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Article

Role of Plant Traits in Photosynthesis and Thermal Damage Avoidance under Warmer and Drier Climates in Boreal Forests

by
Guiomar Ruiz-Pérez
1,*,
Samuli Launiainen
2 and
Giulia Vico
1
1
Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Uppsala, 75007, Sweden
2
Natural Resources Institute Finland (LUKE), Helsinki, 00790, Finland
*
Author to whom correspondence should be addressed.
Forests 2019, 10(5), 398; https://doi.org/10.3390/f10050398
Submission received: 14 March 2019 / Revised: 24 April 2019 / Accepted: 3 May 2019 / Published: 8 May 2019
(This article belongs to the Special Issue Relationship between Forest Ecophysiology and Environment)
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Abstract

:
In the future, boreal forests will face warmer and in some cases drier conditions, potentially resulting in extreme leaf temperatures and reduced photosynthesis. One potential and still partially unexplored avenue to prepare boreal forest for future climates is the identification of plant traits that may support photosynthetic rates under a changing climate. However, the interplay among plant traits, soil water depletion and the occurrence of heat stress has been seldom explored in boreal forests. Here, a mechanistic model describing energy and mass exchanges among the soil, plant and atmosphere is employed to identify which combinations of growing conditions and plant traits allow trees to simultaneously keep high photosynthetic rates and prevent thermal damage under current and future growing conditions. Our results show that the simultaneous lack of precipitation and warm temperatures is the main trigger of thermal damage and reduction of photosynthesis. Traits that facilitate the coupling of leaves to the atmosphere are key to avoid thermal damage and guarantee the maintenance of assimilation rates in the future. Nevertheless, the same set of traits may not maximize forest productivity over current growing conditions. As such, an effective trait selection needs to explicitly consider the expected changes in the growing conditions, both in terms of averages and extremes.

1. Introduction

Plant growth and development involve numerous biochemical reactions that are sensitive to temperature [1]. At the leaf level, high temperatures may lead to a decrease in photosynthesis and an increase in respiration, thus reducing ovetrall net carbon dioxide (CO2) assimilation rate [2]. Under extreme conditions, plant thermotolerance might be exceeded, leading to permanent damage to the photosynthetic machinery [3]. Leaf thermal damage occurs when leaf temperature (TL) exceeds a critical temperature (TCRIT), but the damage becomes permanent if TL exceeds an even higher thermal threshold, often termed ‘maximum temperature’ [3,4,5,6,7].
Leaf temperature is defined by the interplay between plant traits (chiefly, leaf size and shape [8], albedo [9] and stomatal conductance [10]) and environmental conditions (irradiance, air temperature and humidity, plant water availability). When plants are well-watered and the canopy is well-coupled with the surrounding atmosphere, air temperature (TA) and TL are generally well correlated, although thermal regulation can result in leaves being cooler than air in warm conditions and warmer otherwise [11]. Low wind speed and plant water shortage can lead to TL substantially exceeding TA [12,13,14], by reducing the leaf evaporative cooling.
Climate change projections suggest a global increase in the frequency of warm days and nights, heat waves and, in some locations, dry spells [15]. Boreal forests are warming up twice as fast as other ecosystems. As a result of higher temperatures and, in some regions, reduced summer precipitation [15], they will be subject to more frequent periods of low water availability. Warming, together with low water, is already negatively affecting forest productivity [16,17] and threatening the survival of sensitive species [18]. This is especially problematic in the boreal region considering the role of boreal forests in the global carbon (C) cycle [19,20] and as a biomass source in climate change mitigation policies [21]. However, while the joint effects of high air temperatures and low water availability and the occurrence of thermal damage and/or limitation of assimilation rates have been extensively studied in arid and semi-arid environments [22,23], the potential occurrence of such conditions has been mostly overlooked in more mesic environments and boreal forests [24]. To better understand the joint effects of high temperatures and low water availability is essential in order to identify the expected frequency of damaging conditions the potential extent of damage, and possible steps towards preparing boreal forests to future growing conditions.
One potential and still partially unexplored avenue for adaptation to climate change is the identification of combinations of plant traits that can support carbon uptake and reduce the risk of thermal damage in a changing climate [25]. In particular, the role of traits leading to high temperature tolerance has been little explored over boreal regions [26]. Similarly, the link between plant traits, water use and water stress has not been fully resolved in boreal forests [27]. Limited information and understanding may be partially due to the fact that measurements of comprehensive sets of traits and long-term monitoring of forest dynamics are expensive and time consuming, constraining the traits considered [27]. Mechanistic models offer a powerful tool to overcome these limitations, allowing the exploration of a wide set of traits and environmental conditions, including the expected warmer and drier conditions.
Here, a mechanistic model describing energy and mass exchanges among the soil, plant and atmosphere [28,29] is applied to explore the role of plant traits and current and future environmental conditions on leaf temperature and net CO2 assimilation, with a focus on boreal forests. Specifically, we answer three questions, pertaining traits, growing conditions and their joint effects respectively: (i) Which are the dominant plant traits in regulating leaf temperature and CO2 assimilation? (ii) How do environmental conditions (e.g., air temperature and precipitation timing) interact in determining the occurrence of thermal damage and reduced CO2 assimilation? (iii) Which are the most suitable trait combinations for sustained CO2 assimilation and thermal damage avoidance? How do they differ between current and future, warmer and drier conditions? By answering these questions, this study identifies the key plant traits along the thermal safety—C fixation tradeoff, under current and future climates. It also suggests optimal trait combinations for thermal safety and productivity to cope with future climates. Therefore, this study contributes to disentangle the traits—growing conditions nexus, including the joint effect of increasing temperatures and more frequent lack of water in boreal forests.

2. Materials and Methods

2.1. APES-Atmosphere-Plant Exchange Simulator Model

We use the model APES (Atmosphere-Plant Exchange Simulator), previously developed, calibrated and validated in boreal forests. APES is a process-based one-dimensional multilayer, multi-species forest canopy-soil model, designed especially to account for the vertical structure and functional diversity. The model mechanistically solves the coupled energy, water and C cycles in the soil-vegetation-atmosphere system, using physical and physiological constraints. Only the modeling aspects most relevant for the purposes of this work, i.e., those related to the determination of leaf temperature and net CO2 assimilation rate, are briefly described here and in the Supplementary Materials. A complete description of this model and its validation, as well as some applications, can be found in Launiainen et al. [28,29].
In APES, the leaf temperature, TL, is calculated separately for sunlit and shaded leaves within each canopy layer, by solving the coupled energy balance and net CO2 assimilation rate (Anet) (see the Supplementary Materials for the equations).
Anet is obtained based on the Farquhar model, as the minimum of Rubisco-limited and light-limited rate, which in turn depend on the maximum carboxylation rate VCMAX and the maximum electron transport rate JMAX, respectively [30]. The temperature responses of both VCMAX and JMAX are as in Medlyn et al. [31]. Additionally, leaf respiration increases with TL, so that Anet reaches its maximum at an intermediate temperature (the optimal temperature for net CO2 assimilation). The parameters VCMAX, JMAX and Rd at reference temperature are affected by the leaf water potential ( ψ L ), following Kellomaki and Wang [32]. The stomatal conductance is computed by using the “unified stomatal model” proposed by Medlyn et al. [33].
The microclimatic gradients within the plant canopy are explicitly accounted for. The photosynthetic active (PAR) and near-infrared (NIR) radiation, and the long-wave balance are computed for each canopy layer as in Zhao and Qualls [34,35]. The profiles of air CO2 and H2O concentrations, TA and wind speed (U) are computed using first-order turbulence closure schemes iteratively with solution of leaf energy and CO2 balance. The above-ground and soil processes are coupled through water and heat fluxes via feedbacks between soil and vegetation (rainfall interception, root water uptake, feedbacks to leaf physiological parameters).

2.2. Metrics Evaluating Thermal Risk and Assimilation Capacity

To explore the role of key plant traits and identify potential trade-offs between photosynthesis and thermoregulation, we focused on four metrics of thermal damage, tolerance and CO2 fixation.

2.2.1. Maximum TL (TL,max)

TL,max was determined as the maximum leaf temperature within the warmest three-day period during the growing season. For each time step (30 min), we first calculated the average of TL of each canopy layer in sunlit leaves, which are the ones most likely to experience the warmest temperatures. Using this average TL series, we then computed the three-day moving average. TL,max is the absolute maximum TL within the three-day window with the highest moving average. We selected a three-day period, as in O’Sullivan et al. [3], considering that plants can cope with high temperatures for short periods. Knowledge of TL,max allows determining whether conditions may be conducive to thermal damage of the photosynthetic machinery, i.e., if the critical temperature (TCRIT) is exceeded over the growing season.

2.2.2. Cumulated Anet (Anet,cum)

Anet,cum is the cumulated Anet within the growing season. We first computed the average of Anet at each time step (30 min), including both sunlit and shaded leaves, weighted by sunlit and shaded fractions of leaves at each layer and accounting for non-uniform leaf area distribution across canopy layers. Then, Anet,cum was calculated as the cumulated sum of the average Anet within the growing season.

2.2.3. Maximum Anet (Anet,max)

The Anet,max refers to the maximum average Anet at each time step (30 min) over the growing season, weighted by sunlit and shaded fractions of leaves at each layer and accounting for non-uniform leaf area distribution across canopy layers.

2.2.4. Thermal Range of High Photosynthesis (∆T90)

The ∆T90 is the extent of the temperature range where assimilation rates are ≥90% of their seasonal maximum value calculated above, Anet,max. Considering the previously computed Anet,max and, at each time step, the average Anet over the whole canopy, we identified the maximum and minimum TL for which Anet is equal to 90% of Anet,max. ∆T90 corresponds to the difference between those maximum and minimum TL. We interpreted ∆T90 as a proxy of leaf thermotolerance: broad ∆T90 ranges mean that the CO2 assimilation rate is close to its maximum under a wide range of thermal conditions, while narrow ∆T90 range necessarily implies a higher likelihood to experience sub-optimal thermal conditions for assimilation.

2.3. Plant Traits Selection and Determination of Their Role

2.3.1. Selection of Plant Traits

Only traits expected to have a direct most notable effect on leaf thermoregulation and C-fixation in APES were included in the analyses. Under set environmental conditions, TL is strongly affected by leaf size and shape [8], albedo [9] and traits related to stomatal conductance and hence regulation of transpiration [10]. These traits also affect the net CO2 assimilation rate, directly via its maximum rate, and indirectly, via the stomatal opening and the effects of temperature on leaf metabolic rates. Consequently, in the analyses below, we focused on the primary plant traits regulating the CO2 assimilation rate, the boundary layer and stomatal conductances (gb and gs, respectively), the absorption of solar radiation.
A total of six traits were selected: the maximum carboxylation rate at 25 °C, VCMAX,25; the two parameters of the stomatal model, i.e., stomatal model slope in well-watered conditions g1 and a parameter describing the sensitivity of g1 to soil water potential β [33]; the effective leaf thickness, lt, and PAR and NIR albedos, αPAR and αNIR. This selection was based on the following considerations. Assuming leaves as flat plates, the effective leaf thickness, lt, controls the boundary layer conductance gb [36,37]. Conversely, the stomatal conductance, gs, is mostly dependent on hydraulic and photosynthetic traits: in the stomatal model employed in APES [33], these aspects are summarized by the parameters g1 and β. Leaf temperature and photosynthesis also depend on the absorbed radiation, and hence on the leaf albedo for both photosynthetically active and near infrared radiation (αPAR and αNIR respectively). Finally, according to the Farquhar model, the net CO2 assimilation rate depends on the maximum carboxylation rate, VCMAX, the maximum electron transport rate, JMAX, and the day respiration rates, Rd. These rates are functions of the corresponding rates at 25 °C (VCMAX,25, JMAX,25 and Rd,25) and their response to temperature [31]. JMAX,25 and Rd,25 are often well correlated with VCMAX,25, following Medlyn et al. [38] and Launiainen et al. [29], respectively, thus allowing to consider only VCMAX,25 as a key parameter of photosynthesis. Further details on trait selection and their role in leaf temperature regulation are given in the Supplementary Materials.
For each of these six traits, we considered realistic ranges, based on a literature search. These ranges were maintained intentionally broad, beyond those typical of current dominant coniferous and deciduous tree species in boreal forests (Table 1). Conversely, the understory and forest floor features were fixed in the model. Concretely, the understory consists of seedlings of Norway spruce, Silver birch and other deciduous species (LAI ∼0.5 m2 m−2), as specified in [29].

2.3.2. General Sensitivity Analysis (GSA) and Determination of Dominant Traits

A General Sensitivity Analysis (GSA) was run on APES to identify which of the focal traits are most important to maximize both instantaneous and cumulative Anet and regulate TL, reducing the risk of high temperature damage. To this aim, 100 trait sets were randomly generated from uniform distributions of each trait over the ranges specified in Table 1. The traits were sampled independently. The model was run using each trait set for current growing conditions (Section 2.4.1). The obtained simulated time series of Anet and TL allowed the computation of the four metrics described in Section 2.2 for each trait set (maximum leaf temperature, TL,max; the thermal range of high photosynthesis, ∆T90, cumulated and maximum net assimilation, Anet,cum and Anet,max).
The model results were then grouped based on whether the metrics exceeded pre-set thresholds. Specifically, a threshold was set for each metric in order to split the 100 trait sets into two groups: one containing the m trait sets for which metric is above the threshold (Group 1) and the others n = (100 – m) (Group 2). Then, for each trait, the cumulative probability distribution for the m trait values in Group 1 and n trait values in Group 2 were obtained. The higher the distance between the cumulative distribution functions, the more important the role of the trait value in defining the outcome. To measure the distance between the two empirical functions, the Kolmogorov–Smirnov (KS) two-sample test was used. This nonparametric method allows comparing two samples and quantifying the distance between the empirical distributions of the two samples [51]; thus, it can be used to rank the traits according to how dominant they are in determining the final outcome [52,53,54].
The thresholds for the grouping were selected to be the highest ones that allowed the greatest number of trait sets in the Group 1 while the number of dominant traits remained unchanged [53]. Indeed, choosing an excessively low threshold leads to (almost) no distinction between Group 1 and Group 2 so that the separation between the two distribution functions is negligible for all traits (i.e., no trait emerges as dominant). Conversely, choosing an excessively high threshold reduces substantially the number of trait sets for which the metric is above the threshold: in this case, Group 1 might not be representative except for a minority of trait sets, leading to a substantial separation between the two distribution functions for all traits (i.e., all traits are considered dominant). This approach was followed for each metric except TL,max. For the latter, we used TCRIT—i.e., the temperature at which first symptoms of thermal damage appears. Following O’Sullivan et al. [3], based on the latitude of the reference study site (Section 2.4.1), TCRIT was set at 44.0 °C. This threshold also allowed a balanced partitioning of the 100 parameter sets into Group 1 and Group 2, i.e., the approach delineated above would have led to a similar value for the threshold. The resulting thresholds were: TL,max = 44.0 °C, Anet,cum = 1,274 μmol m−2, Anet,max = 0.466 μmol m−2s−1 and ΔT90 =6.3 °C.

2.3.3. Selection of Focal Trait Combinations

Once the dominant traits were identified via Kolmogorov–Smirnov test, four sets of parameters were chosen to represent widely different outcomes in terms of thermotolerance and C fixation (Table 2). Given the focus on boreal forests, the focal trait combinations were chosen among those including trait values within the ranges typically observed in mid-to-high latitude forests (Table 1). We selected four trait combinations that showed contrasting thermal and productivity responses under identical growing conditions (2005 growing season). Specifically, trait combination 1 corresponds to the set of traits with the lowest performance (lowest Anet,cum and TL,MAX exceeding TCRIT in 2005): it is representative of those trait combinations with low productivity and high risk of thermal damage, at least under the current conditions (Low Productivity-High Risk; hereafter, LP-HR). Combination 2 was instead the best performing combination, leading to high assimilation rates while preventing thermal damage, i.e., it is representative of individuals with high productivity and low risk of thermal damage (High Productivity-Low Risk; HP-LR). Combination 3 led to high assimilation rates but also to TL,MAX on par with that of combination 1, thus representing trait sets leading to high productivity but also high risk of thermal damage (High Productivity-High Risk; HP-HR). Finally, combination 4 resulted in assimilation rates similar to combination 1 but it did prevent the occurrence of thermal damage; thus, it corresponds to low productivity but also low risk of thermal damage (Low Productivity-Low Risk). These differences in assimilation and thermal metrics are the result of the following differences between the trait values. Combinations 1 (LP-HR) and 4 (LP-LR) have similar and low VCMAX,25; combinations 2 (HP-LR) and 3 (HP-HR) have a similar VCMAX,25, but these are higher than combinations 1 and 4. Conversely, lt values in combinations 1 (LP-LR) and 3 (HP-HR) are similar and higher than the ones in combinations 2 (HP-LR) and 4 (LP-LR), the latter of which also have similar values. No patterns emerged relatively to the other traits, which were indeed not dominant as shown by the sensitivity analysis (see results in Section 3.1). These four combinations of traits together with their corresponding assimilation and thermal metrics are listed in Table 2.

2.4. Growing Conditions

The GSA was performed under current climatic conditions, corresponding to selected years in a study site in Finland (Section 2.4.1). Conversely, the focal combinations of traits were tested under both current and future warmer and/or drier growing conditions, as defined in Section 2.4.2. This approach allowed us to determine whether thermal damage and suboptimal photosynthetic rates would occur given certain temperature increase and intensity of the water stress, thus allowing the quantification of potential effects of climate change and the importance of trait selection in the future.

2.4.1. Current Growing Conditions

Current growing conditions are based on meteorological data observed in the Hyytiälä field station SMEAR II (Station for Measuring Forest Ecosystem-Atmosphere Relations), located in Hyytiälä, Southern Finland (61°51’ N, 24°17’ E). The annual mean temperature is 3 °C and total mean annual precipitation is 700 mm. All the meteorological data necessary for APES are routinely measured at this site. A complete description of the site and its measurements can be found in Kolari et al. [55]; Kulmala et al. [56] and Launiainen et al. [29].
The model was applied over selected growing seasons (2005 and 2006), here defined as the period of May 1st to September 30th [29], with the warmer period falling in the first half of July. The growing season 2005 can be considered as a normal one in the region, while 2006 was a dry year [57], slightly warmer than 2005. The two growing seasons were also characterized by a different precipitation timing: in 2005, the rainiest period occurred in late July and early August and the highest precipitation was recorded at the end of the growing season; in 2006, a dry spell occurred in late July and early August while two of the rainiest peaks were recorded in early July. Therefore, 2005 and 2006 present contrasting plant water availability, allowing us to analyze if these difference might lead to significant changes in thermoregulation and net CO2 assimilation.

2.4.2. Scenarios of Future Growing Conditions

Boreal regions are experiencing a rapid warming and an increase in the frequency and intensity of periods with low water availability as a consequence of changes in the timing and magnitude of precipitation, as well as increases in evapotranspiration from longer and warmer growing seasons [15]. Therefore, to generate climate change scenarios, we focused on air temperature (TA) and soil water potential (ΨS).
The future scenarios were developed as follows. We considered two single days within the growing season of year 2005 as baseline: (i) the day where TL reached its maximum value (July 6th) and (ii) an average day with TA close to the median value for the growing season (May 20th). Then, we built scenarios of future growing conditions by shifting upwards the observed TA series of 1 to 10 °C combined with decreasing soil water potential ranging from −0.5 to −3 MPa. The soil water potential is considered constant within the day. For each scenario, we computed the instantaneous Anet and TL at midday. While acknowledging their importance, other environmental variables such as wind speed and relative humidity were not modified. Thus, the vapor pressure deficit (VPD) was computed considering the new temperature values and the unchanged relative humidity.
Furthermore, we analyzed how trait combinations might affect soil water availability and how such changes may alter maximum and cumulated net CO2 assimilation. To this aim, the shift in TA series by 1 to 10 °C was combined with imposing 5 to 30 day dry spells, with no precipitation. The dry spell was assumed to begin after the last precipitation event before the warmest period registered within the growing season of 2005 (i.e., June 27th). We then computed the corresponding Ψs at the end of the 5–30 day dry period, and the Anet,cum over the dry down.
In all cases, the length of the growing season was maintained unaltered, even though global warming may lead to longer growing seasons in boreal regions [58,59]. This simplifying assumption bears no consequence of the results discussed below, because the aim was to contrast different trait combinations under the same abiotic conditions.

3. Results

3.1. Dominant Traits Emerging from the General Sensitivity Analysis (GSA)

The General Sensitivity Analysis (GSA) allowed the identification of the traits most relevant for the four metrics: TL,max, Anet,cum, Anet,max and ∆T90. The results are presented in Figure 1 and summarized next.

3.1.1. Maximum TL (TL,max)

Ranking the traits according to KS (Figure 1), the most dominant one was lt, followed by g1 and VCMAX,25. In the case of the leaf albedos (αPAR and αNIR) and β values, the two distribution functions were overlapping and the KS value was negligible, i.e., the traits did not have a large effect on TL,max. The group containing the trait sets that did not result in thermal damage (i.e., TL,max < TCRIT) had lower lt, higher g1 and higher VCMAX,25. For values lower than 55 μmol m–2s–1 (i.e., those typically observed in trees in mid-to-high latitudes; shaded range), the importance of VCMAX,25 decreases as evidenced by the two empirical distribution functions overlapping for such parameter values. The same behavior was shown by g1, the influence of which diminishes for values higher than 4 kPa0.5.

3.1.2. Cumulated Anet (Anet,cum) and Maximum Anet (Anet,max)

Anet,cum was dominated by VCMAX,25, followed by g1 and lt. Higher values of VCMAX,25 led to higher Anet,cum. Similarly, the group with higher Anet,cum presented higher values of g1 and lt up to values of 11 kPa0.5 and 0.1 m, respectively, above which the effect of these parameters on Anet,cum shifted. Similar results were obtained regarding Anet,max (Figure 1, third row).

3.1.3. Thermal Range of High Photosynthesis (∆T90)

∆T90 was equally dominated by g1, lt and VCMAX,25. The group with higher thermotolerance (i.e., broader ∆T90) had higher g1 and lower VCMAX,25 and lt than the less thermotolerant one. Regarding the leaf albedos and β values, the distribution functions were mainly overlapping and the KS value was almost negligible (Figure 1, bottom row). Note that, differently from the case of TL,max, VCMAX,25 is also dominant for parameter values typical of trees in mid-to-high latitudes.

3.2. Role of the Growing Conditions

3.2.1. Differences between 2005 and 2006

In both the 2005 and 2006 growing seasons, leaves did not reach their maximum temperature when TA was warmest, but rather after several consecutive days without precipitation (4 and 7 days in 2005 and 2006, respectively). No large differences between the days when the maximum TL are reached in each year were found in terms of radiation, wind speed and VPD.
Given the same combination of traits, Anet,cum was systematically higher in 2006 than in 2005 growing season (Figure 2, top), while TL,max was systematically higher in 2005 than in 2006. Indeed, some combinations of traits led to thermal damage during 2005, while this threshold was not reached by any combination of traits during 2006. Conversely, no marked differences in terms of Anet,max and ∆T90 were found (Figure 2).

3.2.2. Future Growing Conditions

To investigate the effects of likely future conditions, increases of temperature of 0° C to 10 °C with respect to current conditions were combined with values of soil water potential ranging from −0.5 to −3 MPa. Those changes were applied by considering both a standard day and a warm day of the 2005 growing season as baselines.
Taking the currently normal thermal conditions as baseline, combination 3 (HP-HR) led to the highest assimilation rates under TA lower than 18°C (Figure 3 bottom row). For TA below 16°C, TL reached temperatures similar to combinations 2 (HP-LR) and 4 (LP-LR). Combination 1 (LP-HR) led to the highest difference between TA and TL, while combinations 2 (HP-LR), 3 (HP-HR) and 4 (LP-LR) showed similar TL values over a wide range of TA conditions (Figure 3 top row). TL was not sensitive to Ψs in any of the four combinations. The Anet contour curves showed a parabolic shape with a vertex in TA equal to 8°C, 10°C, 10°C and 12°C for combination 1, 2, 3 and 4, respectively.
For the warm day baseline conditions, combinations with high lt—i.e., combinations 1 (LP-HR) and 3 (HP-HR)—were more likely to result in thermal damage, with TL exceeding TCRIT even under current thermal conditions (Figure 4 top row). Due to this high TL, the assimilation rates were also reduced to the point that combination 3 (HP-HR) showed lower Anet even though its maximum assimilation rate was higher than the corresponding one in combination 4 (LP-LR) (Figure 4, bottom row). Conversely, combination 2 (HP-LR), and to some extent combination 4 (LP-LR), showed higher thermoregulation capacity (i.e., lower TL) and higher assimilation rates. When compared, combination 2 (HP-LR) appeared to be more beneficial, resulting in lower TL and higher assimilation rates. Note that for all four combinations, an increase in TA with respect to the warm day baseline is detrimental to plant productivity, as Anet is reduced and leaves experience thermal damage already under current conditions (combination 1 and 3), or for temperature increases of 1 to 2.5 °C (combination 4 and 2, respectively). TL was sensitive to Ψs under dry conditions, particularly in combination 2 (HP-LR) (Figure 4 top row). Contrarily, in all combinations, Anet rates decreased as did water availability (Figure 4 bottom row).
Regarding the effects of plant traits on soil water depletion, similar Ψs emerged among the four trait combinations after the same number of consecutive days without precipitation. For all four combinations, after intervals shorter than 15 consecutive days without precipitation, soil water potential (i.e., lower Ψs) was only slightly affected by increasing temperatures. The lowest Ψs—around –0.9 MPa after more than 25 consecutive dry days—was reached by combination 2 (HP-LR) and 4 (LP-LR) (Figure 5).

4. Discussion

4.1. Role of Traits: Potential Mechanisms Explaining the Dominance of Plant Traits

The GSA allowed identifying the role of six traits affecting net CO2 assimilation, Anet, leaf temperature and TL, and hence the risk of thermal damage. As expected, both the maximum and cumulated net CO2 assimilation are strongly dependent on the maximum carboxylation rate, VCMAX,25. They are also affected by the effective leaf thickness, lt, although less markedly. In particular, higher lt values were also associated with higher cumulated net CO2 assimilation, Anet,cum, but only up to lt ~0.1m, above which this trend reverts. In general, higher lt leads to higher TL, by reducing the leaf boundary layer conductance gb [60]. Increase in TL may have opposite effects on Anet, depending on the initial temperature and the species: when TL is below the temperature that maximizes net CO2 assimilation, an increase in TL stimulates Anet [11]. In our results relative to current climates, TL was generally below such threshold, so that higher lt values were associated with higher Anet,cum. Nevertheless, as apparent from our results, there is a maximum lt, above which increases in lt are no longer beneficial for CO2 assimilation because of an insufficient leaf cooling. Note that most boreal forests have needles, i.e., low lt. It is important to note that plants grown in high light generally have thick leaves to protect them from high-irradiance damage [61,62]. Conversely, in low light available conditions (frequent in boreal forests), leaf thickness reduces to maximize the light capturing area and reduce self-shading. As such, the low lt typical of many boreal species enhances light use, and hence potentially net CO2 assimilation via light capture as opposed to optimal temperature for photosynthesis. Finally, within the range of values observed in mid-to-high latitudes (shaded area in Figure 1), the two parameters of the photosynthesis model (g1, β), and both albedos (αPAR and αNIR) did not have any conspicuous influence on the net assimilation rates.
Regarding the thermal metrics, the range of high photosynthesis, ∆T90w, was equally influenced by the traits lt, g1 and VCMAX,25. Conversely, the maximum temperature TL,max, was mostly influenced by lt, while g1 and VCMAX,25 played secondary roles. In particular, lower lt values and higher g1 values at a given VCMAX,25 lead to higher boundary layer and stomatal conductances, respectively, preventing thermal damage and resulting in lower TL. Similarly, higher VCMAX,25 were associated to higher gs, which in turn enhances the cooling effect of transpiration, resulting in lower TL. This mechanism might explain the effect of VCMAX,25 on TL,max. However, with focus on values observed in mid-to-high latitudes (shaded area in Figure 1), the role of VCMAX,25 in regulating maximum TL diminished. Therefore, under warm conditions, the cooling effect of the transpiration process is generally neither benefited from nor hampered by the assimilation capacity of plants, but rather regulated by the traits lt and g1.
There is no evidence in the literature of how ∆T90 changes according to plant traits, to the best of our knowledge. The width of the temperature range that realizes >80% of the maximum photosynthetic rate varies among plant functional types, in particular at low growth temperatures [1,63,64]; however, thus far these differences have not been explained based on specific traits. Our results suggest that VCMAX,25, lt and g1 are the key traits that determine ∆T90, possibly through the regulation of stomatal and boundary layer conductances. The influence of VCMAX,25 might also be due to the dependence of VCMAX on TL per se, as discussed in the Supplementary Materials. As apparent from Figure 1, leaves that are well coupled with the atmosphere (i.e., high gb and gs and, therefore, high g1 and low lt) correspond to wider ∆T90, whereas leaves less coupled with the atmosphere present narrower ∆T90. This might mirror two different plant strategies. Plants that are well coupled with the atmosphere respond more easily to changes on the atmospheric conditions so that TL fluctuates in a wider range than plants that are less coupled with the atmosphere. Therefore, plants that have lower assimilation rates and are well coupled have a wider range of optimal temperatures so that the lower assimilation rates and wider TL fluctuations are compensated by a wider range of semi-optimal conditions. In contrast, the ones with higher assimilation rates and less coupled with the atmosphere present a narrower range of optimal temperatures.
Briefly, lt, VCMAX,25 and, to some extent, g1 are key for both assimilation rates and thermoregulation capacities, mainly via the regulation of stomatal and boundary layer conductances. With focus on mid-to-high latitudes, while VCMAX,25 has no large influence on the maximum temperature reached by plants under warm conditions, it is key on their thermoregulation capacity under normal conditions (Figure 1). Moreover, over this region, g1 has no effect on assimilation rates, which are completely dominated by VCMAX,25. Finally, the traits β, αPAR and αNIR seem to play secondary roles for all four metrics analyzed here and they will therefore not be further discussed.
It is important to acknowledge that the results of this analysis might slightly change if other growing conditions were to be used. However, this potential limitation is mitigated by the method employed to split the 100 combinations of traits between Group 1 and Group 2 (Section 2.3.2), which identifies suitable threshold irrespective of the model outputs. As such, similar conclusions on the relevance of the different traits would be drawn when considering other realistic growing conditions. A further aspect not accounted for here is thermal acclimation. Hence, assimilation rates and thermoregulation capacities and how they are influenced by the analyzed traits might change along with growth temperature, even in existing leaves, at scales of few days to weeks [1,63]. This might represent a limitation when comparing species exposed to contrasting growing conditions and/or with different acclimation capability. However, this analysis still provides key information by identifying the most dominant traits, their inter-relation and the potential mechanisms that explain plant responses.

4.2. Role of Growing Conditions: the Timing of Precipitatio Affects the Risk of Thermal Damage

In combination with its inherent traits, plant CO2 assimilation and the risk of thermal damage are also determined by the environmental conditions. The contrasting weather conditions of the growing seasons 2005 and 2006 showed how the same combinations of traits led to different values for the four metrics. While no substantial differences were found in terms of maximum net CO2 assimilation rate, Anet,max, and the range of high photosynthesis, ∆T90, substantial differences emerged in terms of maximum temperature, TL,max and cumulated net CO2 assimilation, Anet,cum (Figure 2). Specifically, despite the lower mean air temperature and higher precipitation of 2005 over 2006, warm periods were more damaging in the growing season 2005 than 2006 regardless of the combination of traits (Figure 2). This difference can be ascribed to the timing of rainfall events: in 2005, the longest dry spell occurred during the warmest days, while this was not the case in 2006. As a result, in 2006, plants with sets of traits that reached thermal damage under growing conditions of 2005 could cope with similar or higher air temperatures and even benefit from these slightly warmer conditions, as photosynthetic capacity was enhanced and water was available for evaporative cooling to stave off the risk of thermal damage.
This result also emerges when exploring in more detail the combined effect of temperature and water availability as part of the future scenario analyses. While leaf temperature, TL, was sensitive to soil water potential, Ψs, under warm conditions (Figure 4, top row), TL did not show any substantial change with Ψs under normal ones (Figure 3, top row). Moreover, higher air temperature enhances soil water depletion but only under warm conditions (Figure 5, top row). These conclusions are not affected by the length of the growing season, which was held constant, since the aim is to identify differences in assimilation rates and leaf temperatures for different trait combinations but given the same abiotic conditions. The results highlight the relevance of the timing of high temperature and lower water availability and the importance of considering these events in conjunction [65,66,67,68]. The joint effects of high temperatures and low water availability are expected to become even more important in the future since high temperatures and water deficiency during the growing season are likely to become more frequent in boreal regions [69]. Hence, there will be a likely increase in the probability that boreal forests will need to cope with more severe combination of heat waves and droughts [15].

4.3. Interactions of Traits and Growing Conditions: Most Suitable Traits for Enhanced CO2 Assimilation and Reduced Risk of Thermal Damage Under Current and Future Climates

Within the general pattern discussed above, specific trait sets can reduce or enhance thermal risks in specific climatic conditions. Therefore, attempts to identify the set of traits that could maximize productivity while preventing thermal damage under current and future conditions necessarily require the joint analysis of traits and specific growing conditions. Thus, we tested four combination of traits that presented contrasting assimilation and thermal responses during the 2005 growing season—a normal season at the reference site—and analyzed their response under multiple scenarios of future climatic conditions. The values for each trait within each combination are restricted to those observed in boreal tree species—in line with our focus on boreal forests—to account for the fact that trait values per se are related to climatic conditions. For example, global VCMAX,25 distribution has been recently proved to be mainly explained by climate [70].
As discussed in Section 4.2, the combination of low water availability and heat stress causes a disproportionate damage compared to each stress component occurring in isolation [65,66,71,72,73]. Due to the compound effect of low water availability and high temperatures,the focus of the multi-scenarios assessment was on these abiotic stressors, holding constant (i.e., as observed in 2005) the other climatic variables (chiefly, relative humidity, wind speed and atmospheric CO2 concentration). Nevertheless, reduced wind speed may lead to a further decoupling of leaf and air temperature, with potentially high and damaging temperatures in sunlit leaves. Declines of long-term wind speed (stilling) have been reported in both hemispheres [74,75]. Therefore, all else being equal, even higher leaf temperatures, TL, are to be expected as the result of the reduced cooling due to stilling, in particular for leaves with large effective leaf thickness lt. Similarly, the stomatal closure potentially caused by enhanced CO2 concentration may further increase TL.
Our model results clearly show that, during the warmest period of the growing season, future warmer and drier conditions may cause reductions in net CO2 assimilation Anet regardless of plant traits (Figure 4 bottom row). Conversely, during periods with lower air temperatures, warming temperatures might be still beneficial (Figure 3 bottom row). However, the analysis of temperature data over the period 1996–2006 (upper row histogram in Figure 3) shows that, for the site of reference, higher temperatures were either not beneficial for productivity or even harmful in 75% to 93% of the days of the growing season depending on the trait combination. Similarly, future warmer and drier conditions may substantially increase the risk of thermal damage regardless of the plant traits. Nonetheless, how damaging these new conditions are depends on the trait set (Figure 4, upper row).
The GSA clearly shows that high photosynthetic capacity (i.e., high maximum carboxylation rate, VCMAX,25) is needed to ensure high Anet; small lt is the key trait to prevent thermal damage. Under the growing conditions of 2005, enhancement of cumulated net CO2 assimilation Anet,cum emerges when VCMAX,25 increases, as apparent when comparing the Anet,cum for combination 1 (Low Productivity-High Risk; LP-HR) and combination 4 (Low Productivity-Low Risk, LP-LR) against combinations 2 (High Productivity-Low Risk; HP-LR) and 3 (High Productivity-High Risk; HP-HR) (Table 2). Indeed, under standard conditions, the combination with the highest VCMAX,25 (combination 3; HP-HR) showed the highest Anet (Figure 3 bottom row). However, this enhancement does not hold under warm conditions: in this case, assimilation rate is also limited by leaf temperature as the optimal temperature for photosynthesis is exceeded (Figure 4 bottom row). Temperatures above this threshold might constrain assimilation rates to the extent that plants with high VCMAX,25 can show lower assimilation rates than plants with lower VCMAX,25, as apparent by comparing combination 4 (LP-LR) versus combination 3 (HP-HR) under the warmest conditions within the growing season. This pattern is also partly due to the sensitivity to temperature of VCMAX increasing with VCMAX,25. Moreover, lower VCMAX,25 was also associated with broader the range of high photosynthesis ∆T90, i.e., a broader range of conditions that allow high assimilation rates. In fact, only those plants with the ability to be well-coupled with the atmosphere (i.e., low lt) can keep high assimilation rates as shown by combination 2 (HP-LR) and, to a lower extent, combination 4 (LP-LR) (Figure 4 bottom row), even though the former has substantially higher assimilation rates and lower TL than the latter.
Nonetheless, combination 3 (HP-HR), which has high lt, was the one with the highest Anet,cum within the growing season 2005. This is because, up to air temperature TA around 18 °C, combination 3 (HP-HR) shows the highest Anet (Figure 3 bottom row). Based on the observed TA at midday in the reference site during the growing seasons in 1996-2016, TA was lower than 18 °C in the 70.3% of days, making combination 3 (HP-HR) the most appealing one in terms of overall productivity. However, this combination can also lead to thermal damage (i.e., TL exceeding the critical temperature for leaf damage, TCRIT; Section 2.3.2) for TA ≥26 °C, particularly under limited water availability (Figure 4 top row). The maximum TA at midday reported in the reference site exceeded this value in half the years between 1996 and 2016 (Figure 4, top left). Therefore, high lt might be beneficial for assimilation over the growing season at the expense of increasing the likelihood of thermal damage, particularly under water stress. Indeed, across all plant types, leaf size is distributed geographically according to a combination of the mean temperature of the warmest month of the year and the mean annual precipitation: higher mean temperature in the warmest month and lower mean annual precipitation correspond to smaller leaf sizes [76]. Thus, the selection of lt is not straightforward since it might represent a trade-off between productivity and prevention of thermal damage. The net result depends largely on the climatic conditions.
As explained in the Methods, the trait combinations were generated randomly because this study aimed to identify the role of each trait when combined with other traits and under specific growing conditions. Thus, the trait combinations do not to represent specific species. However, the combination of traits that leads to the highest thermal damage risk and/or is more likely to experience sub-optimal conditions for assimilation can be used to speculate which species are likely to be more vulnerable to future growing conditions. For example, Pinus sylvestris L. and Picea abies L.—the two dominant species in Northern Europe - have small and rounded needle leaves (i.e., low effective leaf thickness lt) in common but Pinus sylvestris L. usually exhibits higher photosynthetic capacity (i.e., higher VCMAX,25) than Picea abies L. e.g., [77]. Although our results are not aimed to be representative of specific boreal species, Pinus sylvestris L. can be considered similar to combinations 2 (HP-LR) and Picea abies L. to combination 4 (LP-LR). As such, based on our conclusions, one could expect Picea abies L. to be more vulnerable to warming climates than Pinus sylvestris L., even suffering thermal damage under current conditions. Indeed, negative effects of warming on Picea abies L. species have been already reported [78], while Pinus sylvestris L. appears less sensitive to warming [79,80].

5. Conclusions

Understanding how specific plant traits, and combination of traits, prevent or enhance heat stress and C uptake is vital to predict how projected increases in frequency of heat waves and droughts in future climate may affect boreal forests. To disentangle the role of plant traits on thermoregulation and C uptake, we focused on six traits: the maximum carboxylation rate, two parameters that regulate the stomatal conductance and its sensibility to water stress, the effective leaf thickness and the PAR and NIR albedos. Four performance metrics related to leaf temperature and assimilation were evaluated, with a focus on the growing season.
Among the analyzed traits, photosynthetic capacity (as represented by maximum carboxylation rate at 25 ℃, VCMAX,25) and the effective leaf thickness, lt, were the dominant ones regarding both thermoregulation and assimilation. Higher values of VCMAX,25 are needed to enhance assimilation under current and future conditions. To prevent thermal damage, high VCMAX,25 should be combined with low lt. However, the selection between low or high lt is not straightforward since lt seems to represent a trade-off between thermal damage prevention and productivity.
Moreover, the climate change scenario analyses highlighted that the projected joint changes in temperature and water availability needs to be considered in our prognosis of future boreal forest wellbeing, because combination of traits that prevent thermal damage under current growing conditions will not be able to limit the occurrence of thermal damage under warmer and/or drier conditions. Likewise, substantial differences were observed when considering currently normal versus warm conditions within the same growing season. This suggests that trait selection should not only rely on the overall productivity over the whole growing season but should also consider specifically the warmest period.
Further analyses exploiting databases of traits and how they are distributed geographically and regional projections of growing conditions, combined with the understanding of the role played by plant traits provided by our results, can support the identification of species and regions most vulnerable to climate change, where appropriate forest management should be focused.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/10/5/398/s1, Supplementary Materials document.

Author Contributions

This article is the result of the joint effort between G.R.-P., G.V. and S.L. The tasks were divided as follows: conceptualization, G.R.-P., G.V. and S.L.; methodology, G.R.-P. and G.V.; software, S.L.; validation, G.R.-P. and G.V.; formal analysis, G.R.-P.; investigation, G.R.-P. and G.V.; resources, G.V. and S.L.; writing—original draft preparation, G.R.-P.; writing—review and editing, G.R.-P., G.V. and S.L. supervision, G.V. and S.L.; and funding acquisition, G.V.

Funding

This research was supported by the Swedish Research Council Formas through grant 2018-01820 and by the Swedish government through the project Trees and Crops for the Future (TC4F). S.L. was also supported by the Academy of Finland, through the Academy Research Fellow project CLIMOSS (No. 296116 and 307192).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Yamori, W.; Hikosaka, K.; Way, D.A. Temperature response of photosynthesis in C-3, C-4, and CAM plants: Temperature acclimation and temperature adaptation. Photosynth. Res. 2014, 119, 101–117. [Google Scholar] [CrossRef]
  2. Hasanuzzaman, M.; Nahar, K.; Alam, M.M.; Roychowdhury, R.; Fujita, M. Physiological, Biochemical, and Molecular Mechanisms of Heat Stress Tolerance in Plants. Int. J. Mol. Sci. 2013, 14, 9643–9684. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. O’Sullivan, O.S.; Heskel, M.A.; Reich, P.B.; Tjoelker, M.G.; Weerasinghe, L.K.; Penillard, A.; Zhu, L.L.; Egerton, J.J.G.; Bloomfield, K.J.; Creek, D.; et al. Thermal limits of leaf metabolism across biomes. Glob. Chang. Biol. 2017, 23, 209–223. [Google Scholar] [CrossRef] [PubMed]
  4. Schreiber, U.; Berry, J.A. Heat-induced changes of chlorophyll fluorescence in intact leaves correlated with damage of the photosynthetic apparatus. Planta 1977, 136, 233–238. [Google Scholar] [CrossRef]
  5. Huve, K.; Bichele, I.; Rasulov, B.; Niinemets, U. When it is too hot for photosynthesis: Heat-induced instability of photosynthesis in relation to respiratory burst, cell permeability changes and H2O2 formation. Plant Cell Environ. 2011, 34, 113–126. [Google Scholar] [CrossRef] [PubMed]
  6. Knight, C.A.; Ackerly, D.D. Evolution and plasticity of photosynthetic thermal tolerance, specific leaf area and leaf size: Congeneric species from desert and coastal environments. New Phytol. 2003, 160, 337–347. [Google Scholar] [CrossRef]
  7. O’Sullivan, O.S.; Weerasinghe, K.W.L.K.; Evans, J.R.; Egerton, J.J.G.; Tjoelker, M.G.; Atkin, O.K. High-resolution temperature responses of leaf respiration in snow gum (Eucalyptus pauciflora) reveal high-temperature limits to respiratory function. Plant, Cell 2013, 36, 1268–1284. [Google Scholar] [CrossRef]
  8. Chaves, M.; Costa, J.; Zarrouk, O.; Pinheiro, C.; Lopes, C.; Pereira, J.; Costa, J. Controlling stomatal aperture in semi-arid regions—The dilemma of saving water or being cool? Plant Sci. 2016, 251, 54–64. [Google Scholar] [CrossRef]
  9. Monteiro, M.V.; Blanuša, T.; Verhoef, A.; Hadley, P.; Cameron, R.W.F. Relative importance of transpiration rate and leaf morphological traits for the regulation of leaf temperature. Aust. J. Bot. 2016, 64, 32. [Google Scholar] [CrossRef] [Green Version]
  10. Radin, J.W.; Lu, Z.; Percy, R.G.; Zeiger, E. Genetic variability for stomatal conductance in Pima cotton and its relation to improvements of heat adaptation. Proc. Natl. Acad. Sci. USA 1994, 91, 7217–7221. [Google Scholar] [CrossRef]
  11. Okajima, Y.; Taneda, H.; Noguchi, K.; Terashima, I. Optimum leaf size predicted by a novel leaf energy balance model incorporating dependencies of photosynthesis on light and temperature. Ecol. Res. 2012, 27, 333–346. [Google Scholar] [CrossRef]
  12. Vogel, S. Leaves in the lowest and highest winds: temperature, force and shape. New Phytol. 2009, 183, 13–26. [Google Scholar] [CrossRef] [Green Version]
  13. Leigh, A.; Sevanto, S.; Ball, M.C.; Close, J.D.; Ellsworth, D.S.; Knight, C.A.; Nicotra, A.B.; Vogel, S. Do thick leaves avoid thermal damage in critically low wind speeds? New Phytol. 2012, 194, 477–487. [Google Scholar] [CrossRef] [Green Version]
  14. Luquet, D.; Bégué, A.; Vidal, A.; Clouvel, P.; Dauzat, J.; Olioso, A.; Gu, X.; Tao, Y. Using multidirectional thermography to characterize water status of cotton. Remote. Sens. Environ. 2003, 84, 411–421. [Google Scholar] [CrossRef]
  15. Intergovernmental Panel on Climate Change. Climate Change 2014—Impacts, Adaptation and Vulnerability: Part B: Regional Aspects; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
  16. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E. (Ted); et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  17. Boisvenue, C.; Running, S.W. Impacts of climate change on natural forest productivity—Evidence since the middle of the 20th century. Chang. Boil. 2006, 12, 862–882. [Google Scholar] [CrossRef]
  18. Sastry, A.; Barua, D. Leaf thermotolerance in tropical trees from a seasonally dry climate varies along the slow-fast resource acquisition spectrum. Sci. Rep. 2017, 7, 11246. [Google Scholar] [CrossRef] [Green Version]
  19. Bonan, G.B. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science 2008, 320, 1444–1449. [Google Scholar] [CrossRef] [Green Version]
  20. Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Kauppi, P.E.; Kurz, W.A.; Phillips, O.L.; Shvidenko, A.; Lewis, S.L.; Canadell, J.G.; et al. A Large and Persistent Carbon Sink in the World’s Forests. Science 2011, 333, 988–993. [Google Scholar] [CrossRef]
  21. Baul, T.K.; Alam, A.; Ikonen, A.; Strandman, H.; Asikainen, A.; Peltola, H.; Kilpeläinen, A. Climate Change Mitigation Potential in Boreal Forests: Impacts of Management, Harvest Intensity and Use of Forest Biomass to Substitute Fossil Resources. Forests 2017, 8, 455. [Google Scholar] [CrossRef]
  22. Grossiord, C.; Sevanto, S.; Adams, H.D.; Collins, A.D.; Dickman, L.T.; McBranch, N.; Michaletz, S.T.; Stockton, E.A.; Vigil, M.; McDowell, N.G. Precipitation, not air temperature, drives functional responses of trees in semi-arid ecosystems. J. Ecol. 2017, 105, 163–175. [Google Scholar] [CrossRef]
  23. Helman, D.; Osem, Y.; Yakir, D.; Lensky, I.M. Relationships between climate, topography, water use and productivity in two key Mediterranean forest types with different water-use strategies. Agric. Meteorol. 2017, 232, 319–330. [Google Scholar] [CrossRef]
  24. Grossiord, C.; Granier, A.; Gessler, A.; Jucker, T.; Bonal, D. Does Drought Influence the Relationship Between Biodiversity and Ecosystem Functioning in Boreal Forests? Ecosystems 2014, 17, 394–404. [Google Scholar] [CrossRef]
  25. Aubin, I.; Munson, A.; Cardou, F.; Burton, P.; Isabel, N.; Pedlar, J.; Paquette, A.; Taylor, A.; Delagrange, S.; Kebli, H.; et al. Traits to stay, traits to move: a review of functional traits to assess sensitivity and adaptive capacity of temperate and boreal trees to climate change. Environ. Rev. 2016, 24, 164–186. [Google Scholar] [CrossRef] [Green Version]
  26. Girardin, M.P.; Bouriaud, O.; Hogg, E.H.; Kurz, W.; Zimmermann, N.E.; Metsaranta, J.M.; de Jong, R.; Frank, D.C.; Esper, J.; Buntgen, U.; et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl. Acad. Sci. USA 2016, 113, E8406–E8414. [Google Scholar] [CrossRef] [Green Version]
  27. O’Brien, M.J.; Engelbrecht, B.M.J.; Joswig, J.; Pereyra, G.; Schuldt, B.; Jansen, S.; Kattge, J.; Landhäusser, S.M.; Levick, S.R.; Preisler, Y.; et al. A synthesis of tree functional traits related to drought-induced mortality in forests across climatic zones. J. Appl. Ecol. 2017, 54, 1669–1686. [Google Scholar] [CrossRef]
  28. Launiainen, S.; Katul, G.G.; Kolari, P.; Lindroth, A.; Lohila, A.; Aurela, M.; Varlagin, A.; Grelle, A.; Vesala, T. Do the energy fluxes and surface conductance of boreal coniferous forests in Europe scale with leaf area? Chang. Boil. 2016, 22, 4096–4113. [Google Scholar] [CrossRef]
  29. Launiainen, S.; Katul, G.G.; Laurén, A.; Kolari, P. Coupling boreal forest CO2, H2O and energy flows by a vertically structured forest canopy – Soil model with separate bryophyte layer. Ecol. Model. 2015, 312, 385–405. [Google Scholar] [CrossRef]
  30. Farquhar, G.D.; Von Caemmerer, S.; Berry, J.A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 1980, 149, 78–90. [Google Scholar] [CrossRef] [Green Version]
  31. Medlyn, B.E.; Dreyer, E.; Ellsworth, D.; Forstreuter, M.; Harley, P.C.; Kirschbaum, M.U.F.; Le Roux, X.; Montpied, P.; Strassemeyer, J.; Walcroft, A.; et al. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ. 2002, 25, 1167–1179. [Google Scholar] [CrossRef] [Green Version]
  32. Kellomäki, S.; Wang, K.-Y. Photosynthetic responses to needle water potentials in Scots pine after a four-year exposure to elevated CO2 and temperature. Tree Physiol. 1996, 16, 765–772. [Google Scholar] [CrossRef] [PubMed]
  33. Medlyn, B.E.; Duursma, R.A.; Eamus, D.; Ellsworth, D.; Prentice, I.C.; Barton, C.V.M.; Crous, K.Y.; De Angelis, P.; Freeman, M.; Wingate, L. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Chang. Boil. 2011, 17, 2134–2144. [Google Scholar] [CrossRef] [Green Version]
  34. Zhao, W.; Qualls, R.J. A multiple-layer canopy scattering model to simulate shortwave radiation distribution within a homogeneous plant canopy. N.a. Resour. 2005, 41. [Google Scholar] [CrossRef] [Green Version]
  35. Zhao, W.; Qualls, R.J. Modeling of long-wave and net radiation energy distribution within a homogeneous plant canopy via multiple scattering processes. N.a. Resour. 2006, 42. [Google Scholar] [CrossRef] [Green Version]
  36. Schuepp, P.H. Tansley Review No. 59 Leaf Boundary-Layers. New Phytol. 1993, 125, 477–507. [Google Scholar] [CrossRef]
  37. Campbell, G.S.; Norman, J.M. An Introduction to Environmental Biophysics; Springer Nature: Basingstoke, UK, 1998. [Google Scholar]
  38. Medlyn, B.E.; Badeck, F.W.; De Pury, D.G.G.; Barton, C.V.M.; Broadmeadow, M.; Ceulemans, R.; De Angelis, P.; Forstreuter, M.; Jach, M.E.; Kellomaki, S.; et al. Effects of elevated [CO2] on photosynthesis in European forest species: a meta-analysis of model parameters. Plant Cell Eniviron. 1999, 22, 1475–1495. [Google Scholar] [CrossRef] [Green Version]
  39. Baldocchi, D.; Meyers, T. On using eco-physiological, micrometeorological and biogeochemical theory to evaluate carbon dioxide, water vapor and trace gas fluxes over vegetation: A perspective. Agric. Meteorol. 1998, 90, 1–25. [Google Scholar] [CrossRef]
  40. Kattge, J.; Knorr, W. Temperature acclimation in a biochemical model of photosynthesis: A reanalysis of data from 36 species. Plant Cell 2007, 30, 1176–1190. [Google Scholar] [CrossRef]
  41. Beerling, D.; Quick, W. A new technique for estimating rates of carboxylation and electron transport in leaves of C3 plants for use in dynamic global vegetation models. Chang. Boil. 1995, 1, 289–294. [Google Scholar] [CrossRef]
  42. Aerts, R.; Brovkin, V.; Cavender-Bares, J.; Cavender-Bares, J.; Verheijen, L.M.; Cornelissen, J.H.C.; Kattge, J.; Van Bodegom, P.M. Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model. Chang. Boil. 2015, 21, 3074–3086. [Google Scholar]
  43. A Arain, M.; A Black, T.; Barr, A.G.; Jarvis, P.G.; Massheder, J.M.; Verseghy, D.L.; Nesic, Z. Effects of seasonal and interannual climate variability on net ecosystem productivity of boreal deciduous and conifer forests. Can. J. 2002, 32, 878–891. [Google Scholar] [CrossRef]
  44. Medlyn, B.E.; Berbigier, P.; Clement, R.; Grelle, A.; Loustau, D.; Linder, S.; Wingate, L.; Jarvis, P.G.; Sigurdsson, B.D.; McMurtrie, R.E. Carbon balance of coniferous forests growing in contrasting climates: Model-based analysis. Agric. Meteorol. 2005, 131, 97–124. [Google Scholar] [CrossRef]
  45. Niinemets, Ülo Research review. Components of leaf dry mass per area - thickness and density - alter leaf photosynthetic capacity in reverse directions in woody plants. New Phytol. 1999, 144, 35–47. [CrossRef]
  46. Niinemets, Ülo GLOBAL-SCALE CLIMATIC CONTROLS OF LEAF DRY MASS PER AREA, DENSITY, AND THICKNESS IN TREES AND SHRUBS. Ecology 2001, 82, 453–469. [CrossRef]
  47. Lin, Y.-S.; Medlyn, B.E.; Duursma, R.A.; Prentice, I.C.; Wang, H.; Baig, S.; Eamus, D.; de Dios,, V.R.; Mitchell, P.; Ellsworth, D.S.; et al. Optimal stomatal behaviour around the world. Nat. Clim. Change 2015, 5, 459. [Google Scholar] [CrossRef]
  48. Breuer, L.; Eckhardt, K.; Frede, H.-G. Plant parameter values for models in temperate climates. Ecol. Model. 2003, 169, 237–293. [Google Scholar] [CrossRef]
  49. Bartlett, M.K.; Ollinger, S.V.; Wicklein, H.F.; Hollinger, D.Y.; Richardson, A.D. Canopy-scale relationships between foliar nitrogen and albedo are not observed in leaf reflectance and transmittance within temperate deciduous tree species. Botany 2011, 89, 491–497. [Google Scholar] [CrossRef]
  50. Dickinson, R.E. Land Surface Processes and Climate—Surface Albedos and Energy Balance. Adv. Geophys. 1983, 25, 305–353. [Google Scholar]
  51. Hornberger, G.M.; Spear, R.C. Eutrophication in Peel Inlet.1. Problem-Defining Behavior and a Mathematical-Model for the Phosphorus Scenario. Water Res. 1980, 14, 29–42. [Google Scholar] [CrossRef]
  52. McIntyre, N.; Jackson, B.; Wade, A.; Butterfield, D.; Wheater, H.; Wade, A. Sensitivity analysis of a catchment-scale nitrogen model. J. Hydrol. 2005, 315, 71–92. [Google Scholar] [CrossRef]
  53. Medici, C.; Wade, A.; Frances, F.; Wade, A. Does increased hydrochemical model complexity decrease robustness? J. Hydrol. 2012, 440, 1–13. [Google Scholar] [CrossRef]
  54. Ruiz-Pérez, G.; Medici, C.; Latron, J.; Llorens, P.; Gallart, F.; Francès, F.; Ruiz-Pérez, G. Investigating the behaviour of a small Mediterranean catchment using three different hydrological models as hypotheses. Hydrol. Process. 2016, 30, 2050–2062. [Google Scholar] [CrossRef]
  55. Kolari, P.; Kulmala, L.; Pumpanen, J.; Launiainen, S.; Ilvesniem, H.; Hari, P.; Nikinmaa, E. CO2 exchange and component CO2 fluxes of a boreal Scots pine forest. Boreal Environ. Res. 2009, 14, 761–783. [Google Scholar]
  56. Kulmala, L.; Launiainen, S.; Pumpanen, J.; Lankreijer, H.; Lindroth, A.; Hari, P.; Vesala, T. H2O and CO2fluxes at the floor of a boreal pine forest. N.a. B: Chem. Phys. Meteorol. 2008, 60, 167–178. [Google Scholar] [Green Version]
  57. Gao, Y.; Markkanen, T.; Aurela, M.; Mammarella, I.; Thum, T.; Tsuruta, A.; Yang, H.; Aalto, T. Response of water use efficiency to summer drought in a boreal Scots pine forest in Finland. Biogeosciences 2017, 14, 4409–4422. [Google Scholar] [CrossRef] [Green Version]
  58. Richardson, A.D.; Keenan, T.F.; Migliavacca, M.; Ryu, Y.; Sonnentag, O.; Toomey, M. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. Meteorol. 2013, 169, 156–173. [Google Scholar] [CrossRef]
  59. Gill, A.L.; Gallinat, A.S.; Sanders-DeMott, R.; Rigden, A.J.; Gianotti, D.J.S.; Mantooth, J.A.; Templer, P.H. Changes in autumn senescence in northern hemisphere deciduous trees: A meta-analysis of autumn phenology studies. Ann. Bot. 2015, 116, 875–888. [Google Scholar] [CrossRef]
  60. Michaletz, S.T.; Weiser, M.D.; McDowell, N.G.; Zhou, J.; Kaspari, M.; Helliker, B.R.; Enquist, B.J. Corrigendum: The energetic and carbon economic origins of leaf thermoregulation. Nat. Plants 2016, 2, 16147. [Google Scholar] [CrossRef]
  61. Davi, H.; Barbaroux, C.; Dufrene, E.; François, C.; Montpied, P.; Breda, N.; Badeck, F. Modelling leaf mass per area in forest canopy as affected by prevailing radiation conditions. Ecol. Model. 2008, 211, 339–349. [Google Scholar] [CrossRef]
  62. Xu, F.; Guo, W.; Xu, W.; Wei, Y.; Wang, R. Leaf morphology correlates with water and light availability: What consequences for simple and compound leaves? Prog. Sci. Mater. Int. 2009, 19, 1789–1798. [Google Scholar] [CrossRef]
  63. Vico, G.; Way, D.A.; Hurry, V.; Manzoni, S. Can leaf net photosynthesis acclimate to rising and more variable temperatures? Plant Cell Environ. 2019. [Google Scholar] [CrossRef]
  64. Way, D.A.; Yamori, W. Thermal acclimation of photosynthesis: On the importance of adjusting our definitions and accounting for thermal acclimation of respiration. Photosynth. Res. 2014, 119, 89–100. [Google Scholar] [CrossRef]
  65. Barnabas, B.; Jager, K.; Feher, A. The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 2008, 31, 11–38. [Google Scholar] [CrossRef] [PubMed]
  66. De Boeck, H.J.; Bassin, S.; Verlinden, M.; Zeiter, M.; Hiltbrunner, E. Simulated heat waves affected alpine grassland only in combination with drought. New Phytol. 2016, 209, 531–541. [Google Scholar] [CrossRef]
  67. Suzuki, N.; Rivero, R.M.; Shulaev, V.; Blumwald, E.; Mittler, R. Abiotic and biotic stress combinations. New Phytol. 2014, 203, 32–43. [Google Scholar] [CrossRef] [Green Version]
  68. Zscheischler, J.; Westra, S.; van den Hurk, B.J.J.M.; Seneviratne, S.I.; Ward, P.J.; Pitman, A.; AghaKouchak, A.; Bresch, D.N.; Leonard, M.; Wahl, T.; et al. Future climate risk from compound events. Nat. Clim. Chang. 2018, 8, 469–477. [Google Scholar] [CrossRef]
  69. Belyazid, S.; Giuliana, Z. Water limitation can negate the effect of higher temperatures on forest carbon sequestration. Eur. J. 2019, 138, 287–297. [Google Scholar] [CrossRef]
  70. Smith, N.G.; Keenan, T.F.; Prentice, I.C.; Wang, H.; Wright, I.J.; Niinemets, Ü.; Crous, K.Y.; Domingues, T.F.; Guerrieri, R.; Ishida, F.Y.; et al. Global photosynthetic capacity is optimized to the environment. Ecol. Lett. 2019, 22, 506–517. [Google Scholar] [CrossRef] [PubMed]
  71. Keleş, Y.; Öncel, I. Response of antioxidative defence system to temperature and water stress combinations in wheat seedlings. N.a. Sci. 2002, 163, 783–790. [Google Scholar] [CrossRef]
  72. Rizhsky, L.; Liang, H.; Mittler, R. The Combined Effect of Drought Stress and Heat Shock on Gene Expression in Tobacco1. N.a. Physiol. 2002, 130, 1143–1151. [Google Scholar] [Green Version]
  73. Zscheischler, J.; Seneviratne, S.I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 2017, 3, e1700263. [Google Scholar] [CrossRef]
  74. McVicar, T.R.; Roderick, M.L.; Donohue, R.J.; Van Niel, T.G. Less bluster ahead? Ecohydrological implications of global trends of terrestrial near-surface wind speeds. Ecohydrol. 2012, 5, 381–388. [Google Scholar] [CrossRef]
  75. Roderick, M.L.; Rotstayn, L.D.; Farquhar, G.D.; Hobbins, M.T. On the attribution of changing pan evaporation. Geophys. Lett. 2007, 34, L17401–L17403. [Google Scholar] [CrossRef]
  76. Wright, I.J.; Dong, N.; Maire, V.; Prentice, I.C.; Westoby, M.; Díaz, S.; Gallagher, R.V.; Jacobs, B.F.; Kooyman, R.; Law, E.A.; et al. Global climatic drivers of leaf size. Science 2017, 357, 917–921. [Google Scholar] [CrossRef]
  77. Niinemets, U. Stomatal conductance alone does not explain the decline in foliar photosynthetic rates with increasing tree age and size in Picea abies and Pinus sylvestris. Tree Physiol. 2002, 22, 515–535. [Google Scholar] [CrossRef] [Green Version]
  78. Zhang, X.W.; Wang, J.R.; Ji, M.F.; Milne, R.I.; Wang, M.H.; Liu, J.-Q.; Shi, S.; Yang, S.-L.; Zhao, C.-M. Higher Thermal Acclimation Potential of Respiration but Not Photosynthesis in Two Alpine Picea Taxa in Contrast to Two Lowland Congeners. PLoS One 2015, 10, e0123248. [Google Scholar] [CrossRef]
  79. Taeger, S.; Sparks, T.H.; Menzel, A. Effects of temperature and drought manipulations on seedlings of Scots pine provenances. Plant Biol. 2015, 17, 361–372. [Google Scholar] [CrossRef]
  80. Kurepin, L.V.; Stangl, Z.R.; Ivanov, A.G.; Bui, V.; Mema, M.; Hüner, N.P.; Öquist, G.; Way, D.; Hurry, V. Contrasting acclimation abilities of two dominant boreal conifers to elevated CO2 and temperature. Plant Cell 2018, 41, 1331–1345. [Google Scholar] [CrossRef]
Forests 10 00398 g001 550
Figure 1. Results of the Global Sensitivity Analysis. Rows refer to the four metrics; columns to the six traits investigated. In each main plot, the lines are the empirical cumulative function of the trait values: blue solid lines refer to the trait values that lead to metric values below the metric-specific threshold; the red dashed lines refer to trait values that lead to metric values above the threshold. Shaded areas identify ranges of trait typically observed in mid-to-high latitudes. In the insets, the box and whisker plots compare and summarize the trait values leading to metric values above (left box; A--) and below (right box; B-) the threshold. The boxes extend from the first to the third quartile, the end of the whiskers are computed as 1.5 × IQR (Interquantile Range). The median values are indicated by the horizontal red lines.
Figure 1. Results of the Global Sensitivity Analysis. Rows refer to the four metrics; columns to the six traits investigated. In each main plot, the lines are the empirical cumulative function of the trait values: blue solid lines refer to the trait values that lead to metric values below the metric-specific threshold; the red dashed lines refer to trait values that lead to metric values above the threshold. Shaded areas identify ranges of trait typically observed in mid-to-high latitudes. In the insets, the box and whisker plots compare and summarize the trait values leading to metric values above (left box; A--) and below (right box; B-) the threshold. The boxes extend from the first to the third quartile, the end of the whiskers are computed as 1.5 × IQR (Interquantile Range). The median values are indicated by the horizontal red lines.
Forests 10 00398 g001
Forests 10 00398 g002 550
Figure 2. Comparison of model outputs for 2005 and 2006. Value of the four metrics Anet,cum (in μmol m–2), Anet,max (in μmol m–2s–1), TL,max and ∆T90 (in °C) obtained for the 100 combination of traits under the growing conditions 2005 and 2006. The dashed line corresponds to the 1:1 line.
Figure 2. Comparison of model outputs for 2005 and 2006. Value of the four metrics Anet,cum (in μmol m–2), Anet,max (in μmol m–2s–1), TL,max and ∆T90 (in °C) obtained for the 100 combination of traits under the growing conditions 2005 and 2006. The dashed line corresponds to the 1:1 line.
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Forests 10 00398 g003 550
Figure 3. Leaf temperatures (top row; in °C ) and net assimilation rates (bottom row; in μmol m–2 s–1) for different air temperatures and soil water potentials. Four trait combinations are explored (Table 2). The lowest air temperature corresponds to the median day for the growing season 2005. As a term of comparison, the left column shows the frequency of the air temperature at midday reported in the study site during the growing seasons from 1996 to 2016. LP-HR: Low Productivity-High Risk of thermal damage; HP-LR: High Productivity-Low Risk of thermal damage; HP-HR: High Productivity and High Risk of thermal damage; and LP-LR: Low Productivity and Low Risk of thermal damage.
Figure 3. Leaf temperatures (top row; in °C ) and net assimilation rates (bottom row; in μmol m–2 s–1) for different air temperatures and soil water potentials. Four trait combinations are explored (Table 2). The lowest air temperature corresponds to the median day for the growing season 2005. As a term of comparison, the left column shows the frequency of the air temperature at midday reported in the study site during the growing seasons from 1996 to 2016. LP-HR: Low Productivity-High Risk of thermal damage; HP-LR: High Productivity-Low Risk of thermal damage; HP-HR: High Productivity and High Risk of thermal damage; and LP-LR: Low Productivity and Low Risk of thermal damage.
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Figure 4. Leaf temperatures (top row; in °C) and net assimilation rates (bottom row; in μmol m–2 s–1) for different air temperatures and soil water potentials. The baseline temperature corresponds to the warmest day of the growing season 2005. All the other parameters are as in Figure 3.
Figure 4. Leaf temperatures (top row; in °C) and net assimilation rates (bottom row; in μmol m–2 s–1) for different air temperatures and soil water potentials. The baseline temperature corresponds to the warmest day of the growing season 2005. All the other parameters are as in Figure 3.
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Figure 5. Soil water potential (upper panels; MPa) and cumulated net assimilation (lower panels; μmol m–2 s–1) for each focal trait combination (Table 2) for specific TA (y-axis) and after a certain number of consecutive days without precipitation (x-axis). The baseline temperature corresponds to the warmest day in 2005. The rest of the growing conditions correspond to those observed in the study site during the same period within the growing season of 2005.
Figure 5. Soil water potential (upper panels; MPa) and cumulated net assimilation (lower panels; μmol m–2 s–1) for each focal trait combination (Table 2) for specific TA (y-axis) and after a certain number of consecutive days without precipitation (x-axis). The baseline temperature corresponds to the warmest day in 2005. The rest of the growing conditions correspond to those observed in the study site during the same period within the growing season of 2005.
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Table
Table 1. Summary of the traits considered in the General Sensitivity Analysis (GSA), their ranges of variation emerging from the literature globally and for mid-to-high latitudes and corresponding supporting references.
Table 1. Summary of the traits considered in the General Sensitivity Analysis (GSA), their ranges of variation emerging from the literature globally and for mid-to-high latitudes and corresponding supporting references.
TraitRangeRange for Mid-to-high LatitudeReferencesReferences for Mid-to-high Latitude
VCMAX,25
(μmol m–2 s–1)		20–12035–70[39,40,41,42][29,39,40,43,44]
lt
(m)		0.005–0.15*[11,45,46][29]
g1
(kPa0.5)		1–151.5–7[33,38,47][29,33,38]
β
(-)		0.05–3**[29,33,38][29,33,38]
αPAR
(-)		0.02–0.30.04–0.12[48,49,50][29,48,49,50]
αNIR
(-)		0.1–0.70.2–0.55
* No specific values for mid-to-high latitude have been reported. However, boreal forests are dominated by species with needles. ** No specific values for mid-to-high latitude have been reported.
Table
Table 2. Combination of traits used to perform the climate scenarios analyses and the resulting values of the assimilation and thermal metrics for the growing season 2005. LP-HR: Low Productivity-High Risk of thermal damage; HP-LR: High Productivity-Low Risk of thermal damage; HP-HR: High Productivity-High Risk of thermal damage; LP-LR: Low Productivity-Low Risk of thermal damage.
Table 2. Combination of traits used to perform the climate scenarios analyses and the resulting values of the assimilation and thermal metrics for the growing season 2005. LP-HR: Low Productivity-High Risk of thermal damage; HP-LR: High Productivity-Low Risk of thermal damage; HP-HR: High Productivity-High Risk of thermal damage; LP-LR: Low Productivity-Low Risk of thermal damage.
Trait CombinationVCMAX,25
μmol m–2 s–1		lt
m		g1
kPa0.5		β
(-)		αPAR
(-)		αNIR
(-)		Anet,cum
μmol m–2		TL,max
°C		Anet,max
μmol m–2s–1		∆T90
°C
Comb. 1
(LP-HR)		36.160.146.352.410.110.392523549.508.156.02
Comb. 2
(HP-LR)		57.360.022.190.700.270.603277040.0811.545.60
Comb. 3
(HP-HR)		56.930.106.532.340.190.493681846.1812.585.83
Comb. 4
(LP-LR)		36.370.036.181.920.220.522560042.148.186.52

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MDPI and ACS Style

Ruiz-Pérez, G.; Launiainen, S.; Vico, G. Role of Plant Traits in Photosynthesis and Thermal Damage Avoidance under Warmer and Drier Climates in Boreal Forests. Forests 2019, 10, 398. https://doi.org/10.3390/f10050398

AMA Style

Ruiz-Pérez G, Launiainen S, Vico G. Role of Plant Traits in Photosynthesis and Thermal Damage Avoidance under Warmer and Drier Climates in Boreal Forests. Forests. 2019; 10(5):398. https://doi.org/10.3390/f10050398

Chicago/Turabian Style

Ruiz-Pérez, Guiomar, Samuli Launiainen, and Giulia Vico. 2019. "Role of Plant Traits in Photosynthesis and Thermal Damage Avoidance under Warmer and Drier Climates in Boreal Forests" Forests 10, no. 5: 398. https://doi.org/10.3390/f10050398

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