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Article

An Interseasonal Comparison of Soil Respiration in Xeric and Mesic Pine Forest Ecosystems in Central Siberia

by
Anastasia Makhnykina
1,2,*,
Eugene Vaganov
1,2,
Alexey Panov
1,
Daria Polosukhina
1,2 and
Anatoly Prokushkin
1,2,*
1
Sukachev Institute of Forest SB RAS, Krasnoyarsk 660036, Russia
2
Laboratory of Ecosystem Biogeochemistry, Institute of Ecology and Geography, Siberian Federal University, Krasnoyarsk 660041, Russia
*
Authors to whom correspondence should be addressed.
Atmosphere 2024, 15(8), 988; https://doi.org/10.3390/atmos15080988
Submission received: 1 July 2024 / Revised: 7 August 2024 / Accepted: 13 August 2024 / Published: 17 August 2024
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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Abstract

:
An understanding of how boreal forest composition responds to global environmental changes is an important challenge to predicting the future global carbon balance. Boreal forests are the most significant sink for atmospheric carbon dioxide; however, their sequestration capacity is highly sensitive to ongoing climate changes. The combination of the hydrothermal conditions of a territory strongly regulates its biogeochemical processes. The carbon fluxes in boreal forests are strongly mediated by the ground vegetation cover, composed of mosses (mesic) and lichens (xeric). Despite the concurrence of xeric and mesic vegetation types, their responses to climate variations varies significantly. Soil emission is an informative indicator of ecosystem functioning. In this study, we focused on the soil CO2 dynamics during frost-free seasons with different precipitation regimes in the xeric and mesic boreal ecosystems of Central Siberia. Seasonal measurements of soil CO2 emissions were conducted during frost-free seasons using the dynamic chamber method. Our findings reveal that the precipitation regimes of each year may control the seasonal soil emission dynamics. The soil moisture is the most important driver of emissions growth in the water-limited lichen pine forest (R2adj. = 18%). The soil temperature plays the largest role in the feather moss pine forest during the dry (R2adj. = 31%) seasons, and in the lichen pine forest during the wet (R2adj. = 41%) seasons. The cumulative efflux for the xeric and mesic sites is mostly related to the hydrothermal conditions, and not to the differences in ground vegetation cover. During the dry seasons, on average, the soil CO2 emissions are 45% lower than during the wet seasons for both sites. These findings emphasize the need for estimating and including the hydrothermal characteristics of the growing season for detailed emission assessments.

1. Introduction

The ecosystems of the northern hemisphere are characterized by cold and snowy winters and a relatively short and cold growing season [1,2]. Despite the smaller rates of carbon uptake compared to more southern ecosystems, boreal forests have traditionally been considered as carbon (C) sinks due to their slow decomposition rates [3,4,5], but the magnitude of their carbon reservoirs and controlling factors have still not been accurately quantified. Moreover, this climatically sensitive region is predicted to experience significant transformations in the carbon exchange between the terrestrial ecosystem and the atmosphere [6,7].
The soil carbon pool of boreal forests represents the largest reservoir of carbon globally, and even minor changes to it are expected to greatly impact the atmospheric carbon dioxide (CO2) concentration [8,9]. Soil respiration in forest ecosystems is considered to be one of the key components of CO2 balance, and produces the largest C efflux from forest ecosystems into the atmosphere [10,11]. Soil CO2 emissions originate from soil respiration that consists of the autotrophic respiration of tree roots, the activity of the external hyphae of symbiotic mycorrhizal fungi, and the emissions from the activity of nonsymbiotic heterotrophic microbes, such as saprotrophic bacteria and fungi, all of which decompose soil organic matter [12,13]. In addition to the tree layer, the common vegetation structure of northern forests consists of thick forest floor vegetation, which contributes significantly to ecosystem productivity [14,15,16,17].
In the last decades, precipitation levels have displayed significant changes [18,19]. Global climate change has intensified in recent years, causing more frequent extreme droughts [20,21]. These droughts limit plant growth, weaken carbon storage, and destabilize ecosystems [22,23]. For the studied boreal region, there has been a high variation in the amount of precipitation, from drought to flooding, during the snow-free period of the years. These changes have been reflected by the soil emission rates. The existing studies provide general information about soil CO2 emission rates, not including the impact of the specific meteorological conditions of each year or season [24].
The carbon fluxes in boreal forests are also mediated by the ground layer [25,26,27], which consists of mosses and lichens. The ground layer includes living and dead organic material, and is categorically neither soil nor above-ground vegetation. This layer constitutes the transitional border between the soil and the atmosphere. The input of the ground floor vegetation to the total CO2 uptake of a boreal forest ecosystem varies widely, from 3% to 61% [14,28,29], but depends significantly on the region, climate, and vegetation. Terrestrial mosses and lichens have long been recognized as an important part of the global climate system and local nutrient budgets [30,31].
Despite the ground vegetation layer having a large carbon uptake potential, its functional role as a C sink or source under observed and projected global changes is still unclear. Understanding how these changes influence carbon cycling at the soil–atmosphere interface requires accurate estimates of the current status and the existing trends in carbon fluxes and pools. Quantifying forest floor and soil emissions is of great concern for a global understanding of the biogeochemical processes of boreal ecosystems.
The main goal of this study was to compare the soil CO2 emission rates in the lichen and feather moss pine forests of Central Siberia, and to estimate the impact of the different precipitation regimes during the snow-free period of the year on soil emissions. We hypothesized the following: (a) soil CO2 fluxes would be higher in seasons with good amounts of precipitation compared to those with dry conditions; (b) pine forests with feather moss ground cover would release more CO2 than sites with lichen ground cover, even during dry seasons; and (c) hydrothermal soil conditions would predict the majority of soil emission variability during the growing season (GS; June–September).

2. Materials and Methods

2.1. Study Area

The study area is located in the Turukhansk district of the Krasnoyarsk Region (60°47′ N, 89°21′ E), Russia, near the international research station ZOTTO (http://www.zottoproject.org/, accessed on 14 April 2024). The climate of the region is cold continental. According to long-term measurements since 1936 at Bor WMO station (WMO ID: 23884) (http://www.meteo.ru/, accessed on 10 March 2024), the average annual air temperature is −3.4 °C. The sum of temperatures above 10 °C is 800–1200 °C. The absolute minimum air temperature is −54 °C; the absolute maximum temperature is +36 °C. The amplitude of the fluctuations in the average monthly temperatures is 42 °C. The average annual relative humidity is 76%. The amount of atmospheric precipitation per year averages 600 mm, with the maximum amount occurring during July–August [31].
Study plots were established in the lichen pine (LPF) and feather moss pine forests (FMPF) (Figure 1), grown on a geomorphologically homogeneous landscape (hilltop). The soils were formed on glaciofluvial deposits and feature a predominance of sand in the upper part of the profile. Clayey horizons (lenses) can be found at depths of over 1 m. According to the World Reference Base (WRB) soil classification system, the soils of the study plots are Podzols. Generally, the clay content of the mineral soil is less than 5%, with a few examples of stones at a 1.25–1.50 m depth observed [32]. The organic horizon contains over 30% of the total soil organic matter (OM) [33]. The root phytomass constitutes 30–60% of the soil organic matter; the detritus content is about 10%. A more detailed description of the study sites is presented in Table 1.

2.2. Field Measurements

The measurements were performed during the GSs in the study area. The soil CO2 fluxes were measured during the daytime (11:00–16:00 LT) using an LI 8100A infrared gas analyzer (LI-COR Biosciences Inc., Lincoln, NE, USA) coupled with an 8100-103 survey chamber (LI-COR Biosciences Inc., Lincoln, NE, USA). For our observations of each study site, five polyvinylchloride (PVC) rings of 20 cm in diameter were installed at a distance of 1–1.5 m from each other. The depth of penetration of the PVC rings into the soil depended on the vegetation type: it was from 2 to 4 cm for the xeric site, and from 5 to 10 cm for the mesic site, depending on the thickness of the litter layer and the organic horizon. Observations were carried out with three repetitions for each collar, with 2 min for the measurement and a 30 s interval between the measurements. In parallel with the flux measurements, we recorded the soil temperature at depths of 5, 10, and 15 cm using a Soil Temperature Probe Type E (Omega Engineering, Inc., Norwalk, CT, USA), and the soil water content (SWC) at a depth of 5 cm using a Theta Probe Model ML soil moisture sensor (Delta T Devices Ltd., Burwell, Cambridge, UK).
The LPF (xeric site) measurements were conducted annually from 2012 to 2016 and during 2020 and 2021, for 170 measurement days in total, while the FMPF (mesic site) measurements were performed annually during the period from 2012 to 2017, and continued in 2022 (154 measurement days). Hence, on average, 25 measurement days per season were completed, with a frequency of once per 5–7 days.

2.3. Data Analysis

The calculations of the primary data were carried out using a specialized software package—LI8100_win-4.0.0 Original Software. The plot of the mean soil CO2 emissions was calculated as the average of 15 measurements, with three repetitions per five soil collars. The daily means of the emission fluxes were used to examine the impact of soil temperature and soil moisture (SWC). We used exponential and quadratic functions to explore the relationships between the SR fluxes and environmental variables, and statistical significance was set at p < 0.05. The differences in the SR between the xeric and mesic sites were determined using a t-test (equal variances). The relationships between the SR and the related soil microclimatic factors were studied using Pearson’s (r) correlation coefficient. One-way and two-way replicated ANOVAs were used to assess the effects of soil temperature and soil moisture conditions on the SR. The data processing and statistical analysis of the obtained data were performed using Statistica 12 and R statistical software version 4.2.2 [35].

3. Results

3.1. Meteorological Characteristics

The hydrometeorological conditions of the studied GSs differed from year to year and were characterized by an essential variability (Figure 2). The seasons in 2012 and 2016 were the driest, having 39 and 59% lower amounts of seasonal precipitation, respectively, compared to the long-term mean value of 263 mm recorded at Bor WMO station (WMO ID: 23884). In turn, the 2015, 2017, and 2021 growing seasons were characterized by maximal precipitation, showing 35, 27, and 25% higher values, correspondingly, compared to the long-term mean.
The mean seasonal air temperature varied less between the seasons, and the air temperature records for the six seasons were close to the long-term mean value of 13.3 °C. The warmest seasons demonstrated 16 and 21% higher values than the long-term mean, and corresponded to the driest summers of 2012 and 2016.
In order to obtain an overview of the more detailed characteristics of the growing seasons, we selected the amount of precipitation as the main limiting factor for this region [11]. To categorize the precipitation regime, we used the hydrothermal coefficient (HTC) of G. Selyaninov, which is calculated based on Equation (1) [36]:
HTC= Σ R × 10/Σ t,
where Σ R and Σ t are the sum of the precipitation and temperatures, respectively, during the period, when the temperature > 10 °C.
Based on the existing classification of the HTC, we formally divided our seasons into two groups (Figure 3):
  • HTC > 1.0—“Wet seasons”: 2015, 2017, 2020–2022;
  • HTC < 1.0—“Dry seasons”: 2012, 2013, 2016.
Atmosphere 15 00988 g003
Figure 3. Hydrothermal coefficient (HTC) for snow-free seasons.
Figure 3. Hydrothermal coefficient (HTC) for snow-free seasons.
Atmosphere 15 00988 g003

3.2. Soil CO2 Emission Dynamics in Lichen Pine Forest

The total period of observation of the seasonal soil respiration in the LPF was 6 years. Using our HTC-based classification, the GS’s soil CO2 emission dynamics are presented for the two groups separately (Figure 4), i.e., for the dry (Figure 4a) and wet (Figure 4b) seasons. Among the dry GSs (Figure 4a), the lowest CO2 efflux rates (0.6 ± 0.03 µmol m−2 s−1) were observed in 2012. In general, in 2012 and 2013, the seasonal CO2 fluxes varied from 0.7 to 6.2 µmol m−2 s−1, with the maximal values recorded during the second half of the summer. However, the seasonal pattern in 2016 was not so prominent, showing just a few gains during the GS. We also observed smaller variations in the CO2 efflux rates during the dry, moisture-limited seasons. Interestingly, even during water-limited seasons, the soil CO2 emission dynamics generally demonstrated small variations, with maximum rates shifting to the start of the GS.
In turn, the wet seasons demonstrated more variation in the shape of the soil emission cycle. For the three wet GSs of 2015, 2020, and 2021 (Figure 4b), we observed, on average, two-fold flux rates compared to the dry seasons, showing variations from 1.5 to 11.0 µmol m−2 s−1, with a strong peak at mid-season. The precipitation amounts across these growing seasons were 36, 17, and 26% higher than that of the long-term mean value recorded at Bor WMO station (Figure 2).

3.3. Soil CO2 Emission Dynamics in Feather Moss Pine Forest

In contrast to the LPF, the FMPF is characterized by lower soil temperature and higher soil moisture (Table 1). These features determine the soil emissions behavior during the established dry and wet seasons.
One of the main particularities that can be noted for soil emissions during the dry seasons (Figure 5a) is a high daily variability in the values. The CO2 efflux in the FMPF across the dry GSs showed 25% higher rates compared to those in the LPF, which could be attributed to wetter soil conditions due to the thick litter layer. Besides the GS of 2013, when maximal emission rates were achieved by late summer, for most of the studied dry years, the seasonal pattern is not obvious. In contrast to the lichen pine forest, in the moisture-favored feathermoss pine forest we did not observe seasonal fluctuations in 2012.
During the wet GSs (Figure 5b), the seasonal pattern of soil CO2 efflux demonstrated values that varied from 1.1 to 11.6 µmol m−2 s−1, with a peak occurring in the middle of the GS. On average, the soil CO2 emission rates were 2-fold higher than during the dry seasons. However, the summer of 2022 demonstrated the highest flux rates observed during the early GS, due to the earlier spring and snowmelt that triggered a higher soil efflux intensity. It also has be to mentioned that during the wet seasons, the peaks of soil CO2 efflux patterns showed 20% lower values compared to the LPF site.

3.4. Soil Microclimate Factors and Emission Rates

An analysis of the impact of the main soil microclimatic factors, such as soil temperature and soil moisture, on soil emissions showed site-specific differences during the dry and wet seasons (Figure 6).
The top soil temperature was found to be a major modulating factor for the CO2 efflux rates of the mesic FMPF site (R2adj. = 0.31) across the dry GSs, while for the LPF or xeric site, (R2adj. = 0.41) temperature regulation of soil respiration occurs mostly during the wet seasons. Our findings are primarily caused by the underlining surface and soil texture of the sites. For instance, at the xeric site, the lichen vegetation cover does not have an active mechanism for water content regulation, and is mostly controlled by the surrounding environment [37]. The rapid water loss of lichen vegetation causes a rather narrow range of optimal precipitation during the whole GS that is capable of reducing a water deficit and, under these conditions, emission rates are mostly regulated by the top soil temperature.
The FMPF site during the wet seasons demonstrates overmoistened conditions, due to the thick litter layer that reduces soil emission rates even at peak temperatures during the mid-GS. However, during dry GSs the soil temperature may explain up to 31% of the soil CO2 efflux variations. In turn, the soil moisture does not appear to play a regulating role at the mesic site, while for the xeric site it has a stronger effect on modulating soil emission rates (R2adj. = 0.18) during the dry seasons. This indirectly informs us about the humid conditions of the local microclimate at this site. Opposite to the mesic site, the soil emissions of the xeric site are more modified by soil moisture (R2adj. = 0.18) during the dry seasons, when water-limiting conditions are observed.

3.5. Inter-Seasonal Soil Emissions

We observed the highest rates of CO2 release from the soil surface of the study area during the wet GSs (Figure 7). On average, the cumulative soil CO2 emission values during the wet seasons were 45 and 40% higher than during the dry seasons for the LPF and FMPF sites, respectively. The maximal soil efflux for the LPF was observed during the wet seasons of 2015 (2.44 kg CO2 m−2) and 2021 (2.79 kg CO2 m−2), while for the FMPF, the highest values were recorded during the wet seasons of 2017 (2.32 kg CO2 m−2) and 2022 (2.84 kg CO2 m−2).
Interestingly, at the mesic site during the wet season of 2015, the cumulative CO2 emissions were lower (1.78 kg CO2 m−2) when compared to the next dry season of 2016 (2.07 kg CO2 m−2). Perhaps this observed exception is attributable to the precipitation regime of the 2015 season, which was 36% higher than that of the long-term mean value, and likely triggered some inhibition of soil CO2 release.
We found that the previously suggested HTC, as a complex characterization of the hydrothermal regime of a specific year, linearly correlates with the cumulative soil CO2 emissions. This coefficient may explain up to 43% of the emission rates (Figure 8). The values observed during the dry seasons (HTC < 1) were significantly lower than the rates observed during the wet seasons (HTC > 1). An exception was observed for the FMPF site, where the soil efflux showed comparatively low rates besides the high value of the HTC (1.6). This finding could be an illustration of the impact of the relatively overwet season of 2015 on soil CO2 emissions.

4. Discussion

4.1. Seasonal Soil CO2 Emission Dynamics of the Xeric and Mesic Sites: Dry and Wet Seasons

Soil water availability is one of the key limiting factors in the boreal forest zone [38,39,40]. When observing significant changes in the amount of precipitation, the positive feedback of soil CO2 emissions can be expected [41]. As commonly assumed, drought conditions act as a stress factor on many biological processes in ecosystems and suppress most biogeochemical reactions [24,42]. In regard to soil emissions, droughts are usually related to an inhibition of CO2 release until optimal moisture conditions are achieved [43].
At the xeric site, we observed the aforementioned response of the ecosystem to water-deficit conditions: soil efflux rates were, on average, 45% lower during the dry seasons compared to wet seasons. At the mesic site, the similar impact of drought stress was found, except for the wet season of 2015, which demonstrated less CO2 release than the following dry season of 2016. Also, the seasonal pattern during this wet season showed a rather low variability in the FMPF, with almost stable emission rates during the season. A few spikes in the CO2 rates that were observed during that year could probably be related to the temperature rising and the decline in soil moisture. This interesting finding is beyond the scope of this paper, but is a subject for further investigations. As previously noted, the feather moss or mesic site can be characterized by a higher moisture level [44], which is defined by the thickness of the litter layer and the soil moisture during the growing season [45,46]. Some earlier observations suggested that the soil texture, even across the same soil type, may significantly modulate the soil moisture regime and, eventually, CO2 release [47]. Our results are consistent with the previously given estimates for a boreal feather moss pine forest in Finland [48], where the soil CO2 emission rates fluctuated around 2.17 g C m−2 day−1 during the growing season. This value corresponds well with the soil efflux rates found for the dry years at the FMPF (mesic) site (2.32 and 2.46 g C m−2 day−1 in 2012 and 2013, respectively).
The LPF is more sensitive to any biotic and abiotic external factors [49,50,51,52]. This site is characterized by sandy soil with a well-drained texture and, under large amounts of precipitation, water squeezes easily through the soil pores, triggering an immediate response of soil CO2 efflux, which was previously reported by [53]. It is also noted that, similarly to the mesic site, the LPF site demonstrates comparable soil efflux rates during both dry and wet years. This response may possibly be a kind of adaptation strategy for a fast recovery after the impact of stress factors. For instance, in Canadian boreal forests with a predominance of mosses in the ground vegetation [54], soil CO2 emission rates during the GS of 2020 showed 30% higher values compared to those observed in the FMPF. A possible reason could be related to the increased precipitation amount observed in Canada during the GS (JJAS) that reached 619 mm. Our results also correspond well with the findings reported for a Swedish northern forest [26,55] with a similar precipitation regime and ground cover vegetation. Similarly to our observations, they measured peak soil CO2 emissions at the end of July, and the rates decreased in the dry regions, while a more pronounced positive effect on soil efflux was obvious in the wetter areas. This conclusion completely supports our findings. On the other hand, the slightly opposite relationship of soil efflux with dry conditions has been established for the temperate forests in the European part of Russia [56]: after short dry periods, soil CO2 emission rates increased by 10–16%, and which followed soil temperature increase.

4.2. Microclimatic Factors Impact on Soil CO2 Emissions

Alterations to the soil environment strongly effect soil CO2 emission rates [57]. Soil temperature and moisture are commonly considered [45,58,59] to be the key factors for predicting any changes in soil emission dynamics. A comparison of the microclimatic conditions of the two studied sites show the obvious differences (Table 1): the long-term mean soil temperature observed in the FMPF is 16% lower compared to that of the LPF, while the long-term mean soil moisture of the FMPF is 21% higher than that of the LPF.
Former estimates [60] showed the role of drought in causing a nearly immediate and sharp decrease in soil CO2 emission rates. However, in the FMPF we observed increased CO2 efflux rates even during the dry season (Figure 6), which might be attributed to the specific microclimatic conditions of this mesic site, and generally related to the differences in soil texture and moisture regime [34,44,61]. This last feature is also associated with the presence of moss species in the FMPF and mainly lichen species in the LPF.
The observed relationship between soil CO2 emissions and microclimatic variables (Figure 6) demonstrates the feedback of CO2 efflux to soil temperature at the LPF and FMPF sites. The correlation between soil emissions and soil temperature is found for the FMPF during dry seasons. In contrast, at the LPF site, the soil temperature modulates soil emission rates during wet GSs, with an absence of the moisture limitation effect. Such a behavior or biological strategy could be a marker of the absence of a water deficit in the FMPF during the vegetation season, and also connected with the inhibition of soil emission rates due to the waterlogged environment during the wet seasons.
Some groups of factors may potentially modify soil microclimatic conditions. For example, trees density may change soil emissions due to associated processes, which are competition for nutrients supply and respiration, as a part of common soil respiration [26,57]. The complex of aboveground ecological characteristics could actually impact belowground functioning, consequently affecting the magnitude of biotic CO2 emissions [57]. Different trees densities could modify the balance between the inputs and outputs of soil CO2. Tree species influence soil CO2 through the changing microclimate of forest floor vegetation, the amounts and properties of litter, the distribution of the root systems in the soil, etc. [62]. As a consequence, a higher trees density is related to higher soil CO2 emissions. However, sometimes the opposite behavior is observed, when trees compete with the soil for carbon recourses. At this time, a higher stand density may decrease the soil emission rates. In our case, the stand density in the LPF and FMPF sites are almost similar (Table 1), and it is most likely the forest floor vegetation that produces the main differences in soil CO2 emissions: even in dry years, the soil CO2 emissions in the FMPF are up to 25% higher than those in the LPF.

4.3. Cumulative CO2 Emissions during Dry and Wet Seasons

Hydrothermal conditions are the specific and variable characteristics of each single vegetation season. The combination of temperature and moisture regimes regulates most of the biogeochemical cycles and processes in an area [63].
Most of the existing models include soil temperature and moisture as the main predictors of soil emission fluxes [64,65,66]. However, environmental coefficients like the HTC might serve as a tool to show the combined effect of climatic variables on soil emissions [67]. The HTC coefficient could be used optionally to predict soil emission behavior in seasons characterized by different precipitation regimes. However, for more accurate estimates of the soil efflux, more precise variables are required.
In our study, the HTC explained around 40% of the cumulative seasonal soil CO2 emission flux (Figure 8). It has to be mentioned that the individual effect of soil temperature is close to this number for the LPF and FMPF; however, it is revealed differently by the moisture conditions of the seasons (R2adj. in Table 2). From these interesting results, we can conclude that the combined effect of temperature and precipitation conditions in the HTC, and the individual effect of soil temperature, are similar.
In turn, a big portion of soil emission variability has to be controlled by additional factors that have another origin. On one hand, these factors deal with biotic relationships [51], while on the other hand, they could be related to the site: geographical location [54], soil type and texture [37,46,47], slope and microrelief of the area [68], vegetation [8,17,24], and others [69,70,71].

5. Conclusions

This study showed the specific and common features of soil emission dynamics in xeric and mesic ecosystems during dry and wet GSs. During the wet seasons, we found a higher variation in the emission rates and bigger amounts of released CO2, with peaks in the middle of the GS. The dry seasons were accompanied by small variations in CO2 fluxes, and the peaks were moved to the second half and end of the GS. Interestingly, the microclimatic parameters of each site demonstrated the different responses to soil temperature rising during the dry and wet seasons. At the mesic site during the dry seasons, the soil emissions were 15% higher than at the xeric site. At the xeric site, we observed a positive effect of soil temperature increase on soil emission rates during the wet seasons (R2adj. = 41%), without water-limiting conditions. In contrast, at the mesic site, mostly during the dry seasons, we detected a distinct temperature impact on soil emission variations (R2adj. = 31%). However, in general, both sites demonstrated a decrease in the cumulative released CO2 fluxes during the dry seasons. The mean cumulative CO2 emissions during the dry seasons were 43 and 46% lower than during the wet seasons at the xeric and mesic sites, respectively. This particular characteristic, found for a feather moss pine forest during overwetted seasons, was reflected in the inhibition of the soil emission rates. Using the HTC of Selyaninov as a soil environmental function could represent the changes in soil CO2 emission cumulative fluxes (may explain around 40%), and could be used for the wider estimation of soil emission.
The characteristics of the ground vegetation cover should be used to make adequate predictions of the carbon sequestration of boreal forest ecosystems. Consequently, further studies in this field could explore the link between meteorological parameters and soil emission rates under controlled conditions. One of the main practical benefits of the obtained results, associated with an analysis of the sequestration capacity of boreal forest ecosystems, is to offer an additional carbon stock area and mitigate human-induced climate change.

Author Contributions

Conceptualization, A.M., E.V. and A.P. (Anatoly Prokushkin); methodology, A.M.; formal analysis, A.M. and A.P. (Anatoly Prokushkin); investigation, A.M., E.V. and A.P. (Anatoly Prokushkin); resources, A.P. (Alexey Panov), A.P. (Anatoly Prokushkin) and A.M.; writing—original draft preparation, A.M.; writing—review and editing, A.P. (Anatoly Prokushkin), A.P. (Alexey Panov) and D.P.; visualization, D.P. and A.P. (Alexey Panov). All authors have read and agreed to the published version of the manuscript.

Funding

In situ observations and raw data processing were supported by the Russian Academy of Sciences within the framework of a state assignment (#FWES-2024-0040) of the Sukachev Institute of Forest Siberian Branch of the Russian Academy of Sciences. Also, this research was carried out as a part of a most important innovative project of national importance: “Development of a system for ground-based and remote monitoring of carbon pools and greenhouse gas fluxes in the territory of the Russian Federation, ensuring the creation of recording data systems on the fluxes of climate-active substances and the carbon budget in forests and other terrestrial ecological systems” (#123030300031-6).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data discussed in this paper are available upon request. The data are not publicly available due to privacy.

Acknowledgments

The authors would like to thank Mikhail Korets (Sukachev Institute of Forest SB RAS) for his technical support regarding the map of the study area. We also thank the staff of the international research station “ZOTTO” for their technical and transportation support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hisano, M.; Ryo, M.; Chen, X.; Chen, H.Y.H. Rapid functional shifts across high latitude forests over the last 65 years. Glob. Chang. Biol. 2021, 27, 3846–3858. [Google Scholar] [CrossRef]
  2. Sulla-Menashe, D.; Woodcock, C.E.; Friedl, M.A. Canadian boreal forest greening and browning trends: An analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers. Environ. Res. Lett. 2018, 13, 014007. [Google Scholar] [CrossRef]
  3. Fan, S.; Gloor, M.; Mahlman, J.; Pacala, S.; Sarmiento, J.; Takashi, T.; Peng, T. A large terrestrial carbon sink in North America implied by atmospheric and oceanic carbon dioxide data and models. Science 1998, 282, 442–446. [Google Scholar] [CrossRef]
  4. Pan, Y.; Birdsey, R.; 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] [PubMed]
  5. Tupek, B.; Launiainen, S.; Peltoniemi, M.; Sievänen, R.; Perttunen, J.; Kulmala, L.; Penttilä, T.; Lindroos, A.J.; Hashimoto, S.; Lehtonen, A. Evaluating CENTURY and Yasso soil carbon models for CO2 emissions and organic carbon stocks of boreal forest soil with Bayesian multi-model inference. Eur. J. Soil Sci. 2019, 70, 847–858. [Google Scholar] [CrossRef]
  6. Jones, A.; Stolbovoy, V.; Tarnocai, C.; Broll, G.; Spaargaren, O.; Montanarella, L. Soil Atlas of the Northern Circumpolar Region; European Commission, Office for Official Publications of the European Communities: Luxembourg, 2009; 142p. [Google Scholar]
  7. De Vos, B.; Cools, N.; Ilvesniemi, H.; Vesterdal, L.; Vanguelova, E.; Camicelli, S. Benchmark values for forest soil carbon stocks in Europe: Results from a large scale forest soil survey. Geoderma 2015, 251, 33–46. [Google Scholar] [CrossRef]
  8. Raich, J.; Schlesinger, W.H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B Chem. Phys. Meteorol. 1992, 44, 81–99. [Google Scholar] [CrossRef]
  9. Crowther, T.W.; Todd-Brown, K.E.O.; Rowe, C.W.; Wieder, W.R.; Carey, J.C.; Machmuller, M.B.; Snoek, B.L.l; Fang, S.; Zhou, G.; Allison, S.D.; et al. Quantifying global soil carbon losses in response to warming. Nature 2016, 104, 104–111. [Google Scholar] [CrossRef] [PubMed]
  10. Janssens, I.A.; Lankreijer, H.; Matteucci, G.; Kowalski, A.S.; Buchmann, N.; Epron, D.; Pilegaard, K.; Kutsch, W.; Longdoz, B.; Grunwald, T.; et al. Productivity overshadows temperature in determining soil and ecosystem respiration across european forests. Glob. Chang. Biol. 2001, 7, 269–278. [Google Scholar] [CrossRef]
  11. Xu, W.; Hisano, M. Spatial variation in boreal forest responses to global environmental change in western Canada. Agric. For. Meteorol. 2024, 355, 110140. [Google Scholar] [CrossRef]
  12. Kuzyakov, Y. Sources of CO2 efflux from soil and review of partitioning methods. Soil Biol. Biochem. 2006, 38, 425–448. [Google Scholar] [CrossRef]
  13. Kutsch, W.L.; Aubinet, M.; Buchmann, N.; Smith, P.; Osborne, B.; Eugster, W.; Wattenbach, M.; Schrumpf, M.; Schulze, E.D.; Tomelleri, E.; et al. The net biome production of full crop rotations in Europe. Agric. Ecosyst. Environ. 2010, 139, 336–345. [Google Scholar] [CrossRef]
  14. Goulden, M.L.; Crill, P.M. Automated measurements of CO2 exchange at the moss surface of a black spruce forest. Tree Physiol. 1997, 17, 537–542. [Google Scholar] [CrossRef] [PubMed]
  15. Morén, A.-S.; Lindroth, A. CO2 exchange at the floor of a boreal forest. Agric. For. Meteorol. 2000, 101, 1–14. [Google Scholar] [CrossRef]
  16. Kulmala, L.; Pumpanen, J.; Kolari, P.; Muukkonen, P.; Hari, P.; Vesala, T. Photosynthetic production of ground vegetation indifferent-aged scots pine (Pinus sylvestris) forests. Can. J. For. Res. 2011, 41, 2020–2030. [Google Scholar] [CrossRef]
  17. Kulmala, L.; Pumpanen, J.; Kolari, P.; Dengel, S.; Berninger, F.; Koster, K.; Matkala, L.; Vanhatalo, A.; Vesala, T.; Back, J. Inter- and intra-annual dynamics of photosynthesis differ between forest floor vegetation and tree canopy in a subarctic Scots pine stand. Agric. For. Meteorol. 2019, 271, 1–11. [Google Scholar] [CrossRef]
  18. Griffis, T.J.; Black, T.A.; Gaumont-Guay, D.; Drewitt, G.B.; Nesic, Z.; Barr, A.G.; Morgenstern, K.; Kljun, N. Seasonal variation and partitioning of ecosystem respiration in a southern boreal aspen forest. Agric. For. Meteorol. 2004, 125, 207–223. [Google Scholar] [CrossRef]
  19. Reichstein, M.; Ciais, P.; Papale, D.; Valentini, R.; Running, S.; Viovy, N.; Cramer, W.; Granier, A.; Ogée, J.; Allard, V.; et al. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: A joint flux tower, remote sensing and modelling analysis. Glob. Chang. Biol. 2007, 13, 634–651. [Google Scholar] [CrossRef]
  20. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
  21. Ma, Z.H.; Peng, C.; Zhu, Q.; Chen, H.; Yu, G.; Li, W.; Zhou, X.; Wang, W.; Zhang, W. Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc. Natl. Acad. Sci. USA 2012, 109, 2423–2427. [Google Scholar] [CrossRef]
  22. Zhang, F.; Quan, Q.; Ma, F.; Tian, D.; Hoover, D.; Zhou, Q.; Niu, S. When does extreme drought elicit extreme ecological responses? J. Ecol. 2019, 107, 2553–2563. [Google Scholar] [CrossRef]
  23. Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogée, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A.; et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 2005, 437, 529–533. [Google Scholar] [CrossRef]
  24. Shibistova, O.; Lloyd, J.; Evgrafova, S.; Savushkina, N.; Zrazhevskaya, G.; Arneth, A.; Knohl, A.; Kolle, O.; Schulze, E.D. Seasonal and spatial variability in soil CO2 efflux rates for a Central Siberian Pinus sylvestris forest. Tellus B 2002, 54, 552–567. [Google Scholar] [CrossRef]
  25. Kotani, A.; Saito, A.; Kononov, A.V.; Petrov, R.E.; Maximov, T.C.; Iijima, Y.; Ohta, T. Impact of unusually wet permafrost soil on understory vegetation and CO2 exchange in a larch forest in eastern Siberia. Agric. For. Meteorol. 2019, 265, 295–309. [Google Scholar] [CrossRef]
  26. Chi, J.; Zhao, P.; Klosterhalfen, A.; Jocher, G.; Kljun, N.; Nilsson, M.B.; Peichl, M. Forest floor fluxes drive differences in the carbon balance of contrasting boreal forest stands. Agric. For. Meteorol. 2021, 306, 108454. [Google Scholar] [CrossRef]
  27. Martínez-García, E.; Nilsson, M.B.; Laudon, H.; Lundmark, T.; Fransson, J.E.S.; Wallerman, J.; Peichl, M. Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape. Agric. For. Meteorol. 2022, 318, 108916. [Google Scholar] [CrossRef]
  28. Subke, J.-A.; Tenhunen, J.D. Direct measurements of CO2 flux below a spruce forest canopy. Agric. For. Meteorol. 2004, 126, 157–168. [Google Scholar] [CrossRef]
  29. Ikawa, H.; Nakai, T.; Busey, R.C.; Kim, Y.; Kobayashi, H.; Nagai, S.; Ueyama, M.; Saito, K.; Nagano, H.; Suzuki, R.; et al. Understory CO2, sensible heat, and latent heat fluxes in a black spruce forest in interior Alaska. Agric. For. Meteorol. 2015, 214–215, 80–90. [Google Scholar] [CrossRef]
  30. Gorham, E. On the acidity and salinity of rain. Geochim. Cosmochim. Acta 1955, 7, 231–239. [Google Scholar] [CrossRef]
  31. Tamm, C.O. Growth, Yield and Nutrition in Carpets of a Forest Moss (Hylocomium splendens). Medd. Skogsforskn. Inst. Stockh. 1953, 43, 1–140. Available online: https://publications.slu.se/?file=publ/show&id=125109 (accessed on 10 April 2024).
  32. Pleshikov, F.I. (Ed.) Forest Ecosystems of the Yenisey Meridian; Nauka: Novosibirsk, Russia, 2002; 356p. (In Russian) [Google Scholar]
  33. Polosukhina, D.A.; Prokushkin, A.S. Comparative characteristics of reserves and isotopic composition of soil organic matter in forest biogeocenoses in the footprint zone of the ZOTTO tall tower. In Proceedings of the Lomonosov Readings in Altai: Fundamental Problems of Science and Education, Barnaul, Russia, 14–17 November 2017. (In Russian). [Google Scholar]
  34. Bezkorovainaya, I.N.; Ivanova, G.A.; Tarasov, P.A.; Sorokin, N.D.; Bogorodskaya, A.V.; Ivanov, V.A.; Konard, S.G.; McRae, D.J. Pyrogenic Transformation of pine stand soil in middle taiga of Krasnoyarsk region. Contemp. Probl. Ecol. 2005, 12, 143–152. (In Russian) [Google Scholar]
  35. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: http://www.r-project.org (accessed on 10 April 2024).
  36. Selyaninov, G.T. About climate agricultural estimation. Proc. Agric. Meteorol. 1928, 20, 165–177. (In Russian) [Google Scholar]
  37. Esseen, P.-A.; Olsson, T.; Coxson, D.; Gauslaa, Y. Morphology influences water storage in hair lichens from boreal forest canopies. Fungal Ecol. 2015, 18, 26–35. [Google Scholar] [CrossRef]
  38. Subke, J.-A.; Reichstein, M.; Tenhunen, J.D. Explaining temporal variation in soil CO2 efflux in a mature spruce forest in Southern Germany. Soil Biol. Biochem. 2003, 35, 1467–1483. [Google Scholar] [CrossRef]
  39. Gaumont-Guay, D.; Andrew Black, T.; Griffis, T.J.; Barr, A.G.; Jassal, R.S.; Nesic, Z. Influence of temperature and drought on seasonal and interannual variations of soil, bole and ecosystem respiration in a boreal aspen stand. Agric. For. Meteorol. 2006, 140, 220–235. [Google Scholar] [CrossRef]
  40. Manzoni, S.; Schimel, J.P.; Porporato, A. Responses of soil microbial communities to water stress: Results from a meta-analysis. Ecology 2012, 93, 930–938. [Google Scholar] [CrossRef] [PubMed]
  41. Lindroos, A.-J.; Makipaa, R.; Merila, P. Soil carbon stock changes over 21 years in intensively monitored boreal forest stands in Finland. Ecol. Indic. 2022, 144, 109551. [Google Scholar] [CrossRef]
  42. Lindroth, A.; Holst, J.; Linderson, M.-L.; Aurela, M.; Biermann, T.; Heliasz, M.; Chi, J.; Ibrom, A.; Kolari, P.; Klemedtsson, L.; et al. Effects of drought and meteorological forcing on carbon and water fluxes in Nordic forests during the dry summer of 2018. Philos. Trans. R. Soc. B 2020, 375, 20190516. [Google Scholar] [CrossRef] [PubMed]
  43. Bergeron, O.; Margolis, H.A.; Coursolle, C. Forest floor carbon exchange of a boreal black spruce forest in eastern North America. Biogeosciences 2009, 6, 1849–1864. [Google Scholar] [CrossRef]
  44. Ivanova, G.A.; Bezkorovaynaya, I.N.; Bogorodskaya, A.V.; Ivanov, V.A.; Ivanov, A.V.; Kovaleva, N.M.; Conard, S.G.; Krasnoshchekova, E.N.; Kukavskaya, E.A.; Macrae, D.D.; et al. The Impact of Fires on Ecosystem Components Is Medium-Taiga Pine Forests of Siberia; Evdokimenko, M.D., Ed.; Nauka: Novosibirsk, Russia, 2014; 232p. (In Russian) [Google Scholar]
  45. Kulmala, L.; Launiainen, S.; Pumpanen, J.; Lankreijer, H.; Lindroth, A.; Hari, P.; Vesala, T. H2O and CO2 fluxes at the floor of a boreal pine forest. Tellus B Chem. Phys. Meteorol. 2008, 60, 167–178. [Google Scholar] [CrossRef]
  46. Houle, G.P.; Kane, E.S.; Kasischke, E.S.; Gibson, C.M.; Turetsky, M.R. Recovery of carbon pools a decade after wildfire in black spruce forests of interior Alaska: Effects of soil texture and landscape position. Can. J. For. Res. 2018, 48, 1–10. [Google Scholar] [CrossRef]
  47. Feiziene, D.; Feiza, V.; Vaideliene, A.; Polivaitis, V.; Antanaitis, S. Soil surface carbon dioxide exchange rate as affected by soil texture, different long-term tillage application and weather. Agriculture 2010, 97, 25–42. [Google Scholar]
  48. Ryhti, K.; Kulmala, L.; Pumpanen, J.; Isotalo, J.; Pihlatie, M.; Helmisaari, H.S.; Leppalammi-Kujansuu, J.; Kieloaho, A.J.; B¨ack, J.; Heinonsalo, J. Partitioning of forest floor CO2 emissions reveals the belowground interactions between different plant groups in a Scots pine stand in southern Finland. Agric. For. Meteorol. 2021, 297, 108266. [Google Scholar] [CrossRef]
  49. Song, Y.; Zhou, D.; Zhang, H.; Li, G.; Jin, Y.; Li, Q. Effects of vegetation height and density on soil temperature variations. Chin. Sci. Bull. 2013, 58, 907–912. [Google Scholar] [CrossRef]
  50. Trumbore, S. Age of soil organic matter and soil respiration: Radiocarbon constraints on belowground c dynamics. Ecol. Appl. 2000, 10, 399–411. [Google Scholar] [CrossRef]
  51. Han, G.; Zhou, G.; Xu, Z.; Yang, Y.; Liu, J.; Shi, K. Biotic and abiotic factors controlling the spatial and temporal variation of soil respiration in an agricultural ecosystem. Soil Biol. Biochem. 2007, 39, 418–425. [Google Scholar] [CrossRef]
  52. Vargas, R.; Allen, M.F. Environmental controls and the influence of vegetation type, fine roots and rhizomorphs on diel and seasonal variation in soil respiration. New Phytol. 2008, 179, 460–471. [Google Scholar] [CrossRef] [PubMed]
  53. Makhnykina, A.V.; Vaganov, E.A.; Panov, A.V.; Koshurnikova, N.N.; Prokushkin, A.S. The pulses of soil CO2 emission in response to rainfall events in Central Siberia: Revisiting the overall frost-free season CO2 flux. Forests 2024, 15, 355. [Google Scholar] [CrossRef]
  54. Harel, A.; Sylvain, J.-D.; Drolet, G.; Thiffault, E.; Thiffault, N.; Tremblay, S. Fine scale assessment of seasonal, intra-seasonal and spatial dynamics of soil CO2 effluxes over a balsam fir-dominated perhumid boreal landscape. Agric. For. Meteorol. 2023, 335, 109469. [Google Scholar] [CrossRef]
  55. Tagesson, T. Seasonal Variation and Controlling Factors of Soil Carbon Effluxes in Six Vegetation Types in Southeast of Sweden; Swedish Nuclear Fuel and Waste Management Co.: Stockholm, Sweden, 2006; 39p. [Google Scholar]
  56. Khoroshaev, D.A.; Kurganova, I.N.; Lopes de Gerenyua, V.O. Heterotrophic soil respiration response to the summer precipitation regime and different depths of snow cover in a temperate continental climate. Eurasian Soil Sci. 2023, 56, 1667–1682. [Google Scholar] [CrossRef]
  57. Curiel Yuste, J.; Flores-Rentería, D.; García-Angulo, D.; Hereş, A.-M.; Bragă, C.; Petritan, A.-M.; Petritan, I. Cascading effects associated with climate-change-induced conifer mortality in mountain temperate forests result in hot-spots of soil CO2 emissions. Soil Biol. Biochem. 2019, 133, 50–59. [Google Scholar] [CrossRef]
  58. Launiainen, S.; Rinne, J.; Pumpanen, J.; Kulmala, L.; Kolari, P.; Keronen, P.; Siivola, S.; Pohja, T.; Hari, P.; Vesala, T. Eddy covariance measurements of CO2 and sensible and latent heat fluxes during a full year in a boreal pine forest trunk-space, Bor. Boreal Environ. Res. 2005, 10, 569–588. [Google Scholar]
  59. Gaumont-Guay, D.; Black, T.A.; Barr, A.G.; Griffis, T.J.; Jassal, R.S.; Krishnan, P.; Grant, N.; Nesic, Z. Eight years of forest-floor CO2 exchange in a boreal black spruce forest: Spatial integration and long-term temporal trends. Agric. For. Meteorol. 2014, 184, 25–35. [Google Scholar] [CrossRef]
  60. Venäläinen, A.; Lehtonen, I.; Laapas, M.; Ruosteenoja, K.; Tikkanen, O.P.; Viiri, H.; Ikonen, V.P.; Peltola, H. Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Glob. Chang. Biol. 2020, 26, 4178–4196. [Google Scholar] [CrossRef]
  61. Rapalee, G.; Steyaert, L.T.; Hall, F.G. Moss and lichen cover mapping at local and regional scales in the boreal forest ecosystem of central Canada. J. Geophys. Res. 2001, 106, 33551–33563. [Google Scholar] [CrossRef]
  62. Lucas-Borja, M.E.; Candel, D.; Jindo, K.; Moreno, J.L.; Andres, M.; Bastida, F. Soil microbial community structure and activity in monospecific and mixed forest stands, under Mediterranean humid conditions. Plant Soil 2012, 354, 359–370. [Google Scholar] [CrossRef]
  63. Kolstela, J.; Aakala, T.; Maclean, I.; Niittynen, P.; Kemppinen, J.; Luoto, M.; Rissanen, T.; Tyystjärvi, V.; Gregow, H.; Vapalahti, O.; et al. Revealing fine-scale variability in boreal forest temperatures using a mechanistic microclimate model. Agric. For. Meteorol. 2024, 350, 109995. [Google Scholar] [CrossRef]
  64. Roby, M.C.; Scott, R.L.; Moore, D.J.P. High vapor pressure deficit decreases the productivity and water use efficiency of rain-induced pulses in semiarid ecosystems. J. Geophys. Res. Biogeosci. 2020, 125, e2020JG005665. [Google Scholar] [CrossRef]
  65. Priputinaa, I.V.; Frolov, P.V.; Shanin, V.N.; Bykhovets, S.S.; Kurganova, I.N.; Lopes de Gerenyua, V.O.; Sapronov, D.V.; Zubkova, E.V.; Myakshina, T.N.; Khoroshaev, D.A. Simulation modeling of forest soil respiration: Case study of entic carbic podzol under coniferous–broadleaved forest in the south of Moscow oblast. Eurasian Soil Sci. 2023, 56, 1291–1303. [Google Scholar] [CrossRef]
  66. Makhnykina, A.V.; Tychkov, I.I.; Prokushkin, A.S.; Pyzhev, A.I.; Vaganov, E.A. Factors of soil CO2 emission in boreal forests: Evidence from Central Siberia. iForest 2023, 16, 86–94. [Google Scholar] [CrossRef]
  67. Gudko, V.; Usatov, A.; Ioshpa, A.; Denisenko, Y.; Shevtsova, V.; Azarin, K. Agro-climatic conditions of the Southern Federal District of Russia in the context of climate change. Theor. Appl. Climatol. 2021, 145, 989–1006. [Google Scholar] [CrossRef]
  68. Gril, E.; Spicher, F.; Greiser, C.; Ashcroft, M.B.; Pincebourde, S.; Durrieu, S.; Nicolas, M.; Richard, B.; Decocq, G.; Marrec, R.; et al. Slope and equilibrium: A parsimonious and flexible approach to model microclimate. Methods Ecol. Evol. 2023, 14, 885–897. [Google Scholar] [CrossRef]
  69. Houghton, R.A.; Roy, J.; Saugier, B.; Mooney, H.A. Global Terrestrial Productivity and Carbon Balance; Terrestrial Global Productivity; Academic Press: San Diego, CA, USA, 2001; pp. 499–520. [Google Scholar] [CrossRef]
  70. Zhu, B.; Wang, X.; Fang, J.; Piao, S.; Shen, H.; Zhao, S.; Peng, C. Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China. J. Plant Res. 2010, 123, 439–452. [Google Scholar] [CrossRef] [PubMed]
  71. Li, F.; Liu, Y.; Zhang, C.; Sa, R.; Tie, N. Precipitation and understory vegetation diversity drive variations in soil organic carbon density: Results from field surveys and satellite data of two different periods in the Greater Khingan Mountains of northeast China. Appl. Ecol. Environ. Res. 2022, 20, 2303–2328. [Google Scholar] [CrossRef]
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Figure 1. Study sites location (60° N, 89° E): ZOTTO station and two research sites—LPF and FMPF.
Figure 1. Study sites location (60° N, 89° E): ZOTTO station and two research sites—LPF and FMPF.
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Figure 2. The growing seasons hydrothermal variables for 2012–2022 in the study area and the mean values recorded at Bor WMO station (WMO ID: 23884).
Figure 2. The growing seasons hydrothermal variables for 2012–2022 in the study area and the mean values recorded at Bor WMO station (WMO ID: 23884).
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Figure 4. Seasonal pattern of soil emissions during dry (a) and wet (b) seasons in LPF. Average rates with standard deviations are presented.
Figure 4. Seasonal pattern of soil emissions during dry (a) and wet (b) seasons in LPF. Average rates with standard deviations are presented.
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Figure 5. Seasonal pattern of soil emissions during dry (a) and wet (b) seasons in FMPF. Average rates with standard deviations are presented.
Figure 5. Seasonal pattern of soil emissions during dry (a) and wet (b) seasons in FMPF. Average rates with standard deviations are presented.
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Figure 6. Relationships between soil CO2 emissions and soil microclimatic factors in and FMPF (a,b) and LPF (c,d) during dry and wet seasons.
Figure 6. Relationships between soil CO2 emissions and soil microclimatic factors in and FMPF (a,b) and LPF (c,d) during dry and wet seasons.
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Figure 7. Cumulative soil CO2 emission flux for different seasons (122 days each).
Figure 7. Cumulative soil CO2 emission flux for different seasons (122 days each).
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Figure 8. Dependencies of cumulative soil CO2 emissions on HTC.
Figure 8. Dependencies of cumulative soil CO2 emissions on HTC.
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Table 1. Ecological conditions of the study sites. Mean values for GS are presented with standard deviations (SD).
Table 1. Ecological conditions of the study sites. Mean values for GS are presented with standard deviations (SD).
CharacteristicLPFFMPF
Elevation, m a.s.l.116117
Forest composition10P *10P *
Tree species Pinus sylvestris L.Pinus sylvestris L.
Stand density, trees per ha 13011146
Forest age, years73119
Ground coverCladonia stellaris
(Opiz) Pouzar
et Vezda, Cl. arbuscula
(Wallr) Flot		Pleurozium schreberi
(Brid.) Mitt, Dicranum polysetum Michx., Hylocomium splendens (Hedw.) Schimp.
Soil carbon pool, g per m2 (top 50 cm)19601930
Carbon pool in ground cover, g per m2 **445–470240–300
Mean soil temperature, °C13.9 ± 0.911.6 ± 1.2
Mean soil water content, m3 m−30.23 ± 0.010.29 ± 0.01
* 10P—formula for tree stand by stock of wood (Russian classification), i.e., stand is composed solely of pine trees (10 pines of 10 trees). ** Data from Bezkorovainaya et al., 2005 [34].
Table 2. Correlation between soil microclimatic factors and soil CO2 emissions.
Table 2. Correlation between soil microclimatic factors and soil CO2 emissions.
FactorLPFFMPF
Soil temperature:
Dry seasonsR2adj. = 0.027R2adj. = 0.31
Wet seasonsR2adj. = 0.41R2adj. = 0.14
Soil moisture:
Dry seasonsR2adj. = 0.18R2adj. = −0.0075
Wet seasonsR2adj. = −0.019R2adj. = −0.0066
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Makhnykina, A.; Vaganov, E.; Panov, A.; Polosukhina, D.; Prokushkin, A. An Interseasonal Comparison of Soil Respiration in Xeric and Mesic Pine Forest Ecosystems in Central Siberia. Atmosphere 2024, 15, 988. https://doi.org/10.3390/atmos15080988

AMA Style

Makhnykina A, Vaganov E, Panov A, Polosukhina D, Prokushkin A. An Interseasonal Comparison of Soil Respiration in Xeric and Mesic Pine Forest Ecosystems in Central Siberia. Atmosphere. 2024; 15(8):988. https://doi.org/10.3390/atmos15080988

Chicago/Turabian Style

Makhnykina, Anastasia, Eugene Vaganov, Alexey Panov, Daria Polosukhina, and Anatoly Prokushkin. 2024. "An Interseasonal Comparison of Soil Respiration in Xeric and Mesic Pine Forest Ecosystems in Central Siberia" Atmosphere 15, no. 8: 988. https://doi.org/10.3390/atmos15080988

APA Style

Makhnykina, A., Vaganov, E., Panov, A., Polosukhina, D., & Prokushkin, A. (2024). An Interseasonal Comparison of Soil Respiration in Xeric and Mesic Pine Forest Ecosystems in Central Siberia. Atmosphere, 15(8), 988. https://doi.org/10.3390/atmos15080988

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