Next Article in Journal
Responses of Soil Organic Carbon Fractions and Stability to Forest Conversion in the Nanling Nature Reserve, China
Previous Article in Journal
Solid Biofuel from the Amazon: A Circular Economy Approach to Briquette Production from Wood Waste
Previous Article in Special Issue
Anatomical Characteristics of the Xylem of Populus euphratica at Different Groundwater Burial Depths in the Lower Tarim River (China) and Its Response to Temperature Extremes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 8
10.3390/f15081329
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Changes in Water Utilization Characteristics of Trees in Forests across a Successional Gradient in Southern China

by
Deqiang Gao
,
Qing Xu
,
Haijun Zuo
,
Wenbin Xu
,
Ke Diao
and
Beibei Zhang
*
Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(8), 1329; https://doi.org/10.3390/f15081329
Submission received: 17 June 2024 / Revised: 16 July 2024 / Accepted: 27 July 2024 / Published: 31 July 2024
(This article belongs to the Special Issue Forest Hydrology under Climate Change)
Browse Figures
Versions Notes

Abstract

:
Elucidating the water utilization strategy of trees during forest succession is a prerequisite for predicting the direction of forest succession. However, the water utilization characteristics of trees in forests across a successional gradient remain unclear. Here, we utilized the hydrogen and oxygen stable isotopes combined with the Bayesian mixed model (MixSIAR) to analyze the water utilization of dominant trees (Pinus massoniana, Castanea henryi, and Schima superba) in forests along a successional gradient in the Dinghushan Biosphere Reserve of China. Furthermore, we determined the primary factor affecting the water utilization of various trees based on variation partitioning analysis and a random forest model. Our results illustrated that in the early-successional forest, the water utilization ratios from shallow soil layers by P. massoniana were significantly lower than that in the mid-successional forest (51.3%–61.7% vs. 75.3%–81.4%), while its water utilization ratios from deep soil layers exhibited the opposite pattern (26.1%–30.1% vs. 9.0%–15.0%). Similarly, the ratios of water utilization from shallow soil layers by C. henryi (18.9%–29.5% vs. 32.4%–45.9%) and S. superba (10.0%–25.7% vs. 29.2%–66.4%) in the mid-successional forest were relatively lower than in the late-successional forest, whereas their water utilization ratios from deep soil layers showed the contrary tendency. Moreover, our results demonstrated that the diverse water utilization of each tree in different successional forests was mainly attributed to their distinct plant properties. Our findings highlight the increased percentage of water utilization of trees from shallow soil layers with forest succession, providing new insights for predicting the direction of forest succession under changing environments.

1. Introduction

Forest succession is an essential process for the self-recovery of forest ecosystems to adapt to external disturbances, including global climate change, species invasion, and human interference [1,2,3]. During forest succession, species structure, plant phenology, morphology, and physiological characteristics would change to maintain the community stability [4]. In addition, forest succession could also affect the plant water utilization strategy due to the different water and light conditions at various stages of succession [5]. The plant water utilization strategy, one of the important hydrological processes in forest ecosystems, plays a decisive role in the succession direction and ecological function of soil-vegetation systems [6,7]. Especially under climate change, the shifts in precipitation could affect plant water use strategies and thereby alter forest hydrological processes, which ultimately change the direction of forest succession and disrupt forest stability. Therefore, exploring the plant water utilization strategy along a successional gradient is necessary for a comprehensive understanding of the hydrological processes in vegetation restoration and the direction of vegetation succession under climate change.
Despite its importance, the plant water utilization strategy across a successional gradient remains uncertain. Specifically, previous studies mainly focused on plant water use efficiency at different successional stages, with limited studies involving the water utilization pattern of plants [8,9,10]. More importantly, the results from these studies are divergent. It was reported that Pinus densiflora mainly used shallow soil water in the early-successional forest in Tsukuba, Japan, while it shifted to utilize deep soil water in the mid-successional forest [11]. Similarly, Chang et al. [7] indicated that vegetation in the abandoned land from the Loess Plateau used soil water at 0–20 cm depth in the early-successional forest. By comparison, in the mid- and late-successional forests, the depth of plant water absorption extended to 0–100 cm soil. The above results illustrate that the depth of vegetation water uptake deepens with progressing forest succession. In contrast, other studies found that the Ocotea samosa (30–70 cm), Eugenia spp. (30–70 cm), and Juniperus occidentalis (0–80 cm) in the early-, mid- and late-successional forests primarily used water from a similar soil layer [12,13,14]. These different observations imply that the water utilization characteristics of plants at different succession stages need further exploration.
Additionally, it is still unclear which factor primarily affects the plant water utilization across a successional gradient. Generally, environment, soil, and plant properties were supposed to be the drivers influencing tree water utilization [15,16,17,18,19]. Environmental factors, such as air temperature, solar radiation, and vapor pressure deficit, could indirectly affect plant water uptake by regulating transpiration [19,20,21]. However, given the minor variations in environmental factors in the same region, soils and plants rather than the environment should be the main factors mediating plant water absorption. It has been reported that soils with lower bulk density, higher total porosity, and field capacity possess more pores, which could store more water for tree utilization [22,23] and thereby improve tree water utilization. Other studies also illustrated that soils with higher moisture exhibit greater water availability, which is more conducive to plant water absorption [24]. Meanwhile, a lower soil temperature could increase the water viscosity and thus reduce the water diffusion rate and inhibit plant water absorption [25]. In addition, soil texture and soil organic matter could indirectly affect plant water utilization by adjusting soil water holding capacity [26,27]. Besides the above-mentioned soil properties, plant properties may also affect the water utilization of trees. Specifically, given that fine roots are the main organ for water utilization by trees [28,29], the fine root biomass distributed in the soil layer directly affects the tree water utilization ratio from this soil layer. Apart from fine root biomass, trees with higher leaf biomass have a stronger transpiration pull, thereby promoting their water utilization [30,31]. Additionally, a higher leaf water potential indicates less water stress on the tree, resulting in greater water absorption by the tree [32,33]. Plant physiological indicators could affect plant water utilization characteristics by regulating photosynthesis, transpiration, and leaf stomatal conductance [34]. However, it remains obscure which factor mainly regulates the water utilization of trees across a successional gradient.
To resolve this gap, we selected forests along successional gradients, including a coniferous forest (early-successional forest), a mixed coniferous broad-leaved forest (mid-successional forest), and a monsoon evergreen broad-leaved forest (late-successional forest) in the Dinghushan Biosphere Reserve in Guangdong province, China. Then, by using the hydrogen and oxygen stable isotopes combined with the Bayesian mixed model (MixSIAR), we analyzed the water utilization characteristics of dominant trees (Pinus massoniana, Castanea henryi, and Schima superba) in each forest following different magnitudes of precipitation. Furthermore, we measured the plant and soil variables in the study area to reveal the primary factors mediating the water utilization of various trees. It should be noted that P. massoniana, the dominant tree species in early- and mid-successional forests, is suitable as a pioneer tree species due to its lower water consumption compared to other coniferous tree species [35]. Both C. henryi and S. superba, the zonal tree species in the mid- and late-successional forests, have higher water consumption than P. massoniana [36]. This study seeks to answer the following two questions: (1) Is there a difference in the water utilization characteristics of P. massoniana/C. henryi/S. superba in different successional forests? (2) Which factor is the dominant driver affecting the water utilization of various trees in forests along successional gradients?

2. Materials and Methods

2.1. Study Area

Our study site is located at the Dinghushan Biosphere Reserve, Guangdong Province of China (112°30′39″–112°33′41″ E, 23°09′21″–23°11′30″ N, Figure 1). The mean annual temperature, mean annual precipitation, and relative humidity in the area are 21.0 °C, 1956 mm, and 77.7%, respectively [37]. The climate type belongs to the South Asian tropical monsoon, and the soil type is lateritic soil. Three typical forests along successional gradients, a coniferous forest (CF, early-successional forest), a mixed coniferous and broad-leaved forest (MCBF, mid-successional forest), and a monsoon evergreen broad-leaved forest (MEBF, late-successional forest), are distributed across the site [38]. Of the three forest types, the CF is mainly dominated by P. massoniana in the arbor layer and Psychotria rubra and Blechnum orientale in the understory, which has a topsoil containing 20.3% clay, 23.7% silt, and 56.0% sand. The MCBF primarily consists of P. massoniana, C. henryi, and S. superba in the arbor layer and P. rubra and Pteris multifida in the understory. Its topsoil is composed of 22.9% clay, 21.8% silt, and 55.3% sand. In addition, the MEBF is dominated by C. henryi and S. superba in the arbor layer and Psychotria asiatica and Blechnum orientale in the understory [39]. Its topsoil consists of 25.7% clay, 33.7% silt, and 40.6% sand. The content of the topsoil’s organic matter is 24.17 g/kg, 29.76 g/kg, and 32.81 g/kg in CF, MCBF, and MEBF, respectively.

2.2. Field Sampling

According to the standard of light (5–10 mm/24 h), moderate (10–25 mm/24 h), and heavy precipitation (>25 mm/24 h), three magnitudes of precipitation events were collected from the rainfalls that occurred in the study area. The three rainfall events occurred on 1 October 2013, 7 September 2013, and 18 December 2013, with precipitation of 5.4 mm, 20.0 mm, and 45.8 mm, respectively. When rainfall events occurred, we rapidly selected samples from the precipitation, soil, and tree stem (xylem) within five days.
Before collecting precipitation, three rain gauges (Figure S1) embedded into funnels were randomly placed on the vacant land outside the forest, and a table tennis ball was put inside the funnels to inhibit water evaporation. The distances between the rain gauges and each forest were about 500–1000 m. When each rainfall event occurred, we gathered three precipitation samples from three rain gauges, respectively.
Regarding soil sample collection, we set up three soil profiles (100 cm depth), which were randomly excavated from each forest (CF, MCBF, and MEBF) in the study area. The distance between these three soil profiles was approximately 20–25 m. After each rainfall event, new soil profiles were excavated again. We sampled soils at depths of 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm, respectively. In total, 45 soil samples were collected once a day after precipitation.
For collecting tree stem (xylem) samples, we selected three healthy trees with diameters and heights (Table S1) close to the average value of the dominant trees in each forest as the standard trees. Specifically, three P. massoniana in the CF, three P. massoniana, three C. henryi and three S. superba in the MCBF, and three S. superba and three C. henryi in the MEBF were selected as standard trees. We collected 3–5 segments of biennial branches with a length of 3–4 cm on the sunny side of each standard tree. Then, we removed the phloem of the branch and retained the xylem as an experimental sample. Similar to soil samples, the collection frequency of xylem samples was also once a day after precipitation.
All the above-mentioned samples were immediately packed into bottles. Then, the caps of the bottles were quickly tightened and sealed with parafilm. The collected samples were stored in a −18 °C refrigerator.

2.3. Sample Treatment and Isotope Analysis

We extracted the soil and xylem water through a vacuum extraction system using the evaporative cooling method [40]. The water derived from soils and xylems and the precipitation were measured for δD and δ18O using a mass spectrometer (Delta V Advantage, Thermo Fisher Scientific, Inc., Waltham, MA, USA) combined with an element analyzer (Flash 2000 HT, Thermo Fisher Scientific, Inc., Waltham, MA, USA). Both the δD and δ18O were expressed relative to the Vienna Standard Mean Ocean Water (VSMOW) as follows:
δX (‰) = [(Rs/Rd) − 1] × 1000‰
where δX represents δD (δ18O), Rs denotes 2H/1H (18O/16O) in the sample, and Rd is 2H/1H (18O/16O) in VSMOW.

2.4. Calculation of Water Utilization Characteristics of Trees

We further employed the Bayesian mixed model (MixSIAR) to calculate the water utilization ratio by the trees from each soil layer (P. massoniana, C. henryi, and S. superba) across a successional gradient. The calculation includes the following steps. First, the δD (δ18O) of water from tree xylems and soils were imported into the “Mixture data” and “Source data” in the MixSIAR interface, respectively. Second, given that isotopic values do not undergo fractionation during the process of root water absorption [41], the “Discrimination data” was set to 0 in the MixSIAR interface. Finally, the running length of “Markon chain Monte Carlo, MCMC” was set as “long” (chain length = 300,000; burn = 200,000; thin = 100; chains = 3); “Error structure” and “Specify prior” were set as “Residual only” and “Uninformative prior”, respectively. In addition, the model was tested for convergence through “Gelman Rubin” and “Geweke” tests [42]. This above analysis was performed using “MixSIAR” in the R software (v 3.6.0).

2.5. Examination of Soil and Plant Properties

Soil properties, such as bulk density (BD), total porosity (TP), and field capacity (FC), were measured to evaluate their impacts on the water utilization of the dominant trees at different successional stages (Table S2). Regarding the determination of the BD, the undisturbed soil was first collected using a 100 cm3 ring cutter, then the collected soil was oven-dried at 105 °C, and the soil weight was measured. The ratio of dried soil to the volume of the ring cutter was the BD. The TP was determined using the ring cutter method [43]. To determine the FC, a 100 cm3 ring cutter containing undisturbed soil was immersed in water for 12 h, and then it was put on a platform covered with sand for 72 h before being weighed. Furthermore, the soil was dried and weighed at 105 °C. The difference between the soil weight after standing for 72 h and the dry soil weight divided by the dry soil weight was the FC.
Meanwhile, we also determined the plant properties of dominant trees in each successional forest to analyze their effects on water utilization characteristics. The plant properties mainly included leaf biomass (LB, Table S3), fine root biomass (FB, Table S4), and predawn leaf water potential (ΨPLWP, Table S3). Of them, the LB was calculated by introducing the measured height and diameter of each tree into the known allometric growth equation in the study area [44,45]. Before measuring the FB, the soil was first collected with a 10 cm root drill at intervals of 60° at 1 m from the tree trunk. The soil depth was consistent with that in Section 2.2. Fine roots (≤2 mm) were selected from the soil samples, then oven-dried and weighed to calculate the FB. Additionally, the ΨPLWP was determined using a dew point water potential meter (WP4C, Meter, Decagon, Pullman, WA, USA) between 5:00 am and 6:30 am.

2.6. Statistical Analyses

To test the difference in the water utilization ratio from each soil layer (R) by P. massoniana in the early- and mid-successional forests, we compared the R of P. massoniana between the two forests using a one-way ANOVA. Similarly, we also used this method to examine the differences in the R of C. henryi/S. superba between the mid- and late-successional forests. To explore the factors affecting the R of each tree, we established the Pearson correlation between the P and soil properties (BD, TP, and FC)/plant (LB, FB, and ΨPLWP). Furthermore, to determine the primary driver of the R, we employed variation partitioning analysis to examine the pure and combined effects of vegetation and soil properties on R. To verify the results obtained from the variation partitioning analysis, we also used a random forest model to screen for the primary factors of R. The “ntree” included in the model was set to 1000, and default parameters were used for other parameters. The variation partitioning analysis and random forest model were implemented with “vegan” and “randomForest” packages in the R software, respectively.

3. Results

3.1. δ18O and δD of Soil and Xylem Water in Forests along Successional Gradients

Whether in the CF, MCBF, or MEBF, the δ18O and δD of the water derived from soil and xylem were distributed on the right side of the local meteoric water line (Figure 2a–c), indicating the occurrence of hydrogen and oxygen isotope fractionation with the conversion of precipitation into soil water and xylem water in the three forests. In addition, in the three above-mentioned forests along the successional gradients, the δ18O and δD of xylem water in each tree were close to those of soil water (Figure 2a–c), demonstrating that the xylem water in this area originated from soil water.

3.2. Water Utilization Characteristics of Dominant Trees in Forests at Different Successional Stages

The results based on a one-way ANOVA showed that, after light, moderate, and heavy rainfall, the water utilization ratios from the 0–20 cm and 20–40 cm soil layers by P. massoniana in the MCBF were significantly higher than that in the CF (77.6% vs. 56.2%), while those from the 60–80 cm and 80–100 cm soil layers in the MCBF were significantly lower than that in the CF (12.6% vs. 28.6%, Figure 3a–c). Additionally, after light rainfall, there was no difference in the water utilization ratio from the 40–60 cm soil layer by P. massoniana between the CF and MCBF. By comparison, after moderate and heavy rainfall, the water utilization ratio from the 40–60 cm soil layer by P. massoniana in the MCBF was significantly lower than that in the CF.
Similar to P. massoniana, following light, moderate, and heavy rainfall events, the water utilization ratios from the 0–20 cm and 20–40 cm soil layers by C. henryi in the MEBF were significantly higher than that in the MCBF (37.8% vs. 22.8%, Figure 3d–f). Nevertheless, no difference existed in the water utilization ratio from the 40–100 cm layers between the MCBF and MEBF, except for that from the 80–100 cm layer after light and heavy rainfall and that from the 40–60 cm layer after moderate rainfall.
After light and moderate rainfall, the water utilization ratios by S. superba from the 0–20 cm and 20–40 cm soil layers in the MEBF were significantly higher than that in the MCBF (31.6% vs. 10.9%), while there was no difference in the water utilization ratios from the 40–100 cm soil layers by S. superba between the MCBF and MEBF except for the ratio from the 80–100 cm soil layer after a light rainfall event (Figure 3g,h). After a heavy rainfall event, the water utilization ratios from the 0–20 cm, 20–40 cm, 40–60 cm, and 60–80 cm soil layers by S. superba in the MEBF were significantly higher than that in the MCBF (66.4% vs. 25.7%), whereas the ratio from the 80–100 cm soil layer in the MEBF was significantly lower than that in the MCBF (33.6% vs. 74.3%, Figure 3i).

3.3. Factors Affecting Water Utilization Characteristics of Trees in Forests along Successional Gradients

The correlation analysis showed that the water utilization ratios (R) from the 0–20 cm and 20–40 cm soil layers (R0–20 and R20–40) from each type of tree were significantly correlated with soil/plant properties (Figure 4; Tables S2–S4), indicating that both factors may affect the R0–20 and R20–40. Specifically, the R0–20 and R20–40 of P. massoniana/C. henryi/S. superba were negatively associated with bulk density (p < 0.05), while they were positively related to other soil (total porosity and field capacity) and plant properties (leaf biomass, fine root biomass, and predawn leaf water potential). By contrast, the difference existed in the correlation between the R from the 60–100 cm soil layer and the soil/plant properties among various trees. Concretely, the R from the 60–100 cm soil layers (R60–80 and R80–100) of P. massoniana were significantly correlated with soil and plant properties (p < 0.05; Figure 4), suggesting that the two properties may affect the R60–80 and R80–100 of P. massoniana. The R60–80 of S. superba and C. henryi were not related to soil and plant properties (p > 0.05), except for fine root biomass (p < 0.05; Figure 4). On the contrary, the R80–100 of S. superba and C. henryi were significantly correlated with all plant properties and some soil properties (p < 0.05; Figure 4), implying that both the two properties may affect the R80–100 of S. superba and C. henryi.

3.4. Primary Drivers of Water Utilization of Trees in Different Successional Forests

To clarify the drivers influencing the water utilization of trees, this study introduced two statistical methods, including a variation partitioning analysis and a random forest model, to determine the relative importance of soil and plant properties on the water utilization of each tree. Variation partitioning analysis showed that plant properties explained 19.0%, 18.0%, 9.1%, 25.4%, and 5.5% of the variation in the R0–20, R20–40, R40–60, R60–80, and R80–100 of P. massoniana, respectively, while soil properties only explained 6.3%, 11.3%, 3.6%, 3.9%, and 3.7% of the variation (Figure 5a–e). Similarly, the pure effect of plant properties on the water utilization of C. henryi/S. superba was higher than that of soil properties (Figure 5f–o). These results indicated that plant properties were the primary driver influencing the water utilization characteristics of various trees. The random forest model also confirmed that plant properties were the primary factor in the water utilization of each tree. Specifically, the R0–20 and R20–40 of P. massoniana/C. henryi/S. superba and the R40–60, R60–80, and R80–100 of P. massoniana were mainly influenced by vegetation such as fine root biomass, leaf biomass, and predawn leaf water potential (Figure 6a–g,k,l). Taken together, the water utilization of various trees was mainly regulated by plant properties.

4. Discussion

4.1. Differences in Water Utilization of Each Tree in Forests Along Successional Gradients

This study indicated that in both the early- (CF) and mid-successional (MCBF) forests, P. massoniana mainly absorbed water from shallow soil layers (0–40 cm, Figure 3a–c), while in the mid- and late-successional (MEBF) forests, zonal vegetation such as C. henryi and S. superba primarily used water from middle and deep soil layers (40–100 cm, Figure 3d–i). Such a phenomenon could be ascribed to the root distribution characteristics of various trees [46,47,48]. Specifically, the root of P. massoniana is mainly distributed in shallow soil layers, while that of C. hystrix and S. superba is mainly distributed in deep soil layers. Therefore, regardless of the successional stage, P. massoniana mainly utilized shallow soil water, yet C. hystrix and S. superba mainly absorbed deep soil water.
Our results also illustrated that from the early to the mid-successional stage, the water utilization ratio of P. massoniana from shallow soil layers (0–40 cm) significantly improved, while that from deep soil layers (60–100 cm) significantly decreased (Figure 3a–c). Similarly, from the middle to the late-successional stage, the water utilization ratio of C. henryi and S. superba from the shallow soil layer significantly increased, whereas that from the deep soil layer significantly declined (Figure 3d–i). The above observations indicate that with progressing forest succession, some dominant trees increase their water utilization ratio for the shallow soil layer, which may be caused by the following two reasons. On the one hand, the differences in root distribution among various trees lead to changes in their water utilization characteristics at different successional stages. Specifically, given that the root of P. massoniana and zonal broad-leaved tree species (C. henryi and S. superba) were mainly distributed in the soil layers 0–40 cm and 60–100 cm, respectively (Table S4), in the mid-successional stage, the three trees exhibited different water absorption niches after zonal broad-leaved tree species entering the P. massoniana forest. That is, shallow-root trees (P. massoniana) tend to use water from shallow soil layers, while deep-root trees (C. henryi and S. superba) utilize deep soil water [49,50]. Therefore, compared with P. massoniana in the early-successional forest, P. massoniana in the mid-successional forest had an increased water utilization ratio from the shallow soil layer. In the late-successional forest, P. massoniana disappeared, and its original water absorption niche, shallow soil water, was also released. C. henryi and S. superba, which originally had the deep soil water absorption niche, increased their water utilization ratio from the shallow soil layer. On the other hand, shifts in soil properties of forests along successional gradients induce changes in the water absorption of each tree. In our study, the field capacity and total porosity in the 0–40 cm soil layers in the mid-successional forest (MCBF) were significantly higher than those in the early-successional forest (CF, Table S2). Similarly, these two soil properties from the 0–40 cm soil layers in the late-successional forest (MEBF) were significantly higher than those in the mid-successional forest (Table S2). The above-mentioned differences in soil properties of forests at different successional stages may result in a higher soil water storage in the mid-successional forest compared to the early-successional forest and also lead to a higher soil water storage in the late-successional forest than the mid-successional forest. As a result, P. massoniana absorbed more water from the 0–40 cm soil layers in the mid-successional forest compared to the early-successional forest, while C. henryi and S. superba utilized more water from the 0–40 cm soil layers in the late-successional forest than in the mid-successional forest. Taken together, both vegetation and soil properties may cause changes in the water utilization characteristics of P. massoniana, C. henryi, and S. superba among different successional forests.

4.2. Primary Factors Affecting the Water Utilization Characteristics of Various Trees in Different Successional Forests

The observation based on the random forest model and variation partitioning analysis showed that the water utilization of P. massoniana, C. henryi, and S. superba was mainly regulated by plant properties (Figure 5 and Figure 6). Among plant properties, fine root biomass was a critical factor regulating the water utilization of trees, which was attributed to the correlation between plant water absorption depth and fine root distribution [51,52]. Thus, a larger fine root biomass within soil layers led to a higher ratio of water utilization from soil layers [53,54]. This phenomenon has also been found in previous studies, revealing that the higher water utilization rate from the deep soil layer by Mediterranean species was mainly attributed to the more fine root biomass in this soil layer [55]. In our case, the fine root biomass of P. massoniana from the shallow soil layers in the early-successional forest (CF) was significantly lower than that in the mid-successional forest (MCBF), while that in deep soil layers in the former was significantly higher than in the latter (Table S4). Hence, P. massoniana in the MCBF had a significantly higher water utilization ratio from shallow soil layers than in the CF and a significantly lower water absorption percentage from deep soil layers than in the CF. Similarly, in the mid-successional forest, the fine root biomass of C. henryi and S. superba distributed in the shallow soil layer was significantly lower than that in the late-successional forest (MEBF), whereas the fine root biomass from the deep soil layer in MCBF was significantly higher than that from the MEBF (Table S4). Therefore, the water utilization ratio from shallow soil layers of C. henryi and S. superba in the latter was significantly higher than that in the former, and the water absorption percentage from the deep soil layer in the latter was significantly lower than that in the former. Besides fine root biomass regulation, leaf biomass could modulate the water utilization characteristics of trees through leaf transpiration regulation, as transpiration is the power source of tree water absorption [56,57]. Therefore, higher leaf biomass induces stronger tree transpiration, subsequently leading to trees absorbing more water to maintain metabolism [30,31]. In this study, the leaf biomass of P. massoniana in the MCBF was significantly higher than that in the CF (Table S3). Thus, its water absorption was also greater than that in the CF. Similarly, the leaf biomasses of C. henryi and S. superba in MEBF were significantly higher than those in the MCBF (Table S3), indicating their higher water absorptions in the MEBF compared to the MCBF. In addition, leaf water potential could regulate the water utilization of trees. It has been confirmed that leaf water potential could be used to evaluate the water use status of plants [32,33]. Specifically, a lower leaf water potential denotes more severe drought stress on plants, while conversely, it indicates that plants absorb more water [58,59]. Such an opinion has also been confirmed by Bello et al. [60] and Wu et al. [61] for Quercus petraea and Myricaria squamosa, respectively. In this study, the predawn leaf water potential of P. massoniana in the MCBF was significantly higher than that in the CF (Table S3), and the predawn leaf water potential of C. henryi and S. superba in the MEBF was significantly higher than that in the MCBF (Table S3). This indicates that the water absorption of P. massoniana in the MCBF was greater than that in the CF, and the water absorption of C. henryi and S. superba in the MEBF was smaller than that in the MCBF.
Compared with vegetation, soil properties, including bulk density, total porosity, and field capacity, had less pure effects on the water utilization of trees, but they had a larger overlap with plant properties. Such a phenomenon could be attributed to the following two aspects. First, although soil with a lower bulk density, higher total porosity, and field capacity has stronger water storage capacity [22,23], the main organ for tree water absorption is fine roots. Therefore, soil properties exert a small direct impact on the water utilization of trees, which could only indirectly affect the water utilization by influencing plant properties. This result is also supported by previous research, demonstrating that compared to plant parameters, soil properties exhibit a smaller effect on the water utilization of Cunninghamia lanceolata, but they can indirectly affect its water utilization by regulating plant factors [34]. In this study, the bulk density of the 0–80 cm soil layers in the MCBF was significantly lower than that in the CF, and the bulk density of the 0–100 cm soil layers in the MEBF was significantly lower than that in the MCBF (Table S2). Hence, the soil properties of the MCBF and MEBF were conducive to fine root growth, thereby indirectly affecting the water utilization of trees. Second, the sampling periods in this study were after the occurrence of rainfall events rather than the seasonal drought period, and thus, water was not a limiting factor for tree growth during the study period. Consequently, although soil properties could affect water storage capacity, they could not directly impact plant water utilization. Taken together, the direct impact of soil properties on the water utilization of trees was lower than that of plant properties, but they could exert indirect effects by influencing vegetation.
It should be noted that in our case, the explained variations of soil and plant factors on the tree water utilization ratio in some soil layers are relatively low (Figure 5h,m,n). This phenomenon may be due to the fact that the effect of other soil (such as soil texture and soil organic matter) and plant (physiological indicators) parameters on tree water utilization were ignored in this study. For example, it has been reported that soil texture and soil organic matter could indirectly affect plant water utilization by regulating soil porosity and soil water holding capacity [26,27]. In addition, plant physiological indicators could influence plant water utilization by impacting photosynthesis, transpiration, and leaf stomatal conductance [34]. Therefore, in future research, it is necessary to explore the effects of the above-mentioned factors on tree water utilization to improve the explained variation in the model.

4.3. Implications

Our results demonstrate that, with progressing forest succession, the water utilization ratio from shallow soil layers by dominant trees increased, while that from deep soil layers decreased. This phenomenon is contrary to the conventional view that the plant water utilization ratio from deep soil layers would increase during the succession process accompanied by root distribution deepening. These inconsistent observations may be attributed to the following explanation. Specifically, previous studies mainly concentrated on the root distribution of single species, neglecting the root distribution relationships among multiple types of plants. In fact, with the development of succession, various plants in the same habitat may exhibit two types of ecological niche relationships, including competition and complementation. If several plants exhibit a competitive relationship, their root distribution will deepen, and their water utilization rate from the deep soil layer will increase. However, if they display a complementary relationship, some plants’ root systems become shallower, and their water utilization rate from shallow soil layers could increase, while other plants show the opposite pattern. Therefore, in the future, we should pay more attention to exploring the differences in water utilization of dominant trees in forests along successional gradients rather than simply inferring the water utilization of a single species based on its root distribution.

5. Conclusions

In summary, based on the stable hydrogen and oxygen isotopes coupled with the Bayesian mixed model (MixSIAR), our study found that in the early-successional forest (CF), the water utilization ratio to shallow soil layers by P. massoniana was significantly lower than that in the mid-successional forest (MCBF), while its water utilization ratio to deep soil layers in the CF was significantly higher than that in the MCBF. Similarly, the water utilization ratio from shallow soil layers of C. henryi and S. superba in the mid-successional forest (MCBF) was significantly lower than that in the late-successional forest (MEBF), whereas their water utilization ratio from deep soil layers in the MCBF was significantly higher than that in the MEBF. Furthermore, the variation partitioning analysis and random forest model indicated that the difference in the water utilization characteristics of each tree in different successional forests was mainly attributed to their distinct plant properties. The above results challenged the traditional view that the water utilization ratio of plants from deep soil layers increased during the succession process and provided new insights for understanding the direction of forest succession.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f15081329/s1, Figure S1: Rain gauge; Table S1: Average diameter, crown width, and tree height of dominant species in forests with different successional stages; Table S2: Soil properties (mean ± SD, n = 3) of forests in different successional stages; Table S3: Plant properties (mean ± SD, n = 3) of dominant species in forests with different successional stages; Table S4: Fine root biomass (g·m−2, mean ± SE, n = 3) of dominant species in forests with different successional stages.

Author Contributions

Conceptualization, Q.X. and B.Z.; methodology, D.G.; software, H.Z.; validation, D.G., Q.X., and B.Z.; formal analysis, D.G.; investigation, D.G.; resources, W.X.; data curation, K.D.; writing—original draft preparation, D.G.; writing—review and editing, D.G., Q.X., and B.Z.; supervision, Q.X. and B.Z.; project administration, Q.X. and B.Z.; funding acquisition, D.G. and Q.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Funds of CAF (CAFYBB2022SY022; CAFYBB2017ZB003) and the National Natural Science Foundation of China (31290223; 31870716).

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

We are very grateful to the Guangdong Dinghushan National Observation and Research Station for Forest Ecosystem for their support and assistance in the fieldwork.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hill, A.P.; Field, C.B. Forest fires and climate-induced tree range shifts in the western US. Nat. Commun. 2021, 12, 6583. [Google Scholar] [CrossRef] [PubMed]
  2. Liu, D.; Semenchuk, P.; Essl, F.; Lenzner, B.; Moser, D.; Blackburn, T.M.; Cassey, P.; Biancolini, D.; Capinha, C.; Dawson, W.; et al. The impact of land use on non-native species incidence and number in local assemblages worldwide. Nat. Commun. 2023, 14, 2090. [Google Scholar] [CrossRef] [PubMed]
  3. Massey, R.; Rogers, B.M.; Berner, L.T.; Cooperdock, S.; Mack, M.C.; Walker, X.J.; Goetz, S.J. Forest composition change and biophysical climate feedbacks across boreal North America. Nat. Clim. Chang. 2023, 13, 1368–1375. [Google Scholar] [CrossRef] [PubMed]
  4. Hornslein, N.J.; Siegert, C.; Renninger, H.J. Physiological response of mid-canopy sweetgum trees to overstory loblolly pine mortality. Trees 2019, 33, 139–151. [Google Scholar] [CrossRef]
  5. Tor-ngern, P.; Chart-asa, C.; Chanthorn, W.; Rodtassana, C.; Yampum, S.; Unawong, W.; Nathalang, A.; Brockelman, W.; Srinoppawan, K.; Chen, Y.; et al. Variation of leaf-level gas exchange rates and leaf functional traits of dominant trees across three successional stages in a Southeast Asian tropical forest. Forest Ecol. Manag. 2021, 489, 119101. [Google Scholar] [CrossRef]
  6. Grossiord, C.; Sevanto, S.; Dawson, T.E.; Adams, H.D.; Collins, A.D.; Dickman, L.T.; Newman, B.D.; Stockton, E.A.; Mcdowell, N.G. Warming combined with more extreme precipitation regimes modifies the water sources used by trees. New Phytol. 2017, 213, 584–596. [Google Scholar] [CrossRef] [PubMed]
  7. Chang, E.; Li, P.; Li, Z.; Xiao, L.; Zhao, B.; Su, Y.; Feng, Z. Using water isotopes to analyze water uptake during vegetation succession on abandoned cropland on the Loess Plateau, China. Catena 2019, 181, 104095. [Google Scholar] [CrossRef]
  8. Karatassiou, M. Water use efficiency and net production of two semi arid grasslands in different successional stages. Appl. Ecol. Env. Res. 2016, 14, 41–53. [Google Scholar] [CrossRef]
  9. Zhang, X.; Li, H.; Hu, X.; Zheng, P.; Hirota, M.; Kamijo, T. Photosynthetic Properties of Co-Occurring Pioneer Species on Volcanically Devastated Sites in Miyake-jima Island, Japan. Plants 2021, 10, 2500. [Google Scholar] [CrossRef]
  10. Ávila-Lovera, E.; Urich, R.; Coronel, I.; Tezara, W. Ecophysiological traits change little along a successional gradient in a tropical dry deciduous woodland from Margarita Island, Venezuela. Front. For. Glob. Change 2023, 6, 1043574. [Google Scholar] [CrossRef]
  11. Yamanaka, T. Root functional change achieves water source separation under vegetation succession. Ecohydrology 2018, 11, e1985. [Google Scholar] [CrossRef]
  12. Ghimire, C.P.; van Meerveld, H.J.; Zwartendijk, B.W.; Bruijnzeel, L.A.; Ravelona, M.; Lahitiana, J.; Lubczynski, M.W. Vapour pressure deficit and solar radiation are the major drivers of transpiration in montane tropical secondary forests in eastern Madagascar. Agr. Forest Meteorol. 2022, 326, 109159. [Google Scholar] [CrossRef]
  13. Abdallah, M.A.; Durfee, N.; Mata-Gonzalez, R.; Ochoa, C.G.; Noller, J.S. Water use and soil moisture relationships on western juniper trees at different growth stages. Water 2020, 12, 1596. [Google Scholar] [CrossRef]
  14. Mata-González, R.; Abdallah, M.A.; Ochoa, C.G. Water use by mature and sapling western juniper (Juniperus occidentalis) trees. Rangel. Ecol. Manag. 2021, 74, 110–113. [Google Scholar] [CrossRef]
  15. Michael, R.N.; Yu, B.; Wintle, B.A.; Doronila, I.A.; Yuen, S.T.S. The effect of substrate compaction on plant water use and the implications for phytocap design specifications. Ecol. Eng. 2019, 127, 195–203. [Google Scholar] [CrossRef]
  16. Rasa, K.; Heikkinen, J.; Hannula, M.; Arstila, K.; Kulju, S.; Hyväluoma, J. How and why does willow biochar increase a clay soil water retention capacity? Biomass Bioenerg. 2018, 119, 346–353. [Google Scholar] [CrossRef]
  17. Zou, S.; Li, D.; Di, N.; Liu, J.; Li, L.; Liu, Y.; Xi, B.; Coleman, M. Stand development modifies effects of soil water availability on poplar fine-root traits: Evidence from a six-year experiment. Plant Soil 2022, 480, 165–184. [Google Scholar] [CrossRef]
  18. Ganesan, S.P.; Boldrin, D.; Leung, A.K. A closer look at root water potential: Experimental evidence based on drought stress of Chrysopogon zizanioides. Plant Soil 2024, 499, 569–585. [Google Scholar] [CrossRef]
  19. Du, H.; Fu, W.; Song, T.; Zeng, F.; Wang, K.; Chen, H.; Liu, M. Water-use efficiency in a humid karstic forest in southwestern China: Interactive responses to the environmental drivers. J. Hydrol. 2023, 617, 128973. [Google Scholar] [CrossRef]
  20. Kaneko, T.; Gould, N.; Campbell, D.; Snelgar, P.; Clearwater, M.J. The effect of soil type, fruit load and shaded area on hass avocado (Persea americana mill.) water use and crop coefficients. Agric. Water Manag. 2022, 264, 107519. [Google Scholar] [CrossRef]
  21. Song, X.; Lyu, S.; Wen, X. Limitation of soil moisture on the response of transpiration to vapor pressure deficit in a subtropical coniferous plantation subjected to seasonal drought. J. Hydrol. 2020, 591, 125301. [Google Scholar] [CrossRef]
  22. Oliveira, C.E.; Zoz, T.; Castagnara, D.D.; Zoz, A.; Mortinho, E.S.; Fernandes, G.C.; Lustosa Sobrinho, R.; Faria, G.A. Growth of Crambe under Different Soil Bulk Densities and Water Restriction. Russ. J. Plant Physiol. 2023, 70, 63. [Google Scholar] [CrossRef]
  23. Melo, M.L.A.D.; Lier, Q.D.J. FluxPAW: A standalone software to calculate flux-based plant available water. Softw. Impacts 2023, 15, 100464. [Google Scholar] [CrossRef]
  24. Voltas, J.; Lucabaugh, D.; Chambel, M.R.; Ferrio, J.P. Intraspecific Variation in the Use of Water Sources by the Circum-Mediterranean Conifer Pinus halepensis. New Phytol. 2015, 208, 1031–1041. [Google Scholar] [CrossRef] [PubMed]
  25. Ko, H.; Choo, H.; Ji, K. Effect of temperature on electrical conductivity of soils—Role of surface conduction. Eng. Geol. 2023, 321, 107147. [Google Scholar] [CrossRef]
  26. Wang, D.; Fu, B.; Zhao, W.; Hu, H.; Wang, Y. Multifractal Characteristics of Soil Particle Size Distribution under Different Land-Use Types on the Loess Plateau, China. CATENA 2008, 72, 29–36. [Google Scholar] [CrossRef]
  27. Ankenbauer, K.J.; Loheide, S.P.I. The effects of soil organic matter on soil water retention and plant water use in a meadow of the Sierra Nevada, CA. Hydrol. Process. 2017, 31, 891–901. [Google Scholar] [CrossRef]
  28. Li, W.; Wang, L.; Qian, S.; He, M.; Cai, X.; Ding, J. Root characteristics explain greater water use efficiency and drought tolerance in invasive Compositae plants. Plant Soil 2023, 483, 209–223. [Google Scholar] [CrossRef]
  29. Xing, Y.; Chen, M.; Dao, J.; Lin, L.; Chen, C.; Chen, Y.; Wang, Z. Fine-root morphology of woody and herbaceous plants responds differently to altered precipitation: A meta-analysis. Forest Ecol. Manag. 2024, 552, 121570. [Google Scholar] [CrossRef]
  30. Sun, G.; Alstad, K.; Chen, J.; Chen, S.; Ford, C.R.; Lin, G.; Liu, C.; Lu, N.; McNulty, S.G.; Miao, H.; et al. A general predictive model for estimating monthly ecosystem evapotranspiration. Ecohydrology 2011, 4, 245–255. [Google Scholar] [CrossRef]
  31. Hardanto, A.; Röll, A.; Hendrayanto, H.D. Tree soil water uptake and transpiration in mono-cultural and jungle rubber stands of Sumatra. Forest. Ecol. Manage. 2017, 397, 67–77. [Google Scholar] [CrossRef]
  32. Ratzmann, G.; Zakharova, L.; Tietjen, B. Optimal leaf water status regulation of plants in drylands. Sci. Rep. 2019, 9, 3768. [Google Scholar] [CrossRef] [PubMed]
  33. Yokoyama, G.; Yasutake, D.; Minami, K.; Kimura, K.; Marui, A.; Yueru, W.; Feng, J.; Wang, W.; Mori, M.; Kitano, M. Evaluation of the physiological significance of leaf wetting by dew as a supplemental water resource in semi-arid crop production. Agric. Water Manag. 2021, 255, 106964. [Google Scholar] [CrossRef]
  34. Zhang, Y.; Xu, Q.; Zhang, B.; Gao, D.; Wang, T.; Xu, W.; Ren, R.; Wang, S. Contrasting Water-Use Patterns of Chinese Fir among Different Plantation Types in a Subtropical Region of China. Front. Plant Sci. 2022, 13, 946508. [Google Scholar] [CrossRef] [PubMed]
  35. Xu, T.Y.; Huang, J.Q.; Zeng, X.P.; Zhang, Y.H.; Meng, Z.; Otieno, D.; Li, Y.L. Sap flow characteristics of the dominant tree species Pinus massoniana at the pioneer succession stage in the Dinghushan Mountain. Chin. J. Appl. Environ. Biol. 2022, 28, 1167–1174. [Google Scholar]
  36. Cheng, J.; Ouyang, X.; Huang, D.W.; Liu, S.Z.; Zhang, D.Q.; Li, Y.L. Sap flow characteristics of four dominant tree species in a mixed conifer-broadleaf forest in Dinghushan. Acta Ecol. Sin. 2015, 35, 4097–4104. [Google Scholar]
  37. Huang, Y.H.; Li, Y.L.; Xiao, Y.; Wenigmann, K.O.; Zhou, G.Y.; Zhang, D.Q.; Wenigmann, M.; Tang, X.L.; Liu, J.X. Controls of litter quality on the carbon sink in soils through partitioning the products of decomposing litter in a forest succession series in South China. Forest Ecol. Manag. 2011, 261, 1170–1177. [Google Scholar] [CrossRef]
  38. Li, Y.L.; Yang, F.F.; Ou, Y.X.; Zhang, D.Q.; Liu, J.X.; Chu, G.W.; Zhang, Y.R.; Otieno, D.; Zhou, G.Y. Changes in forest soil properties in different successional stages in lower tropical China. PLoS ONE 2013, 8, e81359. [Google Scholar] [CrossRef]
  39. Yan, J.H.; Wang, Y.P.; Zhou, G.Y.; Zhang, D.Q. Estimates of soil respiration and net primary production of three forests at different succession stages in South China. Global Change Biol. 2006, 12, 810–821. [Google Scholar] [CrossRef]
  40. Ehleringer, J.R.; Roden, J.; Dawson, T.E. Assessing ecosystem-level water relations through stable isotope ratio analyses. In Methods in Ecosystem Science; Sala, O.E., Jackson, R.B., Mooney, H.A., Howarth, R.W., Eds.; Springer: New York, NY, USA, 2000; pp. 181–198. [Google Scholar]
  41. Wang, J.; Fu, B.; Wang, L.; Lu, N.; Li, J. Water use characteristics of the common tree species in different plantation types in the Loess Plateau of China. Agr. Forest Meteorol. 2020, 288–289, 108020. [Google Scholar] [CrossRef]
  42. Stock, B.C.; Semmens, B.X. MixSIAR GUI User Manual, Version 3.1. 2013. Available online: https://www.imsbio.co.jp/RGM-files/R_CC/download/MixSIAR/inst/mixsiar_manual_3.1_small.pdf (accessed on 26 March 2024).
  43. Ozcan, M.; Gökbulak, F.; Hizal, A. Exclosure effects on recovery of selected soil properties in a mixed broadleaf forest recreation site. Land Degrad. Dev. 2011, 24, 266–276. [Google Scholar] [CrossRef]
  44. Xue, C.; Xu, Q.; Lin, L.; He, X.; Cao, L.; Li, H. Biomass Dynamic Predicting for Schima superba in Guangdong Based on Allometric and Theoretical Growth Equation. Sci. Silvae Sin. 2019, 55, 86–94. [Google Scholar]
  45. Zhang, Z.; Jin, G.Q.; Zhou, Z.C.; Sun, L.S. Biomass allocation differences with Pinus massoniana in Guangdong and Hubei provenances. J. Zhejiang A F Univ. 2019, 36, 271–278. [Google Scholar]
  46. Dhiman, I.; Bilheux, H.; DeCarlo, K.; Painter, S.L.; Santodonato, L.; Warren, J.M. Quantifying root water extraction after drought recovery using sub-mm in situ empirical data. Plant Soil 2018, 424, 73–89. [Google Scholar] [CrossRef]
  47. Mackay, D.S.; Savoy, P.R.; Grossiord, C.; Tai, X.; Pleban, J.R.; Wang, D.R.; McDowell, N.G.; Adams, H.D.; Sperry, J.S. Conifers depend on established roots during drought: Results from a coupled model of carbon allocation and hydraulics. New Phytol. 2020, 225, 679–692. [Google Scholar] [CrossRef]
  48. Wang, S.; Zhu, G.; Bai, W.; Yuan, R.; Zhang, Y. Root Distribution and Water Uptake Applied by Hydrogen and Oxygen Stable Isotopes for Lianas in Northwest China. Forests 2024, 15, 626. [Google Scholar] [CrossRef]
  49. Vanhellemont, M.; Sousa-Silva, R.; Maes, S.L.; Van den Bulcke, J.; Hertzog, L.; DeGroote, S.R.E.; Van Acker, J.; Bonte, D.; Martel, A.; Lens, L.; et al. Distinct growth responses to drought for oak and beech in temperate mixed forests. Sci. Total Environ. 2019, 650, 3017–3026. [Google Scholar] [CrossRef]
  50. Kühnhammer, K.; van Haren, J.; Kübert, A.; Bailey, K.; Dubbert, M.; Hu, J.; Ladd, N.; Meredith, L.K.; Werner, C.; Beyer, M. Deep roots mitigate drought impacts on tropical trees despite limited quantitative contribution to transpiration. Sci. Total Environ. 2023, 893, 164763. [Google Scholar] [CrossRef]
  51. Dybzinski, R.; Taylor, N.; Prosser, M.; Niosi, O.; Demo, M.; Kilbane, E. Nitrogen, water, and phosphorus uptake as functions of fine-root mass in greenhouse microcosms of Poa pratensis. Plant Ecol. 2021, 222, 977–991. [Google Scholar] [CrossRef]
  52. Zhu, W.; Zhao, D.; Di, N.; Li, D.; Zhou, O.; Sun, Y.; Jia, L.; Ding, C.; Xi, B. Matching root water uptake patterns to fine root and soil water distributions. Plant Soil 2024, 495, 499–516. [Google Scholar] [CrossRef]
  53. Xi, B.; Wang, Y.; Jia, L.; Bloomberg, M.; Li, G.; Di, N. Characteristics of fine root system and water uptake in a triploid Populus tomentosa plantation in the North China Plain: Implications for irrigation water management. Agric. Water Manag. 2013, 117, 83–92. [Google Scholar] [CrossRef]
  54. Masumoto, T.; Ito, T.; Akatsuki, M.; Makita, N. Fine root hydraulic conductivity relates to root functional traits in four coniferous species. Rhizosphere 2022, 21, 100489. [Google Scholar] [CrossRef]
  55. Fort, F.; Volaire, F.; Guilioni, L.; Barkaoui, K.; Navas, M.L.; Roumet, C. Root traits are related to plant water-use among rangeland Mediterranean species. Funct. Ecol. 2017, 31, 1700–1709. [Google Scholar] [CrossRef]
  56. Thomas, A.; Yadav, B.K.; Šimůnek, J. Root water uptake under heterogeneous soil moisture conditions: An experimental study for unraveling compensatory root water uptake and hydraulic redistribution. Plant Soil 2020, 457, 421–435. [Google Scholar] [CrossRef]
  57. Vanderborght, J.; Couvreur, V.; Javaux, M.; Leitner, D.; Schnepf, A.; Vereecken, H. Mechanistically derived macroscopic root water uptake functions: The α and ω of root water uptake functions. Vadose Zone J. 2024, 23, e20333. [Google Scholar] [CrossRef]
  58. Ding, Y.; Nie, Y.; Chen, H.; Wang, K.; Querejeta, J.I. Water uptake depth is coordinated with leaf water potential, water-use efficiency and drought vulnerability in karst vegetation. New Phytol. 2021, 229, 1339–1353. [Google Scholar] [CrossRef]
  59. Altieri, G.; Wiman, N.G.; Santoro, F.; Amato, M.; Celano, G. Assessment of leaf water potential and stomatal conductance as early signs of stress in young hazelnut tree in Willamette valley. Sci. Hortic. 2024, 327, 112817. [Google Scholar] [CrossRef]
  60. Bello, J.; Hasselquist, N.J.; Vallet, P.; Kahmen, A.; Perot, T.; Korboulewsky, N. Complementary water uptake depth of Quercus petraea and Pinus sylvestris in mixed stands during an extreme drought. Plant Soil 2019, 437, 93–115. [Google Scholar] [CrossRef]
  61. Wu, H.; Zhao, G.; Li, X.; Wang, Y.; He, B.; Jiang, Z.; Zhang, S.; Sun, W. Identifying water sources used by alpine riparian plants in a restoration zone on the Qinghai-Tibet Plateau: Evidence from stable isotopes. Sci. Total Environ. 2019, 697, 134092. [Google Scholar] [CrossRef] [PubMed]
Forests 15 01329 g001
Figure 1. Location of the study site, including coniferous forest (CF), mixed coniferous and broad-leaved forest (MCBF), and monsoon evergreen broad-leaved forest (MEBF) in the Dinghushan Biosphere Reserve, Guangdong Province of China.
Figure 1. Location of the study site, including coniferous forest (CF), mixed coniferous and broad-leaved forest (MCBF), and monsoon evergreen broad-leaved forest (MEBF) in the Dinghushan Biosphere Reserve, Guangdong Province of China.
Forests 15 01329 g001
Forests 15 01329 g002
Figure 2. δ18O and δD in soil water and xylem water (Pinus massoniana, Castanea henryi, and Schima superba) from the Coniferous Forest ((a), CF), Mixed Coniferous Broad-leaved Forest ((b), MCBF) and Monsoon Evergreen Broad-Leaved Forest ((c), MEBF). LMWL is the local meteoric water line (y = 7.88x + 9.41).
Figure 2. δ18O and δD in soil water and xylem water (Pinus massoniana, Castanea henryi, and Schima superba) from the Coniferous Forest ((a), CF), Mixed Coniferous Broad-leaved Forest ((b), MCBF) and Monsoon Evergreen Broad-Leaved Forest ((c), MEBF). LMWL is the local meteoric water line (y = 7.88x + 9.41).
Forests 15 01329 g002
Forests 15 01329 g003
Figure 3. Water utilization ratio from each soil layer by trees in the Coniferous Forest (CF), Mixed Coniferous Broad-leaved Forest (MCBF), and Monsoon Evergreen Broad-Leaved Forest (MEBF) following light (a,d,g), moderate (b,e,h), and heavy (c,f,i) rainfall events. The dots, squares, and triangles represent the water utilization ratio by Pinus massoniana (a–c), Castanea henryi (d–f), and Schima superba (g,h,i), respectively. The error bar represents the standard error. * p < 0.05; ** p < 0.01.
Figure 3. Water utilization ratio from each soil layer by trees in the Coniferous Forest (CF), Mixed Coniferous Broad-leaved Forest (MCBF), and Monsoon Evergreen Broad-Leaved Forest (MEBF) following light (a,d,g), moderate (b,e,h), and heavy (c,f,i) rainfall events. The dots, squares, and triangles represent the water utilization ratio by Pinus massoniana (a–c), Castanea henryi (d–f), and Schima superba (g,h,i), respectively. The error bar represents the standard error. * p < 0.05; ** p < 0.01.
Forests 15 01329 g003
Forests 15 01329 g004
Figure 4. Correlations between the water utilization ratio of trees from each soil layer and predictors. R0–20, R20–40, R40–60, R60–80, and R80–100 refer to the water utilization ratios from 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm soil depths, respectively. BD, TP, FC, LB, FB, and ΨPLWP represent bulk density, total porosity, field capacity, leaf biomass, fine root biomass, and predawn leaf water potential, respectively. The color and numbers correspond to the strength of the correlation (decrease/increase of color intensity = loss of/gain of correlation). * p < 0.05; ** p < 0.01.
Figure 4. Correlations between the water utilization ratio of trees from each soil layer and predictors. R0–20, R20–40, R40–60, R60–80, and R80–100 refer to the water utilization ratios from 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm soil depths, respectively. BD, TP, FC, LB, FB, and ΨPLWP represent bulk density, total porosity, field capacity, leaf biomass, fine root biomass, and predawn leaf water potential, respectively. The color and numbers correspond to the strength of the correlation (decrease/increase of color intensity = loss of/gain of correlation). * p < 0.05; ** p < 0.01.
Forests 15 01329 g004
Forests 15 01329 g005
Figure 5. Relative contribution of soil and plant factors to the water utilization ratio of trees from the (a,f,k) 0–20 cm, (b,g,l) 20–40 cm, (c,h,m) 40–60 cm, (d,i,n) 60–80 cm, and (e,j,o) 80–100 cm soil layers.
Figure 5. Relative contribution of soil and plant factors to the water utilization ratio of trees from the (a,f,k) 0–20 cm, (b,g,l) 20–40 cm, (c,h,m) 40–60 cm, (d,i,n) 60–80 cm, and (e,j,o) 80–100 cm soil layers.
Forests 15 01329 g005
Forests 15 01329 g006
Figure 6. Importance of soil and plant properties to the water utilization ratio of trees conducted using random forest models in the (a,f,k) 0–20 cm, (b,g,l) 20–40 cm, (c,h,m) 40–60 cm, (d,i,n), 60–80 cm and (e,j,o) 80–100 cm soil layers. BD, bulk density; TP, total porosity; FC, field capacity; LB, leaf biomass; FB, fine root biomass; ΨPLWP, predawn leaf water potential. Orange and green bars represent soil and plant attributes, respectively. ** and * indicate p < 0.01 and p < 0.05, respectively.
Figure 6. Importance of soil and plant properties to the water utilization ratio of trees conducted using random forest models in the (a,f,k) 0–20 cm, (b,g,l) 20–40 cm, (c,h,m) 40–60 cm, (d,i,n), 60–80 cm and (e,j,o) 80–100 cm soil layers. BD, bulk density; TP, total porosity; FC, field capacity; LB, leaf biomass; FB, fine root biomass; ΨPLWP, predawn leaf water potential. Orange and green bars represent soil and plant attributes, respectively. ** and * indicate p < 0.01 and p < 0.05, respectively.
Forests 15 01329 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gao, D.; Xu, Q.; Zuo, H.; Xu, W.; Diao, K.; Zhang, B. Changes in Water Utilization Characteristics of Trees in Forests across a Successional Gradient in Southern China. Forests 2024, 15, 1329. https://doi.org/10.3390/f15081329

AMA Style

Gao D, Xu Q, Zuo H, Xu W, Diao K, Zhang B. Changes in Water Utilization Characteristics of Trees in Forests across a Successional Gradient in Southern China. Forests. 2024; 15(8):1329. https://doi.org/10.3390/f15081329

Chicago/Turabian Style

Gao, Deqiang, Qing Xu, Haijun Zuo, Wenbin Xu, Ke Diao, and Beibei Zhang. 2024. "Changes in Water Utilization Characteristics of Trees in Forests across a Successional Gradient in Southern China" Forests 15, no. 8: 1329. https://doi.org/10.3390/f15081329

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop