Introduction

Saturated hydraulic conductivity (Ks) is one of the most important soil properties that determines the behavior of water flow systems1. A detailed understanding of Ks is critical in the assessment of irrigation practices, infiltration rates, runoff, groundwater recharge rates, and drainage processes, which makes it of particular concern in forest management2.

Vegetation is expected to be an important factor that influences the hydraulic properties of soil by affecting its physical and chemical characteristics3,4. Forest conversion is a major change globally, yet our understanding of its impacts on soil Ks remains incomplete. However, the prediction of forest soil Ks is complex due to multiple interactions associated with anthropological and geomorphic processes, which impact spatiotemporal Ks variations5,6,7. Previous studies have found differences in Ks among deforested areas in primary forests, secondary forests, and agricultural ecosystems8, and among forests, shrub lands, and grasslands9. Intense agricultural use can reduce Ks soils10. Pasture soils have lower Ks than woodland soils11. The mechanisms responsible for Ks are associated with soil structure12. Across soil depth profiles, Ks tends to decrease with soil depth13,14,15,16. This elucidation has been integrated within several hydraulic models17,18, in which pedotransfer functions (PTF) models are typically applied to the prediction of Ks5,19. Among a number of physical parameters, soil porosity, texture, and bulk density are determinants for Ks in the PTF20,21,22,23. The chemical characteristics of soils such as soil organic carbon (SOC) or soil organic matter (SOM) are also important predictors for Ks in the PTF21,24,25,26. The effects of soil aggregate dimensions on Ks were investigated and it was found that higher Ks associated with higher SOM was positively associated with soil aggregate size27. Ks has also been found to be affected by pore dimensions and distribution28. At the field scale, however, the physical and chemical parameters of soils are not always significantly correlated with Ks29.

Despite the drastic effects of forest vegetation type shifts that occur frequently at a global scale, how changes in forest vegetation types affect Ks remains poorly understood. Moreover, the contribution and importance of specific soil parameters on the resulting Ks is not always certain. Here, our objectives are to test: (1) whether differences in forest vegetation, resulting from changes in management objectives, affect soil Ks across multiple soil depths and; (2) how changes in soil Ks might be associated with the physicochemical attributes of soil. To address our first objective, we used analysis of variance to test the effect of forest vegetation types on soil Ks. For the second objective, we used boosted regression tree (BRT) analysis, which resembles an additive regression model and can achieve higher accuracy and less bias in predictions than traditional multiple regression models30. In particular, BRT analysis is good for handling multi-collinearity concerns and violations of parametric assumptions31,32.

Results

Both vegetation type and soil depth affected soil Ks and other characteristics (Table 1). The effect of vegetation type on Ks was significantly dependent on soil depth as indicated by the significant interaction effect between vegetation type and soil depth (P = 0.03). In the top soil layer (0–10 cm), soil Ks was higher in bamboo forests and tea gardens than in native and mixed forests; in the deep soil layers (10–20 cm and 20–30 cm), native forests had significantly higher soil Ks than other vegetation types, with the lowest values in tea gardens (Fig. 1).

Table 1 Effects of vegetation type and soil depth on soil saturated hydraulic conductivity (Ks), soil physicochemical characteristics, and roots characteristics.
Full size table
Figure 1
figure 1

Soil saturated hydraulic conductivity (Ks), soil physical and chemical characteristics and roots distribution characteristics in relation to vegetation type and soil depth. Values for boxplots are medians, 75% observations in boxes, and whiskers above and below the box indicate 95th and 5th percentiles. Corresponding statistical analysis results are presented in Table 1.

Full size image

The soil bulk density was impacted by changes in vegetation type and soil depth, but not by the interaction between vegetation type and soil depth (P < 0.001, P < 0.001 and P = 0.97, respectively). A similar trend was seen for total porosity and capillary porosity, which were impacted by vegetation type and soil depth, but not by their interaction. Total soil nitrogen was impacted considerably by changes in vegetation type with P = 0.01, while the impacts of soil depth and the interaction between vegetation type and soil depth were not significant. The non-capillary porosity of soil was impacted by the interaction between vegetation type and soil depth with P = 0.09, but not by these individual factors (P = 0.21 and P = 0.36, respectively). Meso and micro water-stable aggregates were impacted only by soil depth with P = 0.04 and P = 0.02, respectively (Table 1, Fig. 1).

Both root length density and root surface area density varied across vegetation types and soil depths. Root length density was impacted by changes in vegetation type, soil depth, and the interaction between them (P < 0.001, P < 0.001 and P = 0.04, respectively). Root surface area density was impacted by changes in vegetation type and soil depth (P = 0.04 and P < 0.001, respectively), but not by their interaction (P = 0.59). Root length density of bamboo forests was higher than other vegetation types in all soil depths, while root surface area density was lower in 0–10 cm soil depth. The root length density of bamboo forests in 0–10 cm and 10–30 cm was lower (Table 1, Fig. 1).

Correlation analysis showed the correlation between all soil properties (Fig. 2). All soil properties contributed to differences in Ks, with non-capillary porosity, total porosity, and macro water-stable aggregates exhibiting the greatest contributions (Fig. 3). BRT analysis indicated that non-capillary porosity, total porosity, and macro water-stable aggregates contributed 25.1%, 24.5%, and 16.8% of the BRT model explained variations in Ks, respectively (Fig. 4). The other factors of capillary porosity, bulk density, total organic carbon, meso water-stable aggregates, and micro water-stable aggregates, had relatively minor contributions in this model (Fig. 4).

Figure 2
figure 2

Correlation between saturated hydraulic conductivity (Ks) and soil characteristics.

Full size image
Figure 3
figure 3

Boosted regression tree (BRT) modeled relationships between saturated hydraulic conductivity (Ks) (centered value, m day−1) and (a) non-capillary porosity (%), (b) total porosity, (c) macro water-stable aggregate content (%), (d) capillary porosity (%), (e) bulk density (Mg m−3), (f) total organic carbon (g kg−1), (g) meso water-stable aggregate content (%), and (h) micro water-stable aggregate content (%).

Full size image
Figure 4
figure 4

Results of boosted regression tree analysis (BRT) of predictors’ relative influences (%).

Full size image

Discussions

Soil Ks is a critical factor for plant growth that involves air-filled porosity, plant-available water, and so forth33. Hence, the improvement of Ks is essential in order to avoid runoff and soil erosion34. As we anticipated, Ks differed among the four vegetation types, with the disparities resulting primarily at the 10–30 cm soil depth. This result was similar to previous research, in which changes in vegetation type were shown to alter Ks significantly35. The effects of vegetation type on soil Ks were probably by means of root distribution and morphological characteristics such as root biomass and distribution in soil36,37,38,39. The root system affects soil texture via mechanical forces such as insertion or extrusion in soil40. Root length density and root surface area density both showed a decreasing trend with respect to depth in different vegetation types. In native forests and mixed forests, the main roots are obvious and the root system might extend to the lower depths of the layer, while in bamboo forests, the main roots are not obvious and the underground rhizome expands near the soil surface. In tea gardens, roots do not extend as deeply in soil compared to the other two types, nor do they expand like bamboo rhizome.

The roots distribution characteristics also affect the soil texture by adjusting litter input from the soil or its surface over time41, thus affecting soil organic carbon (SOC) and other soil physicochemical characteristics3,4. This stimulates belowground microbial biomass and rhizospheres42,43, and the effects of the physicochemical attributes of soil on Ks via microbial community activities44,45,46. So to alter the physiochemical characteristics of soil, by means of producing solid, gas, and gel phases in order to adjust the fraction of the total spatial volume that is available for water flow, and hence the Ks.

The effects of soil depth on soil Ks differed with vegetation types though Ks in native forests did not vary with soil depth. This is in alignment with prior researches13,14,15, which may have been the result of distinct vertical distributions of the physicochemical characteristics of soil and root distribution14. This might partly explain the increasing trend in soil depth from 10–20 cm to 20–30 cm. The decreasing root length and surface area densities were weakened by the effects of roots via mechanical forces or litter input characteristics. The higher Ks in 0–10 cm might be attributed to the great probability in contacting a fresh litter of leaves or branches.

In this study, the variation of the Ks value was higher in the tea garden. This may be attributed to the higher distribution density of the tea stems and the complexity of the root distribution underground, which could affect the Ks value28,37. We also observed that the Ks value at the 10–20 cm depth for the vegetation types other than native forests was lower than at the other soil depths. We attributed this to soil disturbances during vegetation conversion at that time, which might have had the effect of compacting the 0–20 cm depth layer. However, since the relation between Ks and root distribution was not clarified in this study, we propose to explore this area further in future research.

In this study, total soil porosity, non-capillary porosity, and macro water-stable aggregates were the principal factors that influenced Ks. A key parameter in this study was bulk density, from which the calculations on total soil porosity were derived. This was similar to a study in which differences in Ks between samples were found to be correlated with bulk density and macro porosity47. The characteristics of pores in soils, such as their dimensions, distribution, and interconnections have been known to impact Ks. It was found in many studies that lower bulk density was aligned with higher Ks, and vice versa48,49, while water stable macro aggregates were positively correlated with Ks50. It was found that the Ks values were reduced in soils with smaller aggregates in contrast to those with large aggregates12. This was likely attributed to the impacts of different fresh organic matter, which were produced by different vegetation types51. Vegetation generated litter may simulate soil aggregation52, which subsequently influences bulk density and porosity53,54, while bulk density and porosity are closely correlated to adjustments in Ks55,56. It remains a scientific challenge to describe in detail the complex continuous soil space28. However, we may conclude that soil pore characteristics are important factors.

In conclusion, our results show that change in vegetation type is a driving factor that strongly influences the hydraulic properties of soils in subtropical forests. Vegetation type, soil depth, and their interaction were observed to influence Ks significantly, and the effects of soil depth on Ks varied for different types of vegetation. The Ks of native forests did not significantly differ at soil depths from 0–30 cm. For the other vegetation types, the Ks at the 10–20 cm depth was significantly lower than that at 0–10 cm and 20–30 cm depths. There are multiple factors that impact Ks; however, total soil porosity, non-capillary porosity, and macro water-stable aggregates comprised the primary factors in this study. Soil Ks is strongly influenced by changes in vegetation type, indicating that shifts in aboveground vegetation may strongly impact the water dynamics of soil. Based on our data, we suggest that the restoration of the native evergreen broad-leafed forests will assist in the retention and maintenance of soil hydrologic properties. Additional research will be required to confirm other factors and mechanisms that influence Ks, such as the role of root systems and microbial communities in the processes that follow changes in vegetation species.

Materials and Methods

Study area

Major forest conversion is occurring globally. In China, vegetation change from native evergreen broadleaf forests to mixed evergreen broadleaf and coniferous forests or other vegetation types is common. This study was conducted at the Fengyang Mountain Nature Reserve, Zhejiang Province, China (longitude extending from 119°06′ to 119°15′E, latitude from 27°46′ to 27°58′N, and elevations of from 600 m to 1929 m), which comprised a land area of 15,171 ha. This nature reserve is characterized as having a subtropical climate, with an annual average temperature of 12.3 °C, and annual rainfall of 2,400 mm. Prior to 1970, this area was dominated by native evergreen broad-leaved forests (primarily comprised of Camellia japonica Linn., Cyclobalanopsis glauca (Thunberg) Oersted, Eurya japonica Thunb., and Rhododendron simsii Planch.). Intensive selective cutting and reforestation was conducted during 1971–1973, and portions of the forests were converted to mixed evergreen broad-leaved and coniferous forests (henceforth referred to as mixed forests), primarily consisting of Schima superba Gardn. et Champ., Rhododendron simsii, and Pinus taiwanensis Hayata). Subsequent to clear-cutting, pure plantations with Cunninghamia lanceolata (Lamb.) Hook., Cryptomeria fortune Hooibrenk ex Otto et Dietr., bamboo forests (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens Mazel ex H.de leh.), or tea gardens were established. Following the establishment of the nature reserve in 1975, the entire study area, including the tea gardens, was protected from anthropogenic disturbances. The roots characteristics of different vegetation types were shown in Supplementary Figure.

Sampling

In June 2013, we randomly sampled five native evergreen broad-leaved forest stands, five mixed forest stands, five bamboo forest stands, and four tea garden stands at elevations ranging from 1250 to 1450 m, which resulted in a total of 19 sampled stands. All of the sample stands resided on well-drained mesic sites with slopes inclines of less than 5% to minimize the effects of inherent site conditions on soil characteristics57,58. In each stand, we established a sample plot of 20 × 20 m. Using a knife and a trowel, we extracted soil samples at depths of 0–10 cm, 10–20 cm, and 20–30 cm by digging a 15 × 15 cm section at each sampling point to enable the analysis of soil physicochemical characteristics59,60, which resulted in a total of 57 samples. For the determination of soil Ks, bulk density, and capillary porosity analysis, we extracted soil samples with a metal corer (5.5 cm in diameter x 5 cm in height) at each sampling point61, which resulted in a total of 171 samples (57 samples for Ks analysis, 57 samples for soil bulk density analysis and capillary porosity analysis, and 57 samples for other physicochemical properties).

To further understand how roots affect the impact of vegetation type on Ks, we did supplementary sampling of soil with a metal corer (5.5 cm in diameter × 5 cm in height) in December 2018. Similar to the early sampling, we randomly sampled five native evergreen broad-leaved forest stands, four mixed forest stands, three bamboo forest stands, and three tea garden stands at elevations ranging from 1250 to 1450 m, which resulted in a total of 15 supplementary sampled stands.

Saturated hydraulic conductivity measurements

Ks was determined based on the constant hydraulic head method by imposing a stable hydraulic head to the top of the cores that were sampled at each of the sampling points, which were saturated with water prior to experiments in the laboratory1.

Analysis of soil physicochemical and roots properties

Soil bulk density was determined by drying the samples in an oven at 105 °C until a constant weight was attained, and then adjusting for root and stone volume58. Soil samples for other physicochemical analyses were air-dried, sieved (2 mm mesh) in the laboratory, and then stored in air-tight plastic bags. Total organic carbon (TOC) content was measured using the sulfuric acid-potassium external heating method62. Total nitrogen and total phosphorus were simultaneously determined using a Bran + Luebbe Autoanalyser 3 Continuous Flow Analyzer (Bran + Luebbe GmbH, Norderstedt, Germany). Root length density and root surface area density were analyzed with the Win RHIZO root system (Regent Instruments, Québec, Canada). Before the analysis, all roots were washed out from the metal corer and then scanned with EPSON LA (Seiko Epson Corporation, Nagano-ken, Japan).

Soil porosity

The total porosity was calculated using the following equation63:

$${P}_{t}=100\times (1-{D}_{b}/{D}_{p})$$
(1)

where Pt is the total soil porosity (%); 100 is the unit conversion factor; Db is the soil bulk density (g cm−1); and Dp is the soil particle density (g cm−1), which was assumed to be 2.65 g cm−1 according to China’s standard64. The soil capillary porosity was determined based on the water suction method, with the surface of the water located just below the tops of the soil cores63. Each soil core was initially weighed and placed onto a salver via filter paper until it attained a constant weight. Following weighing, the soil samples were allowed to drain completely under gravity. The soil samples were subsequently weighed again; their capillary water contents were determined by the differences in weight between the saturated and drained states.

$${P}_{c}=100\times {W}_{c}\times {D}_{b}/V$$
(2)
$${P}_{n}={P}_{t}-{P}_{c}$$
(3)

where Pc is the capillary porosity (%); Pn is the non-capillary porosity (%); 100 is the unit conversion factor; Wc is the soil capillary water content (%); V is the volume of the soil core (cm3).

Water stable aggregate measurements

Water stable aggregate was measured using a routine wet-sieve method via a mechanical sieving procedure65. Briefly, for each soil sample, 200 g of air-dried soil was placed on a series of sieves to determine the dry aggregate size distribution (combined in three nest sizes in the order of >2 mm, 0.5–2 mm, and <0.5 mm) prior to wet-sieving. Subsequently, 50 g samples were prepared according to their dry-sieving percentages by the weight of aggregates at each size distribution for wet-sieving. The samples were immersed in water for 10 minutes and then placed under oscillation at 30 rpm for 30 min. The aggregate fractions that remained on each sieve were removed with aqua distillate into aluminum bins, to be oven-dried at 105 °C for 24 h. The aggregate fractions were then weighed to calculate the aggregate weights from each size class58,66.

Data analysis

To examine the impact of land use type and soil depth on the Ks and other soil characteristics, an analysis of variance (ANOVA) was performed following a split plot design, with soil layers nested within the sample plot. We modelled the fixed effects of vegetation type, soil layer, and their interaction on Ks with plot as the random factor using maximum likelihood with the lme4 package67. ANOVA assumption tests were done with the lmerTest package68. Shapiro –Wilk’s test was conducted. In this study, the Shapiro –Wilk’s test involving capillary porosity and non-capillary porosity failed, so a Box-Cox transformation was performed by the following equation69:

$${V}_{trans}=({{V}_{origin}}^{\lambda +1})/\lambda $$
(4)

where Vtrans is the transformed value of capillary porosity or non-capillary porosity; Vorigin is original value of capillary porosity or non-capillary porosity; and λ is the parameter of box-cox.

We used boosted regression tree analysis (BRT) to elucidate how Ks was potentially affected by soil physicochemical characteristics. Furthermore, we examined Pearson’s correlation between potential factors and Ks to reduce the fitting predictors. Then we fitted all BRT models using the adjusted settings for ecological modeling: tree complexity = 5, learning rate = 0.0001, bag fraction = 0.7. All analyses were performed using BRT with the R package gbm70.