion of biomass loss at the species-level with the protection effect at the stand-level.

2.4. Data analysis

In each scenario, we measured two variables associated with forest ecosystem functions. Firstly, we measured the biomass of mature cohorts (defined as being 40 years or older) of all species present in each stand of the landscape. The mature biomass is an important proxy for several forest functions like carbon storage and seed production, as mature trees can reproduce and create seeds while storing more carbon than smaller trees [99,100]. In addition, mature trees are important to the forest industry given the higher market value of large diameter trees. The total mature biomass in the landscape (B_L) was calculated by summing the stand-scale mature biomass (B_S) across all stands. Secondly, we measured the functional response diversity in each stand (FD_S) using the exponent of the Shannon’s diversity index, applied to the relative biomass abundance of each functional group in the stand:

(1)

where p_i is the relative abundance of functional group i (expressed by the biomass of its age cohorts) from the n functional groups present in stand S. FD_S measures the effective number of functional groups in a stand, quantifying its functional response diversity in a simple yet meaningful way [35,101]. Furthermore, we computed the mean functional response diversity across stands of the landscape (F̅D̅_S) as a stand-area weighted mean of FD_S. We used F̅D̅_S to observe if functional planting did effectively increase the functional response diversity of stands at the landscape scale, and if this increase could further be linked to changes in our resilience measures (see below). In addition, we computed the total biomass of all age cohorts in the landscape for each functional group (referred as B_FG). The three resulting measures (B_L, F̅D̅_S and B_FG) were subsequently averaged across the five simulation replicates for each scenario combination.

Moreover, we measured the resilience of the mature biomass at the landscape scale (B_L) following one of the catastrophic events at t = 100. Resilience is a notably complex concept to capture that has led to the development of varied measures (e.g., speed of recovery, critical slowing down, etc.) [102]. In the context of this study, we define resilience as the ability of a system to maintain essential functions in the face of a disturbance [33]. In particular, we followed the methodology of Cantarello et al. [103] and measured the resilience of B_L through three different metrics (Fig 3): resistance (R), net change (NC) and rate of recovery (RR). As noted above, the mature biomass acts here as a proxy for several important ecosystem functions such as seed production, carbon storage, and wood production.

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Fig 3. Measures of resilience used in our study.

A, B and E refers to the value of the variable of interest before the disturbance (A), right after it (B), and at the end of the simulation (E), and are used in equations 2 and 3.

https://doi.org/10.1371/journal.pone.0326627.g003

R was defined as the variation between the value of the variable immediately before (B, at t = 90) and after (A, at t = 100) the catastrophic event using the following equation:

(2)

Hence, R varied between 1 (no change in the value of B_s) and 0 (maximum change) (Fig 3a). In contrast, NC corresponded to the percentage difference between the value of the variable at the end of the simulation (E, at t = 200) and its value before the catastrophic event (B, at t = 90), relative to its value before the event (Fig 3b, equation 3).

(3)

NC was therefore negative if E at t = 200 was lower than its pre-disturbance value A and positive if it exceeded A. Lastly, RR corresponded to the inverse of the recovery time (in years) that B_S took to reach its pre-catastrophic event value, which can also be interpreted as the percentage of mature biomass recovered every year (Fig 3c). If the variable did not retrieve its pre-disturbance value before the end of the simulation, RR was set at 0.01, corresponding to the inverse of the maximum recovery time possible in our simulations (100 years). As such, an increase in each of these three measures represented an increase in the resilience of the mature biomass in the landscape.

We expected R, NC and RR to complement each other since they measure distinct aspects of resilience. Indeed, R measures the magnitude of the initial impact of the catastrophic event but does not consider the temporal dynamic of BS following the event. In contrast, NC and RR are both influenced by the legs of the disturbance event and other sources of mortality (e.g., regular fires) that may occur during the 100-year period of recovery. But while NC revealed how well a stand had recovered during that period, RR showed how fast it had recovered.

In the end, we simply compared the temporal trends and average values for each measure (B_L, F̅D̅_S, B_FG, R, NC and RR) between the scenarios to infer the effects of our different factors. In addition, we use the variability observed between replicates as an indication of uncertainty steaming from the stochastic processes inside our model. We did not use any statistical tests, as the scenario were known to have distinct factors a priori, meaning that any increase in the number of replicates would end up making any difference between scenarios statistically significant [104].

3.1. Temporal dynamics of mature biomass and mean functional response diversity

Our results show that the temporal dynamic of B_L differed little between the four forest management strategies implemented (Fig 4). In most catastrophe and climate scenarios, the BAU-PlantFunct management strategy tended to increase B_L slightly (by up to 8%) compared the other strategies (Fig 4, yellow curves). In contrast, the normal TRIAD strategy tended to produce smaller B_L (Fig 4, red curves). Furthermore, the variation in B_L between replicates was negligible for each scenario, indicating that the stochasticity of the forest dynamics had little effect when considering the mature biomass at the landscape scale.

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Fig 4. Temporal variation of the Total Mature Biomass in the landscape B_L for each combination of management, climate and catastrophe scenario.

Solid lines are mean values and envelops are standard deviation across 5 simulation replicates.

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The main differences in B_L were observed between the catastrophic disturbance scenarios and between the climate scenarios (Fig 4). Indeed, the large fire and severe drought catastrophes resulted in important reductions in B_L at t = 100 (around 30%), impacting its dynamics for the following decades (Fig 4, first and second row). However, B_L ultimately recovered in both cases after 50 years or so and reached values similar to the “no catastrophe” scenarios during the last 50 years of the simulations. In contrast, the MPB outbreak had almost no impact on B_L when compared to the “no catastrophe” scenario (Fig 4, third row). The most important factor impacting the dynamic of B_L was climate. Indeed, B_L either increased in the baseline climate scenario (Fig 4, left column), remained relatively stable in the RCP 4.5 scenario (Fig 4, middle column) or decreased in the RCP 8.5 scenario (Fig 4, right column) throughout the 200 years of the simulations. These trends remained regardless of the forest management strategy involved or nature of the simulated catastrophe.

In contrast, the temporal dynamics of the mean functional diversity, F̅D̅S, varied according to the different forest management strategies (Fig 5). Indeed, regardless of the climate and catastrophe scenario, F̅D̅S increased by up to 15% in management scenarios with functional planting (TRIAD+ and BAU-PlantFunct, Fig 5, blue and yellow curves) compared to scenarios without functional planting (TRIAD and BAU-NoPlant, Fig 5, red and brown curves). This boost in F̅D̅_S increased with time but seemed to plateau towards the end of the simulations. However, the wide variability envelopes indicate important variations in functional response diversity between stands and between replicates (Fig 5).

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Fig 5. Temporal variation of the mean Functional Diversity across all stands of the landscape (F̅D̅_S) for each combination of management, climate and catastrophe scenario.

Solid lines are mean values and envelops are standard deviation across stands and 5 simulation replicates.

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The presence of catastrophes had a small effect on the temporal dynamics of F̅D̅S, in contrast to the results for the total mature biomass (Fig 5, first to last row). However, climate once again played an important role in shaping the long-term trend in F̅D̅_S. Indeed, similarly to BL, F̅D̅_S increased in the baseline scenario (Fig 5, left column), decreased slightly in the RCP 4.5 scenario (Fig 5, middle column), and decreased more steeply in the RCP 8.5 scenario (Fig 5, right column) throughout the simulations.

Finally, the dynamic of total biomass of each functional group (B_FG) showed very little variation between management scenarios (Fig 6). TRIAD+ and BAU-PlantFunct scenarios show a very slight increase in the biomass of the rarest functional group in the landscape (group 2, softwoods with low tolerance to shade and fire) compared to the TRIAD and BAU-NoPlant scenarios, with a baseline and RCP 4.5 climate (Fig 6, orange areas, first and second row). Overall, the evolution of the biomass of each functional group thus changed according to the climate, but not the management strategy used.

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Fig 6. Evolution of the total biomass for the six functional groups of trees defined in our study for each combination of climate and management scenario, but without a catastrophic disturbance event at t = 100.

Values at each time step are mean values across 5 simulation replicates.

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3.2. Resilience of mature biomass

Our results show that all resilience measures for B_L were again more sensitive to the simulated climate and catastrophic disturbance event than to the forest management strategy (Fig 7). Nonetheless, in most cases, the mean values of R, NC and RR were higher for the BAU-PlantFunct and TRIAD+ scenarios, pointing to a slightly higher resilience when functional planting was used (Fig 7, yellow and blue bars).

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Fig 7. Bar plots showing the resilience values of the total biomass of the landscape (BL) for each resilience measure (row) and each catastrophic event (column).

The height of the bar are mean values and error lines are standard deviation across 5 simulation replicates.

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The resilience measures for B_L varied according to the type of catastrophic disturbance event simulated. The large fire resulted in the smallest values of resistance, R, followed by the severe drought and the MPB outbreak (Fig 7, first row). While the large fire and severe drought resulted in lower values of R than the MPB outbreak (Fig 7, first row), the latter was associated with lower values of NC (Fig 7, middle row). Although B_L was less impacted by the MPB outbreak (leading to higher R values), the landscape did not recover its pre-disturbance B_L as well as with the other catastrophes. On the other hand, the rate of recovery, RR, was slightly higher for the MPB outbreak scenario than for the other two catastrophes, implying a faster recovery of B_L following this outbreak (Fig 7, last row).

Overall, the most coherent signal throughout all resilience measures came from the climate scenarios. Scenarios with the most intense level of climate change (RCP 8.5) was systematically associated with low R, NC and RR values, indicating an overall loss of mature biomass resilience at the landscape scale (Fig 7). However, the RCP 4.5 scenario led to resilience values similar or higher than those for the Baseline scenario for all three measures and all three catastrophes.

4.1. Climate as the main driver of the dynamics and resilience of the landscape

Among the three factors that we varied in our scenarios, climate stood out as having the greatest effect throughout our simulations (see “Results”). This is particularly apparent when comparing the temporal dynamics of the total mature biomass in the landscape (B_L) in scenarios with or without a catastrophe (Fig 4 and 6). In the scenarios with a large fire and severe drought, B_L dropped drastically at t = 100 but rapidly (within 50 years) recovered to similar values found in the corresponding scenarios without catastrophes. In addition, the scenarios without any catastrophe clearly illustrate that B_L decreased with increasing intensity in climate change (Fig 4, bottom row). Taken together, these observations suggest that climate conditions were the strongest drivers determining the dynamics of B_L, and not the simulated management strategies nor the punctual disturbances.

Our resilience measures also captured the impact of climate on species’ growth. Indeed, scenarios with more intense climate change (i.e., RCP 8.5) presented lower resilience values compared to the baseline and RCP 4.5 scenarios (Fig 7). In the RCP 4.5 scenarios, climate change increased the number of fires (see S1 Appendix E in S1 File) and reduced the growth capabilities of certain species compared to the baseline; however, it also improved the growth of other (thermophilic) species, reducing the differences with baseline scenarios. In contrast, in the RCP 8.5 scenarios, climate increased fire activity and was associated with reduced growth parameters. Our results thus reflect the reported uncertainty regarding the general effect of climate change on forest growth since it may be beneficial through certain processes (e.g., longer growing seasons, CO2 fertilization, etc.; not all considered in this study), but detrimental through others (e.g., more frequent natural disturbances, more hydric stress, etc.) [105,106].

Besides changes in B_L, climate change was also responsible for important shifts in B_FG (Fig 6). Specifically, the total biomass of functional groups of hardwood species like red oak, trembling aspen, red maple or sugar maple strongly increased throughout all simulations, but even more so under the RCP 4.5 and 8.5 climate scenarios (Fig 6, blue and green areas). On the other hand, the total biomass of functional groups of softwood species like black spruce, white spruce or balsam fir decreased markedly over time, especially under the RCP 4.5 and RCP 8.5 climate scenarios (Fig 6, yellow, orange and red areas). This decrease in softwood biomass resulted in the collapse of the current main economic tree species in Quebec, echoing previous concerns in the literature as to the sustainability of Canada’s forestry sector under climate change [107].

4.2. Species growth limiting the effect of functional enrichment on functional response diversity

Functional planting, implemented in the TRIAD+ and BAU-PlantFunct scenarios, led to an increase in the mean functional diversity (F̅D̅S) over time compared to the BAU and TRIAD scenarios (Fig 5). However, this increase plateaued with time – a trend that can be observed for both management scenarios in the temporal evolution of F̅D̅_S (Fig 5) and of B_FG, the total biomass of functional groups (Fig 6). This saturation suggests that fine scale mechanisms, such as species growth and competition, may limit the ability of functional enrichment to diversify this landscape in the long term, especially in the context of climate change. In our model, we implemented functional enrichment by systematically planting a single age cohort of tree species from rare or missing functional groups at the stand scale. Amongst the pool of potential species, we always selected the species with the highest growth performance relative to the local soil and climatic conditions. However, this planting followed a clear cut that kept 10% of the stand biomass, hence preserving species from the original functional groups. As such, once planted, rarer functional groups would often be unable to compete as their growth parameters (maxANPP) were relatively smaller than those of the original species that were thriving under local climate and soil conditions. This was often the case even though our algorithm selected the most adapted species of the rare functional groups to plant in the given cell. As such, these original species would rapidly outgrow the newly introduced ones. Given that we used a measure of functional diversity weighted by the abundance of functional groups, the increase in FD_S was thus saturated by the limited performance of species from additional functional groups.

4.3. Low effect of the functional response diversity on stand resilience

The relatively small effect of our management strategies on the resilience of the mature biomass in the landscape (B_L; Fig 7) can be interpreted in two ways. On the one hand, our implementation of functional planting might not have increased the functional response diversity of stands enough to influence their resilience sufficiently (see previous section). On the other hand, the effect of functional response diversity on stand resilience might be small to begin with. Indeed, several empirical studies have observed that the effect of functional response diversity on forest resilience was either absent, small, or highly contextual [108–111]. More generally, Yang et al. [112] argue that functional traits can be poor predictors of tree demographics when the focus is on species rather than individuals, when contextual information about the trait values is missing (e.g., biogeographic, phenotypic), or when functions important to tree demographics are not being captured by the most measured traits.

In contrast to these empirical studies, simulations of tropical forest dynamics using stand-scale individual-based models revealed a positive effect of functional response diversity on forest resilience [113,114]. However, these effects were conditional on how vegetation dynamics was represented in the models. In particular, Schmitt et al. [114] indicated that functional response diversity improved stand resilience to disturbances through complementarity mechanisms between species with different traits (niche partitioning and facilitation). They also noted that the effect of functional response diversity was only temporary as complementarity effects quickly gave way to interspecific competition. Therefore, the effect of functional response diversity on forest resilience may depend on the studied disturbance, the local environmental conditions (slope, etc.) or the forest age. It is also possible that individual-based models are better suited to capture the effect of functional response diversity compared with models, such as LANDIS-II, that are based on age-cohort dynamics (see “"Limitations on model limitations).

In addition, functional enrichment planting with the wide goal of increasing functional response diversity may sometimes be less effective than other forms of planting to increase forest resilience. For example, several studies have found that planting species adapted to specific disturbances or climate conditions could increase the resilience of forest landscapes following these disturbances [115–117]. We argue that this reflects a trade-off between protecting against a broad range of uncertain future conditions and protecting against specific disturbances or aspects of climate change. While the broader strategy might prove less effective when the threats are well known and defined, it might be more effective when facing the unexpected threats that await forests.

4.4. Is TRIAD+ a good compromise?

An important goal of our study was to assess whether TRIAD+ could produce a good trade-off between conservation, production and adaptation in the landscape. At first glance, our results might indicate that TRIAD+ was slightly less effective than the BAU-PlantFunct strategy, as the latter resulted in slightly higher values of BL, F̅D̅_S, R, NC and RR in several cases. Specifically, when compared with values for the TRIAD + , the BAU-PlantFunct scenario led to slightly higher values of BL (Fig 4), similar values of F̅D̅_S (Fig 5), and an equal or slightly improved resilience for the mature biomass in the landscape (Fig 7). However, the performance of the BAU-PlantFunct scenario must be contrasted with the fact that TRIAD scenarios contained more than twice the quantity of protected areas (reserves) than the BAU scenarios. Indeed, the TRIAD+ scenario had 24% of its forested surface defined as protected areas compared to 9% for the BAU-PlantFunct scenario, and still resulted in similar values of BL, F̅D̅_S and resilience. Although the tested management scenarios mostly produced small differences in mature biomass, functional diversity, and resilience (S1 Appendix F in S1 File), TRIAD+ can be considered as an appealing avenue to satisfy multiple management goals including resilience to future changes.

4.5. Limitations

Our results also suggest some limitations in our modelling methodology. Firstly, the strong effect of climate on the dynamics of BL, F̅D̅_S and resilience could be explained by how succession dynamics and biomass accumulation are represented in the LANDIS-II Biomass Succession extension we employed. In this extension, each tree species is associated with a maximum annual net primary productivity (ANPP) and a maximum biomass per age cohort that depend on ecoregion and climate [118]. Thus, climate acts on the mature biomass of age cohorts both as a “hard ceiling” via the maximum biomass parameter, and as an “escalator” via the maximum ANPP. Therefore, following any disturbance event, the mature biomass of remaining age cohorts could quickly recover. As such, the strong effect of climate reduced the potential variations in mature biomass caused by other factors, such as forest management and catastrophic events.

Secondly, the Biomass Succession extension may also be responsible for the relatively poor growth of species from rare functional groups. Indeed, the main drivers of forest dynamics in this extension are relatively simple spatially implicit competition for shade and growth, with growth being mostly influenced by climatic conditions in our study area. More complex succession extensions of LANDIS-II, such as the PnET succession extension [119], might therefore have yielded different results by dynamically simulating other mechanisms that influence species’ performance at the local scale such as water availability, spatially explicit self-thinning, nutrient competition, or complementarity effects.

4.6. Guidelines and considerations for future studies

Our study highlights important considerations on the potential effects of forestry and climate change on future forest ecosystems which can serve as guiding principles in investigating novel resilience-based management strategies. Firstly, the strong effect of climate on the dynamics of mature biomass and functional diversity, compared to the four tested management strategies, suggests that human efforts to shape future forest composition and resilience under climate change will need to be considerable. It will require frequent interventions carried across large scales to successfully guide forest ecosystems into resilient states and maintain this resilience.

Secondly, our results emphasize the challenges surrounding the practice of functional planting. In particular, species selected for functional plantations may not always be able to thrive in the long-term. In our case, part of our functional planting strategy was to select species from rare functional groups. However, even if these new species have functional traits well adapted to future conditions, they may be poor competitors in these novel communities. In such situations, frequent interventions in stands (e.g., via commercial thinning) across a large spatial scale might be required to balance the relative abundance of different functional groups over time. This implies that sustaining the effects of functional enrichment may be a complex and costly operation in certain forests. Other planting strategies focused on specific disturbances or on species expected to “win” under climate change could be considered, although the uncertainty of future forest disturbances may limit the success of such strategies. As such, while the limitations of our study do not allow us to dismiss functional planting as an effective or practical strategy to enhance forest resilience (see previous section), we believe that our results highlight caveats and difficulties that must be considered in future studies. We also believe that while the potentially beneficial effect of functional planting on forest resilience seems to be validated by our model, its importance and efficacy when compared to alternative strategies still remains to be explored, as our results show a relatively small effect.

Thirdly, the contrast between our findings and those of other studies suggest that the simulation of fine-scale processes of forest dynamics should be improved in forest landscape models such as LANDIS-II. Indeed, the intra-cell dynamics in LANDIS-II remains spatially-implicit and does not simulate some small-scale processes such as nutrient and water competition or complementarity effects. As such, in its current form, the ability of LANDIS-II to capture the regeneration dynamics of forest stands following silvicultural intervention is limited.

Fourthly, the collapse in the abundance of several commercial tree species suggests that the construction industry, in tandem with the forest industry, will have no choice but to adapt their practices to stay viable in the future. Both industries will have to harvest and use trees of a more diverse set of species than what is currently the norm [120].

Fifthly, the model assumed that catastrophic disturbances affected all age cohorts equally. However, natural disturbances are known to affect trees of different ages in different ways. For example, recent studies suggest that older and younger trees can differ in their resistance and resilience to drought, due in part to their root system [121,122]. Fire resistance is also expected to increase with age due to bark thickness [92]. In addition, tree age can be an important factor in predicting tree mortality from some insect outbreaks such as the spruce budworm which causes increased mortality to mature trees [123]. Should we have taken these nuances into account, it is possible that our catastrophic events might have influenced forest resilience differently, by favoring either older or younger tree survival depending on the event.

Finally, our study echoes concern that developing multifunctional forest management strategies is no longer a sufficient goal, as shown by the very similar performance of the classic TRIAD and BAU scenarios regarding forest resilience. But management decisions now can shape tomorrow’s forests, and therefore must include objectives of adaptation. As the climate warms and humanity’s population and resource consumption continue to increase, the future of forests worldwide becomes more and more uncertain. It is therefore our responsibility to find ways to help forests adapt to a world where life on Earth – from humans to trees – is now facing the unknown of the Anthropocene.