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Article

Disentangling the Effects of Tree and Soil Properties on the Water Uptake of a Waterlogging Tolerant Tree in the Yangtze River Delta, China

by
Beibei Zhang
1,
Jing Jiang
2,
Qing Xu
1,*,
Deqiang Gao
1,
Haijun Zuo
1 and
Ranran Ren
1
1
Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
2
Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Forests 2021, 12(11), 1547; https://doi.org/10.3390/f12111547
Submission received: 29 September 2021 / Revised: 27 October 2021 / Accepted: 2 November 2021 / Published: 9 November 2021
(This article belongs to the Section Forest Hydrology)
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Abstract

:
Waterlogging tolerant tree species exert a critical role in forest preservation and the associated water conservation in flood prone areas. Clarifying the patterns and drivers of water uptake by waterlogging tolerant trees is crucial for forest management in flood-prone areas, especially in the scenario of precipitation changes in the estuary delta. Here, we uploaded the values of δD and δ18O obtained from soil and xylem waters to a Bayesian mixed model (MixSIAR) to determine the water use pattern of Taxodium distichum, a waterlogging tolerant tree, following different magnitudes of rainfall events in three sites of the Yangtze River Delta, China. We further conducted variation partitioning analysis and a random forest model to discern the dominant factor driving plant water uptake. Our results indicated that T. distichum mainly absorbed soil water from shallow soil layers (0–40 cm, 43.63%–74.70%), while the percentage of water uptake from deep soil layers was lower in the Yangtze River Delta (60–100 cm, 13.43%–35.90%), whether in light, moderate, or heavy rainfall conditions. Furthermore, our results demonstrated that tree traits, such as fine root biomass, are dominantly driving plant water uptake. These findings imply that waterlogging tolerant tree species could increase the percentage of water uptake from shallow soils by changing their plant attributes, which would effectively improve the water conservation of forests in the estuary delta.

1. Introduction

Changes in global precipitation increase the frequency and intensity of extreme precipitation in humid areas, subsequently leading to frequent floods [1,2,3]. Floods induced by extreme precipitation expose trees to waterlogging stress, and cause root hypoxia and inhibit plant respiration, ultimately increasing the tree mortality [4,5,6]. This phenomenon is particularly universal in the river basins and estuarine deltas of tropical/subtropical areas [7,8,9]. In order to alleviate the large-scale tree death caused by frequent floods, tree species owning waterlogging resistance have been cultivated in some estuarine deltas in recent years. Under the excessive soil moisture condition, waterlogging resistance trees could transport the oxygen to the root system and rhizosphere soils to ensure plant water absorption through changing their morphology, such as the formation of adventitious root, aerial root, or aerenchyma [10]. Therefore, trees with waterlogging resistance could optimize plant water uptake by adjusting root distribution, finally adapting to waterlogging stress. Collectively, knowledge on water use pattern of waterlogging tolerant tree species is thus of importance for forest management in flood-prone areas.
However, our understanding on the water uptake of waterlogging tolerant trees is still limited by the following two aspects. First, previous studies primarily concentrated on the water use pattern of waterlogging tolerant trees in coastal delta hummocks, while ignored that in the alluvial plain of the estuary delta. It has been reported that, Taxodium distichum and Pterocarpus officinalis in coastal forest hummocks mainly absorbed the water accessed from unsaturated soil in the hummock top [11,12]. Nevertheless, this phenomenon might not occur in waterlogging tolerant trees in the alluvial plain of the estuarine delta. Given the influences of salinity and tide, the water consumption of trees in coastal delta hummocks is lower [13], while trees in estuarine deltas are less affected by these above factors. Therefore, the tree water use patterns in estuarine deltas could then differ from those observed in coastal delta hummocks. More importantly, compared with coastal delta hummocks, the alluvial plain of estuarine delta has a flatter terrain and poorer drainage capacity, and therefore trees in the alluvial plain are more vulnerable to waterlogging stress. Based on such reasons, their water use patterns may also be different from those observed in hummocks. Especially in the scenario of changing precipitation, frequent occurrence of extreme precipitation may aggravate waterlogging stress in the estuarine alluvial plain. Taken together, it is necessary to study the water use patterns of waterlogging tolerant trees in the alluvial plain of the estuarine delta under different magnitudes of precipitation.
Second, it remains unclear which factor mainly affects the water absorption of waterlogging tolerant trees in the alluvial plain of the estuary delta. Generally, tree traits and soil properties are considered as two main factors influencing water use patterns of trees [14,15,16]. It has been reported that, the larger the fine root biomass distributed in a soil layer, the greater the water absorption from the soil layer by trees [17,18]. Besides the fine roots, both leaf biomass and diameter at breast height of trees could also affect the plant water absorption by regulating the transpiration [19,20]. Moreover, soil properties also exert a critical role in driving tree water uptake. Previous studies have illustrated that the soil with smaller bulk density and larger field water capacity could store more water [21,22], and subsequently enhance the tree water uptake. Distinguished from common trees, the water uptake of waterlogging tolerant trees is also influenced by soil aeration since the soil with better aeration is conducive to root respiration that could stimulate plant water absorption [23,24]. Nevertheless, the relative contributions of the above factors to water uptake patterns of waterlogging tolerant trees remain unknown, largely due to the lack of comprehensive studies at a regional scale.
To address these issues, we selected three T. distichum plantations in the Yangtze River Delta, China. Then we employed stable hydrogen and oxygen isotopes (δD and δ18O) coupled with the Bayesian mixed model (MixSIAR) to explore the water use patterns of T. distichum [25,26]. Furthermore, we examined both tree traits and soil properties in each plantation, and explored which factor dominantly drives the T. distichum water uptake. This study primarily tests the following two hypotheses: (i) The water use patterns of T. distichum were different following light, moderate, and heavy rainfall events. (ii) Compared with soil properties, tree traits are the dominant factor affecting the T. distichum water absorption.

2. Materials and Methods

2.1. Study Site

Three study sites, such as Chizhou in Anhui Province (CZ, 117°2′33′′ E, 30°8′4′′ N), Gaoyou in Jiangsu Province (GY, 119°27′34″ E, 32°53′21″ N) and Shaoxing in Zhejiang Province (SX, 120°40′20″ E, 29°47′32″ N), were selected in the Yangtze River Delta (Figure 1). The mean average temperature in the three study sites is 16.5, 15, and 16.5 °C, and the mean average precipitation is 1800, 1030, and 1400 mm, respectively. In the T. distichum plantation of the CZ site, the soil type is sandy loam, and the understory vegetation mainly includes Camellia oleifera, Rubus parvifolius, Oenanthe javanica, and Cyclosorus acuminatus. In the GY site, the soil belongs to sandy loam, and the understory chiefly consists of Bromus japonicus, Duchesnea indica, and Geranium wilfordii. In the SX site, the soil type is regarded as red-yellow soil, and the understory vegetation is composed of Poa annua and Zoysia japonica.

2.2. Sample Collection

Based on the classification standard of precipitation issued by China Meteorological Administration, three magnitudes of precipitations were selected in each study site, including light (precipitation: 5–10 mm), moderate (precipitation: 10–25 mm), and heavy rainfalls (precipitation > 25 mm) (Table 1). Within 8–9 days after each rainfall event (before the occurrence of another rainfall), samples of rainfall, groundwater, soil and tree stems (xylem) were collected in each site.
For rainfall sampling, three rain gauges with a built-in funnel were randomly placed in an open space adjacent to the study site, and a table tennis ball was put into the funnel to prevent rainfall evaporation. After each rainfall event, three rainfall samples were collected from the rain gauges, then mixed as one sample (about 3 mL). The sampling time ranged from 7:00 a.m. to 8:00 a.m. Regarding tree stems (xylem) sampling, three healthy trees with similar diameter at breast height and crown width were selected from the T. distichum plantation as standard trees used in this study (Table S1). Then 3–5 tree stem (xylem) samples with a length of 3–4 cm were collected on the sunny side of each standard tree. For soil collection, three soil profiles with 100 cm depth were randomly excavated in the T. distichum plantation in each site. In each soil profile of each site, ~5 g soil samples were collected with a knife at 0–20, 20–40, 40–60, 60–80, and 80–100 cm soil layers, respectively. With respect to shallow groundwater, a well was selected nearby the T. distichum plantation and then the groundwater samples were collected from this well.
After each rainfall event, xylem, soil, and groundwater samples were gathered once a day from 8:00 a.m. to 10:00 a.m. All samples were immediately put into glass bottles, tightened with caps, and sealed with parafilm. Glass bottles containing samples were stored in an incubator (−5–0 °C) in the field and in a refrigerator at −18 °C after being transported to the laboratory.

2.3. Determining Water Isotopes and Calculating Water Uptake Pattern of T. distichum

The water in soil and tree stem (xylem) samples was extracted by water vacuum extraction system [27]. Both δD and δ18O in the soil water, xylem water, rainfall, and groundwater were assayed by isotope ratio mass spectrometer (Delta V Advantage, Thermo Fisher Scientific, Inc., Waltham, MA, USA) coupled with an element analyzer (Flash 2000 HT, Thermo Fisher Scientific, Inc., Waltham, MA, USA). The δD and δ18O were calculated based on Equation (1):
δX (‰) = [(Rs/Rd) − 1] × 1000‰
where Rs was the D/H or 18O/16O molar ratios in the sample, and Rd was the D/H or 18O/16O molar ratios in the standard (V-SMOW, Vienna standard mean ocean water).
Then the δD and δ18O of precipitation in each site were linearly fitted to obtain the local meteoric water line (LMWL). If the values of δD and δ18O from other sources of water (including soil and xylem water and groundwater) were distributed on the LMWL, these waters were not affected by evaporation. When the δD and δ18O of these waters were located on the right side of LMWL, they were enriched by evaporation during the transformation process. As the δD and δ18O in any two of the three water bodies were similar, they had similar water sources.
We further used the Bayesian mixed model MixSIAR (version 3.1.7) to calculate the percentage of water uptake by T. distichum to each soil layer. This analysis was performed by the “MixSIAR” package using R 4.0.3. Detailed data analysis process was conducted with the following three steps. First, the obtained data was uploaded to the MixSIAR interface. In detail, the values of δD and δ18O from xylem water, soil water in each layer, and fractionation coefficient were loaded into the model as “Mixture data”, “Source data”, and “Discrimination data”, respectively. Second, other related parameters were set on the MixSIAR interface. Specifically, we set “MCMC (Markon chain Monte Carlo) run long”, “error structure” and “specify priority” to “long”, “residual only”, and “uninformative prior”, respectively. Third, we tested the convergence of the model through “Gelman-Rubin” and “Geweke” [26].

2.4. Determination of Tree Traits and Soil Properties

To evaluate the factors affecting T. distichum water uptake, we measured both tree traits and soil properties in each site. The tree traits mainly include fine root biomass (FB), diameter at breast height (DBH), and leaf biomass (LB). For the measurement of FB, soils were collected with a root drill (10 cm diameter) at 60° intervals on a circle with the trunk as the center and 1 m as the radius. Soil sampling depth was the same as that in Section 2.2. Fine roots (≤2 mm) were then screened from the collected soils, dried, and weighed to obtain FB. With regard to the determination of DBH, the diameter of standard wood was measured at 1.3 m from the ground. For the examination of LB, we first measured the DBH and tree height of T. distichum, and then substituted these two parameters to the allometric equation of T. distichum in each site, and finally calculated the LB of T. distichum.
Furthermore, we analyzed the soil properties of T. distichum plantation to evaluate their impacts on plant water absorption, including bulk density (BD), field capacity (FC) and soil water-filled pore space (WFPS). For the BD, undisturbed soil was collected with a cutting ring of 100 cm3, and then dried and weighed. The ratio of dried-soil weight to cutting ring volume is the BD. Regarding the FC, the specific analysis procedure is as follows: first, soil was collected with a cutting ring. Second, the cutting ring containing soil was soaked in water for 12 h. Third, the soaked cutting ring was placed in a flat bottom plate covered with sand for 72 h and weighed. Finally, soil was dried at 105 °C and weighed. The FC was calculated by the ratio of soil weight after standing for 72 h to the dried soil weight minus one [28]. The WFPS was calculated by Equation (2) [29]:
WFPS = SWC × BD 1 − BD / PD
where SWC represents soil water content (%), BD is bulk density (g cm−3), PD denotes particle density of soil (2.65 g cm−3).

2.5. Statistical Analyses

To explore factors affecting T. distichum water uptake, we used ordinary least squares (OLS) regression analyses to examine the relationships between the percentage of water uptake by T. distichum in each soil layer and tree traits/soil properties. Furthermore, we employed variation partitioning analysis to evaluate the pure and joint effects of tree traits and soil properties on water absorption of T. distichum. To verify the results based on variation partitioning analysis, we further used the random forest model to clarify the dominant driver regulating the water use of T. distichum. Variation partitioning analysis and random forest model were performed using R software with the “vegan” and “randomForest” packages, respectively [30,31].

3. Results

3.1. Characteristics of δD and δ18O in Soil Water, Xylem Water, and Groundwater

The δD and δ18O of soil and xylem water were located on the right side of local meteoric water line (LMWL) (Figure 2), suggesting that they were enriched by evaporation during the transformation process. In addition, the δD and δ18O of xylem water in T. distichum were close to that of soil water in each site (Figure 2), indicating that the xylem water of T. distichum mainly originated from soil water in the Yangtze River Delta. By contrast, the δD and δ18O of shallow groundwater in the T. distichum plantations were distributed on the LMWL, whether in CZ, GY, or SX sites (Figure 2).

3.2. Water Uptake Patterns of T. distichum under Light, Moderate, and Heavy Rainfalls

After light rainfall, T. distichum absorbed a higher percentage of water in shallow soil layers (0–40 cm) while a lower percentage of water in deep soil layers (60–100 cm), accounting for 71.53%–74.70% and 13.43%–17.03%, respectively (Figure 3a). Similarly, after moderate and heavy rainfalls, the percentage of water uptake from shallow soil layers by T. distichum was higher than that from deep soil layers (43.63%–62.73% vs. 21.8%–35.9% after moderate rainfall, 58.77%–66.67% vs. 18.33%–23.3% after heavy rainfall) (Figure 3b,c).

3.3. Dominant Drivers Influencing T. distichum Water Uptake

Variation partitioning analysis revealed that tree traits and soil properties jointly explained 77.2%, 72.0%, and 75.8% of variations in T. distichum water uptake under the light, moderate, and heavy rainfall events. Specifically, tree traits explained 36.0%, 45.3%, and 35.0% of the variability in T. distichum water uptake, while soil properties explained 6.3%, 1.1%, and 8.0% of the variability (Figure 4), respectively. Tree traits have higher pure effects on T. distichum water absorption than soil properties (Figure 4).
The random forest model further showed that FB explained 25.63%, 19.46%, and 34.10% of variations in T. distichum water absorption, respectively (Figure 5a,d,g), suggesting that tree traits were the dominant driver influencing water use patterns of T. distichum. Besides the FB, soil properties, such as WFPS, also exerted an important role in regulating T. distichum water uptake, accounting for 11.02%, 12.96%, and 10.99% of variations for plant water uptake under light, moderate, and heavy rainfalls, respectively (Figure 5a,d,g). Specifically, T. distichum water uptake was positively correlated with FB (Figure 5b,e,h), while negatively related to WFPS (Figure 5c,f,i), whether in light, moderate, or heavy rainfall events.

4. Discussion

4.1. Soil Water Was the Dominant Water Source of T. distichum

Our results revealed that δD and δ18O in shallow groundwater were distributed on the LMWL, while those in soil and xylem water were located on the right side of the LMWL in the T. distichum plantations of the Yangtze River Delta, indicating that both xylem and soil water did not exchange with groundwater. Thus, the “ecohydrological separation” phenomenon between plant transpiration and groundwater, observed in Mediterranean mountain forest [32], temperate forest [33], and tropical forest [34], also exists in the plantation of the estuarine delta. Moreover, our results also showed that the δD and δ18O of xylem water were close to those of soil water, illustrating that soil water was the dominating water source of T. distichum in the Yangtze River Delta. This observation was distinct to previous studies reporting that the water sources of many tree species are not only derived from soil water but also groundwater, such as Quercus ilex, Arbutus unedo, Phillyrea latifolia, Fagus sylvatica, and Abies alba [35,36]. This contradiction may be ascribed to the differences of precipitation in these study areas. Specifically, due to the low annual average precipitation (610 mm) [35] or simulated precipitation reduction experiment [36] conducted in their sites, soil moisture was relatively lower, which was not enough for water requirement of trees. Therefore, these trees not only absorbed soil water, but also acquired groundwater to satisfy their water requirements. However, in this study, the mean annual precipitation of the three sites ranged from 1030 to 1800 mm, which was sufficient for trees. Hence, T. distichum plantations conducted in this study did not need to absorb groundwater to maintain their growth.

4.2. Tree Traits Primarily Regulated the Water Uptake of T. distichum

Our results demonstrated that T. distichum mainly absorbed shallow soil water, while the percentage of water uptake from deep soil layer was relatively lower, whether in light, moderate, or heavy rainfall, supporting our first hypothesis. The water use pattern of T. distichum is opposite to that of Cunninghamia lanceolata and Populus deltoides [26,37]. This discrepancy may be attributed to the following two reasons. First, T. distichum is a shallow-rooted tree species, and its roots are mostly distributed within shallow soil layers. Thus, it tends to absorb shallow soil water in the absence of water competition with other tree species. On the contrary, C. lanceolata and P. deltoides belong to deep-rooted tree species, their roots are generally distributed in deep soil layers, and their xylem water is mainly originated from deep soil water [15,38,39]. Second, T. distichum generally grows in humid areas with high or even excessive soil moisture, and thus its water absorption would be largely influenced by soil aeration. In this study, the aeration in shallow soils was better than that in deep soils (Table 2), and consequently T. distichum tended to absorb shallow soil water. Contrary to the growth environment of T. distichum, C. lanceolata and P. deltoides grow in habitats with moderate soil water content, and their water absorption was mainly affected by field capacity rather than soil aeration [40]. Given the higher water storage in the deep soil layer than the shallow layer, C. lanceolata and P. deltoides mainly adsorbed deep soil water. Taken together, both tree traits (such as root distribution) and soil properties (such as soil water-filled pore space) could affect the water use patterns of T. distichum living in the estuarine deltas.
However, it remains unclear which kind of factor dominantly drives the T. distichum water uptake. Based on variation partitioning analysis and random forest model, our results illustrated that T. distichum water uptake was mainly regulated by tree traits such as FB, LB, and DBH (Figure 4), which supported our second hypothesis. Among the tree traits, FB was one of the most important tree properties in influencing the water use of T. distichum. It is well known that trees mainly absorb soil water through fine roots, despite the environment and soil water status. The more fine roots distributed in a soil layer, the greater the percentage of water uptake from the soil layer [21,41]. In these three study sites of the Yangtze River Delta, the FB of T. distichum distributed in 0–40 cm soil layers was significantly higher than that in 60–100 cm soil layers (Table 2). Therefore, T. distichum mainly absorbed shallow soil water, while obtained less from deep soils. Besides the FB, LB also exhibited a critical role in affecting T. distichum water uptake. This may be due to the fact that leaves are the major apparatus for tree transpiration, while transpiration is the driving force for tree water uptake. Hence, the larger LB has stronger tree transpiration, which could in turn absorb more water to maintain metabolism [19]. Moreover, DBH also affected the T. distichum water absorption. Given that DBH could reflect the tree growth status, the higher DBH represents a more flourishing tree that needs more water applied for plant metabolism [42]. Although both LB and DBH could influence the T. distichum water absorption, these two factors had less effect than FB (Figure 5). Such a phenomenon could be explained by the fact that although LF and DBH could play an important role in affecting the total water absorption of T. distichum, they cannot directly influence the percentage of water uptake from each soil layer. By comparison, the distribution of fine root biomass in each soil layer could regulate the water uptake proportion because the root is the main apparatus of water absorption. Therefore, FB exerted greater effects on the water uptake of T. distichum than LB and DBH.
Our results also showed that soil properties had less effect on T. distichum water uptake compared with tree traits (Figure 4). Although the soil with lower BD and higher FC has greater water holding capacity that finally supplied more water for trees [40,43,44], the amount of water absorption largely depends on fine roots of T. distichum. Hence, soil properties (BD and FC) could only indirectly affect the water use pattern of T. distichum by influencing tree traits. Besides the above two soil properties, WFPS, an indicator representing soil aeration [28], also had an effect on the water absorption of T. distichum. This may be because soil with lower WFPS has higher aeration, which is conducive to the growth of tree fine roots. Given that fine roots mainly perform the process of water absorption [25,45], WFPS could also impact plant water uptake. Taken together, soil properties, including BD, FC, and WFPS, indirectly regulated the water absorption of T. distichum via affecting tree traits.

5. Conclusions

In conclusion, based on the δD and δ18O coupled with the Bayesian mixed model, our study revealed that T. distichum, a waterlogging tolerant tree species, mainly absorbed shallow soil water in the Yangtze River Delta of China, whether in light, moderate, or heavy rainfalls. The main factor affecting the water uptake of T. distichum was tree traits. These findings provide us with one important implication. Under the scenario of global precipitation changes, extreme precipitation events occur frequently in humid areas, and the forests in the estuary delta with flat terrain usually suffer from floods induced by extreme precipitation. Some waterlogging tolerant tree species such as T. distichum could improve the percentage of water uptake from shallow soils by altering their fine root distribution, which could effectively improve water conservation in the estuary delta. In the future, when we conduct vegetation restoration and structural optimization in humid areas, more attention should be paid on the selection of waterlogging tolerant tree species to resist floods. However, given the limitations of workload and cost, only three study sites were selected in our case. Further studies on water uptake of waterlogging tolerant trees should be conducted in more field sites to improve the reliability of our observations and advance our understanding on this issue.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/f12111547/s1, Table S1 Diameter at breast height and crown width of selected Taxodium distichum used in this study.

Author Contributions

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

Funding

This research was funded by the National Natural Science Foundation of China (31670720, 31870716) and the National Nonprofit Institute Research Grant of CAF (CAFYBB2021ZE002).

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

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.

References

  1. Bao, J.; Sherwood, S.C.; Alexander, L.V.; Evans, J.P. Future increases in extreme precipitation exceed observed scaling rates. Nat. Clim. Chang. 2017, 7, 128–133. [Google Scholar]
  2. Pendergrass, A.G. What precipitation is extreme? Science 2018, 360, 1072–1073. [Google Scholar] [CrossRef]
  3. Romero, G.Q.; Marino, N.A.C.; MacDonald, A.A.M.; Céréghino, R.; Trzcinski, M.K.; Mercado, D.A.; Leroy, C.; Corbara, B.; Farjalla, V.F.; Barberis, I.M.; et al. Extreme rainfall events alter the trophic structure in bromeliad tanks across the Neotropics. Nat. Commun. 2020, 11, 3215. [Google Scholar] [CrossRef] [PubMed]
  4. Gregoire, L.P.; Isabelle, L.; Celine, L.; Corinne, S.; Karine, L.; Jean, A.; Jean-Charles, L.; Christophe, P. Implication of the suberin pathway in adaptation to waterlogging and hypertrophied lenticels formation in pedunculate oak (Quercus robur L.). Tree Physiol. 2016, 36, tpw056. [Google Scholar]
  5. Tapani, R.; Timo, D.; Jouni, K.; Sirpa, P.; Raimo, S.; Tarja, L. Dynamics of fine root production and mortality of Scots pine in waterlogged peat soil during the growing season. Can. J. Forest Res. 2020, 50, 510–518. [Google Scholar]
  6. Yang, B.; Peng, C.; Harrison, S.; Wei, H.; Wang, H.; Zhu, Q.; Wang, M. Allocation Mechanisms of Non-Structural Carbohydrates of Robinia pseudoacacia L. Seedlings in Response to Drought and Waterlogging. Forests 2018, 9, 754. [Google Scholar]
  7. Asaeda, T.; Gomes, P.I.A.; Takeda, E. Spatial and temporal tree colonization in a midstream sediment bar and the mechanisms governing tree mortality during a flood event. River Res. Applic. 2010, 26, 960–976. [Google Scholar] [CrossRef]
  8. Assahira, C.; Piedade, M.T.F.; Trumbore, S.E.; Wittmann, F.; Cintra, B.B.L.; Batista, E.S.; Faria de Resende, A.; Schöngart, J. Tree mortality of a flood-adapted species in response of hydrographic changes caused by an Amazonian river dam. Forest Ecol. Manag. 2017, 396, 113–123. [Google Scholar]
  9. Faria de Resende, A.; Schöngart, J.; Streher, A.S.; Ferreira-Ferreira, J.; Piedade, M.T.F.; Silva, T.S.F. Massive tree mortality from flood pulse disturbances in Amazonian floodplain forests: The collateral effects of hydropower production. Sci. Total Environ. 2019, 659, 587–598. [Google Scholar] [CrossRef]
  10. Yin, D.M.; Chen, S.M.; Chen, F.D.; Guan, Z.Y.; Fang, W.M. Morpho-anatomical and physiological responses of two Dendranthema species to waterlogging. Environ. Exp. Bot. 2010, 68, 122–130. [Google Scholar]
  11. Colón-Rivera, R.J.; Feagin, R.A.; West, J.B.; López, N.B.; Benítez-Joubert, R.J. Hydrological modification, saltwater intrusion, and tree water use of a Pterocarpus officinalis swamp in Puerto Rico. Estuar. Coast. Shelf Sci. 2014, 147, 156–167. [Google Scholar] [CrossRef]
  12. Hsueh, Y.H.; Chambers, J.L.; Krauss, K.W.; Allen, S.T.; Keim, R.F. Hydrologic exchanges and baldcypress water use on deltaic hummocks, Louisiana, USA. Ecohydrology 2016, 9, 1452–1463. [Google Scholar] [CrossRef]
  13. Duberstein, J.; Krauss, K.; Baldwin, M.; Allen, S.; Conner, W.; Salter, J.; Miloshis, M. Small gradients in salinity have large effects on stand water use in freshwater wetland forests. Forest Ecol. Manag. 2020, 473, 118308. [Google Scholar] [CrossRef]
  14. Meißner, M.; Köhler, M.; Schwendenmann, L.; Hölscher, D.; Dyckmans, J. Soil water uptake by trees using water stable isotopes (δ2H and δ18O)-a method test regarding soil moisture, texture and carbonate. Plant Soil 2014, 376, 327–335. [Google Scholar] [CrossRef]
  15. 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] [PubMed] [Green Version]
  16. Zhou, H.; Zhao, W.; He, Z.; Yan, J.; Zhang, G. Variation in depth of water uptake for Pinus sylvestris var. mongolica along a precipitation gradient in sandy regions. J. Hydrol. 2019, 577, 123921. [Google Scholar] [CrossRef]
  17. Ehleringer, J.R.; Dawson, T.E. Water uptake by plants: Perspectives from stable isotope composition. Plant Cell Environ. 1992, 15, 1073–1082. [Google Scholar] [CrossRef]
  18. Case, M.F.; Nippert, J.B.; Holdo, R.M.; Staver, A.C. Root-niche separation between savanna trees and grasses is greater on sandier soils. J. Ecol. 2020, 108, 2298–2308. [Google Scholar] [CrossRef]
  19. Sun, S.J.; Meng, P.; Zhang, J.S.; Wan, X.C. Variation in soil water uptake and its effect on plant water status in Juglans regia L. during dry and wet seasons. Tree Physiol. 2011, 31, 1378–1389. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Liu, X.; Sun, G.; Mitra, B.; Noormets, A.; Gavazzi, M.J.; Domec, J.C.; Hallema, D.W.; Li, J.; Fang, Y.; King, J.S.; et al. Drought and thinning have limited impacts on evapotranspiration in a managed pine plantation on the southeastern United States coastal plain. Agric. For. Meteorol. 2018, 262, 14–23. [Google Scholar] [CrossRef]
  21. Coners, H.; Leuschner, C. In situ measurement of fine-root water absorption in three temperate tree species-temporal variability and control by soil and atmospheric factors. Basic Appl. Ecol. 2005, 6, 395–405. [Google Scholar] [CrossRef]
  22. Zhao, Y.; Wang, L.; Knighton, J.; Evaristo, J.; Wassen, M. Contrasting adaptive strategies by Caragana korshinskii and Salix psammophila in a semiarid revegetated ecosystem. Agric. For. Meteorol. 2021, 300, 108323. [Google Scholar]
  23. Pezeshki, S.R. Wetland plant responses to soil flooding. Environ. Exp. Bot. 2001, 46, 299–312. [Google Scholar]
  24. Dwire, K.A.; Kauffman, J.B.; Baham, J.E. Plant species distribution in relation to water-table depth and soil redox potential in montane riparian meadows. Wetlands 2006, 26, 131–146. [Google Scholar] [CrossRef]
  25. Wang, J.; Fu, B.; Lu, N.; Zhang, L. Seasonal variation inwater uptake patterns of three lant species based on stable isotopes in the semi-arid loess plateau. Sci. Total Environ. 2017, 609, 27–37. [Google Scholar] [CrossRef] [PubMed]
  26. Zhang, B.B.; Xu, Q.; Gao, D.Q.; Jiang, C.W.; Liu, F.T.; Jiang, J.; Wang, T. Altered water uptake patterns of Populus deltoides in mixed riparian forest stands. Sci. Total Environ. 2020, 706, 135956. [Google Scholar] [CrossRef] [PubMed]
  27. 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]
  28. Özcan, 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]
  29. Yang, G.; Peng, Y.; Olefeldt, D.; Chen, Y.; Wang, G.; Li, F.; Zhang, D.; Wang, J.; Yu, J.; Liu, L.; et al. Changes in Methane Flux along a Permafrost Thaw Sequence on the Tibetan Plateau. Environ. Sci. Technol. 2018, 52, 1244–1252. [Google Scholar] [CrossRef]
  30. Oksanen, J.; Kindt, R.; Legendre, P.; O’Hara, B.; Simpson, G.L.; Solymos, P.; Steven, M.H.H.; Wagner, H.; Vegan: Community Ecology Package. R Package Version 1.15-1. 2008. Available online: http://www.R-project.org (accessed on 2 November 2021).
  31. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
  32. Brooks, J.R.; Barnard, H.R.; Coulombe, R.; Mcdonnell, J.J. Ecohydrologic separation of water between trees and streams in a Mediterranean climate. Nat. Geosci. 2010, 3, 100–104. [Google Scholar] [CrossRef]
  33. Evaristo, J.; Jasechko, S.; Mcdonnell, J.J. Global separation of plant transpiration from groundwater and streamflow. Nature 2015, 525, 91–94. [Google Scholar] [PubMed]
  34. Goldsmith, G.R.; Muñoz-Villers, L.E.; Holwerda, F.; Mcdonnell, J.J.; Asbjornsen, H.; Dawson, T.E. Stable isotopes reveal linkages among ecohydrological processes in a seasonally dry tropical montane cloud forest. Ecohydrology 2012, 78, 474–485. [Google Scholar] [CrossRef]
  35. Barbeta, A.; Mejia-Chang, M.; Ogaya, R.; Voltas, J.; Dawson, T.E.; Peñuelas, J. The combined effects of a long-term experimental drought and an extreme drought on the use of plant-water sources in a Mediterranean forest. Glob. Chang. Biol. 2015, 21, 1213–1225. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Magh, R.K.; Eiferle, C.; Burzlaff, T.; Dannenmann, M.; Rennenberg, H.; Dubbert, M. Competition for water rather than facilitation in mixed beech-fir forests after drying-wetting cycle. J. Hydrol. 2020, 587, 124944. [Google Scholar] [CrossRef]
  37. Yang, F.; Feng, Z.; Wang, H.; Dai, X.; Fu, X. Deep soil water extraction helps to drought avoidance but shallow soil water uptake during dry season controls the inter-annual variation in tree growth in four subtropical plantations. Agric. Forest Meteorol. 2017, 234–235, 106–114. [Google Scholar] [CrossRef]
  38. González de Andrés, E.; Camarero, J.J.; Blanco, J.A.; Imbert, J.B.; Lo, Y.H.; Sangüesa-Barreda, G.; Castillo, F.J. Tree-to-tree competition in mixed european beech-scots pine forests has different impacts on growth and water-use efficiency depending on site conditions. J. Ecol. 2017, 106, 59–75. [Google Scholar] [CrossRef] [Green Version]
  39. 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]
  40. Zhang, B.B.; Xu, Q.; Gao, D.Q.; Jiang, C.W.; Liu, F.T.; Jiang, J.; Ma, Y.B. Higher soil capacity of intercepting heavy rainfall in mixed stands than in pure stands in riparian forests. Sci. Total Environ. 2019, 658, 1514–1522. [Google Scholar] [CrossRef]
  41. 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]
  42. Sumida, A.; Miyaura, T.; Torii, H. Relationships of tree height and diameter at breast height revisited: Analyses of stem growth using 20-year data of an even-aged Chamaecyparis obtusa stand. Tree Physiol. 2013, 33, 106–118. [Google Scholar] [CrossRef] [PubMed]
  43. Fang, X.; Zhao, W.; Wang, L.; Feng, Q.; Ding, J.; Liu, Y.; Zhang, X. Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China. Hydrol. Earth Syst. Sci. 2016, 20, 3309–3323. [Google Scholar]
  44. Gwak, Y.; Kim, S. Factors affecting soil moisture spatial variability for a humid forest hillslope. Hydrol. Process. 2017, 31, 431–445. [Google Scholar]
  45. Ellsworth, P.Z.; Sternberg, L.S. Seasonal water use by deciduous and evergreen woody species in a scrub community is based on water availability and root distribution. Ecohydrology 2015, 8, 538–551. [Google Scholar] [CrossRef]
Forests 12 01547 g001 550
Figure 1. Sampling sites and sceneries in the three Taxodium distichum plantations in the Yangtze River Delta of China. The black dots labeled as CZ, GY, and SX denote the site location at Chizhou, Gaoyou, and Shaoxing, respectively.
Figure 1. Sampling sites and sceneries in the three Taxodium distichum plantations in the Yangtze River Delta of China. The black dots labeled as CZ, GY, and SX denote the site location at Chizhou, Gaoyou, and Shaoxing, respectively.
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Figure 2. Isotopes (δ18O and δD) in soil water, tree xylem water, and groundwater from Taxodium distichum plantations in (a) Chizhou, (b) Gaoyou, and (c) Shaoxing. CZ, GY, and SX indicate the sites located at Chizhou, Gaoyou, and Shaoxing, respectively.
Figure 2. Isotopes (δ18O and δD) in soil water, tree xylem water, and groundwater from Taxodium distichum plantations in (a) Chizhou, (b) Gaoyou, and (c) Shaoxing. CZ, GY, and SX indicate the sites located at Chizhou, Gaoyou, and Shaoxing, respectively.
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Figure 3. Percentage of water uptake from different soil layers (0–20, 20–40, 40–60, 60–80, 80–100 cm) by Taxodium distichum in Chizhou (CZ), Gaoyou (GY), and Shaoxing (SX) following light, moderate, and heavy rainfalls.
Figure 3. Percentage of water uptake from different soil layers (0–20, 20–40, 40–60, 60–80, 80–100 cm) by Taxodium distichum in Chizhou (CZ), Gaoyou (GY), and Shaoxing (SX) following light, moderate, and heavy rainfalls.
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Figure 4. Variation partitioning analysis for percentage of water uptake by Taxodium distichum following (a) light, (b) moderate, and (c) heavy rainfalls. The variation is divided into the following four fractions: pure effect of soil properties (X1), pure effect of tree traits (X2), joint effects of soil and tree properties (X3), and unexplained variation.
Figure 4. Variation partitioning analysis for percentage of water uptake by Taxodium distichum following (a) light, (b) moderate, and (c) heavy rainfalls. The variation is divided into the following four fractions: pure effect of soil properties (X1), pure effect of tree traits (X2), joint effects of soil and tree properties (X3), and unexplained variation.
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Figure 5. Importance of tree and soil properties to the proportion of water uptake by Taxodium distichum and relationships of plant water uptake proportion with fine root biomass and total porosity following (a–c) light, (d–f) moderate, and (g–i) heavy rainfall by mean percent increase in mean squared error (MSE). The solid lines represent the fitted ordinary least-squares model, and the shaded areas represent the 95% confidence intervals. LB, leaf biomass; FB, fine root biomass; DBH, diameter at breast height; BD, bulk density; WFPS, soil water-filled pore space; FC, field capacity. ** denotes significant correlation between tree water uptake and the corresponding variables at p < 0.01.
Figure 5. Importance of tree and soil properties to the proportion of water uptake by Taxodium distichum and relationships of plant water uptake proportion with fine root biomass and total porosity following (a–c) light, (d–f) moderate, and (g–i) heavy rainfall by mean percent increase in mean squared error (MSE). The solid lines represent the fitted ordinary least-squares model, and the shaded areas represent the 95% confidence intervals. LB, leaf biomass; FB, fine root biomass; DBH, diameter at breast height; BD, bulk density; WFPS, soil water-filled pore space; FC, field capacity. ** denotes significant correlation between tree water uptake and the corresponding variables at p < 0.01.
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Table
Table 1. Characteristics of three rainfall events that occurred at the three study sites.
Table 1. Characteristics of three rainfall events that occurred at the three study sites.
SiteRainfall Event ⅠRainfall Event ⅡRainfall Event Ⅲ
TimePrecipitation (mm)TimePrecipitation (mm)TimePrecipitation (mm)
CZ11 July 20188.714 December 201714.54 September 201827.0
GY27 November 20179.38 November 201619.58 October 201629.2
SX10 July 20177.522 May 201714.516 September 201635.5
CZ, GY, and SX indicate the site located at Chizhou, Gaoyou, and Shaoxing, respectively.
Table
Table 2. Dynamics of fine root biomass (FB) and soil water-filled pore space (WFPS) in each soil layer within CZ, GY, and SX sites after light, moderate, and heavy rainfall events.
Table 2. Dynamics of fine root biomass (FB) and soil water-filled pore space (WFPS) in each soil layer within CZ, GY, and SX sites after light, moderate, and heavy rainfall events.
TraitSiteRainfall Event0–20 cm20–40 cm40–60 cm60–80 cm80–100 cm
FBCZLight103.7 ± 26.2 b28.3 ± 7.8 a19.3 ± 1.1 a3.7 ± 0.4 a1.3 ± 0.2 a
Moderate96.4 ± 21.7 b26.1 ± 0.8 a17.5 ± 1.2 a3.1 ± 0.2 a1.7 ± 0.1 a
Heavy95.0 ± 20.2 b30.5 ± 0.6 a15.5 ± 0.9 a2.9 ± 0.1 a1.8 ± 0.1 a
GYLight42.6 ± 2.3 e29.5 ± 2.2 d15.9 ± 0.6 c8.3 ± 0.7 b2.9 ± 0.5 a
Moderate44.6 ± 1.6 e31.7 ± 0.4 d14.2 ± 0.3 c9.0 ± 0.3 b2.1 ± 0.2 a
Heavy45.3 ± 1.4 e30.3 ± 1.7 d15.7 ± 0.9 c8.7 ± 0.2 b2.8 ± 0.1 a
SXLight55.5 ± 1.7 e32.8 ± 0.8 d18.0 ± 1.9 c8.3 ± 0.3 b2.4 ± 0.3 a
Moderate55.2 ± 0.6 e32.2 ± 0.6 d16.9 ± 0.3 c7.6 ± 0.1 b2.1 ± 0.1 a
Heavy55.2 ± 0.6 e32.5 ± 1.1 d15.3 ± 0.1c7.4 ± 0.1 b1.9 ± 0.2 a
WFPSCZLight43.6 ± 0.3 a51.6 ± 0.8 b53.0 ± 0.8 b49.9 ± 0.8 b50.7 ± 3.1 b
Moderate49.2 ± 1.5 a62.4 ± 4.1 b59.1 ± 2.4 ab58.5 ± 3.9 ab59.1 ± 1.8 ab
Heavy86.2 ± 1.1 a95.0 ± 2.6 c87.6 ± 1.6 ab92.4 ± 0.9 abc92.9 ± 2.9 bc
GYLight42.5 ± 2.0 a49.6 ± 1.9 b49.2 ± 1.3 b50.7 ± 2.1 b49.1 ± 2.2 b
Moderate46.1 ± 1.8 a53.7 ± 1.1 b54.7 ± 1.6 b55.9 ± 1.4 b53.8 ± 1.5 b
Heavy79.2 ± 1.5 a92.5 ± 1.8 b90.3 ± 2.0 b92.2 ± 2.9 b93.1 ± 1.5 b
SXLight46.8 ± 2.6 a50.3 ± 0.6 a50.2 ± 1.6 a50.8 ± 0.7 a50.1 ± 2.6 a
Moderate48.8 ± 3.8 a55.5 ± 0.5 ab56.2 ± 0.6 a55.4 ± 1.4 a55.2 ± 1.8 a
Heavy86.9 ± 2.9 a87.1 ± 4.5 a93.9 ± 2.4 ab89.5 ± 1.7 b92.2 ± 0.6 ab
Values are means ± SE (n = 3). Significant differences in independent variables among different soil layers are denoted by different lowercase letters (p < 0.05).
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Zhang, B.; Jiang, J.; Xu, Q.; Gao, D.; Zuo, H.; Ren, R. Disentangling the Effects of Tree and Soil Properties on the Water Uptake of a Waterlogging Tolerant Tree in the Yangtze River Delta, China. Forests 2021, 12, 1547. https://doi.org/10.3390/f12111547

AMA Style

Zhang B, Jiang J, Xu Q, Gao D, Zuo H, Ren R. Disentangling the Effects of Tree and Soil Properties on the Water Uptake of a Waterlogging Tolerant Tree in the Yangtze River Delta, China. Forests. 2021; 12(11):1547. https://doi.org/10.3390/f12111547

Chicago/Turabian Style

Zhang, Beibei, Jing Jiang, Qing Xu, Deqiang Gao, Haijun Zuo, and Ranran Ren. 2021. "Disentangling the Effects of Tree and Soil Properties on the Water Uptake of a Waterlogging Tolerant Tree in the Yangtze River Delta, China" Forests 12, no. 11: 1547. https://doi.org/10.3390/f12111547

APA Style

Zhang, B., Jiang, J., Xu, Q., Gao, D., Zuo, H., & Ren, R. (2021). Disentangling the Effects of Tree and Soil Properties on the Water Uptake of a Waterlogging Tolerant Tree in the Yangtze River Delta, China. Forests, 12(11), 1547. https://doi.org/10.3390/f12111547

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