Influence of scrubs on runoff and sediment loss in soils of Mediterranean climate

Catena 57 (2004) 91 – 107 www.elsevier.com/locate/catena

Influence of scrubs on runoff and sediment loss in soils of Mediterranean climate M.A. Casermeiro a,*, J.A. Molina b,*, M.T. de la Cruz Caravaca a, J. Hernando Costa a, M.I. Hernando Massanet a, P.S. Moreno b a

Soil Science Department, Universidad Complutense de Madrid, Plaza Ramo´n y Cajal s/n, E-28040 Madrid, Spain b Plant Biology Department, Universidad Complutense de Madrid, Plaza Ramo´n y Cajal s/n, E-28040 Madrid, Spain Received 25 March 2002; received in revised form 2 June 2003; accepted 17 June 2003

Abstract Scrubland communities are the most common plant communities in eroded areas with Mediterranean climate. These protect the soil in different ways including the interception of raindrops (which lowers their erosive capacity) and the provision of organic carbon (necessary for the formation of organomineral aggregates). Vegetation were analysed and rainfall simulations performed in 29 natural plots in areas of significant erosion in the Madrid (Spain) region. The results show that plant cover is the main factor reducing surface runoff and the movement of sediments. Vegetation structure is also important, with pluri-stratified communities offering more protection against water erosion than mono-stratified communities. The plant growth forms were also found to be influential. Nanophanerophytes as Rosmarinus officinalis were the most efficient of those studied. Biodiversity did not seem to be important in soil protection in the studied area. D 2003 Elsevier B.V. All rights reserved. Keywords: Soil erosion; Rainfall simulation test; Plant growth form; Biodiversity; Spain

1. Introduction Erosion is one of the most important soil problems in areas with Mediterranean-type climate. In these seasonally dry environments, the relationships between the soil and plant

* Corresponding authors. M.A. Casermeiro is to be contacted at Tel.: +34-913942088; fax: +34-913941759. E-mail addresses: [email protected] (M.A. Casermeiro), [email protected] (J.A. Molina). 0341-8162/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0341-8162(03)00160-7

92

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

communities play a major role in preventing soil erosion. Plant communities protect the soil because the canopy and litter intercept raindrops, reducing of their kinetic energy, while the organic carbon they provide increases the formation of soil aggregates. Soil structure is closely related to the movement of water in the soil through physical properties such as water storage capacity, bulk density, porosity and erodibility (Truman and Bradford, 1990; Deuchras et al., 1999). When forests decline, or when cultivated lands are abandoned, scrub communities become one of the most important vegetation types. In the Iberian Peninsula, scrublands are wide spread (Rivas-Martı´nez, 1987) and perform a role in preventing soil erosion (Bochet et al., 1999). Several works have studied the role of different factors in soil erosion, such as rock fragmentation (Cerda´, 2001), soil use (Lasanta et al., 2000), raindrop interception by plant communities (Riezebos and Epema, 1985) or vegetation cover (Elwell and Stocking, 1976; Thornes, 1990; Viles, 1990). Other works focus on the importance of the patch mosaic distribution and plant growth forms (Cerda´, 1997; Sa´nchez and Puigdefabregas, 1994). The influence of scrub biodiversity has received less attention. The main objective of this paper is to study the relationships between composition and structure of natural scrub communities involved in soil erosion processes in a Mediterranean area.

2. Site description The study area is located in the Arroyo de la Vega valley in the south east of the Region of Madrid known as La Alcarria de Madrid. This area is catalogued as a significant erosion zone (ZES) by regional environmental authorities (Comunidad Auto´noma de Madrid, 1997). The study site is flat (average altitude 860 –880 m) known as pa´ramo, crossed by the stream Arroyo de la Vega (Fig. 1), with a step between the pa´ramo and the stream, sloping at 30%. The main geological materials are: limestones and crust on the pa´ramo, marls, lime, clay and gypsum of the upper and medium Miocene on the slope, and mainly quaternary materials (sands, gravels and clays) in the flat bottom of the river (ITGME, 1990). In the study area, it was not possible to identify crust or caliche. Climatic data were supplied by the Arganda del Rey meteorological station, the closest to the study area. According to Rivas-Martı´nez (1997), the climate of the study area is defined as Mediterranean pluviseasonal-oceanic. It lies in the upper Mesomediterranean low dry bioclimatic belt. The average annual rainfall is 428 mm, and shows an ombric regime: autumn>winter>spring>summer. There are 2 months (July and August) with imperceptible rainfall ( < 6 mm) and 76 days with rain per year. Maximum erosion risk is in the autumn after the dry summer season. The representative soil units of this valley are: chromic luvisols on the paramo, calcisols, rendsic, calcaric and lithic leptosols plus calcaric regosols and cambisols on the slope as well as gypsisols where gypsum appears, and fluvisols near the river (Comunidad Auto´noma de Madrid, 2000a,b).

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

93

Fig. 1. Case study area.

The natural vegetation includes evergreen oak forest of Quercus rotundifolia and Quercus faginea, shrublands characterised by Quercus coccifera, steppe grasslands of Stipa tenacissima, and Rosemary (Rosmarinus officinalis) and thyme (Thymus vulgaris and Thymus zygis) scrublands (Table 1) (Rivas-Martı´nez, 1982). The valley has been traditionally used for agriculture: olive groves and cereals crops on the pa´ramo and slopes, and small areas of irrigated intensive cropping in the valley bottom. The area was famous for sheep and cattle in the past, but stock raising is now much in decline. Since the beginning of the XX century, there has been an increase in the total cultivated area. Abandoned cultivated zones lie mainly on the slope. Most of the vacated fields have been colonised by the mentioned scrub communities.

3. Materials and methods Twenty-nine scrub communities plots were selected on intermediate slope in the Arroyo de la Vega basin according to physiognomical criteria in order to be spatially representative of the natural vegetation. Positions were recorded using a GPS. 3.1. Vegetation sampling Each plot covered 25 m2, the minimum area for collecting most information in scrub communities (Barkman, 1989) (Fig. 2). The gradient of every plot was measured with a

94

Table 1 Plot characteristics U.T.M.a coordinates zone 30N

Plot

a

Y

480 693 480 701 480 758 481 568 481 506 481 566 480 751 480 788 480 053 480 037 447 050 483 561 486 499 479 544 480 313 479 544 479 317 479 792 446 850 446 852 447 265 482 142 481 618 481 575 446 992 446 994 446 852 482 704 480 636

4 468 324 4 468 306 4 468 234 4 469 273 4 469 296 4 469 210 4 468 933 4 468 928 4 467 641 4 467 647 4 448 040 4 464 001 4 464 001 4 465 326 4 465 818 4 465 326 4 465 145 4 469 307 4 448 105 4 448 107 4 448 320 4 472 594 4 472 412 4 472 201 4 480 730 4 480 732 4 482 605 4 463 388 4 469 628

ED-50 = European Datum 1950. b 10% Lichen sp. cover. c 7% Nostoc sp. cover.

Slope, %

Plant cover, %

Litter, %

Surface stoniness, %

Soil type

Vegetation type

27 20 20 15 30 25 30 25 10 10 21 20 23 22 30 33 27 18 20 37 27 20 15 30 15 20 32 21 17

20 100 100 90 95 100 100 70 90 100 30 80 40 100 80 100 70 20 100 80 100 75 90 20 100 20 80 50 70

5 100 0 0 <5 <5 10 10 10 80 0 10 1 0 5 0 5 5 5 5 50 5 70 0 70 40 10 5 5

35 5 5 5 2 8 2 10 0 10 80 70 50 0 0 5 40 10 10 15 30 10 10 30 10 50 30 40 50

Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Rendsic Leptosol Rendsic Leptosol Rendsic Leptosol Gypsiric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Gypsiric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Luvic calcisol Haplic calcisol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol Calcaric Leptosol

Thymus zygis scrubland Rosmarinus officinalis scrubland Thymus vulgaris scrubland Sub-nitrophilous grass Sub-nitrophilous grass Sub-nitrophilous grass Thymus zygis scrubland Thymus zygis scrubland Thymus vulgaris scrubland Rosmarinus officinalis scrubland Thymus vulgaris scrubland Thymus scrubland with Stipa Thymus scrubland with Stipa Thymus zygis scrubland Thymus zygis scrubland Thymus zygis scrubland Thymus vulgaris scrubland Thymus scrubland with Stipa Thymus zygis scrubland Thymus zygis scrubland Thymus zygis scrubland Thymus zygis scrubland Thymus zygis scrubland Thymus zygis scrubland Rosmarinus officinalis scrubland Thymus vulgaris scrubland Thymus zygis scrubland Thymus zygis scrubland Thymus vulgaris scrubland

Moss cover, %

7

7 7 15 15

7 7 15 7 15 15b

15 c

15

7

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

X

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

95

clinometer. Surface stoniness and litter were estimated in percentage. In each plot, a releve´ of the vegetation was made according to the methodology developed by BraunBlanquet (1979) and Ge´hu and Rivas-Martı´nez (1982), using the centralised replicate sampling procedure (Mueller-Dumbois and Ellenberg, 1974). The frequencies of different species were measured in each plot for calculation of the Margaleff index, (Magurran, 1996; Grytnes, 2000). Biomass was measured using an objective indirect method. All aerial biomass was collected in a sub-plot of 6.25 m2, in the left triangle sector of the plot. Fresh plant biomass was heated in an oven (85 jC) until its mass was stable. Biomass data were related to the plant life forms according to the classification of Ellenberg and Mueller-Dombois (1967). Annual species were not included in the sampling since they are unimportant in autumn when the maximum risk of erosion occurs. The nomenclature used for vascular plants was that of Castroviejo et al. (1986/ 2001), except for Q. rotundifolia Lam. 3.2. Soil sampling and rainfall simulation test In each plot, a surface soil sample (0– 20 cm) was collected following FAO recommendations (FAO, 1977). Particle size distribution, organic carbon, pH, electrical conductivity, total nitrogen and equivalent calcium carbonate were analysed following ISRIC recommendations (ISRIC, 1993). The content of calcium carbonate, extractable in ammonium oxalate, was analysed following NFX-82 methods (Franc¸aise, 1982).

Fig. 2. An example of plot study area.

96

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

Fig. 3. Rainfall simulation plot in Thymus zygis scrub.

Fig. 4. Rainfall simulation plot in Rosmarinus officinalis scrub.

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

97

To compare the erosion preventing behaviour of different scrub communities, rainfall simulation tests were performed on wet soils (most tests were carried out in the autumn after a period of natural rain), using a portable Kamphorst rainfall simulator, in sub-plot of 0.094 m2 (Figs. 3 and 4). Rain intensity was 56.55 mm h 1 and kinetic energy 3.92 J m 2 mm 1 (Kamphorst, 1987). This intensity represent 15% of the total rainfall precipitation, these data are significant in Mediterranean type of climate. Rainfall time was continued for at least 5 min until good statistical runoff curves were obtained. Sediment was collected, dried (105 jC) and weighed. After the rainfall simulation test, two core samples were taken in each plot according to Smith and Mullins (1991). These were used for determining the physical properties of the soil including bulk density, particle density, porosity and water storage capacity. 3.3. Statistical analysis Data normality was tested and the most appropriate transformation selected by the Box and Cox (1964) test (sediment yield, organic carbon, nanophanerophyte, chamaephyte and hemicryptophyte biomass, and equivalent calcium carbonate). Statistical analysis of the runoff curves and Multivariate analyses (Principal component Analyses) including soil, rainfall and vegetation data, were performed using Statgraphics Plus 5.1 programme. Correlation analysis was performed by SPSS 11.0.

4. Results and discussion Most of the plots are calcaric Leptosols, soils that are vulnerable to erosion. These soils are well drained and show a fine structure and high biological activity. Plots 4, 5 and 6 are Rendsic Leptosols and present mollic horizons with a better structure than the calcaric Leptosols. Plots 21 and 22 are calcisols due to secondary recarbonization processes. Plot 21 is a Luvic calcisol with the highest value of clay content (>50%) (Table 2), whereas plot 22 is a Haplic calcisol. The plots showed variable plant cover, litter and surface stoniness (Table 1). The main characteristics of the surface soil horizons are shown in Table 2. These were carbonated (mean calcium carbonate equivalent 417.4 g kg 1), moderately basic with a mean pH of 8.2, and moderately electroconductive (x = 0.29 dS m 1, except plots 7 and 15, corresponding to Gypsiric Leptosols in T. zygis areas with Ononis tridentata, where increased electroconductivity was attributed to gypsum). Soil textures were mainly balanced with mean values of 20.8% coarse sand, 31.5% fine sand, 19.4% silt and 28.7% clay. The surface horizon showed a water retention capacity of 232.8 g kg 1, a mean porosity of 45%, a real density of 2.35 Mg m 3 and a bulk density of 1.23 Mg m 3. Organic carbon values were moderately high (x = 30.5 g kg 1), especially in plots 4, 5 and 6 which had contents of 71.8, 84.4 and 85.5 g kg 1, respectively. One hundred seven plant species were identified in five types of scrubland in the 29 plots studied: T. zygis scrubs, T. vulgaris scrubs, Thymus with Stipa scrubs, R. officinalis

98

Table 2 Main values of particle size distribution, physical properties, pH, electrical conductivity, carbonates and organic carbon of soils sampled Plot

Coarse sand

Fine sand

Silt

Clay

15.7 17.2 15.7 18.7 21.5 17.9 25.8 31.3 17.3 25.9 35.5 23.0 19.2 18.7 31.5 27.5 18.5 22.0 19.9 24.0 15.1 25.4 19.0 15.1 19.7 10.7 24.0 11.3 16.9 20.8 5.9

21.3 23.0 35.7 30.3 28.7 28.7 30.8 21.4 36.9 38.9 29.8 39.0 17.6 47.2 29.1 48.2 33.7 36.3 35.5 30.6 12.9 23.7 46.0 38.5 32.2 22.5 29.7 38.4 25.9 31.5 8.6

43.3 26.2 14.9 20.8 18.2 21.3 32.1 21.5 7.0 7.5 7.6 12.4 35.8 3.0 29.9 1.8 7.0 15.9 21.0 22.2 20.3 22.6 9.2 14.6 20.4 31.6 26.5 22.4 25.2 19.4 10.1

19.7 33.6 33.7 30.2 31.7 32.0 11.4 25.9 38.8 27.8 27.2 25.6 27.5 31.1 9.5 22.5 40.8 25.8 23.7 23.2 51.7 28.3 25.8 31.9 27.8 35.2 19.9 27.9 32.1 28.3 8.2

Mineral density, Mg m 3

Bulk density, Mg m 3

Porosity (%)

Water retention ( 33 kPa), g kg 1

pH

2.73 2.25 2.26 2.11 2.32 2.01 2.23 2.01 2.05 2.14 2.4 2.26 2.81 2.47 2.59 2.44 2.39 2.45 2.42 2.32 2.4 2.34 2.34 2.54 2.56 2.35 2.12 2.98 2.21 2.36 0.23

1.35 1.24 1.5 0.94 1.17 0.94 1.25 1.25 1.46 1.38 1.29 1.5 1.06 1.47 1.18 1.54 1.64 1.58 0.99 1.29 1.21 1.18 1.1 1.38 1.44 1.08 1.13 1.18 1.26 1.28 0.19

50 45 34 56 50 53 44 38 29 35 46 34 62 41 55 37 31 36 59 45 49 50 53 46 44 54 47 60 43 45 9

195 231 214 380 309 221 270 299 220 279 259 204 394 225 256 64 116 177 298 212 232 322 313 129 163 220 181 211 176 233.5 73.7

8.3 8 8.2 8.1 8 8 8 7.9 8.1 8.2 8.2 8.2 8.5 8.3 7.9 8.2 8 8.4 8.1 8.3 8.2 8.3 8.2 8.3 8.4 8.3 8.2 8.3 8.4 8.2 0.2

EC, dS m

0.22 0.21 0.20 0.17 0.20 0.21 1.28 0.61 0.20 0.16 0.15 0.12 0.13 0.21 1.96 0.20 0.20 0.15 0.20 0.19 0.16 0.16 0.16 0.15 0.15 0.16 0.16 0.17 0.15 0.29 0.39

1

CaCO3 equivalent, g kg 1

CaCO3 extractable, g kg 1

Organic carbon, g kg 1

483.6 488.2 453.4 411.5 302.2 416.2 474.3 474.3 232.5 304.6 397.6 476.6 476.6 474.3 385.9 462.7 281.3 118.6 455.7 460.3 318.5 455.7 485.9 495.2 392.9 488.2 481.3 483.6 472 417.4 92.8

39.6 206.4 110.4 120.6 66 118.8 66.2 72 82.8 117.6 96 144 48 128 86 145 140 32 140 146 146 140 79 77 61 66 63 43 61 97.98 42.48

17.2 31 23.6 71.8 84.4 85.5 26.4 21 39.6 25.5 28.8 35.5 22.5 27.9 25.6 23.3 24.6 12.1 27.5 19.9 41.6 14.1 34.5 8.5 19.8 27.3 40.7 9 15.8 30.52 19.41

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Average STD

Particle size distribution, %

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

99

scrubs and subnitrophilous grasslands. The dominant biotopes were chamaephytes and hemicryptophytes, which were responsible for the biodiversity of the plots. These communities were quite diverse with mean Margaleff values of 4.27; the maximum was 5.88 in plot 20 (Table 3). Biomass varied between 0.09 and 1.47 k m 2, dominated by chamephytes and hemicryptophytes, and on occasion nanophanerophytes, and the bottoms of young Mediterranean oaks, holm oaks and Q. coccifera which provided a lot of biomass compared to other biotypes. 4.1. Rainfall test Table 4 shows the results of the rainfall tests. Figs. 5– 10 show the variable behaviour observed. Areas of Rosemary scrublands and subnitrophilous pastures showed the least Table 3 Number of species, individuals and biomass by growth form type, and Margaleff Diversity index for every plot Plot Number of growth form species

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1

Biomass, kg ha

Number of growth form individuals

Ch

H

P

Others

Total

Ch

H

P

Total

Ch

H

P

Total

13 13 13 4 5 9 14 14 10 8 13 16 15 20 14 18 13 13 15 17 17 18 15 15 7 10 20 11 9

4 8 13 5 6 6 5 9 9 4 17 7 6 9 5 15 9 7 9 11 15 8 5 7 3 6 13 5 10

4 6 3 0 0 0 1 2 2 4 2 4 3 4 3 3 2 5 2 3 4 2 4 3 5 5 4 2 3

0 0 2 3 1 1 0 1 1 0 0 0 2 2 0 1 1 1 1 1 0 1 0 0 1 1 0 3 4

21 27 31 12 12 16 20 26 22 16 32 27 26 35 22 37 25 26 27 32 36 27 24 25 16 22 37 21 26

0.041 0.044 0.054 0.008 0.003 0.199 0.087 0.167 0.308 0.092 0.145 0.165 0.140 0.206 0.111 0.145 0.424 0.115 0.238 0.191 0.080 0.172 0.030 0.021 0.020 0.064 0.126 0.047 0.096

0.002 0.001 0.104 0.016 0.177 0.008 0.468 0.029 0.037 0.000 0.008 0.062 0.011 0.054 0.028 0.067 0.032 0.004 0.046 0.014 0.027 0.083 0.470 0.005 0.000 0.000 0.052 0.004 0.022

0.002 0.616 0.000 0.000 0.000 0.000 0.042 0.117 0.007 0.927 0.000 0.026 0.005 0.386 0.051 0.069 0.344 0.214 0.004 0.064 0.037 0.073 0.016 0.077 1.456 0.035 0.006 0.001 0.000

0.045 0.661 0.158 0.024 0.180 0.207 0.596 0.312 0.352 1.019 0.154 0.253 0.156 0.646 0.190 0.282 0.800 0.333 0.288 0.268 0.144 0.328 0.516 0.104 1.476 0.099 0.185 0.053 0.117

41 147 70 134 68 74 96 315 354 97 127 540 323 220 106 205 330 328 180 150 357 204 97 291 80 218 296 491 275

16 18 124 25 63 43 40 54 68 5 139 315 38 162 78 267 115 63 50 39 254 88 54 47 0 4 242 80 207

3 30 2 0 0 0 1 4 6 28 3 34 4 13 19 51 33 84 2 5 38 13 4 18 19 28 8 2 1

60 195 196 159 131 117 137 373 428 130 269 889 365 395 203 523 478 475 232 194 649 305 155 356 99 250 546 573 483

Ch: Chamaephyte; H: Hemicryptophyte, P: Phanerophyte. MDI: Margaleff Diversity index.

MDI

4.88 4.93 5.68 2.17 2.26 3.15 3.86 4.22 3.47 3.08 5.54 3.83 4.24 5.69 3.95 5.75 3.89 4.06 4.77 5.88 5.41 4.55 4.56 4.09 3.26 3.80 5.71 3.15 4.05

100

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

Table 4 Sediment yield, correlation coefficient, probability level and regression equations of rainfall simulation test Sample

Sediment yield, gm 2

Correlation coefficient

P

Regression equation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1445 2.7 6.8 8.9 15.6 7.9 0.3 9.7 0.0 8.0 10.9 5.7 204.3 0.0 58.1 47.9 17.1 267.8 2.4 12.1 0.9 7.7 33.8 110.4 106.6 107.8 33.1 425.9 114.4

0.736 0.836 0.968 0.835 0.736 0.905 0.932 0.944 1.000 0.743 0.657 0.748 0.834 0.894 0.908 0.795 0.762 0.873 0.817 0.835 0.871 0.927 0.860 0.881 0.905 0.737 0.848 0.856 0.738

< 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.05 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01

1 = 66.8065 + 12.5209*sqrt(TIME) 2 = 14.8773 + 5.20802*sqrt(TIME) 3 = 1.10822 + 3.01425*sqrt(TIME) 4 = 37.1625 + 14.8131*sqrt(TIME) 5 = 23.6967 + 5.7611*sqrt(TIME) 6 = 11.2997 + 5.64751*sqrt(TIME) 7 = 1.41042 + 0.438471*sqrt(TIME) 8 = 18.0723 + 9.11188*sqrt(TIME) y=0 10 = 35.726 + 7.38717*sqrt(TIME) 11 = 20.582 + 4.07858*sqrt(TIME) 12 = 124.394 + 15.105*sqrt(TIME) 13 = 26.6497 + 17.6323*sqrt(TIME) 14 = 1.54545 + 0.350814*sqrt(TIME) 15 = 8.18946 + 12.9132*sqrt(TIME) 16 = 53.047 + 13.1377*sqrt(TIME) 17 = 57.0261 + 9.65581*sqrt(TIME) 18 = 95.8686 + 20.0353*sqrt(TIME) 19 = 12.8751 + 8.37287*sqrt(TIME) 20 = 22.6831 + 4.19743*sqrt(TIME) 21 = 3.19858 + 1.08063*sqrt(TIME) 22 = 22.9318 + 15.1185*sqrt(TIME) 23 = 0.466884 + 7.64993*sqrt(TIME) 24 = 57.6448 + 19.502*sqrt(TIME) 25 = 16.4299 + 19.388*sqrt(TIME) 26 = 47.3729 + 8.16772*sqrt(TIME) 27 = 54.8551 + 11.7662*sqrt(TIME) 28 = 54.7081 + 16.2891*sqrt(TIME) 29 = 48.9616 + 11.2049*sqrt(TIME)

runoff. Thyme areas gave very heterogeneous results, except for thyme with Stipa (Fig. 3), which gave very homogeneous figures. Independent of the vegetation type, time to runoff seems to be quite similar despite of the different behaviour in infiltration. Perhaps the fact that the soil was always wet and in most of the times also the plants (because the rainfall

Fig. 5. Runoff curves. T. zygis scrub I.

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

Fig. 6. Runoff curves. T. zygis scrub II.

Fig. 7. Runoff curves. T. zygis scrub with Stipa.

Fig. 8. Runoff curves. T. vulgaris scrub.

Fig. 9. Runoff curves subnitrophilous grassland.

101

102

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

Fig. 10. Runoff curves. R. officinalis scrub.

test were performed after a rainfall episode) should explain the origin of the quick start of the runoff. In general terms, plant cover is inversely related to runoff (r = 0.63; p < 0.01) and the loss of sediments (r = 0.73; p < 0.01). Further, runoff is directly related to surface stoniness (r = 0.52; p < 0.05), while litter is inversely related to the gradient (r = 0.45; p < 0.05). In addition, significant differences in soil erosion can be seen in the same type of scrub community. Several factors should be the responsible of this behaviour as the slope gradient, the presence of moss or the structure of the plant community mono- or pluri-stratified. Thus, in Figs. 1 and 2 with the same type of plant species (T. zygis scrub type), plot 14 present nil runoff attributed to the presence of moss meanwhile plots 16, 1 and 24 present the highest values of runoff attributed to slope higher than 27%. The same reason (high slope) would be responsible in plots 17 and 26 with T. vulgaris scrub type. 4.2. Texture and run-off Due to the high variability of runoff curves, one first approach was to identify if there existed any statistical relationships between texture data, runoff and sediment yield (Table 5). There is not a significant correlation between textural data and runoff. Only a slight correlation between the amount of silt and the sediment wash out was found. These results focus on the importance of soil structure versus soil texture. The difference of structure can explain the variability of sediment wash-out data, as occurs in plots 2 and 24. Although in some conditions, textures seem to be a good indicator for soil erosion; other authors (Barthe`s and Roose, 2002; Le Bissonais, 1996; Amezketa et al., 1996) proposed the Table 5 correlation (Pearson) between texture data, sediment yield out and total runoff Coarse sand Fine sand Silt Clay Sediment yield-out Total run-off

0.60 0.192 0.544** 0.82 0.334

Fine sand

0.725** 0.203 0.110 0.076

Significance level notations are: *p < 0.05, **p < 0.01.

Silt

0.326 0.139 0.386*

Clay

0.226 0.132

Sediment yield-out

0.456*

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

103

Fig. 11. Principal component analysis plot.

structure as one of the more important indicators of soil erosion. In addition, the formation of tiny crust on the soil surface can modify quickly the infiltration rates even on sandy soils (Kidron and Yair, 1997, 2001). In this case, the carbonated origin of the parent materials plus the concentration of organic carbon appears to be important in the formation of soil aggregates. Further studies would be important in order to clarify the relation between soil structure and vegetation type. 4.3. Statistical analysis To determine which variables most influenced runoff and sediment loss, an analysis was made of their principal components (Fig. 11), which covered 78.19% of the variability in the first four axes. The first two include 51.47% of the total variability (Table 6A, Fig. 11). Axis 1 shows the positive relationship between plant cover and organic carbon content, as well as the negative relationship between surface runoff, the movement of sediment and surface stoniness. Axis 2 shows the positive relationship between biomass content—and to a lesser degree the loss of sediment—and the gradient (Table 6B). It also demonstrates the inverse relationship between nanophanerophyte and chamephyte biomass and litter. These data indicate that, independent of the biodiversity of the shrub communities, the factors that most protect against erosion are plant cover

Table 6A Results of principal component analysis of in selected variables Component number

Eigenvalue

Percent of variance

Cumulative percentage

1 2 3 4 5

3.227 1.920 1.606 1.065 0.942

32.27 19.20 16.06 10.65 9.42

32.27 51.47 67.54 78.19 87.61

104

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

Table 6B Component weights of the variables in the components Variable log OC Vegetation cover Litter log Biom P Sinbiom Ch MDI Runoff log sediment Surface stoniness Slope

Component 1 0.437 0.468 0.156 0.147 0.167 0.011 0.451 0.446 0.328 0.060

Component 2 0.355 0.154 0.312 0.598 0.472 0.133 0.070 0.227 0.164 0.262

Component 3 0.126 0.003 0.360 0.060 0.101 0.641 0.248 0.155 0.008 0.585

Component 4 0.126 0.104 0.454 0.277 0.567 0.139 0.094 0.364 0.327 0.317

and soil litter. These data agree with the proposed by Elwell and Stocking (1976). The gradient appears to be less important than plant cover in the generation of surface runoff. If the plots are classified (cluster analysis) (Fig 12) with respect to the same variables selected for the analysis of principle components (Table 7), five groups can be seen. Group I, the most numerous with 13 elements of different types of thyme scrubland, shows the greatest surface runoff and sediment loss of as well as the least cover and organic carbon content. Group II, with five elements which include all the different types of Rosemary scrubland and two of thyme, shows low runoff and sediment loss, a lesser gradient and greater amounts of litter. Group III, which contains five elements of thyme scrubland types, shows the least runoff and sediment loss, high biodiversity and the greatest plant cover. This group is the most efficient at intercepting rain, which may be due to the pluri-stratified nature of these plant communities. In plot 9 (in which no runoff was seen), there was even a layer of mosses. Group IV, formed by communities of subnitrophilic grass areas, showed greater runoff than Group II. However, it showed no sediment loss. This group has high plant cover and the greatest quantity of carbon on its flat surfaces. Group V contains thyme scrubland with a greater plant cover than that of Group I, and shows small loss of sediments despite steeper gradients. The structure and floral composition of shrub communities are essential in understanding the development of degraded areas. In areas with similar biodiversity, the elements which most protect against erosion are directly linked to the interception of raindrops.

Fig. 12. Cluster dendrogram.

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

105

Table 7 Cluster analysis centroids Cluster

Litter

log OC

1 2 3 4 5

7.38 74.00 5.00 1.33 5.00

0.26 0.47 0.45 0.90 0.42

log Biom P 2.16 2.66 1.72 1.0 1.64

log sediment

MDI

Runoff

0.82 0.08 1.27 0.03 0.38

4.17 4.24 4.69 2.52 5.78

2680.54 4.24 4.69 2.52 5.78

Sinbiom Ch 0.41 0.05 0.78 0.84 0.82

Slope

Surface stoniness

Vegetation cover

23.0 17.4 20.4 23.3 34.0

36.53 13.0 3.4 5.0 16.66

49.61 98.0 98.0 95.0 86.66

log Biom P: log Biomase Phanerophyte; MDI: Margaleff Diversity index; Sinbiom Ch: sin biomase chamephyte.

Aerial cover is particularly important, followed by a pluri-stratified structure with different biotypes. The architecture of the dominant shrub species is also important; thus, Rosemary areas with a nanophanerophyte biotype appear to be more efficient at preventing erosion than the chamephytes and hemicryptophytes of thymeshrub area. Also related to the role of vegetation is the supply of organic carbon to the soil. Those soils with higher organic carbon contents appear to offer good protection against erosion. This is provided by both the litter, as in the case of Rosemary shrub areas, or the carbon forming part of the organomineral aggregates of the soil. Subnitrophilic grassland favours the formation of such aggregates. Given that these soils are moderately basic and have high levels of equivalent calcium carbonate, the formation of highly polymerised humus is prevented. Further studies should clarify the relative importance of these soil constituents in the formation of aggregates and their importance in the protection of soils.

5. Conclusions Scrubland is a very characteristic type of vegetation of soils susceptible to erosion in Mediterranean climates. The behaviour of scrubland plant communities with respect to erosion by water is modulated by multiple factors. 1. Plant cover is responsible for reduced runoff and the supply of organic material. 2. Plant growth forms influence the level of protection provided against erosion. Areas dominated by nanophanerophytes, such as Rosemary scrubland, are the most effective at avoiding erosion. 3. If biodiversity is equal, pluri-stratified areas with a layer of moss are more efficient than mono-stratified areas at avoiding erosion. Biodiversity does not seem to be a differentiating element.

Acknowledgements We thank Mr. Adrian Burton for the revision of the English manuscript. This work was financed by grant 07M/0011/99 from the Autonomous Community of Madrid.

106

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

References Amezketa, E., Singer, M.J., Le Bissonais, Y., 1996. Testing a new procedure for measure water-stable aggregation. Soil Science America Journal 60 (3), 888 – 894. Barkman, J.J., 1989. A critical evaluation of the minimum area concepts. Vegetatio 85, 89 – 104. Barthe`s, B., Roose, E., 2002. Aggregate stability as indicator of soil susceptability to runoff and erosion: validation at several levels. Catena 47, 133 – 149. Bochet, E., Rubio, J.L., Poesen, J., 1999. Modified topsoil islands within patchy Mediterranean vegetation in SE Spain. Catena 38, 23 – 44. Box, G.E.P., Cox, R.D.R., 1964. Analysis of transformation. Journal of the Royal Statistical Society. Series B 26, 211 – 252. Braun-Blanquet, J., 1979. Fitosociologı´a. Bases para el estudio de las Comunidades vegetales. Herman Blume, Madrid. Castroviejo, S., et al., 1986/2001. Flora Iberica 1 – 8. C.S.I.C., Madrid. Cerda´, A., 1997. The effect of patchy distribution of Stipa tenacissima L. on runoff and erosion. Journal of Arid Environments 36 (1), 37 – 51. Cerda´, A., 2001. Effects of rock fragment cover on soil infiltration, interill runoff and erosion. European Journal of Soil Science (52), 59 – 68. Comunidad Auto´noma de Madrid, 1997. Plan forestal de la Comunidad de Madrid. Consejerı´a de Medio Ambiente. Comunidad Auto´noma de Madrid, 2000a. Anuario estadı´stico de la Comunidad de Madrid 2000. Consejerı´a de Hacienda, Instituto de Estadı´stica de la Comunidad de Madrid, Madrid. Comunidad Auto´noma de Madrid, 2000b. Mapa de suelos de la Comunidad de Madrid, http://www.comadrid.org. Deuchras, S.A., Townend, J., Aitkenhead, M.J., Fitzpatrick, E.A., 1999. Changes in soil structure and hydraulic properties in regenerating rain forest. Soil Use and Management 15, 183 – 187. Ellenberg, H., Mueller-Dombois, D., 1967. A key to Raunkiaer plant life forms with revised subdivisions. Berichte des Geobotanischen Institutes der Eidg. Techn. Hochschule (37), 56 – 73. Elwell, H.A., Stocking, M.A., 1976. Vegetal cover to estimate soil erosion hazard in Rhodesia. Geoderma 15, 61 – 70. FAO, 1977. Guı´a para la descripcio´n de perfiles. Roma, 60 pp. Francßaise, N., 1982. NFX-82. In: Francßaise, N. (Ed.), Qualite des sols. Determination du calcaire actif. Paris. Ge´hu, J.M., Rivas-Martı´nez, S., 1982. Notions fondamentales de phytosociologie. In: Dierschke, H. (Ed.), Syntaxonomie, Ver. Int. Symp., vol. IVVS. J. cramer, Vaduz, pp. 5 – 33. Grytnes, J.A., 2000. Fine-scale vascular plant species richness in different alpine vegetation types: relationships with biomass and cover. Journal of Vegetation Science 11, 87 – 92. ISRIC, 1993. Procedures for Soil Analysis. FAO. ITGME, 1990. Mapa geolo´gico de Espan˜a. E.1:50.000. Hoja 560. Alcala´ de Henares, Madrid. Kamphorst, A., 1987. A small rainfall simulator for the determination of soil erodibility. Netherlands Journal of Agricultural Science 35, 407 – 415. Kidron, G.J., Yair, A., 1997. Rainfall – runoff relationship over encrusted dune surfaces, Nizzana, western Negev, Israel. Earth Surface Processes and Landforms 22 (12), 1169 – 1184. Kidron, G.J., Yair, A., 2001. Runoff-induced sediment yield over dune slopes in the Negev desert: 1. Quantity and variability. Earth Surface Processes and Landforms 26 (5), 461 – 474. Lasanta, T., Garcı´a Ruı´z, J.M., Pe´rez-Rontome´, C., Sancho, C., 2000. Runoff and sediment yield in a semiarid environment the effect of land management after farmland abandonment. Catena 38, 265 – 278. Le Bissonais, Y., 1996. Aggregate stability and assessment of soil crusability and erodibility: I. Theory and methodology. European Journal of Soil Science 47, 425 – 437. Magurran, A.E., 1996. Ecological Diversity and its Measurement. Chapman & Hall. 179 pp. Mueller-Dumbois, D., Ellenberg, H., 1974. Aims and Methods of Vegetation Ecology. Wiley, New York. Riezebos, H.T., Epema, G.F., 1985. Drop shape and erosivity: Part II. Splash detachment, transport and erosivity indices. Earth Surface Processes and Landforms 10, 69 – 74. Rivas-Martı´nez, S., 1982. Mapa de las series de vegetacio´n de la Provincia de Madrid, Publ. Ser. Forestal del medio Ambiente y Contra Incendios, Diputacio´n de Madrid.

M.A. Casermeiro et al. / Catena 57 (2004) 91–107

107

Rivas-Martı´nez, S., 1987. Memorias del mapa de vegetacio´n potencial de Espan˜a. In: Rivas-Martı´nez, S. (Ed.), ICONA, Madrid. Rivas-Martı´nez, S., 1997. Syntaxonomical synopsis of the potential natural plant communities of North America, I. Itinera Geobotanica (10), 5 – 148. Sa´nchez, G., Puigdefabregas, J., 1994. Interactions of plant growth and sediment movement on slopes in a semiarid environment. Geomorphology (9), 243 – 260. Smith, K., Mullins, C.E., 1991. Soil Analysis Physical Methods. Marcel Dekker. Thornes, J.B., 1990. The interaction of erosional and vegetational dynamics in land degradation: spatial outcomes. In: Thornes, J.B. (Ed.), Vegetation and Erosion. Wiley, London, pp. 41 – 53. Truman, C.C., Bradford, H., 1990. Antecedent water content and rainfall energy influence on soil aggregate breakdown. Soil Science America Journal 54, 1385 – 1392. Viles, H.A., 1990. The agency of organic beings. A selective review of recent work in biogeomorphology. In: Thornes, J.B. (Ed.), Vegetation and Erosion. Wiley, London, pp. 5 – 24.