The influence of plant diversity on slope stability in a moist evergreen deciduous forest

Ecological Engineering 36 (2010) 265–275

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The influence of plant diversity on slope stability in a moist evergreen deciduous forest Marie Genet a,∗ , Alexia Stokes b , Thierry Fourcaud c , Joanne E. Norris d a

Université Bordeaux I, US2B, 33405 Talence Cedex, France INRA, UMR AMAP, TA-A51/PS2, Bld de la Lironde, Montpellier Cedex 5, F-34398, France c CIRAD, UMR AMAP, TA-A51/PS2, Bld de la Lironde, Montpellier Cedex 5, F-34398, France d Halcrow Group Limited, Cygnet Park, Hampton, Peterborough PE7 8GX, UK b

a r t i c l e

i n f o

Article history: Received 30 September 2008 Received in revised form 20 April 2009 Accepted 19 May 2009

Keywords: Root reinforcement Sichuan Landslides Bamboo Tensile resistance Shear

a b s t r a c t The influence of plant diversity on slope stability was investigated at early phases of succession in a mixed forest in Sichuan, China. The first phase comprised big node bamboo (Phyllostachys nidularia Munro) only. In the second phase, bamboo co-existed with deciduous tree species and in the third phase, deciduous species existed alone. Root density at different depths and root tensile strength were determined for each species. The factor of safety (FOS) was calculated for slopes with and without vegetation for each succession phase. For phase 2, FOS was determined for different species mixtures and positions. In phase 3, simulations were performed with a single tree at the top, middle or toe of the slope. Due to its shallow root system, bamboo contributed little to slope stability. In simulations with the tree at the top or middle of the slope, FOS decreased because tree weight added a surcharge to the slope. FOS increased with the tree at the bottom of the slope. Different mixtures of species along the slope had no influence on FOS. Differences in root tensile strength between species played a small role in FOS calculations, and tree size and density were the most important factors affecting slope stability, excluding hydrological factors. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The Sichuan region in the south of China is subject to heavy rains in the monsoon season lasting from June to September. Landslides are frequent (Liu and Diamond, 2005; Zhang et al., 2006), particularly where the Tibet-Qinghai plateau descends rapidly onto the plains and steep slopes and gorges are abundant. Deforestation has been severe in the last 50 years (Démurger et al., 2005), but the recent government guidelines concerning the Sloping Land Conversion Programme have resulted in large areas of cropland being replanted with trees in order to combat erosion and landslides (State Council of the PRC, 2007; Stokes et al., 2008, 2009a). The question remaining to be asked is whether these plantations are useful at fixing soil on steep slopes, or whether natural regeneration would be a more efficient as well as an economic and ecological method of reinforcing soil? Although many studies on how vegetation fixes soil on slopes have been carried out, few have examined how plant diversity may

∗ Corresponding author. Present address: Université Montpellier II, UMR AMAP, TA-A51/PS2, Bld de la Lironde, Montpellier Cedex 5, F-34398, France. Tel.: +33 04 67 61 58 00; fax: +33 04 67 61 56 68. E-mail addresses: [email protected], [email protected] (M. Genet). 0925-8574/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2009.05.018

influence slope stability (Schmidt et al., 2001; Roering et al., 2003; Cammeraat et al., 2005; Van Beek et al., 2005) and in particular, how a given combination of plant species might serve to increase soil reinforcement. Root architecture is highly variable depending on soil type, nutrient and water availability, but the inherent rooting pattern is nevertheless species dependent (Köstler et al., 1968; Stokes et al., 2009b). To stabilize a slope against landslides, the number and size of roots which cross the slip surface are extremely important (Cammeraat et al., 2005; Van Beek et al., 2005; Reubens et al., 2007). The thin roots play a major role in preventing soil slippage particularly in the surface layers of the soil profile (Coppin and Richards, 1990; Operstein and Frydman, 2000; Mickovski et al., 2007). The position of thin roots within a root system, i.e. where most thin roots are located with regard to depth and radial position around the root system, will therefore depend partly on species and partly on local environment. The thicker-diameter roots provide anchorage to the soil mass where the potential slip surface is shallow e.g. <2.0 m deep (Coppin and Richards, 1990; Norris et al., 2008). Plant species can grow differently depending on local conditions; therefore it can be expected that a wide diversity of plant species will allow for any detrimental effects of environment on root biomass or architecture to be buffered (Stokes et al., 2009b). Although it might be expected that plant diversity increases slope stability (Pohl et al., 2009), this may not be the case through-

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out all phases of vegetation succession, especially in the period just after, e.g. a clear-felling has taken place. Pioneer species need to colonise the bare soil and may not possess the necessary rooting characteristics required to improve slope stability. Managers therefore need to ask the question whether it is better to let natural regeneration occur on bare soil, where a mixture of plant forms and species will develop; or whether young trees should be planted, and if so, which species or mixture of species? The immediate effects versus the long-term benefits and advantages also need to be considered. Aside from economical aspects, is a planted forest more effective at reinforcing soil on steep slopes than a naturally regenerated forest? The long-term stability of a slope can also be affected by the position of trees: for instance, if the lower slopes have been cleared of vegetation leaving a heavily loaded forested upper slope, the weight of the vegetation on the upper slope may result in a decrease in the factor of safety (FOS), a measure of the risk of failure of a slope (Norris et al., 2008). Conversely, the additional load provided by the weight of the vegetation at the toe or lower part of the slope adds stability to the slope, increasing the FOS (Greenwood et al., 2004). However, few studies have focussed on this aspect and little quantitative data exist (Kokutse et al., 2006). We carried out a study to examine the influence of plant diversity in a naturally regenerated moist evergreen broadleaf forest at an early age of succession in the Sichuan province of China. Root biomass distribution was determined at different soil depths and root tensile strength measured for several species. Results were used in a model of slope stability and the slope FOS calculated for different succession phases, combinations of species and positions of trees along a slope. Results are discussed with regard to how root biomass evolves over time in natural forests and how best to manage unstable slopes. 2. Materials and methods 2.1. Site characteristics The study site was a 4 km-long valley located northwest of Chongzhou City, on the eastern limits of the Tibet-Qinghai plateau, Sichuan Province, China (30◦ 48 104 N, 103◦ 24 732 E), which belongs to the middle segment of Longmen Mountain, the south-east offshoot of Qionglai Mountain. The topography of the area is mountainous and characterized by gorges, steep hills and valleys, ranging from 960 to 3868 m in altitude (Zhu et al., 2006). The region is situated in the moist monsoon (lasting from May to September) zone and the climate is subtropical. Annual mean temperature is 12.3 ◦ C with minimum temperatures of 6 ◦ C in January and a maximum of 32.7 ◦ C in July and August. Average annual precipitation is 1300–1450 mm with 70% of the annual average amount in June to August and only 5% from November to January (Zhu et al., 2006). Climate is characterized by misty days and high humidity (annual average relative humidity 86%), little sunshine (average annual sunshine = 641.6 h), and low wind speeds (annual average wind speed = 1.4 m s−1 ). Soil parent material is mainly constituted of limestone, sandstone and granite and soil type was a reddish brown silty clay (Soil Taxonomic Classification Research Group, 1993). Soil thickness ranged from 0.5 to 1.3 m over bedrock with a

humus layer of 0.01–0.03 m (Genet et al., 2006). Average soil cohesion (cs ) of fallow soil at a depth of 0.05 m was 28.3 kPa and the soil friction angle () was 19.6◦ . Neither of these values differed significantly along the valley (Genet et al., 2008). Small but numerous shallow landslides occur in the area during the monsoon season (June–September), and the slip surface of these landslides was estimated at a mean depth of 0.6 m (Stokes et al., 2007). This area was severely affected by the Wenchuan earthquake on 12 May 2008, but as yet an inventory of mass movement due to the earthquake has not been completed. The valley studied was extremely rich in flora, with over 300 different species inventoried (X. Cai, personal communication). The dominant vegetation comprised mixed and monospecific tree plantations of Cryptomeria japonica D. Don, Cunninghamia lanceolata Lamb., Lindera limprichtii H. Winkl., Metasequoia glyptostroboides Hu & Cheng., Betula luminifera H. Winkl. and Carya cathayensis Sarg. Major shrub species included Cornus controversa Helms., Trachycarpus fortunei H. Wendl. and Salix guebriantiana Schneid. Dominant grasses and herbs comprised big node bamboo (Phyllostachys nidularia Munro.), Phragmites communis Trin., Juncus effusus L., Plantago asiatica L., Iris tectorum Maxim., Pteridium latiusculum Desv. and Dobinea delavani Baill. (Zhu et al., 2006; Stokes et al., 2007). Three sites were chosen, representing three different phases of early succession in a forest undergoing natural regeneration, although not all species were native to the Sichuan. All sites were located close together on the same soil type and slope angle (35◦ ) and at approximately the same altitude (1205, 1300 and 1215 m, respectively). At each site, plots of different sizes were selected randomly, which contained species representative of the surrounding flora (Site No. 1—first succession phase had two plots, each being 10 m2 . Site No. 2—second succession phase had two plots: 84 and 32 m2 each. Site No. 3—third succession phase had one plot only: 101.25 m2 ). The dominant woody species were identified and stem density measured (Tables 1 and 2). In sites representing succession phases 2 and 3, the same species were not present and it was not possible to find naturally regenerated sites where all species could be found at different ages. At site 1, where vegetation was in the first phase of succession, only big node bamboo (P. nidularia) was present. Big node bamboo dies back after flowering, although the exact number of years between flowerings is not known (Huang et al., 2002). As soon as big node bamboo dies back, trees grow quickly and become dominant, thus causing shady conditions for the understory and preventing further growth of big node bamboo (Stokes et al., 2007). The second phase of succession then begins. To determine approximate tree age in phases 2 and 3, wood cores were removed at the base of each tree using a Suunto©increment borer and the number of annual rings determined using dendrochronological techniques (Stokes and Smiley, 1968). Trees at site 2 were between 5 and 7 years old and trees at site 3 were between 15 and 20 years old. 2.2. Root sampling Using the method given in Genet et al. (2008), to determine root biomass and tensile strength, soil cores were taken from each site representing the three phases of succession. Each core had a diameter of 0.19 m and length of 0.15 m, taken at 0.15 m depths to 0.60 m

Table 1 Characteristics of bamboos (Phyllostachys nidularia) growing at site 1 (first succession phase) and trees growing at sites two and three, corresponding to the second and third succession phases respectively. Succession phase

Age (years)

Mean DBH (mm)

Mean height (m)

Density (stems ha−1 )

Basal area (m2 ha−1 )

Number of core samples

First Second Third

2 5 20

20.4 ± 0.13 38.9 ± 0.17 98.4 ± 0.89

4–5 3–4 12–17

89000 7269 2963

29.09 7.46 29.00

68 75 68

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Table 2 Stem density of trees present at each site. Big node bamboo (Phyllostachys nidularia), is also included because it was dominant in the second phase of succession but is a monocot. The first succession phase is not shown because it comprised big node bamboo only. Tree species at each succession phase

Percentage of species present (%)

Density of each species (stems ha−1 )

Second phase of succession Aralia elata Miq. Quercus infectoria Oliv. Litsea cubeba Pers. Carya cathayensis Sarg. Cynanchum stauntonii Decne. Betula laminifera H. Winkl. Idesia polycarpa Maxim. Phyllostachys nidularia Munro.

23 28 5 1 1 5 1 35

1596 1950 355 89 89 356 89 2483

2.13 2.85 0.32 0.08 0.12 0.49 0.23 0.61

Third phase of succession Cinnamomum wilsonii Gamble Notaphoebe cavaleriei Yang. Phoebe nanmu Oliv. Idesia polycarpa Maxim.

29 21 46 4

790 595 1284 99

6.42 1.24 16.95 2.89

which is the position of the potential slip surface (Stokes et al., 2007). The presence of roots deeper than 0.60 m was negligible. A large number (Table 1) of cores were extracted randomly at each site. Roots (<10 mm in diameter) were separated manually from each soil core using a sieve.

2.3. Root biomass Roots were washed and air-dried for three days before being transported to the laboratory for measurement of biomass. It was not possible to oven dry roots due to the lack of equipment in the field laboratory. Therefore, a selection of 30 roots per plantation were air-dried, weighed and then kept aside until they could be oven-dried at 85 ◦ C for 5d or until no further change in weight (Genet et al., 2008). The two measurements of biomass were then compared. The weight difference of air-dried and oven dried roots was less than 5% and no significant differences were found between air-dried and oven-dried roots for any root class size. Therefore, in the given field conditions, it was possible to obtain reliable estimations of dry biomass without oven-drying the roots. Roots from each core sample were separated into four diameter classes: <1 mm, 1–2 mm, 2–5 mm and 5–10 mm, root diameter being determined with an electronic slide gauge (Sudmeyer et al., 2004; Genet et al., 2008). Each group of roots was weighed using a balance with a precision >0.001 mg. An average air-dried root biomass (RBD) was determined according to the volume V (m3 ) of the core and expressed as kg m−3 . It was difficult to identify plant species from the roots collected in the core samples, therefore, individual RBD for each species within a site could not be determined. M RBD = V

(1)

where M (kg) is mean dry living root mass and V (m3 ) is the volume of the soil cylinder containing the root–soil matrix. RBD was calculated per root diameter class and depth. To compare the root vertical distribution patterns between the three phases of vegetation succession, a cumulative root fraction as a function of depth was calculated. The cumulative root fraction at a given depth was obtained by dividing RBD at each depth by the total RBD of the studied soil column and then computing the cumulative values for all depths (Silva and Rego, 2003; De Baets et al., 2007; Genet et al., 2008).

Basal area (m2 ha−1 )

2.4. Root tensile tests Tensile strength, i.e. ultimate stress at failure, was measured on roots of different diameters from each succession phase. Thirty roots per species were tested, therefore, for phase 1 (1 species), n = 30, phase 2 (8 species), n = 240 and for phase 3 (4 species), n = 120 root samples. Roots were collected from around each tree, so that tensile strength could be determined for each species. We also ensured that each diameter class was represented. Roots were soaked in water for one night so that all roots had approximately the same moisture content (Genet et al., 2006, 2008). The overbark diameter of roots tested varied between 0.2 and 6.0 mm. The length of each sample was at least 15 times its diameter. Mechanical tests were performed with a Universal Testing Machine (ADAMEL Lhomargy, France). A load cell with a maximal capacity of 1.0 kN capable of measuring forces with a precision of 0.1% was used, and crosshead speed was kept constant at 10 mm min−1 . Self clamping jaws were used to avoid damaging the roots. Only samples which broke in the middle third of the root length between the clamps were considered successful and the root rupture was attributed to the force applied in tension and not induced by root structural damage or stress concentration near the clamps. Root diameter was measured using an electronic slide gauge with 0.02 mm accuracy. Tensile strength at rupture was calculated as the maximal force required to cause failure in the root, divided by the root crosssectional area (CSA) at the point of breakage (Genet et al., 2005). 3. Additional cohesion of soil due to roots 3.1. Model of root reinforcement The presence of plant roots in the soil matrix results in an increase in soil cohesion through a reinforcing effect which usually augments superficial slope stability (Schmidt et al., 2001; Van Beek et al., 2005). The root–soil reinforcement model developed by Wu (1976), and elaborated upon by Waldron (1977), is widely used to estimate the additional cohesion taking into account the presence of roots in the soil (Gray and Sotir, 1996; Roering et al., 2003; Genet et al., 2008). This model states that the shear strength of soil reinforced by roots  sr is calculated by the Mohr–Coulomb equation as follows: sr = cs + cr +  tan 

(2)

where cs is the cohesion of fallow soil, cr is additional cohesion due to the presence of roots,  is the normal stress on the shear plane

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and  is the soil friction angle. Shear forces developed in the soil when the soil layer moves are translated into tensile forces in the roots. The mobilization of this tensile force in the roots can then be split into tangential and normal components. Assuming that roots are elastic, initially oriented perpendicular to the slip plane, fully mobilized in tension and that  is unaffected by root reinforcement (Waldron, 1977; Greenway, 1987), cr can be defined as: cr = tr (sin ı + cos ı tan )

Table 3 Slope angle and soil properties used for the slope stability analysis. Cohesion cs and friction angle  of fallow soil, i.e. without root reinforcement, were determined in laboratory soil shear tests. No significant differences were found between samples taken at 50 and 300 mm, therefore data shown are means from both depths. Data are means ± SE. Where letters in superscript differ, data are significantly different (p < 0.05). If no letters in superscript are present, data are not significantly different. Properties

Succession phase

(3) ◦

where ı is the angle of deformed roots with regard to the shear surface and tr is the average mobilized tensile strength of roots per unit area of soil; tr can be expressed as the product of the average tensile strength of roots Tr and the fraction of soil occupied by roots (Ar /A) called the Root Area Ratio (RAR). The values of (sin ı + cos ı tan ) can be approximated to 1.2 (Wu et al., 1979) and so Eq. (3) can be rewritten as: cr = 1.2Tr

A  r

(4)

A

Tr was estimated for each diameter class and for each species using a power law function of root tensile strength versus diameter. As root tensile strength varied with species, Tr was expressed for each site taking account the percentage Pj of species j. With several species present, e.g. at sites 2 and 3, Eq. (4) can be rewritten as: cr =

1.2  A j ∈ Sp



Pj





j j

Ti Ai

(5)

i ∈ Cd

where Sp is the set of species, Cd is the set of class diameter and j

Ti = ˛j di

−ˇj

(6)

Constants ˛j and ˇj were determined from the power regressions of root tensile strength versus diameter for species j. RAR was calculated from the total volume of roots per volume of soil for each diameter class at each depth, assuming that all roots crossed the slip surface once and that their length was greater or equal to the length of the soil core. This assumption was tested and found plausible by analysing root distribution and length on 30 soil cores from each site. To calculate the total volume of roots per volume of soil, a regression between root weight and root volume per root diameter class was established for the three sites using the diameter, length and weight of a sample of 30 roots per site (Genet et al., 2008). 3.2. Laboratory soil shear tests To determine the shear strength  s of non-rooted, unsaturated, fallow soil for use in the slope stability model, eight soil samples from within each stand were collected. Soil samples were taken by manually pushing cylindrical shear boxes of a known volume (62 mm diameter × 20 mm height) into non-rooted soil at depths of 50 and 300 mm. Small disturbed soil samples at the same depths were also taken simultaneously so that soil moisture content at the time of sampling could be determined. Samples were kept at 4 ◦ C and sealed in a plastic bag, along with the shear box samples, until laboratory testing could be carried out (approximately 5 d later). To obtain soil moisture content, samples were weighed on the day that shear tests were carried out and then dried at 80 ◦ C for 5 d, or until there was no further change in mass and weighed again. This drying temperature is standard in China (Anon., 1996). Soil moisture content was expressed as a percentage (grams of water per 100 g of dry soil) of the sample weight, and was found to vary between 30% and 31% only between samples (Table 3).

Slope angle ( ) Soil moisture (%) Pure soil cohesion cs (kPa) Pure soil friction angle  (◦ )

First

Second

Third

35 29.5 ± 2.8 20.7 ± 4.6 25.1 ± 0.8a

35 31.5 ± 1.5 18.6 ± 1.5 28.3 ± 2.9b

35 29.8 ± 1.9 25.3 ± 6.0 15.9 ± 3.3a

Strain-controlled direct shear tests were carried out using standard Chinese shear testing procedure (Anon., 1996). The undisturbed soil samples were removed from the shear boxes and placed in a shear testing device (Nanjing Soil Shear Machine SDJ-1, China). Normal loads of 100, 200, 300 and 400 kPa were applied as weights on consecutive samples. A lateral displacement was applied at a speed of 0.8 mm min−1 until failure occurred and the peak shear force recorded (Genet et al., 2008). 3.3. Slope stability analysis To analyse the FOS of a slope with and without vegetation, slope stability analyses using the limit equilibrium method (LE) were carried out. LE methods based on the equilibrium of hydrostatic forces are shown to be reliable for estimating the factor of safety and are readily adapted to include the effects of vegetation (Greenwood, 1983, 1990, 2006). LE methods analyze a slope by dividing it into a series of slices and calculating the forces and moments acting on each slice of the analysis and the total forces and moments acting on the slip surface. The FOS is then determined by dividing the available forces or moments, which are deduced from Eqs. (2)–(6), by the resulting gravity loads acting at the slip surface. We used the program, SLIP4EX, developed by J. Greenwood, Nottingham Trent University, U.K., which uses Microsoft Excel©software, to compute the FOS of slopes (Greenwood, 2006). The SLIP4EX program compares LE methods (Bishop, Fellenius (Swedish), Janbu and Greenwood) for a single slip surface without vegetation on Sheet 1 of the spreadsheet, and on Sheet 2 the effects of the vegetation (or other reinforcement or hydrological changes) are incorporated and calculated using modified Swedish or Greenwood’s method equations (full equations are given in Greenwood, 2006). A slope is considered to be stable if the FOS is >1.0 and unstable when the FOS is <1.0. Engineered slopes in Europe are generally designed to have a FOS between 1.2 and 1.4 (Norris et al., 2008). Natural slopes have variable FOS depending on their geological and engineering properties. The slopes in the Sichuan province are prone to landslides (Stokes et al., 2007; Genet et al., 2008), therefore, they are of marginal stability only and probably have a FOS close to 1.0. In this study, the soil parameters cs and  derived from the laboratory testing (Table 3) were assigned to each slice and used as input values. Unit weight of the soil is one of the material inputs for the model but was not measured in this study; therefore this was assumed to be 18 kN/m3 , which is typical for clay soils (Tomlinson, 2001). However, by using the measured unsaturated soil (shear strength) cohesion (cs ) in the analysis, this produced high FOS, which were in the order of 4.0. The measured unsaturated soil cohesion is an overestimate of the actual shear strength of the soil at failure, since during and just prior to failure the soil would be fully saturated and thus extremely weak. Therefore, we used published values of shear strength of granite derived tropical residual

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not considered in this study. In SLIP4EX the tensile root force is the available root force which acts on the base of the slice. The available root force is derived from the number of roots crossing the slip surface multiplied by the root diameter and either the tensile strength of the root or the root pull out resistance, and divided by a partial FOS of 8 to allow for uncertainties in root distribution (Greenwood et al., 2004). Norris (2005, 2006) showed that the pull out resistance of a root, with diameters up to 60 mm, falls within 50–70% of its tensile strength value, therefore with the uncertainties associated with root distribution and failure strain between root and soil, the mean tensile strength value provides an indication of the available tensile root force. In this study, the tensile root force was estimated from the root tensile strength power regression equation for either each species or the mean of all species considered together, using a nominal root diameter based on the root class which contained the most roots (Table 4) at 0.6 m depth, 0.6 m being the typical depth of slip surface (Stokes et al., 2007), and an assumed number of roots which cross the slip surface. The vegetation parameters were applied to each slice in the same way that the soil parameters were assigned and used as input values. In SLIP4EX, the vegetation parameters are only selected if they have an effect on the slip surface. The following models were set up to determine whether different combinations of species affect the FOS of a slope and the effect of positioning trees on different parts of a slope, that is, adding additional weight to the slope. Each model was considered to be representative of the different processes occurring during each succession phase:

Fig. 1. (a) Illustrative sketch of the infinite slope model representing the large scale mountain slope. Drawing not to scale. (b) Illustrative sketch of the semicircular slip model representing the small surface failure on the hill slopes. Drawing not to scale.

soils.1 Such values would be more realistic for slopes near failure at our study site. Maail et al. (2004) report cohesion values of 4–7 kPa for granite soils with a friction angle () of 23–28◦ . An assumed cohesion value of 5 kPa and an average friction angle of 20◦ were subsequently used. The change in FOS due to the root reinforcement was calculated as a percentage increase (or decrease). Both an infinite slope (Fig. 1a) and a semicircular slip (Fig. 1b) were considered in the modelling to represent in the first instance the larger scale mountain slope and secondly the small dish shaped surface failures that occur on the hill slopes. For the infinite slope, slope length was defined as 20 m (each slice was 2 m in length) and slope angle was 35◦ for all three phases of succession. For the semicircular slip the ‘dish’ was divided into three slices: slice 1 had a width of 1.0 m, slip surface angle of 15◦ , slice 2 had a width of 1.5 m and slip surface angle of 35◦ and slice 3 had a width of 0.5 m and a slip surface angle of 55◦ . A soil cohesion value of 3.3 kPa was assigned to the semicircular slip model, as the assumed soil cohesion value of 5 kPa resulted in too high FOS for failure to occur, as compared to the infinite slope model. The water table (piezometric surface) was assumed to be at the ground surface in both cases, thus representing the worst case conditions for failure to occur. The vegetation parameters modelled using SLIP4EX were the tensile root force (T) and weight of the vegetation (Wv ). Other parameters such as wind loading and soil hydrological changes due to the vegetation (Greenwood, 2006) can also be modelled but were

1 In engineering, a failed slope back analysis would usually be carried out to determine the soil properties at failure. As the slopes in the study sites had not previously failed, but were of marginal stability, typical published soil parameters for tropical residual soils were used for the modelling.

• Model 1—A monospecific vegetation was assumed to be growing over the full length of the slope in a uniformly distributed manner. Both the infinite slope model and semicircular slip model were used to calculate the FOS of the slope. In both models, it was assumed that five 3.5 mm diameter roots crossed the 0.6 m depth slip plane for all species with one exception—the infinite slope with bamboo. Big node bamboo possesses running rhizomes distributed throughout the surface layer of soil (0.15 m depth) (Stokes et al., 2007), therefore, since the root system does not interact with the 0.6 m slip surface it would have no effect on the FOS of the infinite slope. However in the semicircular slip model, slice 1, which is situated at the toe of the slope, has a shallow slip plane so the bamboo would interact in this slice only. The effect of monospecific vegetation on three different slope angles, 25◦ , 35◦ and 45◦ using the infinite slope model was also modelled. Slip plane angles were the same as the slope angles. • Model 2—A mix of five species representing all species except big node bamboo present in the Phase 2 succession was modelled. As a comparison, the mean tensile root strength of all the species combined was also included in the model. To represent the change and development of the forest as the trees grow, the model was run by varying the numbers of roots that cross the slip surface and by increasing the root diameter, which varied between 1.5 and 10 mm. The effect of this being to decrease the tensile strength while increasing root diameter. One species was allocated to one slice of soil which had a width of 2 m. The order of species, repeated twice from the bottom of the slope to the top, was Idesia polycarpa, Betula laminifera, Litsea cubeba, Aralia elata and Carya cathayensis. Different orders of species were also tested. The infinite slope model was used with slope and slip surface angles of 35◦ . • Model 3—Loadings by single birch trees. It was assumed that from the different species found growing on the slopes, the species which would grow to a significant height was Betula laminifera. It is recognised that birch trees in general are fast growing, pioneer species and can grow up to 30 m quite rapidly (http://www.treesforlife.org.uk/tfl.birch.html) so would

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Table 4 Root biomass (g m−3 ) and percentage (in brackets) with regard to total biomass in the same diameter class at each soil depth and at each succession phase. Data are means ± standard error. Where letters in superscript differ, data are significantly different for roots in a given diameter class with regard to soil depth (p < 0.05). Soil depth (cm)

Root diameter class Total

<1 mm

1–2 mm

2–5 mm

5–10 mm

First phase of succession 0–15 16–30 31–45 46–60

7037 1380 577 386

± ± ± ±

1378a (75) 368b (15) 92b (6) 113b (4)

611 141 72 48

± ± ± ±

113a (70) 26b (16) 14b (8) 11b (6)

1305 361 247 137

± ± ± ±

146a (64) 51b (18) 39b (12) 22b (7)

385 255 258 178

± ± ± ±

121a (36) 55b (24) 64b (24) 90b (17)

4737 621 0 21

± ± ± ±

1318a (88) 386b (12) 0c (0) 5c (1)

Second phase of succession 0–15 16–30 31–45 46–60

2153 872 298 477

± ± ± ±

265a (56) 172b (24) 80b (8) 390b (12)

495 97 40 35

± ± ± ±

97a (74) 13b (15) 10b (6) 8b (5)

770 235 124 408

± ± ± ±

101a (50) 31b (15) 25b (8) 355b (26)

604 356 92 33

± ± ± ±

135a (55) 71b (33) 48c (9) 27c (3)

284 183 40 0

± ± ± ±

117a (55) 156b (36) 10c (8) 0 c (0)

Third phase of succession 0–15 16–30 31–45 46–60

4949 1958 1156 949

± ± ± ±

543a (55) 325b (22) 192b (13) 238b (10)

584 93 68 61

± ± ± ±

79a (73) 12b (12) 16b (8) 8b (7)

1067 430 320 265

± ± ± ±

70a (51) 76b (21) 48b (15) 36b (13)

2360 817 483 277

± ± ± ±

278a (60) 193b (21) 91c (12) 85c (7)

938 618 285 346

± ± ± ±

495a (43) 236b (28) 185b (13) 189b (16)

add additional weight to a slope in a relatively short timescale. Therefore to replicate loadings by a birch tree at different locations along the slope, the weight of a single tree was added to the slice weights at the base, middle and top of the slip zone. It was assumed that a 30 m high tree of 0.5 m diameter would have a weight of [ × d2 × h/4 × 10 kN/m3 = ] approximately 60 kN. As the slips are of a small scale and could feasibly be around a single tree, as shown in Fig. 1b, a 20 kN load was assigned to each slice. Only the semicircular model with a slope angle of 35◦ was used as loading any slice in the infinite slope model has no effect on the FOS as the slip surface remains constant. The FOS results of the slope with a mass of vegetation were compared to the results of the FOS when the root reinforcement was taken into consideration and when both factors were combined together.

3.4. Statistical analysis The normality of data was tested using Kolmogorov–Smirnov tests and data were log-transformed when they were not normally distributed. Root density, additional root cohesion data and soil properties (pure soil cohesion and pure soil friction angle) were analyzed using analysis of variance (ANOVA) and analysis of covariance (ANCOVA) with pair wise Tukey’s Studentized tests (HSD) to detect differences between the three succession phases, according to depth. Power regressions were carried out to determine the relationship between root tensile strength and diameter for each species separately and grouped together. Data were log-transformed, before statistical analysis, to reflect the

Fig. 2. Vertical distribution of mean root density (g m−3 ) according to succession phase was significantly different between the three phases at each depth (p < 0.001). Data are means ± standard error. Where letters in superscript differ, data are significantly different between soil depths at each phase (p < 0.05).

power relationship in non-linear regressions. ANOVA and ANCOVA with HSD were used to evaluate root tensile strength differences between different species. Data shown are mean ± standard error. 4. Results 4.1. Root biomass density Mean RBD (regardless of depth) was 2345 ± 469, 1087 ± 156 and 2253 ± 297 gm−3 at the first, second and third phases of succession, respectively. Mean RBD was significantly greater in the first and third phases of succession compared to the second phase (F = 7.38, p < 0.001, ANOVA, Fig. 2, Table 4). With regard to soil depth, RBD decreased significantly from the upper 0.15 m to the lower depths at all three sites (F = 46.81, p < 0.001, ANOVA, Fig. 2, Table 4). When the third site was compared to the second site, the increase in RBD was largely due to the presence of roots in diameter classes 2–5 mm and 5–10 mm. An increase of 17% and 26% in RBD was found in the two smallest root classes, respectively, whereas an increase of 72% and 74% occurred when RBD of root diameter classes 2–5 mm and 5–10 mm, respectively, was considered (Table 4). The percentage of roots from each diameter class was similar for big node bamboo at the first phase of succession. When RBD was calculated with regard to soil depth, i.e. over 0.6 m, the lowest RBD was found in the second succession phase (640 g m−2 ), and similar values were found in both the first (1407 g m−2 ) and third phases (1352 g m−2 ).

Fig. 3. Cumulative root density distribution of all roots (regardless of diameter class) was significantly greater in the upper layers of soil during the first phase of succession.

M. Genet et al. / Ecological Engineering 36 (2010) 265–275

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Table 5 Significant differences in root tensile resistance between species (+: significantly different where p < 0.05 and −: not significant). Species

Phyllostachys nidularia

Phyllostachys nidularia Betula laminifera Carya cathayensis Aralia elata Litsea cubeba Notaphoebe cavalieri Phoebe nanmu Cinnanomun wilsonii Idesia polycarpa

Betula laminifera

Carya cathayensis

Aralia elata

Litsea cubeba

Notaphoebe cavalieri

Phoebe nanmu

Cinnanomun wilsonii

+

+ −

+ + +

+ − − −

+ − − − −

+ − − − − −

+ − − − − − −

Idesia polycarpa + + + + + + + +

Table 6 Significant power regression equations for tensile resistance (MPa) with regard to diameter (mm) in roots from the dominant species at each phase of succession. Regressions were not significant for species not shown. Species

Regression equation

Betula laminifera Aralia elata Idesia polycarpa Litsea cubeba Carya cathayensis Phyllostachys nidularia

−0.63

y = 79.40x y = 93.08x−0.76 y = 14.34x−132 y = 64.36x−0.65 y = 74.28x−0.65 y = 32.42x−0.52

R2

p

0.86 0.81 0.80 0.77 0.55 0.27

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001

When root diameter was taken into consideration, 88% of 5–10 mm and 70% of <1.0 mm diameter roots were found in the top 0.15 m of soil in the first phase of succession (Table 4). In the second and third phases of succession, 74% and 73% of roots from the <1.0 mm diameter class, respectively, were also in the most superficial layer of soil (Table 4, Fig. 4). The remaining roots were also most abundant in the top 0.15 m of soil, but in slightly smaller quantities (43–60%, Table 4, Fig. 4). 4.2. Root tensile resistance Mean root tensile resistance for all species considered together followed a power regression (Tr = 53.70d−0.48 , R2 = 0.30, p < 0.001). When each species was examined individually (Table 4), significant differences in tensile resistance were found between some but not all species (F = 16.96, p < 0.001, ANCOVA, Table 5) with regard to diameter (F = 94.11, p < 0.001) (Table 6). 4.3. Slope stability analysis

Fig. 4. Cumulative root density distribution per root diameter class of all roots with regard to soil depth in (a) first, (b) second and (c) third phases of succession.

Analysis of slopes without vegetation for each succession phase using the measured soil friction angles (Table 3) for a 35◦ slope resulted in a FOS of 1.10 for the first phase of succession, 1.12 for the second phase and 1.06 for the third phase; all being representative of the marginal stability that was observed on these slopes (Stokes et al., 2007; Genet et al., 2008). When the mean soil friction angle of 19.6◦ was used, this gave a FOS of 1.07. As this value was deemed

Cumulative RBD with regard to soil depth differed between the three phases of succession and was significantly higher (75% of cumulative RBD) in the top 0.15 m of soil in the first phase compared to the equivalent depth in the later phases (55–57% of cumulative RBD Fig. 3). Cumulative RBD varied with root diameter (Fig. 4).

Table 7 FOS of monospecific vegetation (each species assumed to be growing over the full length of the slope) assuming 3.5 mm root diameter and five roots crossing the slip surface. Species

Soil only Aralia elata Betula laminifera Idesia polycarpa Litsea cubeba Carya cathayensis Phyllostachys nidularia Mean of all species combined together

25◦ infinite slope

35◦ infinite slope

45◦ infinite slope

Semicircular slip

FOS

% root reinforcement

FOS

% root reinforcement

FOS

% root reinforcement

FOS

% root reinforcement

1.46 1.53 1.53 1.47 1.52 1.53 1.46 1.52

– +4.8 +4.8 +0.7 +4.1 +4.8 0.0 +4.1

1.07 1.12 1.12 1.08 1.11 1.12 1.07 1.11

– +4.7 +4.7 +0.9 +3.7 +4.7 0.0 +3.7

0.89 0.92 0.92 0.89 0.91 0.92 0.89 0.92

– +3.4 +3.4 0.0 +2.2 +3.4 0.0 +3.4

1.08 1.15 1.15 1.08 1.13 1.14 1.09 1.13

– +7.5 +7.5 0.0 +4.6 +5.6 +0.9 +4.6

272

M. Genet et al. / Ecological Engineering 36 (2010) 265–275

Table 8 Mix of five species representing Phase 2 succession compared to the mean of all species combined together. FOS was calculated using different root diameters and numbers of roots to represent growth. Species

Root diameter (mm)

No roots

FOS

% of root reinforcement

Mixed species

1.5 3.5 5.0 7.5 10.0

4 5 6 7 8

1.08 1.10 1.13 1.19 1.28

0.9 2.8 5.6 11.2 19.6

Mean of all species combined together

1.5 3.5 5.0 7.5 10.0

4 5 6 7 8

1.08 1.11 1.15 1.25 1.42

0.9 4.7 7.5 16.8 32.7

representative of all three succession phases, all further analyses were compared to this value for the infinite slope models. For the semicircular slip model, the FOS for the parameters used was 1.08. The percentage increase (or decrease) due to root reinforcement was calculated based on a FOS of 1.07 or 1.08 for the infinite slope and semicircular slip models, respectively. In Model 1, for the infinite slope each species showed a small increase in FOS due to the presence of vegetation except bamboo which does not interact with the slip plane. The smallest increase in FOS was with Idesia polycarpa which only had a 1% increase, while the FOS with Betula laminifera, Aralia elata and Carya cathayensis increased by up to 5% on a 35◦ slope (Table 7). When slope angle was increased to 45◦ , the FOS decreased, whereas for a lesser slope angle (25◦ ) there was a percentage increase in the FOS (Table 7). The average tensile strength value of 29.4 MPa for 3.5 mm diameter roots resulted in a 3.7% increase in FOS due to the presence of roots. For the semicircular slip, increases in FOS were observed for all species except Idesia polycarpa, which showed no reinforcing effect by the roots whereas bamboo had a slight reinforcing effect at the toe of the slip (in slice 1). In Model 2, the FOS increased up to 20% as root diameter and number increased (Table 8). In comparison, when the same model was run using the average root tensile strength equation, the FOS increased by 32% for eight 10 mm diameter roots crossing the slip surface. However, there was no difference in root reinforcement when the root diameters were 1.5 mm (Table 8). When the model was run by changing the position of species along the slope, no change to the overall FOS of the slope was observed. In Model 3, the application of a 20 kN load to each slice of the analysis to represent the mass of a single birch tree resulted in a 7% increase in FOS when the trees were positioned at the base of the slope, and a decrease in FOS when the trees were positioned in the middle or at the top of the slope (Table 9). The reinforcement provided by root tensile resistance showed an increase in FOS for all positions along the slope. However, when the root reinforcement was combined with the mass of the vegetation, the only positive

increase in FOS was achieved at the base of the slope. The mass of the vegetation destabilised the slope in all other cases for a slope angle of 35◦ (Table 9). 5. Discussion Our study showed that RBD was highest during the first succession phase, when big node bamboo was the dominant species. In the later succession phases, mean RBD was lowest in the second phase but increased in the third phase, due in part to the presence of thicker roots. In the second phase of succession, which occurred just after big node bamboo had died back after flowering, trees were small and stem density was low, therefore explaining why RBD was low in this phase. By the time the third succession phase had been reached, both tree size and stem density had augmented, thus increasing RBD. Similar studies of root biomass in forests of different ages show that a peak in RBD can occur when trees reach the stage of canopy closure (Helmisaari et al., 2002; Claus and George, 2005; Fujimaki et al., 2007; Genet et al., 2008). We did not observe such a peak in our study, and in a study by Berish (1982) who measured total root biomass of roots <5 cm in diameter to a depth of 85 cm in three tropical forests aged 1, 8 and 70 years, it was found that total root biomass was estimated at 219, 1291 and 1555 g m−2 respectively. However, in our study, it was not possible to examine a succession phase where trees were older than 30 years, therefore a peak in root biomass may have occurred after this age. Other studies also noted an increase in RBD with tree age (Finér et al., 1997; John et al., 2001; Yanai et al., 2006). The increase in RBD with tree age observed in our study was partly due to the increase in thicker roots. In three forests of Pinus kesiya Royle Ex. Gordon, aged 6, 15 and 23 years, John et al. (2001) observed a significant increase in the biomass of roots 2–10 mm in diameter. These thicker roots will contribute towards tree anchorage which is more important in older trees. Yanai et al. (2006) compared three 19–27 year old broadleaf forests in northern hardwood stand and three older forests (56–69 year old) and also found that the biomass of roots 2–20 mm and <2.0 mm in diameter was 2.7 and 1.5 times greater in the older plantations, respectively. Yanai et al. (2006) concluded that the biomass of fine roots continued to increase once canopy closure had occurred. Such differences in root biomass can also be explained by the differences in vegetation which occur as the forest ages, with certain species taking advantage of, e.g. gaps in the canopy of increased shade. Therefore it is difficult to attribute effect of species on total RBD at any of the sites that we studied, without specific measurements taking into account species identification. Not only did RBD increase with tree age once the first succession phase had terminated, but the naturally regenerated forests we studied had significantly greater RBD compared to monospecific plantations of Cryptomeria japonica of similar ages and located

Table 9 Variation in FOS with both root reinforcement and mass of vegetation in relation to the position of Betula laminifera trees on a 35◦ slope (six roots of 5 mm diameter assumed to cross the slip surface). Slope position (Slice no.)

Root reinforcement only

% change in FOS

Mass of vegetation per slice (20kN) only

% change in FOS

Root reinforcement and mass of vegetation

% change in FOS

Soil only Base or toe (1) Middle (2) Top (3) Base and middle Middle and top Base and top All positions

1.08 1.11 1.14 1.11 1.18 1.18 1.15 1.22

– +2.7 +5.5 +2.7 +9.3 +9.3 +6.5 +13.0

– 1.16 0.82 0.62 0.91 0.59 0.73 0.68

– +7.4 −24.1 −42.6 −15.7 −45.4 −32.4 −37.0

– 1.19 0.84 0.63 0.95 0.61 0.75 0.70

– +10.2 −22.2 −41.7 −12.0 −43.5 −30.6 −35.2

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in the same area (Genet et al., 2008). RBD of the 30-year-old plantation was approximately four times lower than in the naturally regenerated forest. Similar results have also been found by Schmidt et al. (2001), who studied the cohesive reinforcement of soil due to roots in natural and planted forests in the Oregon Coast Range, USA. Schmidt et al. (2001) were also able to determine that landslides were more frequent in monospecific plantations compared to natural forests. In the south of China, Yang et al. (2004) found that when natural forests were replaced by plantations of conifers, the biomass, production and turnover of fine roots (<2 mm in diameter), as well as soil nutrients decreased significantly. Sundarapandian et al. (1999) suggested that when a natural, tropical forest is converted to monospecific plantations, soil fertility and organic matter diminishes, thus leading to the observed decrease in root biomass. The upper layer of soil is generally where most nutrients and organic matter are located (Lambers et al., 2008). Therefore, as we found, RBD was highest in this layer, with at least 50% of all roots in the top 0.15 m and in the case of big node bamboo, with 88% of all roots in the upper layer of soil. This rhizomatous species has already been found to have a very dense but shallow root system, with a mean maximum rooting depth of 0.16 m (Stokes et al., 2007). This pattern of distribution did not vary with tree age in the second and third succession phases, contrary to results found in similar studies. Several authors have found that fine root biomass increases with soil depth as trees age (Persson, 1978; Srivastava et al., 1986; Yin et al., 1989; John et al., 2001), probably because in young trees fine roots have not had the time to penetrate the deeper soil layers (Yin et al., 1989). However, other authors have shown that fine root biomass increases in the upper soil layers with tree age, as organic matter and therefore nutrients augment (Berish, 1982; Gale and Grigal, 1987; Bouillet et al., 2002; Yanai et al., 2006). Although we did not find any differences in RBD with depth over time, some variation was observed with regard to root diameter. The thinnest roots (<1.0 mm) were most abundant in the top 0.15 m of soil. As these roots are largely responsible for water and nutrient uptake, they will be present in larger quantities in this soil layer (Yang et al., 2004). The first succession stage in this area, where big node bamboo was the dominant species, will be highly susceptible to failure through landslides, as RBD was mostly in the top 0.15 m of soil. As roots must cross the slip surface to stabilise a slope (Greenwood et al., 2004; Cammeraat et al., 2005; Van Beek et al., 2005), and the slip surface is located at a mean depth of 0.6 m in this region, slope stability is compromised. Studying slope failures along the same valley, Stokes et al. (2007) observed that landslides were highly frequent in big node bamboo forests, but although this species may not be useful in preventing landslides, its dense and shallow root system could be useful for fixing soil against erosion or overland flow. As big node bamboo plays a negligible role in slope stability, it can be argued that natural regeneration of a bare slope leads to instability in the early years of succession at the sites we studied. For monospecific stands of C. japonica in the same region, it can be seen that there is an increase of 27% due to vegetation in the FOS of a 9 year old stand (Genet et al., 2008). This increase is largely due to the high density (6112 trees ha−1 ) at which young trees were planted. As C. japonica stands were thinned over time, and gaps appeared between trees where root density was low, the FOS decreased significantly. However, in the naturally regenerated forest we examined, for similar stem densities and when vegetation mass was not considered, FOS increased over time as the number and diameter of roots increased. The contribution of vegetation to slope stability has been determined by several authors who have calculated the FOS of slopes with and without plant roots present in the soil (Greenwood, 2006;

273

Bibalani et al., 2007; Tosi, 2007; Danjon et al., 2008; Genet et al., 2008). However, vegetation is usually considered as monospecific and even-aged. Very rarely have spatial and temporal factors been taken into consideration, even though such situations are far more realistic and can influence slope stability (Cammeraat et al., 2005; Sakals and Sidle, 2004; Genet et al., 2008). In this study, we were unable to identify plant species from individual roots, therefore the RAR values were for all species combined. To examine more accurately species effect on slope stability, it would be necessary to measure root architecture and determine mean RAR for a given species before calculating FOS (Danjon et al., 2008). The position of a plant or tree on a slope can also influence slope stability, depending on the type of root architecture and whether the plant is at the top, toe or in the middle of the slope (Kokutse, 2008; Kokutse et al., 2006; Norris et al., 2008). Our study showed that positioning trees at the toe of the slope increased the FOS by 7%, while placing trees in the middle or at the top of the slope decreased the FOS by up to 43%, for a 35◦ slope angle. Although the weight value used for the single tree was indicative, the results are useful in that they indicate that single trees or groups of trees as in a forest situation destabilise the slope, particularly when they were positioned at the top of the slope and there was no additional reinforcement on the lower slope and if the slope was steep. However, using real root architecture for each species in combination with root cohesion, and the hydrological influences of the vegetation would provide more accurate results. Although Wu’s (1976) model has been shown to poorly estimate cr when considering 3D root architecture (Danjon et al., 2008), or the mechanism by which roots fail (Pollen and Simon, 2005), it is still used widely when considering the contribution of thin roots to slope stability (Bischetti et al., 2005; Tosi, 2007; Genet et al., 2008). However, the largest source of error in calculations of the contribution of vegetation to slope stability is probably due to the calculation of a mean value of FOS of a forested slope. The position of trees along a slope, as well as their size and density (Genet et al., 2008), influences FOS more than root tensile strength. In our study we did not consider root architecture or changes in pore water pressure due to the vegetation which would significantly affect the FOS particularly during the monsoon season. Slope stability should be increased when soil is occupied to a maximum by roots in the zone of the potential slip surface. Therefore, a diversity of root system shapes, each with different rooting strategies, would probably be more effective in reinforcing soil on a steep slope than a monospecific stand with roots competing for the same space (Stokes et al., 2009b). Acknowledgements Thanks are due to L. Paquet, A. Lucas (ENSAM, France), J. Ji (Beijing Forestry University, China) and the Sichuan Academy of Forestry, China, for help with fieldwork. John Greenwood (Nottingham Trent University, U.K.) is thanked for his advice on aspects of the modelling. This project was funded by a Bourse Dufrenoy, a LIAMA-CASIA (Beijing, China) seed project and INRA-MRI. AMAP (Botany and Computational Plant Architecture) is a joint research unit which associates CIRAD (UMR51), CNRS (UMR5120), INRA (UMR931), IRD (2M123), and Montpellier 2 University (UM27); http://amap.cirad.fr/. References Anon., 1996. Survey, observation and analysis of terrestrial biocommunities. Standards Press of China, Dongming, Beijing, China (in Chinese). Berish, C.W., 1982. Root biomass and surface area in three successional tropical forests. Can. J. For. Res. 12, 699–704.

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