Under climate change, some forest ecosystems appear to be transitioning into net source of carbon dioxide (CO2), raising questions about the future role of soil respiration rate (Rs), which depends on hydroclimatic conditions. The main objective of this study was to assess the effects of artificial warming on Rs in a sugar maple forest at the northern limit of Quebec temperate deciduous forests in eastern Canada, and to evaluate the effect of species composition on soil response to warming. We measured Rs during the snow-free period of 2021 and 2022 in 32 plots distributed across three forest types, half of which were artificially heated by approximately 2°C with heating cables. We observed an increase in Rs in response to warming in the heated plots, but only up to a threshold of about 15°C, beyond which Rs started to slow down in respect to the control plots. We also observed a weakening of the exponential relationship between Rs and soil temperature beyond this threshold. This trend varied across the forest types, with hardwood-beech stands being more sensitive to warming than mixedwoods and other hardwoods. This greater response of hardwood-beech stands to warming resulted in a more significant slowdown of Rs, starting from a colder temperature threshold, around 10–12°C. This study highlights a potential plateauing of Rs despite rising soil temperature, at least in eastern Canada’s temperate deciduous forest, but this trend could vary from one forest type to the another.
Research Article
Soil Respiration Response to Experimental Warming in a Temperate Harwood Forest Varies With Species Composition
https://doi.org/10.21203/rs.3.rs-5045925/v1
This work is licensed under a CC BY 4.0 License
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temperate forest
soil respiration
artificial warming
drying
species composition
Soils are an important component of the global carbon cycle as they represent the largest terrestrial carbon reservoir and produce, through respiration, the second-largest carbon dioxide (CO2) flux after plant photosynthesis [1]. Soil respiration (Rs) consists of autotrophic respiration, which includes root and rhizosphere respiration, and heterotrophic respiration, which involves organic matter decomposition by soil communities [2]. Soil respiration is mostly influenced by soil hydroclimatic conditions, namely temperature and water content [3], as well as secondary factors such as species composition, which influences soil hydroclimatic conditions [4, 5].
Soil temperature is the variable that best explains the spatiotemporal variations in Rs [6, 7]. Except for certain ecosystems such as deserts, an increase in soil temperature leads to an increase in Rs [8]. Specifically, the relationship between soil temperature and Rs is positive and curvilinear, as demonstrated by several studies focusing on the influence of soil temperature on Rs in temperate forests [9, 10]. Soil temperature sensitivity is generally described by the Q10 value, which is the coefficient of the exponential relationship between Rs and temperature, multiplied by 10 [11]. Seasonal variations in Rs in ecosystems characterized by seasonality are however not solely attributable to temperature changes. At certain times of the year, the relationship between temperature and Rs is confounded by soil moisture [12] and phenology [13, 14].
During periods of drought, soil moisture becomes a limiting factor [15, 16]. Soil drying induced by warming and increased evaporative demand can thus explain the gradual decrease in soil sensitivity to increasing temperature that may be observed [17]. However, while the relationship between temperature and Rs is generally exponential, the relationship between moisture and Rs is more complex. Microbial activity, and therefore heterotrophic respiration, reaches a maximum within a certain range of soil water content [1]. The net effect of soil warming on Rs thus depends on the balance between the influence of temperature and that of soil moisture [18, 19]. Forest species composition may also influence Rs because it modulates (1) root activity, (2) soil chemical properties, such as nutrient availability and acidity, (3) litter inputs and quality, (4) microclimate, and (5) microbial communities and activity [4, 5, 9].
In the context of climate change and increasing droughts and fires, forest ecosystems transitioning to net sources of CO2 are increasing, including some forested regions that are not adapted to drought and fire [20, 21]. In such context, the role of Rs on atmospheric CO2 concentration is uncertain. While Rs could intensify because of warmer conditions, it could also decrease because of increased droughts and decreased net primary productivity, or it could remain unchanged because changes in temperature and moisture may offset each other [17, 22, 23]. Several studies reported tree growth decline and forest dieback in boreal and temperate ecosystems due to droughts [24–27]. Recurrent exposure to water stress can however promote ecological memory mechanisms that make trees more resilient to extreme climatic events [28, 29]. Species that recover more quickly from drought are likely to be favored under climate change with a greater frequency and intensity of water stress events. For example, Hesse et al. [30] showed that European beech (Fagus sylvatica L.) physiologically recovers more quickly following water stress events than Norway spruce (Picea abies (L.) Karst.). Similarly, since drought legacy is modulated by species composition, some forest types may have a greater capacity to resist to changes in soil microclimate under extreme weather events than others [31, 32]. Canarini et al. [33] proposed that cumulative drought can increase soil resilience by promoting their multifunctionality (e.g., microbial activity). However, the relationships between forest species composition, soil microclimate, high water stress, soil memory, and Rs remain to be elucidated.
The objective of this research was to assess the effects of artificial soil warming on Rs across a range of forest plots that encompass differences in the prevalence of conifers and American beech (Fagus grandifolia Ehrh.) within the northern range limit of Quebec temperate deciduous forest. It was hypothesized that artificial warming would generally lead to an increase in Rs in both mixedwoods and hardwoods, except during the warmest periods of summer when Rs would slow down due to a decrease in its temperature sensitivity following drying and water stress induced by increased evaporative demand. It was also hypothesized that the effects of artificial droughts on Rs would be smaller in plots where beech is abundant because it was shown to be more resistant to drought [34]. A better understanding of the relationships between forest species composition and soil processes can better guide decision-making in forestry that aims at making forests more resilient to climate change as well as using them as an efficient natural climate solution.
Study site
The study took place at the Station de Biologie des Laurentides of Université de Montréal in Saint-Hippolyte, Quebec (45°59’17’’N, 74°00’20”W), which is located at the northern limit of the maple-yellow birch bioclimatic domain [35]. The site is mostly composed of sugar maple (Acer saccharum Marsh.), red maple (Acer rubrum L.), American beech, yellow birch (Betula alleghaniensis Britton), white birch (Betula papyrifera Marshall), eastern white cedar (Thuja occidentalis L.), white pine (Pinus strobus L.), and large-tooth aspen (Populus grandidentata Michx.) [36]. Due to its proximity to the boreal forest, species that are typical of that biome, notably balsam fir (Abies balsamea (L.) Mill.), are also present. The average temperature at the study site is 4.3°C, while the average precipitation is 1195 mm, with 900 mm of rain and 297 cm of snow [9]. Soils are orthic humo-ferric and ferro-humic podzols [37] that developed from a well-drained glacial till with a loamy sand texture [38] The forest floor is of the moder type, although the mor type can be found in wetter areas dominated by cedar.
Experimental design
The experimental design consisted of eight stands distributed across three distinct zones. Based on basal area and forest floor litter data, Bélanger et al. [9] classified stands into three forest types: mixedwoods, hardwoods, and hardwood-beech stands (Table 1). Stands were classified as mixedwoods when the basal area of balsam fir represented more than 20% of the total basal area. Hardwoods and hardwood-beech stands were differentiated based on the contribution of beech litter to the total litter. If beech litter accounted for more than 20% of the total forest floor litter in a stand, it was classified as a hardwood-beech stand, and if it was accounted for less than 20% of the total litter, it was classified as a hardwood. The litter was also collected in autumn 2021 as part of this study, and the results obtained (Table 1) were very similar to those collected by Bélanger et al. [9] between 2017 and 2020, suggesting that forest types had not changed significantly (e.g., from a disturbance such as windthrow). Thus, the original forest type classification was maintained. There were two forest types in zone 1 (mixedwoods and hardwoods), two types in zone 2 (mixedwoods and hardwood-beech stands), and all three types in zone 3. Due to the challenges of electrifying forest stands, not all zones included all three forest types. The experimental design was therefore unbalanced with respect to the distribution of forest types among zones. In May 2022, a major windstorm changed species composition significantly by toppling all mature fir trees. This event led to a 5-day power outage as well as a large opening in the canopy, thus providing significantly more light to the forest floor.
Table 1 Description of experimental design by zone, forest type and tree species contributions to total litterfall mass in each stand
N.B. Litter was collected in autumn 2021, but the classification of stands by forest type is based on Bélanger et al. [9]. The values in italics indicate that the data in this study differed slightly from the ones in Bélanger et al. [9] in respect to the > 20% criterion for classification of hardwood-beech stands. AS is Acer saccharum (sugar maple), AR is Acer rubrum (red maple) Betula is Betula alleghaniensis and Betula papyrifera (yellow birch and white birch), FG is Fagus grandifolia (American beech) and PG is Populus grandidentata (large-tooth aspen). Needles mostly come from Abies balsamea (balsam fir).
For the soil warming treatment, the heating cable method was preferred because it is relatively simple to set up when power is available, and it allows to create a considerable temperature differential with relatively high precision [39]. Each stand consisted of four 3 m × 3 m plots, including two control plots and two plots artificially heated using twin conductor 120 V heating cables (RX Roof & Gutter De-Icing Cables, Danfoss). In the heated plots, 36 m of cables were buried in a slalom pattern (~ 15 cm spacing) in May 2017 at a depth of about 12 cm, which corresponds to the forest floor and mineral soil interface. Soils were heated for 15 minutes every hour over 24 hours using mechanical timers starting only for one month toward the end of 2020 (i.e., mid-October to mid-November). The warming treatment was then applied during the snow-free period of 2021 and 2022, from mid-April to mid-November, thus extending the biologically active period by 4 to 6 weeks. The mechanical timer method has the advantage of providing an equal amount of energy in each plot and avoiding imprecision associated with electronic systems due to the installation of reference probes at various/inconsistent distances from the heating cables [40]. Each plot was equipped with two temperature probes (Spectrum Technologies) and two soil water potential probes (Watermark 200SS Sensor, Irrometer), all connected to a WatchDog 1650 Micro Station (Spectrum Technologies) that logged data at 30-minute intervals. The warming treatment created a consistent temperature differential of approximately 2°C for all three forest types (Fig. 1). Regarding soil water potential, it is typically expressed in negative values, meaning that as the soil dries, the water potential becomes more negative. However, for simplicity, soil water potential is thereafter expressed in positive values. A more positive water potential value thus means a drier soil, whereas a water potential close to zero indicates a wet soil. Unfortunately, problems with dataloggers in 2021 due to large changes in air temperature and thus condensation in the spring led to some gaps in soil climate data that limited analyses. This problem was completely resolved in 2022.
Field and laboratory methods
Gas samples at the soil-atmosphere interface were collected before the artificial warming in 2019 and 2020 by Bélanger et al. [9], as well as after the onset of the warming treatment in 2021 and 2022 (this study). Soil respiration from the heated plots in 2021 and 2022 could therefore be compared to temporal controls, which correspond to Rs measured by Bélanger et al. [9] in all plots in 2019 and 2020 (i.e., before the onset of warming). Soil respiration from the heated plots could also be compared to spatial controls, which correspond to Rs measured in this study in control plots in 2021 and 2022 (i.e., after the onset of warming). It was considered that the effects of cable installation on soil processes and properties disappeared rapidly (within 1–2 years) and thus, no trenches were dug in the control plots in 2017 to simulate disturbances during cable installation. This was based on literature [41–43] which suggests a lag time of approximately 6 to 12 months between cable implementation and activation [39] and verified using Plant Root Simulator (PRS) probes (Western Ag Innovations) which measure the ionic activity of soil solutions (unpublished data, N. Bélanger). Sampling was done every two weeks during the snow-free period of 2021 and weekly during the snow-free period of 2022 following the same protocol as in Bélanger et al. [9]. Insulated and ventilated chambers equipped with an Interlink IV injection site (Baxter) were placed on four collars that were installed in each plot in May 2017 (chamber volume of 4.56 L with the collar). Gas was sampled over 20 minutes at 5-minute intervals (t0, t5, t10, t15, t20) using a 20 mL syringe equipped with a 25-gauge 5/8-inch needle. Five mL of gas was extracted from each chamber at each time point, for a total of 20 mL. This cumulative sampling technique allows to capture spatial variability in CO2 flux within plot [44]. The collected samples were then transferred into a vacuumed and sealed 12 mL Exetainer vials (LabCo). For each stand, air temperature, relative humidity and pressure were measured at the time of sampling using proper probes and a WatchDog 1650 Micro Station (Spectrum Technologies).
The gas in the vials was analyzed within 48 h after sampling using cavity ring-down (CRD) spectroscopy (G2201-i isotopic gas analyzer, Picarro). The sample gas was carried with an autosampler (OpenAutoSampler) using ultra-zero air. Before analysis, the water vapor from gas samples was removed using a monotube NafionTM gas dryer (PermaPure). Carbon dioxide and methane (CH4) concentrations were then analyzed for each time point. From these concentration values (ppm), CO2 and CH4 fluxes (mg m-2 h-1) were estimated using the HMR package in R, which fits the data from t0 to t20 to a linear model [45]. The linear model was selected as it provided the best fit for 95% of the collected gas flux samples and led to consistent results for the remaining 5% [46–48]. This procedure follows the analytical protocol used in Bélanger et al. [9] so that temporal controls could be used as a reliable reference.
During the summers of 2021 and 2022, soil ionic activity (NO3-, NH4+, SO42-, Ca2+, Mg2+, K+, H2PO4-, Fe3+, and Mn2+) was measured in each of the plots as a proxy for nutrient availability. One set of four cation and one set of four anion PRS probes were incubated in each plot for 5 weeks, from June 2 to July 14 in 2021 and from June 8 to July 20 in 2022, at a depth of up to 15 cm. Following the incubation period, the probes were removed and cleaned with deionized water and then stored in the fridge until ion extraction. Probes were eluted for 1 hour with 0.5 M HCl. The NO3- and NH4+ ions were analyzed by colorimetry (Autoanalyser III, Bran and Luebbe), whereas other ions were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-OES, Optima 3000-DV, PerkinElmer). The use of PRS probes is now common in forest ecology [49, 50]. Unlike conventional extraction methods that indicate nutrient availability in the soil at a specific moment only, PRS probes capture the dynamics of soil ionic activity over time.
Data analysis
Data collected by Bélanger et al. [9], as well as those collected during the summer of 2021 and 2022 (this study), were analyzed using R. Variables were first transformed by taking the square root (^1/2) to ensure normal distribution. Exponential regression models were first tested for each forest type and treatment using the equation Rs = a × e (b × soil temperature). The Q10 value was then calculated for each model using the equation Q10 = e (b × 10). These models were tested for the full range of soil temperatures as well as specific (limited) ranges.
A series of multiple linear regressions were then tested to explain Rs (dependent variable). Soil temperature was always used as the first variable. Soil water potential, soil ionic activity (i.e., nitrogen ions, NO3- and NH4+, as well as PO43- and Ca2+ ions) and the presence or absence of conifers, especially balsam fir (binary variable where 0 = absence and 1 = presence), were tested as second explanatory variables, for each forest type and stand, along with soil temperature, in an attempt to explain variations in Rs. The residuals of each exponential regression model between Rs and soil temperature were also analyzed using exponential regression models with residuals as the dependent variable and soil water potential as the independent variable. Because the inclusion of secondary variables never improved the prediction of Rs for any forest types, stands, or treatments, results are not further discussed.
To account for spatial and temporal variability of Rs within stands, the difference in Rs between spatial and temporal controls (ΔRs) was calculated for all samplings in 2019 and 2020 within each stand, and ΔRs was also calculated as the difference between spatial controls and heated plots for all samplings in 2021 and 2022. Both sets of ΔRs were then plotted against Rs in the two controls (temporal for 2019 and 2020; spatial for 2021 and 2022).
For both the heated plots and the spatial controls, Rs rates in 2021 and 2022 followed a seasonal pattern with an increase from May to July-August, followed by a decrease until the end of the snow-free period in October-November (Fig. 2). Soil respiration rates in the heated plots varied from as low as 36.8 mg CO2 m-2 h-1 in hardwood-beech stands in May 2022 (Fig. 4C) to as high as 791 mg CO2 m-2 h-1 in mixedwoods in July 2022 (Fig. 4A).
The seasonal variation in Rs (Fig. 2) followed a pattern similar to that of soil temperature (Fig. 1). The determination coefficients (R2) of the exponential regression models between Rs and soil temperature for the heated plots and spatial controls of the three forest types (Fig. 3; Fig. 4) were relatively low, ranging between 0.30 and 0.45, but statistically highly significant (P < 0.05, Table 2). The Q10 values of the regression models varied from 1.85 for heated plots and spatial controls of hardwoods to 3.28 for spatial controls of hardwood-beech stands. Furthermore, Q10 values were higher for spatial controls than for heated plots in mixedwoods and hardwood-beech stands, whereas they were identical between spatial controls and heated plots in hardwoods (Table 2). Soil respiration rates were generally highest in mixedwoods and lowest in hardwood-beech stands, except for spatial controls of hardwood-beech stands where Rs exceeded that of hardwoods starting at a soil temperature of about 14°C (Fig. 3).
Table 2 Parameters and Q10 values of exponential regression models between soil temperature and soil respiration rate (Rs) for heated plots and spatial controls (2021 and 2022) in mixedwoods, hardwoods and hardwood-beech stands
* indicates that the coefficient of determination (R2) of the model is significant at p < 0.05.
The overlay of the regression curves from 2019–2020 and 2021–2022 demonstrated different responses of Rs to warming based on forest type (Fig. 4). For hardwood-beech stands, Rs in heated plots was lower than that in spatial and temporal controls starting at a 10–12°C threshold, and this difference largely increased with increasing soil temperatures (Fig. 4C). For mixedwoods, a similar pattern was observed but the soil temperature threshold was shifted by about 2°C (12–14°C) and the difference between heated plots and spatial controls was marginal (Fig. 4A). For hardwoods, Rs in heated plots fell below that in spatial controls throughout the whole soil temperature range, although the difference was again marginal (Fig. 4B).
The ΔRs plotted against Rs in control plots, calculated to capture spatial and temporal variability within each stand, showed that the effect of artificial warming on Rs not only varied between forest types but also between stands (Fig. 5). For the pre-warming period (i.e., 2019 and 2020), ΔRs values were around 0 mg CO2 m-2 h-1 for most of the stands. A median of ΔRs close to zero was expected and confirmed the robustness of the experimental design. However, for the warming period (i.e., 2021 and 2022), ΔRs values and medians in many stands diverged from zero, suggesting a change in Rs due to artificial warming. Despite some differences between stands, positive ΔRs values and median were generally observed when Rs of spatial controls was below 300 to 500 mg CO2 m-2 h-1, whereas negative ΔRs values and median were observed above this threshold. This pattern indicates that Rs in heated plots was higher than that in spatial controls below 300 to 500 mg CO2 m-2 h-1, while it was lower above this threshold. For stand 2 in hardwoods for example, the threshold between positive and negative ΔRs values and median was observed at approximately 400 mg CO2 m-2 h-1 in spatial controls (Fig. 5A; Table 3). The same pattern was observed for stand 5, a hardwood-beech stand, and stand 7, a mixedwood, but the thresholds were lower at approximately 300 and 350 mg CO2 m-2 h-1, respectively (Fig. 5B; Fig. 5C; Table 3).
Across all forest types and stands, Rs threshold values varied between 300 and 500 mg CO2 m-2 h-1, which corresponded to a range in soil temperatures of approximately 12 to 18°C in spatial controls (Fig. 3). The breaking point in the scatter of ΔRs was observed in stand 4 during warming in 2021 and 2022 (Fig. S3). However, the second set of ΔRs values and median only approached zero, likely because ΔRs values and median during pre-warming were well above zero in the first place (meaning that before the onset of warming, Rs in heated plots was higher than that in control plots). Stands 1 and 6 are the only two stands where the breaking point in the scatter of ΔRs was not apparent (Fig. S1; Fig. S4). Soil respiration increased in both stands during the warming period, resulting in positive ΔRs values and medians, although stand 6 exhibited a much larger and consistent positive response in Rs compared to stand 1. Because all forest types exhibited a breaking point in the scatter of ΔRs during warming, it could not be asserted that the break was associated to a specific forest type.
Table 3 Breaking point (threshold) in the scatter of soil respiration rate (Rs) data for all stands (8×) during warming based on spatial controls (2021 and 2022). NA means that a threshold was not observed
As indicated above, the sensitivity of Rs to soil temperature appeared to decrease from 12 to 18°C, depending on the stand. A second series of exponential regressions was thus performed considering a soil temperature cutoff of 15°C (i.e., the center of the 12–18°C range). For the 0 to 15°C range, the determination coefficients of the exponential relationships between soil temperature and Rs varied from 0.16 to 0.65 and models were statistically significant, whereas the coefficients for the 15 to 25°C range were between 0.01 and 0.13 and models were mostly not significant (Table 4).
Table 4 Parameters of exponential regression models between soil temperature and soil respiration rate (Rs) for the 0–15°C and 15–25°C intervals in heated plots and spatial controls (2021 and 2022) in mixedwoods, hardwoods and hardwood-beech stands
* indicates that the coefficient of determination (R2) of the model is significant at p < 0.05
Effect of soil temperature and artificial warming on Rs
The overall initial increase in Rs observed in this study in response to soil warming agrees with common knowledge that warmer soils generally lead to greater Rs in an array of ecosystems, and that soil temperature is the abiotic variable that exerts the greatest control over spatiotemporal variations in Rs [3, 6–8]. The observed increase in Rs in response to soil warming also corroborates with the meta-analysis by Carey et al. [22] which suggests a positive effect of artificial warming on Rs across several ecosystems. Specifically for temperate forests, previous studies highlighted a strong positive curvilinear relationship between Rs and soil temperature, under either natural conditions [9, 11, 15, 51] or artificial warming [10, 52–54].
Although the relationship between Rs and soil temperature were significant for both spatial controls and heated plots of the three forest types, exponential regression models only explained 30 to 39% of the variability in Rs in spatial controls and 35 to 45% of the variability in heated plots. In comparison, exponential models in Bélanger et al. [9], which used the same experimental design prior to artificial warming, were more robust, explaining 55 to 65% of the variability in Rs. The latter models were developed from data collected in all plots in 2019 and 2020 (i.e., temporal controls). On the one hand, the decrease in predictability of Rs based on soil temperature can be attributed to smaller sample sizes in 2021 and 2022 compared to 2019 and 2020 as well as the lower number of observations in spatial controls compared to heated plots in 2021 and 2022 (about half of the measurements). On the other hand, the threshold of 15°C, beyond which a weakening of the influence of soil temperature on Rs was observed in the heated plots, may partly explain the lower coefficients of determination of exponential regression models that include the full range of measured soil temperature (i.e., from 0 to 25°C). Indeed, for all forest types and treatments, exponential relationships between temperature and Rs were remarkably more statistically robust when considering a range from 0 to 15°C compared to relationships within the range of 15 to 25°C.
A relatively consistent decline in the response of Rs to artificial warming was observed at a specific threshold. For most stands, Rs slowed down in response to artificial warming when Rs in spatial controls reached a threshold from 300 to 500 mg CO2 m-2 h-1. Based on exponential regression models, this range in Rs values corresponded to a range of soil temperature from 12 to 18°C, with a median of 15°. For the mixedwoods and hardwood-beech stands, the lower Q10 values of the regression models in heated plots compared to spatial controls also illustrated a decrease in Rs sensitivity to higher temperatures due to artificial warming (Table 2). Soil temperatures produced by artificial warming in this study were not unusually high compared to other temperate hardwood stands in the literature, mainly because the study site is at the very northern limit of the temperate deciduous forest with generally cooler soils. However, the decrease in predictability in Rs at a higher temperature range with a threshold set at 15°C is not common in the literature. In their meta-analysis, Carey et al. [22] determined that Rs rates increase with increasing soil temperature up to a threshold of approximately 25°C in both heated and non-heated plots, above which Rs rates started to slow down. Previous studies that focused on the effect of artificial warming on Rs generally demonstrated a decrease in sensitivity (i.e., a decrease in Q10) over the long term, after at least 5 years of experiment, due to a combination of phenomena such as microbial thermal acclimation, reduction in labile carbon reserves and soil drying [10, 55, 56].
The weakening of the influence of temperature on Rs in response to artificial soil warming in this study could depend on the influence of several other climatic and environmental variables, such as water availability and forest type, and the interactions between them. We hypothesized that the slowing down of Rs at the 300–500 mg CO2 m-2 h-1 threshold (or a decrease in the robustness of exponential models above 15°C) is due to increased evaporative demand and soil drying during artificial warming. Soil drying induced by artificial warming was shown to offset the dependence of Rs on soil temperature [57], and moist soils were shown to exhibit a greater increase in Rs than dry soils [58]. Indeed, Rs depends on the balance between temperature and soil water availability [19]. Microbial activity and Rs are at their optimum within a certain range of soil water content and can be reduced or inhibited when soil water content is too low or too high [1, 59, 60]. For well-drained forest soils such as those in this study, water deficits during very specific periods can modulate Rs and the measured CO2 flux is no longer related to soil temperature [9, 15]. However, in this study, integrating soil water potential along with temperature into a multiple linear regression model did not improve the prediction of Rs in any stand. Even after dividing the database into ‘‘cold’’ and ‘‘warm’’ temperature ranges (i.e., either below or above 15°C), soil temperature remained the main variable modulating Rs, despite a considerable weakening of the relationship above 15°C. Overall, no statistical test performed could clearly demonstrate that soil water potential governed, at least partially, Rs at the study site, not even under higher soil temperatures. This outcome does not imply that water availability does not influence Rs. Rather, it can mean that drought characteristics (i.e., frequency and intensity) were not entirely conducive to statistical testing with Rs, and/or that the effect of water availability may be confounded by other variables and processes that are a function of forest type and species composition.
At first glance, our results suggest that the magnitude of the positive feedback loop between climate warming and soil CO2 emissions from forest ecosystems could be limited [8, 61], at least for Quebec temperate deciduous forests. However, before the effect of warming on Rs becomes limited by induced soil drying, Rs could increase in response to rising soil temperature and contribute to radiative forcing in these systems. Further monitoring of Rs under artificial warming is planned at the study site to verify these responses, and ideally, more sites with other species compositions should be tested for a better portrait of the bioclimatic domain in question.
Forest type influence on Rs response to artificial warming
Even though the results highlighted a common pattern between all three forest types, which is a weakening of the influence of temperature on Rs after 15°C, we observed a greater slowdown of Rs in response to warming in hardwood-beech stands. Indeed, the temperature threshold from which Rs of heated plot fell below that of spatial controls was lower than in mixedwoods and hardwoods (i.e. 10–12°C). The Q10 values also illustrated this greater sensitivity decrease, since the difference between spatial controls and heated plots was more substantial (3.28 and 2.07 respectively). This difference in Rs response to warming between the three forest types could be attributed to species-specific behaviors in response to changes in hydroclimatic conditions induced by artificial warming.
In terms of water potential, although there are annual and seasonal variations, soils in mixedwoods are generally the driest at the study site, whereas soils in hardwood-beech stands are the wettest [34]. In 2022, for example, hardwoods were generally wetter than hardwood-beech stands (results not shown), unlike the previous five years monitored. For both 2021 and 2022, soil water potential was higher in heated plots than spatial controls for all three forest types, except during the peak of summer (i.e., July) where the opposite was observed (shown for 2022 in Fig. 6). These results are counterintuitive since it was initially hypothesized that artificial warming would accentuate the drought conditions in heated plots during the warmest months due to increased evaporative demand. This pattern was further apparent in hardwood-beech stands in 2022 because soil water potential was lower than in hardwoods in July, while it was generally between that of mixedwoods and hardwoods otherwise (Fig. 6). All three forest types appeared to exhibit a behavior that limits water loss in warmer and drier conditions (i.e., heated plots), but this behavior seemed even more pronounced in hardwood-beech stands in 2022. We hypothesized that this ability of hardwood-beech stands to better conserve water could be explained by the isohydric behavior of beech spp. [34], and that this water-use strategy could partly explain the more significant slowdown of Rs observed in this forest type in response to warming.
Across species, stomatal control to regulate water use is often described along a continuum from isohydry to anisohydry [62]. In periods of water stress, isohydric species tend to close their stomata, which decreases CO2 assimilation, photosynthesis and productivity, but limits transpiration losses. On the other hand, anisohydric species keep their stomata open, which maintains gas exchanges and leads to a greater water loss [62]. While the literature has often emphasized the anisohydric behavior of beech spp., Courcot et al. [34] suggested that beech had an isohydric behavior that more efficiently resisted soil drying at the study site. Indeed, soils in hardwood-beech stands shifted to a ‘‘very high’’ state of water potential (> 120 kPa) at a soil temperature of 15.9°C compared to 12.9°C for soils in mixedwoods. Other studies showed that isohydric behavior can lead to a decrease in Rs. For example, to compensate for the decrease in CO2 assimilation due to stomatal closure, there can be an upward transport of CO2 through root water uptake, which decreases autotrophic respiration and consequently Rs [63]. A part of CO2 derived from root respiration is dissolved in the soil solution and can apparently be transported from the roots to the leaves via the xylem stream [64]. This process may be accentuated during dry periods for isohydric species with strict stomatal regulation [63]. We hypothesized that this process could also be active with beech at the study site, and thus this could partly explain the more important slowdown of Rs in respect to increasing soil temperatures in heated plots of hardwood-beech stands compared to mixedwoods and hardwood stands. This is speculation and only the measurement of some proxies of this process (e.g., sapflow or dissolved CO2 in the xylem) can confirm the hypothesis of CO2 upward transport from roots to leaves in beech trees.
While we initially hypothesized that beech drought resistance demonstrated by Courcot et al. [34] would result in a smaller impact on Rs, we believe that the more obvious slowdown of Rs in response to artificial warming observed in hardwood-beech stands may result from the mechanisms enabling beech to adapt to water stress (e.g., stomatal closure). The species composing mixedwoods and hardwood stands also appeared to show a certain resistance to soil drying (i.e., lower soil water potential in the heated plots than in the spatial controls in the peak of summer), but this did not lead to a significant impact on Rs, unlike in hardwood-beech stands. The influence of this resistance and/or acclimatation to dryer conditions on Rs remains unclear, further underscoring the importance, in the context of climate change and as a means to understand the overall role of forests on atmospheric CO2, of augmenting studies focusing on the response of Rs under various forest types.
We studied Rs under a 2°C artificial soil warming treatment in a temperate deciduous forest at its northern limit, where the abundance of conifers and American beech varied across the study site. Soil respiration increased in response to artificial warming, but only up to a threshold of about 15°C, beyond which Rs slowed down and the relationship between Rs and soil temperature was significantly weaker. This weakening of the soil temperature’s influence on Rs after reaching a certain temperature was associated with augmented evaporative demand and thus soil drying induced by the treatment, although it could not be demonstrated statistically, likely due to species-specific behaviors in response to changes in hydroclimatic conditions. Contrary to our initial hypothesis, hardwood-beech stands were the most responsive to artificial warming and drying, which resulted in a more obvious slowing down of Rs from a threshold of about 10–12°C, even though it was the wettest forest type. The greater sensitivity in terms of Rs in hardwood-beech stands is possibly due to the isohydric behavior of beech during water stress, which limits root respiration (autotrophic) due to the upward transport of CO2 to compensate for reduced gas exchange. The results of this study demonstrate the species-specific response to changes in soil hydroclimatic conditions, which complexifies the study of the impact of predicted rise in temperatures and decrease in soil water content on Rs. A better understanding of the relationship between species composition, soil hydroclimatic conditions and forest carbon cycle processes, such as Rs, is therefore crucial in order to make forests an efficient natural climate solution to mitigate atmospheric CO2 rise under climate change.
Acknowledgments
We would like to thank Alexandre Collin for his help in setting up the experimental design, as well as Joannie Beaulne-Raymond, William Brais, Maude Giguère, Jeanne-Lapierre St-Michel, Simon Lebel-Desrosiers et Joseph Mino-Roy for their help with sampling of gas fluxes. We would also like to thank the staff at SBL for providing access to the site and for hosting us during our visits.
Funding: Financial support was provided to N. Bélanger by the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery grants RGPIN 2015-03699 and 2020-04931) as well as several Undergraduate Research Awards), together with grants from the Canada Foundation for Innovation John R. Evans Leaders Fund (35370) and the Innovation Fund (36014, SmartForests Canada).
Conflicts of Interest: The authors declare no conflicts of interest that are relevant to the content of this article.
Data Availability: Please contact de corresponding author.
Code availability: Not applicable.
Author Contributions: Conceptualization: N.B.; methodology, data curation and laboratory analysis: S.L. and N.B.; figures production: S.L. and B.C.; writing—original draft preparation, S.L.; writing—review and editing: S.L., B.C. and N.B.; funding acquisition: N.B. All authors have read and agreed to the published version of the manuscript.
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No competing interests reported.