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Article

Species- and Age-Specific Growth Reactions to Extreme Droughts of the Keystone Tree Species across Forest-Steppe and Sub-Taiga Habitats of South Siberia

by
Liliana V. Belokopytova
1,*,
Dina F. Zhirnova
1,
Konstantin V. Krutovsky
2,3,4,5,6,
Nariman B. Mapitov
7,
Eugene A. Vaganov
8,9 and
Elena A. Babushkina
1
1
Khakass Technical Institute, Siberian Federal University, 655017 Abakan, Russia
2
Department of Forest Genetics and Forest Tree Breeding, Georg-August University of Göttingen, 37077 Göttingen, Germany
3
Center for Integrated Breeding Research (CiBreed), Georg-August University of Göttingen, 37075 Göttingen, Germany
4
Laboratory of Population Genetics, N. I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 119333 Moscow, Russia
5
Laboratory of Forest Genomics, Genome Research and Education Center, Department of Genomics and Bioinformatics, Institute of Fundamental Biology and Biotechnology, Siberian Federal University, 660036 Krasnoyarsk, Russia
6
Scientific and Methodological Center, G. F. Morozov Voronezh State University of Forestry and Technologies, 394036 Voronezh, Russia
7
Department of Biology and Ecology, Toraighyrov University, Pavlodar 140008, Kazakhstan
8
Institute of Ecology and Geography, Siberian Federal University, 660036 Krasnoyarsk, Russia
9
Department of Dendroecology, V.N. Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Science, 660036 Krasnoyarsk, Russia
*
Author to whom correspondence should be addressed.
Forests 2022, 13(7), 1027; https://doi.org/10.3390/f13071027
Submission received: 12 May 2022 / Revised: 27 June 2022 / Accepted: 27 June 2022 / Published: 29 June 2022
(This article belongs to the Special Issue Forest Species Distribution and Diversity under Climate Change)
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Abstract

:
Over the coming decades, climate change can decrease forest productivity and stability in many semiarid regions. Tree-ring width (TRW) analysis allows estimation of tree sensitivity to droughts, including resistance (Rt) and resilience (Rc) indexes. It helps to find adaptive potential of individual trees and forest populations. On a forest stand scale, it is affected by habitat conditions and species’ ecophysiological characteristics, and on individual scale by tree genotype, age, and size. This study investigated TRW response to droughts in forest-steppe and sub-taiga of southern Siberia for keystone species Scots pine (Pinus sylvestris L.), Siberian larch (Larix sibirica Ledeb.), and silver birch (Betula pendula Roth.). Chronologies reacted positively to the Standardized Precipitation-Evapotranspiration Index (SPEI) of the previous July–September and current April–July. Depressed tree growth across region and droughts lasting over both intra-seasonal intervals were registered in 1965, 1974, and 1999. TRW-based Rt and Rc for these droughts did not reveal age- or size-related patterns. Higher growth stability indexes were observed for birch in sub-taiga and for conifers in forest-steppe. Larch at all sites had disadvantage against pine for 1965 and 1999 droughts aggravated by pest outbreaks, but adapted better to drought in 1974. Site aridity affected both tree growth stability and intensity of climatic response.

1. Introduction

Forest ecosystems are constantly facing changing environmental and climatic conditions, but it is becoming obvious that they are becoming greatly affected by more frequent and prolonged droughts [1,2,3]. Over the coming decades, climate change will accelerate tree dieback due to severe drought and heat stress in many areas [4,5,6,7], especially in semiarid regions where significant decreases in forest productivity and growth stability are expected [8,9,10,11,12]. The sensitivity of trees to drought should be assessed within ecological gradients (natural zones), since the expected climate change effects depend much on the local habitat conditions [13,14].
These conditions and climate change effects can be studied in detail using tree ring analysis. Tree rings are advantageous proxies to study growth-limiting climatic variables, but radial growth also reflects other factors, adding undesirable noise to climatic response [15,16,17]. The response of trees to climatic variables can be significantly modified by topography and soil features, species characteristics, and even individual genetic factors [18,19,20,21,22]. Clearly, species-specific modulation of climate response may be associated with their physiological strategies of coping with stress [23,24], although abiotic factors acting at the community level may cause similar responses [7]. The resilience of forest growth to extreme events, including droughts, also depends not only on the intensity and duration of stress, but also on the forest stand structure [25,26]. Variability in adaptation to drought manifests itself on the population and individual tree levels [22]. In particular, tree age and size can make a significant contribution here [27,28,29,30,31]. However, studies on the interaction between climate change and tree decline have not received much attention in regard to age effects [32,33]. This may be due to the ambiguity of the observed age-related changes in the climate response: old trees may be more [33,34,35,36] or less [37] sensitive to climate than young trees. In some cases, the absence of age-related changes in climate response and vulnerability to stress was also observed [38,39]. In addition, age trends in radial growth may interfere with expression of environmental signals, and standardization is not always able to completely eliminate this effect [31]. However, this issue is still open for discussion [28,40,41,42].
When characterizing tree reaction to extreme events, including droughts, it is important to take into account not only their immediate impact, but also longer aftereffects, i.e., legacy effect [43,44,45]. The consequences of drought may limit the resilience of growth or the ability to restore pre-existing growth rate [46], which suppresses ecosystem-scale carbon sequestration [47]. The duration of full growth recovery after a drought varies from two–three years up to decades [44,48]. However, it remains unclear what factors determine this legacy dependence on the habitat and tree species [49]. A comparison of pointer years in tree rings with relevant historic events and instrumental observations allows us to quantify the response of forests to drought and predict their vulnerability to future extreme events [11,49]. The identification of growth patterns and trends before, during, and after drought on stand and individual tree levels ultimately provides an opportunity to assess forest dynamics and tree adaptation to current and projected climate scenarios.
This study analyzes the reaction to droughts of the main forest-forming tree species in the semiarid forest ecosystems of Southern Siberia. We assumed that a comparison of the responses to the drought index and indicators of growth stability during droughts for different-age trees of three species in sub-taiga and forest-steppe habitats would be useful for assessing tolerance to water stress and the prospects of these species under conditions of rapid regional warming.

2. Materials and Methods

Forest ecozones with varying degrees of water deficit (forest-steppe and sub-taiga at 500–800 m a.s.l.) were chosen as the study area on the borders of the vast Khakass-Minusinsk Depression with the surrounding Kuznetsk Alatau and the Western Sayan Mountains (Figure 1). These low montane areas are represented by low hills with mostly gentle slopes (<30°). Soils are predominantly gray forest (alfisol) and sod-calcareous (rendzina, leptosol), clayey, and stony [50]. The shaded northern slopes are covered with close-canopy mixed forests, but sparse forest stands and isolated trees are interspersed with steppe vegetation in the lower part of the sunlit southern slopes. The forest stands of the forest-steppe and sub-taiga zones in the region are represented primarily by conifer species, Scots pine (Pinus sylvestris L.) and Siberian larch (Larix sibirica Ledeb.), and by the less common silver birch (Betula pendula Roth.) that occurs mainly in relatively moist micro-habitats. Hereafter, these species are written as pine, larch, and birch, respectively.
Climatic data on temperature and precipitation seasonal dynamics were obtained from daily series of the Minusinsk station (MIN, 53°43′ N 91°42′ E, 255 m a.s.l., 1936–2018, Figure S1). The regional climate is sharply continental with high magnitudes of daily and seasonal fluctuations in air temperature [52]. The average annual air temperature in Minusinsk is +1.9 °C, the total precipitation is 360 mm. A cold winter with little snow lasts from November to March; snow comprises only ~20% of annual precipitation, and about 50% of the maximum snow depth accumulates in November. From May to September, average daily temperatures exceed +5 °C, i.e., this interval can be taken as an estimate of the vegetative season. The hot summer is combined with the seasonal maximum of precipitation in July. Moisture deficit was estimated by the 1-month Standardized Precipitation-Evapotranspiration Index (SPEI) series [53] over 53–54.5° N 89.5–91.5° E, obtained from the European Climate Assessment & Dataset KNMI Climate Explorer database [54]. The correlations between SPEI series for the geographic grid cells of sampling sites are 0.90–0.98 (p < 0.001), which makes it possible to use the regional average series (Figure 1) for dendroclimatic analysis. To obtain seasonal SPEI series, series of selected months were averaged with arithmetic mean and then standardized to mean = 0 and SD = 1 (linear transformation).
The sampling sites are open-canopy mixed forest stands (pine, larch, and sometimes birch) on the southwestern–southeastern slopes within forest-steppe (BGD, BID, and KAZ) and sub-taiga (BER, BIR, and KALY) natural zones (Table 1). Sampling was performed during the period 2008–2019. Wood samples (cores) from birch trees were collected only at BER and BID, while pine and larch samples were collected from all six sites. The age distribution of samples for all species and sites is presented in Figure S2.
Samples were collected and processed using standard dendrochronology techniques [55]. The individual series of tree-ring width (TRW) were measured on the LINTAB measuring station using the TSAP program [56]. Their cross-dating was checked using the COFECHA program [57]. Then, the age trends described by the negative exponent and the short-term autocorrelation were removed from the raw series in order to highlight high-frequency fluctuations (including the climate signal), and generalized chronologies for each site/species were obtained by averaging with the bi-weight mean in the ARSTAN program [58].
The following statistical characteristics of the TRW time series were used: standard deviation (SD), mean sensitivity (sens [59]), and mean inter-serial correlation coefficient (r-bar [15]). Pearson’s paired correlation coefficients were used to identify relationships of tree-ring chronologies and individual TRW series with SPEI. Threshold values of mean—1.5·SD were used to identify negative extremes in larch growth (pointer years [60]) and seasonally averaged SPEI (droughts). To analyze the stability of tree growth under drought at the local and individual tree levels, the indexes of resistance (Rt = Gd/Gprev) and resilience (Rs = Gpost/Gprev) proposed by Lloret et al. [46] were implemented, where Gprev is the average growth during three years before drought, Gd is the growth during drought, and Gpost is the average growth during three years after drought. The recovery index (Rc = Gpost/Gd) was not analyzed because many trees had missing rings (TRW = 0) in drought years. The measured raw TRW and basal area increment (BAI [61]) were used as growth measurements for these indexes. The statistical significance of differences in the values of the growth stability indexes between samples (species/site combinations) was assessed with one-way ANOVA.

3. Results

3.1. Tree-Ring Width Chronologies and Their Characteristics

Local residual tree-ring width chronologies (Figure S3) for all three species have a higher range of variation in the forest-steppe zone (BGD, BID, and KAZ) compared to the sub-taiga zone (BER, BIR, and KALY). This is manifested both in the variability in general (SD) and its annual component, the sensitivity coefficient (Table 2). Comparing the chronologies of different species within the same site, birch TRW has the greatest variability, and pine TRW has the lowest variability. No age dependence was found for these indicators of variability. On the contrary, the average growth rate is strongly negatively associated with tree age: longer chronologies with higher values of average cambial age have a lower mean TRW (r = −0.825, p < 0.001). Significant dependences of the mean TRW on the species or natural zone were not revealed. The content of the common signal, estimated by r-bar, has no significant patterns in regards to the tree species, age, or site.

3.2. Climatic Response and Pointer Years

The radial growth of all three species has a significant response to the regional 1-month drought index SPEI, and several main intervals of this response are observed during the biological year (Figure 2). In the second half of the vegetative season preceding tree-ring formation, the correlations with August SPEI are the most pronounced. For the chronologies of the forest-steppe zone, significant correlations can extend to July (larch) and September (primarily pine). A positive precipitation signal, stronger for pine, is observed in November for all of chronologies within the forest-steppe zone and in October for the KALY site. Moisture conditions of the current vegetative season are positively associated with tree growth from April or May to July, although not all correlations are significant. The seasonal averaging of the drought index made it possible to obtain higher and almost universally significant correlations at p < 0.05 for the intervals from July to September of the previous season (r = 0.19–0.53) and from April to July of the current season (r = 0.12–0.52). These correlations are higher for current season in most cases. When averaging SPEI for both periods in total (i.e., mean of seven monthly values for the previous July, August, and September and the current April, May, June, and July), r = 0.20–0.61, significant at p < 0.05 for all chronologies except BIRLS. Simple averaging of SPEI for 13 months from the previous July to the current July derived consistently lower correlations (r = 0.14–0.54) in comparison to selection of only warm-season months.
During the cover period of instrumental climate data, pointer years (growth suppression) are more numerous and better synchronized in conifer chronologies than in birch ones (Figure 3a). On a regional scale, several common years can be distinguished: 1944, 1945, 1965, 1974, 1994, and 1999. In 1944, 1945, and 1994, the SPEI of the previous and current vegetative season also has values below–1.5; in 1965, 1974, and 1999 it was below–2.0; i.e. these were years of severe and extreme droughts, respectively (Figure S4). A more detailed analysis of the climatic dynamics for pointer years (Figure 3b) showed that the suppression of tree growth in 1944 and 1994 may be associated with shorter droughts both in the current and in the previous season (which in 1994 was aggravated by heat wave in June–July), and early frosts in November. In 1945, the most significant stress factors are extremely severe frosts during winter and spring–summer drought against the background of high temperatures in April–May, despite precipitation of the previous July–September being higher than the average. In 1965, 1974, and 1999, a strong deficit of precipitation and elevated temperatures prevailed both in previous July–August and current May–July. For contrast, the large amount of precipitation during both previous and current seasons, heavy November snowfalls and mild winters were observed in 1938, 1970, and 2003 (Figure 3c), which were associated with faster tree growth compared to previous and subsequent years for all chronologies, and with index values of TRW above average for most of them.

3.3. Age Dependencies of Tree Growth Response to Droughts

There are significant differences in the age structure of the sampled trees between species and sites, since even cover periods of chronologies vary from 63 to 316 years. Therefore, it was first necessary to check for age dependences in the tree growth stability during extreme droughts (Table 3). The indexes of resistance (Rt) and resilience (Rs) obtained for raw TRW and BAI series do not have significant relationships with the age or size of trees in most samples. The few statistically significant relationships of Rt and Rs with tree age do not have visible patterns of direction or intensity. Sample size, age range of model trees, natural zone, and species—all these factors were tested and rejected as possible modifiers of the age-related dynamics in growth stability. The difference between sites is observed, but it seems to increase with the increase of the geographical distances between sites rather than to be related to natural zone.
However, the choice of growth indicator for calculating stability indexes introduces a difference observed in a significant part of the samples: BAI-based Rt and Rs have tendencies to reduce with age, while both negative and positive correlations with tree age were observed for TRW-based stability indexes. Dependences of Rt and Rs on the tree size measures, especially diameter, have a more pronounced tendency to be negative. Nevertheless, the largest number of significant negative correlations with size was also observed for BAI-based growth stability indexes.

3.4. Inter-Species and Local Differences in Growth Stability

Birch radial growth in the sub-taiga zone (BER) was affected by drought in 1965 and 1974 significantly less than in conifers, and the negative consequences of drought in 1975 were also less pronounced (Figure 4). In the forest-steppe zone (BID), the pattern is opposite. Conifers have significantly higher indexes of resistance (Rt) and resilience (Rs) compared to birch, with the exception of Rs in 1965. Comparison of two conifer species within the same site showed that during the droughts of 1965 and 1999, higher Rt and Rs were typical for pine trees compared to larches; on the contrary, the drought of 1974 had a stronger limiting effect on pine growth. These inter-species differences are not significant in all sites, but no violations of this pattern in the form of significant differences of the opposite sign were detected.
Judging by the statistical significance of between-site differences for the same species, birch Rt for all three analyzed droughts is significantly higher in the sub-taiga BER than in the forest-steppe BID, while difference between sites is ambiguous for Rs. For pine, Rt is consistently high in the BIR site, low in BER, BGD, and KAZ sites, and medium in the KALY site, while the BID site’s position differs for different droughts. For Rs, the site comparison gives the same results, except for the less stable position of BIR. Thus, pine stands in the forest-steppe zone are more prone to low growth during drought and in subsequent years, while for sub-taiga pine stands, the intensity of the reaction is heterogeneous. Larch stands have low Rt values for all drought years in the BER and BGD sites, high values in BIR, and medium in KALY. For Rs, high values were also distinguished for BIR, medium values for BER, and low values for KAZ. For other sites, both relatively high and relatively low values were observed depending on the event.
For Rt and Rs based on BAI (Figure S5), differences between species and between sites are similar to those based on TRW, but are less pronounced, despite the fact that the variability range of Rt and Rs themselves was almost twice as high.

4. Discussion

4.1. Growth–Climate Relationships

The main common limiting factor for radial growth of all considered species is the moisture deficit during the first half of the current (April–July) and the second half of the previous (July–September) vegetative season. Such a climatic response is typical for the semiarid conditions of the continental Asian temperate latitudes, where most of the precipitation falls during the warm season, determining the unimodal seasonal dynamics of xylogenesis [62,63]. The calendar timeframe for the beginning and ending of a significant dendroclimatic response indicates that a shift in balance between carbon sinks from current TRW formation to the storage of nutrients for usage at the beginning of the next season [64,65] in the study area occurs, as a rule, close to mid-July [66]. An analysis of the pointer years’ climatic features confirms primary moisture limitation of forests in the forest-steppe and sub-taiga zones of the region. The growth of all three forest-forming species in these ecosystems [67] is most strongly suppressed during droughts prolonged due to insufficient precipitation during both the current and previous season in combination with high (1965, 1999) or average temperature (1974). Less pronounced synchronicity of the suppression of tree growth by droughts in 1943–1945 may be due to their shorter duration, i.e., the presence of intervals with relatively favorable conditions in July–September of 1943 and during all of the warm season in 1944. On the one hand, in our opinion, the spatial heterogeneity of precipitation leads to shorter intervals of insufficient moisture possibly actually covering only part of the region. On the other hand, during shorter droughts, trees have the ability to regulate the balance of growth and accumulation of non-structural carbohydrates, while long-term intense droughts lead to carbon loss and starvation, maximizing growth suppression [68,69].
A positive growth response to SPEI in late autumn was found in all three species, but only in the forest-steppe zone, which corresponds to the spatial gradients of the total precipitation. In November, air temperature becomes negative and snow cover accumulation begins (Figure S1). Precipitation fallen in the pre-freezing period forms the overwinter soil moisture reserves. These reserves and snowmelt can serve as an additional water source for evergreen pine trees, where photosynthesis is activated immediately when the soil thaws [70,71] or even earlier at positive air temperatures [72]. Deciduous trees, on the other hand, begin photosynthesis only after new foliage unfolds, which occurs several weeks later [73]. Thus, with shallow snow cover depth in the forest-steppe zone, they can use this source of moisture to a lesser extent at the beginning of the season. It should also be noted that severe frosts before and during the establishment of snow cover (a combination of negative deviations in temperature and precipitation in November–December, see 1944, 1945, and 1994 in Figure 3) can lead to tissue damage, in particular fine roots [74,75,76], which can cause November SPEI response for all species. These conclusions are also supported by abundant November precipitation observed in the biological years most favorable for tree growth (Figure 3c).
The intensity gradient of the climatic response is obviously associated not so much with the natural zone (sub-taiga or forest-steppe), but with the geographical location along the gradient of precipitation, increasing from the driest central part of the Khakass-Minusinsk Depression to its outskirts and up into the mountains: forest stands on the Batenevsky Ridge, jutting out into the drier steppe zone (especially BGD and BID at its eastern tip), react to SPEI more strongly than the sites located in the corresponding natural zone but far in the southwest from the valley center.

4.2. Species-Specific Strategies of Adaptation to Droughts

Plants vary greatly in their sensitivity to drought and in their ability to maintain carbon assimilation and hydraulic functions under stress [77,78,79]. Tree species develop adaptation mechanisms to drought in the range between two opposite strategies: the isohydric strategy of avoiding drought stress at the price of photosynthesis rate and the anisohydric strategy of photosynthesis and growth processes continuation despite stress [79,80]. Regardless of the strategy, after exceeding a certain threshold of stress intensity, trees are damaged and could die [4].
Some of the differences between considered species determine the strategy of drought adaptation; first of all, we can note the regulation of transpiration, i.e., stomatal closure at a certain water pressure deficit. However, in the leaves of silver birch, 12% of transpiration occurs through the cuticle and cannot be regulated by the stomata closure; in conifers, the waxy cuticle of the needles reduces this value, e.g., to 5% in Scots pine [81]. Therefore, despite the contrast between the isohydric strategy (closing of stomata synchronously with water pressure deficit) of pine and birch compared to the anisohydric strategy of larch [82,83,84], a less pronounced decrease in transpiration can be expected for birch under moderate drought stress.
It can be assumed that the regulation of the transpiration rate of these three species should correspond to the abilities of water uptake from various depths by their root systems. Indeed, Zhang et al. [85] noted a deep root system in larch, while pine receives water from the upper soil horizons [86,87]. Silver birch on poor stony soils also forms a shallow root system [88,89], but has a more pronounced plasticity of root architecture compared to pine [90,91]. Thus, the gradient of the degree of transpiration and growth maintenance during drought (pine—birch—larch in ascending order) corresponds to the ability of trees to use water from deep soil horizons.
When comparing tree growth during dry and favorable seasons, it should also be taken into account that the intensity of transpiration of these three species also differs under sufficient moisture, generally increasing in the order of pine—larch—birch [73,83]. It should also be taken into account that the strongest suppression of tree growth in the region is caused by a combination of droughts in both previous and current seasons. We believe that with a strong moisture deficit in the second half of the vegetative season preceding the formation of a narrow tree ring, the anisohydric strategy of larch, can lead to a partial loss of needles and fine roots preceding normal seasonal senescence [92,93], which means reducing the accumulation of assimilates and aggravating the likelihood of carbon starvation during the ongoing drought in the current season. This may give an advantage to the most water-conservative pine that retains old needles. However, there is also evidence in favor of greater drought tolerance and, in particular, greater resistance indexes for larch species growing in the forest-steppes of continental Asia [94,95]. Apparently, the comparative resistance to droughts of these two conifers may depend on the duration, intensity, and intra-seasonal framework of drought event.

4.3. Tree Growth Stability Indexes under Drought

In dendrochronology, approaches based either on BAI or TRW are often used to calculate the indexes of tree growth stability during and after drought, but they are rarely compared [96]. This methodological discrepancy limits the ability to compare the patterns identified in such studies, since growth indicators are non-linearly related to each other and, therefore, may have different sensitivity to stress factors. Raw measurements of BAI are mostly used in such studies, while raw measurements of TRW are less used. In this study, we used both approaches and compared the results. As it turned out, the patterns of differences in growth stability indexes Rt and Rs between species and sites do not depend on the approach to their calculation, while the age/size dependences, despite the low significance in both approaches, are more pronounced for BAI-based indexes. Despite significant age trends in radial growth, the degree of TRW suppression during and after full-season extreme droughts was stable for most chronologies. The independence from age was noted earlier for growth stability indexes calculated on the base of aboveground biomass estimation [39]. We believe that such observations are an additional argument in favor of the Schwarz et al. [96] recommendation to use primarily TRW-based growth stability indexes, as well as in favor of the inconsistency in age patterns of tree sensitivity to stress factors in the study area.
Some studies indicate that age-related changes in climate sensitivity may not be related to ontogenetic features per se, but rather to an increase in tree size [97,98]. It has been mentioned that an increase in the size and volume of a tree raises cost of respiration and reduces the efficiency of water supply, which under moisture deficit conditions ultimately leads to a decrease in growth and strengthening of the observed climatic response [27,99,100]. In addition, it is worth considering the phytosocial position of trees in the forest stand, when with an increase in tree size and the loss of weaker competitors, there are more resources available for survived large old dominant trees [101]. BAI depends on trunk diameter negatively; therefore, in our opinion, the more pronounced negative correlations between tree size and BAI-based stability indexes observed for some of the samples in this study are also a sign of methodological bias in this approach.
The combination of significantly higher resistance and resilience for birch in BER site, and conversely, their lower values in BID site, is most likely associated with a gradient of local conditions. We can assume that birch has a competitive advantage over both conifers under milder conditions of the sub-taiga, but loses it due to more intense droughts in the forest-steppe. The difference in the intensity of drought reaction between conifers was opposite in 1974 compared to 1965 and 1999, and it was not related to drought intensity (the SPEI gradient ranks these events as 1965–1974–1999 in ascending order). A comparison of the cold season conditions also does not give grounds for a different reaction observed in 1974. However, in addition to climate, other stress factors are associated with drought in the region. For example, dry and hot conditions provoke outbreaks of phyllophagous insects, for many of which larch needles are the preferred feeding source in the region. Indeed, outbreaks of larch tortrix (Zeiraphera griseana Hubner) were recorded on the territory of the administrative region in 1966–1969 over extremely vast areas [102], Siberian moth (Dendrolimus sibiricus Tsch.) outbreaks were registered in 1966–1972 and 2000–2004 [103], and the Gypsy moth (Lymantria dispar L.) population increased in 1999–2006 [104]. According to the same sources, 1974 falls in the middle of the low-population inter-outbreak period for the aforementioned pests. The forest-steppe larch stands of southern Siberia are considered to be very vulnerable to phyllophages’ outbreaks, which is associated with increased attractiveness of larch needles for pests during drought due to relatively strong transpiration and high concentration of sugars [105]. Therefore, we believe that biotic stress could have made a significant contribution to the lower growth stability of larch compared to pine after the droughts of 1965 and 1999. In addition, these two droughts were also characterized by relatively low moisture during 1–2 previous years (Figure S4). The combination of an anisohydric adaptation strategy to a lack of moisture and deciduous foliar habit leads to the disadvantage of more pronounced carbon depletion in larch compared to pine in the beginning of these extremely dry periods (cf. statements about the dependence of the response to drought on conditions before and after it [96,106]). At the same time, in 1974, the drought came on abruptly after several years of good growth and full carbon reserves in plants, which probably favored a more intensively growing larch.
The stable significant between-site differences in tree growth stability (high stability during and after drought in BIR site, low in BGD and BER, and medium in KALY) correspond in principle to the intensity gradient of TRW response to SPEI. It seems that in this region, the sensitivity of the forest stands to the entire range of the moisture regime variability in the site (dendroclimatic correlations) is synchronized with the degree of growth suppression by extreme droughts and their legacy effect; moreover, both of these characteristics, if compared between sites for the same biological species, can serve as indirect estimations of the degree of habitat aridity.

5. Conclusions

In the sharp continental semiarid climate, moisture deficit impact on the growth of conifer and broad-leaf trees was found throughout all vegetative season. The immediate effect on the developing ring was registered in May–July, as well as the effect on the next year’s radial growth in July–September. Similar differences in intensity and seasonality of this signal were observed between species and between sites. The most drastic growth depressions were observed in silver birch, Siberian larch, and Scots pine for droughts lasting for all of the second half of the previous vegetative season and the first half of the current one.
A comparison of the tree growth stability indexes of resistance (Rt) and resilience (Rs) during and after droughts, respectively, demonstrated that birch was less affected by droughts in the sub-taiga, while conifers were less affected in the forest-steppe ecotone. Differences in drought resistance between pine and larch were homogenous in all sampling sites, but drought impact on larch was probably aggravated by additional biotic stress caused by pest outbreaks. No conclusive patterns in TRW reflecting age- or size- related shifts in sensitivity to moisture availability in general or to droughts were found.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f13071027/s1, Figure S1: Climatic data of the study area; Figure S2: Age structure of six samples; Figure S3: Residual tree-ring width chronologies and the number of cores; Figure S4: Dynamics of regional SPEI averaged for previous July–September and current April–July; Figure S5: Ranges of individual trees’ BAI-based drought-related stability indexes for the studied sites and species.

Author Contributions

Conceptualization, L.V.B., D.F.Z. and E.A.B.; methodology, L.V.B., D.F.Z. and E.A.V.; validation, K.V.K.; formal analysis, L.V.B.; investigation, D.F.Z., N.B.M. and E.A.B.; resources, D.F.Z.; data curation, L.V.B.; writing—original draft preparation, L.V.B., D.F.Z., K.V.K. and N.B.M.; writing—review and editing, L.V.B., D.F.Z. and K.V.K.; visualization, L.V.B.; supervision, E.A.V.; project administration, E.A.B.; funding acquisition, E.A.V. and E.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Russian Ministry of Science and Higher Education, grant number FSRZ 2020-0010; Russian Science Foundation, grant number 19-18-00145.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

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

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Figure 1. The study area. Black solid triangles depict sampling sites in the forest-steppe zones, open triangles depict sub-taiga zones, and the open circle depicts the Minusinsk weather station. The area within the large rectangle outlined by the solid red line was used for the regional average in the grid SPEI series, and areas in the smaller rectangles outlined by dotted red lines are grid cells used to assess the climate in the sampling sites. The background map image was generated using the ArcGIS online map tool [51]; it is the intellectual property of Esri and is used herein under license (© 2020 Esri and its licensors; all rights reserved).
Figure 1. The study area. Black solid triangles depict sampling sites in the forest-steppe zones, open triangles depict sub-taiga zones, and the open circle depicts the Minusinsk weather station. The area within the large rectangle outlined by the solid red line was used for the regional average in the grid SPEI series, and areas in the smaller rectangles outlined by dotted red lines are grid cells used to assess the climate in the sampling sites. The background map image was generated using the ArcGIS online map tool [51]; it is the intellectual property of Esri and is used herein under license (© 2020 Esri and its licensors; all rights reserved).
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Figure 2. Correlation coefficients of local residual tree-ring width chronologies with regional SPEI (1936–2018) for individual months and longer intervals (current April–July and previous July–September) of growth season. Dashed rectangles represent intra-seasonal intervals with maximum integral positive response. Bars representing significant correlation coefficients (p < 0.05) are outlined by black line. BP—Betula pendula Roth., PS—Pinus sylvestris L., LS—Larix sibirica Ledeb.; * months of the previous year.
Figure 2. Correlation coefficients of local residual tree-ring width chronologies with regional SPEI (1936–2018) for individual months and longer intervals (current April–July and previous July–September) of growth season. Dashed rectangles represent intra-seasonal intervals with maximum integral positive response. Bars representing significant correlation coefficients (p < 0.05) are outlined by black line. BP—Betula pendula Roth., PS—Pinus sylvestris L., LS—Larix sibirica Ledeb.; * months of the previous year.
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Figure 3. Pointer year analysis (1936–2018): (a) pointer years in tree-ring width (TRW) chronologies of Betula pendula Roth. (BP; triangles), Pinus sylvestris L. (PS; rectangles), and Larix sibirica Ledeb. (LS; circles), and full-season droughts (diamonds), defined, respectively, as TRW and total seasonal SPEI (previous July–September and current April–July) values below (mean–1.5SD). (b) Anomalies (differences between climatic dynamics of current biological year and average curve for 1936–2018) of temperature (T) and precipitation (P) series smoothed from Minusinsk station daily data by the 21-day moving average for pointer years; (c) the same for years of the most optimal tree growth. Numbers on plots represent values of SPEI: total (previous season/current season); all standardized to SD = 1) and average TRW index per species for the respective biological year. * months of the previous calendar year.
Figure 3. Pointer year analysis (1936–2018): (a) pointer years in tree-ring width (TRW) chronologies of Betula pendula Roth. (BP; triangles), Pinus sylvestris L. (PS; rectangles), and Larix sibirica Ledeb. (LS; circles), and full-season droughts (diamonds), defined, respectively, as TRW and total seasonal SPEI (previous July–September and current April–July) values below (mean–1.5SD). (b) Anomalies (differences between climatic dynamics of current biological year and average curve for 1936–2018) of temperature (T) and precipitation (P) series smoothed from Minusinsk station daily data by the 21-day moving average for pointer years; (c) the same for years of the most optimal tree growth. Numbers on plots represent values of SPEI: total (previous season/current season); all standardized to SD = 1) and average TRW index per species for the respective biological year. * months of the previous calendar year.
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Figure 4. Ranges of individual trees’ TRW-based drought-related stability indexes, resistance (Rt) and resilience (Rs), for the studied sites (BGD, BID, KAZ, BER, BIR, and KALY) and species (BP—Betula pendula Roth., PS—Pinus sylvestris L., LS—Larix sibirica Ledeb.). The alternation of grey and white backgrounds delineates the sampling sites. On the box plots, mid-point—median value, box—inter-quartile range 25%–75%, whiskers—range of variation min–max. Comparison of site-species combinations was performed using ANOVA. Samples not marked by the same letter are different at p < 0.05.
Figure 4. Ranges of individual trees’ TRW-based drought-related stability indexes, resistance (Rt) and resilience (Rs), for the studied sites (BGD, BID, KAZ, BER, BIR, and KALY) and species (BP—Betula pendula Roth., PS—Pinus sylvestris L., LS—Larix sibirica Ledeb.). The alternation of grey and white backgrounds delineates the sampling sites. On the box plots, mid-point—median value, box—inter-quartile range 25%–75%, whiskers—range of variation min–max. Comparison of site-species combinations was performed using ANOVA. Samples not marked by the same letter are different at p < 0.05.
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Table
Table 1. Sampling sites and tree species.
Table 1. Sampling sites and tree species.
Sampling SiteCoordinatesSampled Species 1
NameCodeLatitudeLongitudeh, m a.s.l.BPPSLS
BerenzhakBER54°20′ N89°44′ E720–750+++
BogradBGD54°12′ N90°50′ E500–600 ++
BidzhaBID54°00′ N91°01′ E650–700+++
BirikchulBIR53°20′ N89°54′ E600–700 ++
KazanovkaKAZ53°13′ N90°05′ E550–650 ++
KalyKALY53°03′ N91°17′ E500–550 ++
1 Plus sign marks tree species sampled at particular site: BP—silver birch (Betula pendula Roth.), PS—Scots pine (Pinus sylvestris L.), LS—Siberian larch (Larix sibirica Ledeb.).
Table
Table 2. Statistical characteristics of tree-ring width (TRW) samples and indexed (residual) chronologies.
Table 2. Statistical characteristics of tree-ring width (TRW) samples and indexed (residual) chronologies.
Species 1SampleSample StatisticsResidual TRW Chronology
Number of
Trees/Cores		Cover Period,
Years		Length,
Years		Mean
TRW, mm		SDsensr-bar
BPBERBP *11/111864–20081450.930.360.420.33
BIDBP15/151955–2017631.800.440.440.44
PSBERPS *14/141752–20082570.970.250.300.52
BGDPS73/1121847–20181720.980.260.310.40
BIDPS30/331849–20181701.490.280.350.49
BIRPS *21/211897–20141182.440.240.290.42
KAZPS47/671767–20132471.280.310.390.53
KALYPS *13/131947–2012662.960.200.220.33
LSBERLS *14/141737–20082720.720.280.320.50
BGDLS60/731768–20192520.690.300.370.43
BIDLS71/831704–20193160.730.290.350.44
BIRLS *8/131791–20132231.270.240.300.26
KAZLS20/201835–20131791.380.460.520.47
KALYLS *13/131946–2012672.490.310.380.51
1 BP—silver birch (Betula pendula Roth.), PS—Scots pine (Pinus sylvestris L.), LS—Siberian larch (Larix sibirica Ledeb.). * TRW chronologies from sampling sites located in the sub-taiga zone.
Table
Table 3. Correlation coefficients of individual tree drought-related growth stability indexes: resistance (Rt) and resilience (Rc) measured for droughts in 1965, 1974, and 1999, with individual tree cambial age, diameter, and basal area. Stability indexes were calculated based on the raw tree-ring width (TRW) and basal area increment (BAI).
Table 3. Correlation coefficients of individual tree drought-related growth stability indexes: resistance (Rt) and resilience (Rc) measured for droughts in 1965, 1974, and 1999, with individual tree cambial age, diameter, and basal area. Stability indexes were calculated based on the raw tree-ring width (TRW) and basal area increment (BAI).
Correl.
with		StabilityBP *PSLS
IndexBased onYearBERBPBIDBPBERPSBGDPSBIDPSBIRPSKAZPSKALYPSBERLSBGDLSBIDLSBIRLSKAZLSKALYLS
tree cambial ageresistance (Rt)TRW19650.400.540.170.08−0.44 †−0.12−0.06−0.100.000.34 †0.050.250.490.66
19740.08−0.130.26−0.09−0.15−0.15−0.070.110.24−0.090.250.080.11−0.23
19990.28−0.260.130.01−0.34−0.07−0.04−0.120.500.200.160.42−0.02−0.20
BAI19650.240.210.140.05−0.60 †−0.43−0.11−0.27−0.010.30 †−0.180.200.280.51
19740.03−0.410.24−0.24 †−0.23−0.46 †−0.220.060.22−0.120.11−0.03−0.08−0.40
19990.28−0.300.220.00−0.40 †0.05−0.06−0.140.500.190.090.40−0.06−0.20
resilience (Rc)TRW19650.430.88 †0.39−0.13−0.040.35−0.020.37−0.26−0.13−0.35 †−0.150.38−0.03
19740.10−0.11−0.020.17−0.20−0.180.130.140.78 †0.030.190.33−0.120.07
1999−0.25−0.300.49−0.11−0.140.05−0.01−0.030.48−0.010.35 †0.430.220.10
BAI19650.180.550.35−0.26 †−0.45 †−0.27−0.13−0.16−0.31−0.28 †−0.43 †−0.290.08−0.41
19740.05−0.40−0.04−0.19−0.40 †−0.53 †−0.180.080.76 †−0.02−0.040.18−0.26−0.47
1999−0.28−0.340.48−0.13−0.290.03−0.08−0.070.48−0.030.26 †0.350.070.09
tree diameterresistance (Rt)TRW19650.000.45−0.140.11−0.340.01−0.06−0.23−0.400.350.010.410.450.71
19740.14−0.170.20−0.09−0.18−0.120.180.370.09−0.040.33 †−0.22−0.05−0.01
19990.220.07−0.53 †−0.08−0.49 †−0.390.070.030.490.020.100.430.000.31
BAI1965−0.160.25−0.150.08−0.54 †−0.45 †−0.10−0.37−0.410.31 †−0.200.360.230.59
19740.11−0.450.19−0.25 †−0.25−0.26−0.030.310.09−0.070.21−0.37−0.25−0.24
19990.210.04−0.54 †−0.09−0.54 †0.030.060.020.480.020.040.41−0.040.31
resilience (Rc)TRW19650.190.51−0.100.14−0.070.38−0.100.33−0.24−0.06−0.140.170.35−0.53
1974−0.17−0.010.25−0.15−0.43−0.19−0.020.490.17−0.030.110.01−0.32−0.11
19990.14−0.23−0.040.06−0.11−0.10−0.07−0.08−0.33−0.220.31 †0.100.200.41
BAI1965−0.040.31−0.130.01−0.50 †−0.22−0.19−0.11−0.27−0.21−0.35 †−0.020.05−0.78 †
1974−0.21−0.300.24−0.38 †−0.59 †−0.47 †−0.240.440.16−0.07−0.07−0.20−0.44−0.63 †
19990.08−0.26−0.050.06−0.27−0.05−0.13−0.10−0.34−0.230.230.040.070.44
tree basal arearesistance (Rt)TRW1965−0.110.50−0.100.06−0.28−0.02−0.07−0.18−0.320.35 †0.080.310.500.69
19740.13−0.070.22−0.06−0.19−0.180.260.380.07−0.020.24−0.290.020.04
19990.160.08−0.48−0.05−0.49 †−0.320.070.050.47−0.030.070.410.070.34
BAI1965−0.240.29−0.110.04−0.44 †−0.43−0.10−0.31−0.330.32 †−0.030.260.300.56
19740.11−0.330.21−0.17−0.25−0.320.080.330.06−0.040.17−0.43−0.15−0.17
19990.150.05−0.49−0.05−0.54 †0.110.050.050.46−0.030.030.400.030.35
resilience (Rc)TRW19650.090.53−0.150.10−0.050.30−0.110.42−0.25−0.06−0.030.370.36−0.43
1974−0.28−0.010.27−0.16−0.42−0.210.020.490.09−0.010.09−0.04−0.21−0.16
19990.17−0.230.000.07−0.09−0.070.00−0.06−0.30−0.240.26 †0.120.250.46
BAI1965−0.090.34−0.170.00−0.39 †−0.24−0.18−0.03−0.28−0.18−0.140.180.10−0.70
1974−0.31−0.270.26−0.30 †−0.53 †−0.51−0.140.430.08−0.04−0.02−0.22−0.32−0.63 †
19990.12−0.260.000.07−0.24−0.02−0.05−0.07−0.30−0.250.210.070.120.48
* BP—Betula pendula Roth., PS—Pinus sylvestris L., LS—Larix sibirica Ledeb. † bold values are significant at p < 0.05
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Belokopytova, L.V.; Zhirnova, D.F.; Krutovsky, K.V.; Mapitov, N.B.; Vaganov, E.A.; Babushkina, E.A. Species- and Age-Specific Growth Reactions to Extreme Droughts of the Keystone Tree Species across Forest-Steppe and Sub-Taiga Habitats of South Siberia. Forests 2022, 13, 1027. https://doi.org/10.3390/f13071027

AMA Style

Belokopytova LV, Zhirnova DF, Krutovsky KV, Mapitov NB, Vaganov EA, Babushkina EA. Species- and Age-Specific Growth Reactions to Extreme Droughts of the Keystone Tree Species across Forest-Steppe and Sub-Taiga Habitats of South Siberia. Forests. 2022; 13(7):1027. https://doi.org/10.3390/f13071027

Chicago/Turabian Style

Belokopytova, Liliana V., Dina F. Zhirnova, Konstantin V. Krutovsky, Nariman B. Mapitov, Eugene A. Vaganov, and Elena A. Babushkina. 2022. "Species- and Age-Specific Growth Reactions to Extreme Droughts of the Keystone Tree Species across Forest-Steppe and Sub-Taiga Habitats of South Siberia" Forests 13, no. 7: 1027. https://doi.org/10.3390/f13071027

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