Sabrina Coste

Jean-Christophe Roggy
email: roggy.j@cirad.fr

Pascal Imbert

Céline Born

Damien Bonal

Erwin Dreyer

Leaf photosynthetic traits of 14 tropical rain forest species in relation to leaf nitrogen concentration and shade tolerance

Keywords: functional diversity, leaf carbon, leaf nitrogen, nitrogen-use efficiency, photosynthetic capacity, tropical rain forest

Variability of leaf traits related to photosynthesis was assessed in seedlings from 14 tree species growing in the tropical rain forest of French Guiana. Leaf photosynthetic capacity (maximum rate of carboxylation and maximum rate of electron transport) was estimated by fitting a biochemical model of photosynthesis to response curves of net CO 2 assimilation rate versus intercellular CO 2 mole fraction. Leaf morphology described by leaf mass per unit leaf area (LMA), density and thickness, as well as area-and mass-based nitrogen (N) and carbon (C) concentrations, were recorded on the same leaves. Large interspecific variability was detected in photosynthetic capacity as well as in leaf structure and leaf N and C concentrations. No correlation was found between leaf thickness and density. The correlations between area-and massbased leaf N concentration and photosynthetic capacity were poor. Conversely, the species differed greatly in relative N allocation to carboxylation and bioenergetics. Principal component analysis (PCA) revealed that, of the recorded traits, only the computed fraction of total leaf N invested in photosynthesis was tightly correlated to photosynthetic capacity. We also used PCA to test to what extent species with similar shade tolerances displayed converging leaf traits related to photosynthesis. No clear-cut ranking could be detected among the shade-tolerant groups, as confirmed by a one-way ANOVA. We conclude that the large interspecific diversity in photosynthetic capacity was mostly explained by differences in the relative allocation of N to photosynthesis and not by leaf N concentration, and that leaf traits related to photosynthetic capacity did not discriminate shade-tolerance ranking of these tropical tree species.

Introduction

Leaf characteristics of tropical trees, such as thickness, density, dry mass per unit area (LMA), anatomy and nitrogen (N) and chlorophyll contents display great diversity from species to species [START_REF] Popma | Gap-dependence and leaf characteristics of trees in a tropical lowland rain forest in Mexico[END_REF][START_REF] Ellsworth | Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest[END_REF][START_REF] Poorter | Leaf optical properties in Venezuelan cloud forest trees[END_REF]. In general, in tropical rain forests, light availability below the canopy is the most important factor influencing species recruitment because deep shade severely limits growth and survival of understory species [START_REF] Bazzaz | Physiological ecology of tropical succession: a comparative review[END_REF]Pickett 1980, Fetcher et al. 1983). Nevertheless, the ecophysiological traits that allow growth and survival at low irradiances remain a matter of debate. Tolerance of seedlings to shade, which may be one of the features that segregates different ecological groups of species, may be related to a combination of structural properties maximizing leaf area with respect to respiring biomass, and of leaf properties optimizing photosynthesis with respect to N investment [START_REF] Messier | Functional ecology of advance regeneration in relation to light in boreal forests[END_REF]. Tropical rain forest tree species have been classified into several ecological groups based on their supposed light requirements [START_REF] Bazzaz | Physiological ecology of tropical succession: a comparative review[END_REF]Pickett 1980, Swaine and[START_REF] Swaine | On the definition of ecological species groups in tropical rain forests[END_REF], but whether functional leaf traits significantly differ among these ecological groups has not yet been established. In particular, leaf traits like photosynthetic capacity and N allocation to photosynthesis may differ significantly among the different groups. For instance, shade-intolerant or pioneer species are frequently thought to display higher light-saturated net CO 2 assimilation rates (A max ) and photosynthetic N-use efficiencies (PNUE or A max /N a , where N a is the amount of N per unit leaf area) than shade-tolerant or late successional species (Reich and Walters 1994, Reich et al. 1994[START_REF] Ellsworth | Photosynthesis and leaf nitrogen in five Amazonian tree species during early secondary succession[END_REF], Niinemets et al. 1998, but see Walters andReich 1999, Hanba et al. 2002). Similarly, shade-intolerant species are thought to have high photosynthetic capacities (as assessed by maximal ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) activity, V cmax , and electron transport rate, J max ), and a high allocation of leaf N to photosynthetic functions (i.e., to carboxylation and to electron transport) (Niinemets et al. 1998). A high LMA has been widely observed in seedlings of shade-tolerant species among tropical evergreen and deciduous trees (Kitajima 1994, Walters andReich 1999 but see Niinemets andKull 1998, Niinemets 1999b). Shade-intolerant species are reported to have thicker leaves (Niinemets et al. 1998[START_REF] Wilson | Spatial and seasonal variability of photosynthetic parameters and their relationship to leaf nitrogen in a deciduous forest[END_REF] but see [START_REF] Davies | Photosynthesis of nine pioneer Macaranga species from Borneo in relation to life history[END_REF], a higher total N content [START_REF] Niinemets | Role of foliar nitrogen in light harvesting and shade tolerance of four temperate deciduous woody species[END_REF], Niinemets et al. 1998) and a higher allocation of N to photosynthesis [START_REF] Seemann | Environmental effects on photosynthesis, nitrogen-use efficiency and metabolite pools in leaves of sun and shade plants[END_REF][START_REF] Evans | Photosynthesis and nitrogen relationships in leaves of C 3 plants[END_REF][START_REF] Niinemets | Role of foliar nitrogen in light harvesting and shade tolerance of four temperate deciduous woody species[END_REF]) (and consequently a smaller allocation of N to chemical defence) than shade-tolerant species. As a consequence, leaves of shade-intolerant species may be more vulnerable to herbivory [START_REF] Grime | Evidence of a causal connection between antiherbivore defence and the decomposition rate of leaves[END_REF]. High lignin concentrations in the foliage of shade-tolerant species also suggest that the leaves of these species should contain more carbon and be more expensive to construct than leaves of shade-intolerant species (Niinemets andKull 1998, Niinemets 1999b).

Relationships between leaf structure and photosynthesis have been the focus of intensive research, and correlations between leaf N content and photosynthetic capacity are frequently observed [START_REF] Field | The photosynthesis-nitrogen relationship in wild plants[END_REF][START_REF] Evans | Photosynthesis and nitrogen relationships in leaves of C 3 plants[END_REF], Reich and Walters 1994, Reich et al. 1994, 1999). According to Niinemets et al. (1998), the higher foliar N content generally observed in shade-intolerant relative to shade-tolerant species may be responsible for this correlation. Nevertheless, there is still large interspecific diversity in PNUE among shade-tolerant species. The relative ways in which total leaf N content and relative allocation of leaf N to the photosynthetic machinery contribute to the interspecific diversity of photosynthetic capacity remain poorly understood.

To assess the interspecific diversity of leaf traits related to photosynthetic capacity in a range of species differing in shade tolerance, we used the semi-mechanistic leaf photosynthesis model of [START_REF] Farquhar | A biochemical model of photosynthetic CO 2 assimilation in leaves of C 3 species[END_REF]. We estimated in vivo maximal Rubisco activity and electron transport rate, two key parameters for quantifying the photosynthetic capacity of a leaf. The procedure was performed with seedlings from 14 French Guiana tropical rain forest species grown under common conditions in an open greenhouse at Kourou, French Guiana. The adjusted values of V cmax and J max were used to compute the relative allocation of total leaf N to carboxylation (P c ) and bioenergetics (P b ) (Hikosaka andTerashima 1995, Niinemets and[START_REF] Niinemets | A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum[END_REF].

The main objectives of our study were: (1) to record interspecific diversity of leaf structure and photosynthetic capacity in a range of species from French Guiana; (2) to determine potential correlations among these traits, particularly with respect to the photosynthesis-nitrogen relationship; and (3) to test whether some of these traits could, alone or in combination, discriminate among species of different ecological groups.

Materials and methods

Plant material

The study was carried out at Kourou, French Guiana (5°10′ N, 52°40′ W) from February to May 2002 in a greenhouse. Measurements were made on seedlings from 14 species of the Guiana tropical rain forest (see Table 1 for the sizes of the seedlings used in the experiment). Two-year-old seedlings were transplanted in September 2001 to 20-l liter pots filled with a 1:2 (v/v) mixture of sand and A horizon soil from a nearby forest. Each pot was fertilized every second month with 5 g of complete fertilizer (12:12:17:2 N,P,K,Mg), treated bimonthly with a commercial insecticide (Cuberol: 5% rotenone), and irrigated to field capacity (0.25 m 3 m -3 ) every day. Temperature and relative humidity in the greenhouse were close to ambient (mean temperature: 27.6 °C (min/max: 23.5/31.5 °C), mean relative humidity: 72% (min/ max: 57/ 92%)). The greenhouse roof was equipped with a neutral sheet (black polyethylene, Uco Agrotextiles, Belgium) that transmitted 30% of global irradiance. Irradiance at plant level was within the range of 25 to 45% of full sunlight that supports optimal growth of seedlings of tropical rain forest species [START_REF] Barigah | Croissance et assimilation nette foliaire de jeunes plants de dix arbres de la forêt guyanaise cultivés à cinq niveaux d'éclairement[END_REF][START_REF] Poorter | Growth responses of 15 rainforest tree species to a light gradient: the relative importance of morphological and physiological traits[END_REF]. Around 6-8 seedlings per species were selected based on uniformity in height. The stud- Table 1. List of study species, with the abbreviations used in the figures, the systematic position, and the ecological group as defined by [START_REF] Favrichon | Classification des espèces arborées en groupes fonctionnels en vue de la réalisation d'un modèle de dynamique de peuplement en forêt guyanaise[END_REF][START_REF] Favrichon | Modèle matriciel déterministe en temps discret[END_REF]. Mean shoot height recorded at the end of the experiment is indicated (mean ± SD); n = number of individuals per species. 1).

Species name

Estimating leaf photosynthetic capacity

We used the [START_REF] Harley | Modelling photosynthesis of cotton grown in elevated CO 2[END_REF] version of Farquhar's biochemical model of leaf photosynthesis [START_REF] Farquhar | A biochemical model of photosynthetic CO 2 assimilation in leaves of C 3 species[END_REF] in which net CO 2 assimilation rate (A; µmol CO 2 m -2 s -1 ) is expressed as:

A V V R = - - c o d 05 . (1)
where V c is Rubisco bisphosphate (RuBP) carboxylation rate, V o is RuBP oxygenation rate and R d corresponds to CO 2 production by non-photorespiratory respiration. For a detailed description of the equation set, readers are referred to Le [START_REF] Le Roux | Parameterization and testing of a biochemically based photosynthesis model for walnut trees (Juglans regia) and seedlings[END_REF] and to [START_REF] Dreyer | Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species[END_REF]. This model permits the estimation of V cmax (µmol CO 2 m -2 s -1 ) and J max (µmol e -m -2 s -1 ). The potential limitation arising from triose phosphate utilization was omitted.

Gas exchange measurements

We estimated V cmax and J max from A versus sub-stomatal CO 2 mole fraction (C i ) relationships. Leaf gas exchange was measured with a portable gas exchange system (CIRAS-1, PP-system, Hitchin, U.K.) equipped with a Parkinson leaf chamber (2.5 cm 2 ). All measurements were performed on mature leaves, usually the ultimate leaf of the penultimate shoot flush. Air temperature in the leaf chamber and air-water vapor pressure deficit at the leaf surface were kept as stable as possible, but nevertheless varied significantly because of changes in the external microclimate (means ± SD = 30 ± 3 °C and 1.6 ± 0.2 kPa, respectively). To compensate for the differences in temperature during measurements, all parameters were scaled to a common reference temperature (T ref ) of 25 °C (298.16 K).

Because of the lack of data available for tropical tree species, we used temperature response parameters computed by [START_REF] Dreyer | Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species[END_REF] for seedlings of temperate forest species.

A halogen lamp (Philips 12 V, 20 W) yielded a saturating photosynthetic photon flux (PPF) of 700 µmol m -2 s -1 . For each leaf, after full light induction for at least 30 min, an A-C i curve was generated by 12 stepwise increases in atmospheric CO 2 concentration (C a = 40, 100, 200, 300, 400, 500, 600, 700, 900, 1200, 1500, 1800 and2000 µmol mol -1 ). The procedure yielded about six values in each fraction of the curve (CO 2 -limited or irradiance-limited zone of the response). Values of R d were recorded in the dark 5 min after switching off the illumination. We estimated A max as the light-saturated net assimilation rate measured at the CO 2 concentration close to 400 µmol mol -1 (mean values of C a and C i ± SD = 396 ± 8.8 and 275 ± 25 µmol mol -1 , respectively). While the A-C i curve data were being gathered, stomatal conductance declined in response to increasing CO 2 concentration; therefore, these values must be interpreted with caution.

Primary model parameters and their temperature dependencies were taken from [START_REF] Dreyer | Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species[END_REF] and are listed in Table 2. For each individual, a nonlinear least squares regression (Newton method, ProcNLIN, SAS v.8.1, SAS Institute Inc., Cary, NC) was used to estimate V cmax and J max by fitting the A-C i curve to empirical equations as described by [START_REF] Dreyer | Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species[END_REF].

Leaf structure, nitrogen and carbon content

After gas exchange measurements, leaves were harvested and their area measured with an electronic area meter (LI-3000A, Li-Cor, Lincoln, NE). Thickness (µm) of each sampled leaf was obtained as the mean of three measurements with a digital micrometer (Digimatic micrometer, Mitutoyo, Japan), care being taken to avoid the main veins. Leaf dry mass was recorded after drying for 48 h at 70 °C. Total N (N m ; mmol N g -1 ) and carbon per unit leaf dry mass (C m ; mmol C g -1 ) were measured with an elemental analyzer (NA 1500 NCS, Carlo Erba, Italy). Values of LMA (g m -2 ), N a and leaf density (LMA/thickness; g cm -3 ) were derived from these data.

Estimation of relative allocation of nitrogen to carboxylation and bioenergetics

Relative allocation of leaf N to Rubisco (P c ; g N in Rubisco g -1 total leaf N) and to bioenergetics (P b ; g N in cytochrome f, ferredoxin NADP reductase, and coupling factor g -1 total leaf TREE PHYSIOLOGY ONLINE at http://heronpublishing.com

DIVERSITY OF LEAF TRAITS IN RAIN FOREST TREES 1129

Table 2. Primary parameters of the photosynthesis model used in this study: α = apparent quantum yield; K c = affinity of Rubisco for CO 2 ; K o = affinity of Rubisco for O 2 ; ∆H a = activation energy; and τ = specificity factor of Rubisco. For details, see [START_REF] Dreyer | Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species[END_REF].

Parameters Value Unit α 0.24 mol e -mol -1 photons K c (25 °C) 40 Pa ∆H a (K c ) 70,000 J mol -1 K o (25 °C) 25,000 Pa ∆H a (K o ) 15,000 J mol -1 τ (25 °C) 2710 - ∆H a (τ) -29,000 J mol -1 ∆H a (V cmax ) 1 73,000 J mol -1 ∆H a (J max ) 1 57,900 J mol -1 ∆H a (R d ) 1
32,000 J mol -1 N) were derived from N a and from V cmax or J max , respectively, as described by [START_REF] Niinemets | A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum[END_REF] using the parameters provided by them:

P V V N c c cr a = max . 6 25 (2) P J J N b mc a = max . 8 06 (3)
where V cr is the specific activity of Rubisco (µmol CO 2 g -1 Rubisco s -1 ), J mc is the potential rate of photosynthetic electron transport per unit cytochrome f (mol e -mol -1 cyt f s -1 ), and the factors 6.25 (g Rubisco g -1 N in Rubisco) and 8.06 (µmol cyt f g -1 N in bioenergetics) convert N mass to protein mass [START_REF] Niinemets | A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum[END_REF]. Reference values at 25 °C were 20.78 µmol CO 2 g -1 Rubisco s -1 and 156 mol electrons mol -1 cyt f s -1 , for V cr and J mc , respectively (cf. Niinemets and Tenhunen 1997).

Statistical analysis

All statistical analyses were performed with STATISTICA (Kernel Version 5.5, StatSoft, Tulsa, OK). Normality of the data and homogeneity of variances were examined by the Shapiro-Wilk and Levene tests, and log 10 or square root transformations were occasionally applied to normalize the distributions of the data. A principal component analysis was conducted to explore simultaneously species variation in multiple leaf traits. For the PCA, the dependent variables P c and P b (calculated from V cmax and J max ), as well as density (product of LMA and thickness), were used as inactive variables. Because data on leaf thickness and density of Couepia caryophylloides were unavailable, this species does not appear in the PCA, which reduces the number of shade-tolerant species to three. Differences between groups were analyzed by a one-way ANOVA. All regressions as well as the PCA and the final ANOVA were applied on the computed mean values of variables for each species. All statistical relationships were considered significant at P < 0.05.

Results

Inter-specific diversity of photosynthetic capacity

Values of A max ranged from 5.4 to 12.9 µmol m -2 s -1 among species (Figure 1A) (equivalent to 75.1 to 290 nmol g -1 s -1 ; data not shown). Both V cmax and J max differed significantly among the 14 species (P < 0.001; Figures 1B and1C). Species means of V cmax varied more than twofold (from 22.1 to 55.3 µmol m -2 s -1 ). There was a strong positive correlation between J max and V cmax (r 2 = 0.9, P < 0.001; Figure 2) and the J max /V cmax ratio varied between 1.7 and 2.2, but with only a small species effect (P = 0.07; Figure 1C). We observed considerable variability in R d and no species effect was detected (P = 0.13; Figure 1D).

Interspecific diversity of leaf structure and chemical composition

Leaf density and thickness differed significantly among species (P < 0.001; Figures 3B and3C), with more than a twofold range between extreme values. Values of LMA varied from 44.6 to 117 g m -2 . Shade-tolerant species had higher LMA than shade-intolerant species, with the exception of Vouacapoua americana (Figure 3A).

Values of C m , N m and N a differed significantly (P < 0.001) among species, ranging from 438 to 518 mg g -1 , 11.6 to 21.9 mg g -1 and 0.93 to 1.93 g m -2 , respectively (Fig- ures 3D-F).

Relative investment of leaf nitrogen in carboxylation and bioenergetics

Values of PNUE differed significantly among the 14 species, ranging from 60.9 to 186.8 µmol mol -1 s -1 (Figure 3G). In parallel, the estimated P c and P b varied significantly among species (P < 0.001). Cecropia obtusa invested more than 40% of its leaf N in Rubisco and Vouacapoua americana only about 16% (Figure 3H). As expected from the tight correlation between J max and V cmax , the distribution of mean values of P b among species paralleled that for P c , but with lower values ranging from 0.03 to 0.08 (Figure 3I).

Relationships between leaf morphology, chemical composition and photosynthetic capacity

We found that N m was linearly and negatively correlated with LMA (P < 0.01; Figure 4): LMA increased more than twofold as N m dropped from 21.9 to 11.6 mg g -1 . In contrast, C m seemed to be independent of LMA (Figure 4). Results of a multiple regression showed that LMA was not correlated with its two components, leaf thickness and density (data not shown), and that thickness and density were not correlated either. Although N m was not correlated with either thickness or density, C m increased with density and decreased with thickness (Figure 4).

No correlations were found between photosynthetic capacity expressed per unit leaf area (A max , V cmax , J max and R d ) and any leaf morphology parameter (LMA, leaf thickness and density) or N a . Only PNUE, P c and P b were tightly correlated with V cmax , J max and A max (Table 3A). Close correlations were found between PNUE and P c and P b (r = 0.95 and 0.94, respectively), as well as between V cmax and J max and A max (r = 0.94 and 0.93, respectively). In addition, N a was negatively correlated with P c (P < 0.01; Figure 5). Similarly, photosynthetic capacity expressed per unit leaf mass was tightly correlated to P c and P b (P < 0.001). Both V cmax and J max decreased with increases in N m , LMA and leaf density (but not leaf thickness) (Table 3).

Principal component analysis

A principal component analysis was applied to the variables V cmax , J max , R d , LMA, leaf thickness, N a , N m and C m , with the dependent variables density, P c and P b as supplementary variables. We also included area-based A max and PNUE as nonactive variables. The first two axes explained 58% of the ob-served variability (Figure 6). The distribution of variables on the correlation circle projected on the factor plane (1 × 2) yielded several clusters. The main contributors to the first principal axis were thickness, V cmax and J max (with factorial coordinates of -0.81 in all cases). In this group, V cmax and J max and the inactive variable A max were strongly inter-correlated. The main contributors to the second principal axis were LMA and N a , and N m to a lesser extent (with factor coordinates of -0.94, -0.70 and +0.52, respectively). The intercorrelated supplementary variables P c , P b and PNUE were correlated to the first principal axis. Density and C m were also tightly correlated. Leaf mass per area was negatively correlated to N m discriminated by Factor 2. Photosynthetic capacity was not related to LMA or total leaf N concentration (axes were orthogonal).

The distribution of the species on the factor plane (1 × 2) yielded three clusters, one with high photosynthetic capacities and thick leaves, one with moderate photosynthetic capacities and high LMA leaves, and one with low photosynthetic capacities and thin leaves. The shade-tolerant Vouacapoua americana (low photosynthetic capacity) and the pioneer Cecropia obtusa (high photosynthetic capacity and high PNUE) were independent of these groups. These groups did not match the ecological groups defined by [START_REF] Favrichon | Classification des espèces arborées en groupes fonctionnels en vue de la réalisation d'un modèle de dynamique de peuplement en forêt guyanaise[END_REF] and, furthermore, the three species clusters each contained species from different ecological groups. On the second axis, shade-tolerant species seemed to display lower N m and higher LMA than mid-tolerant species-except for Symphonia globulifera and Eperua grandiflora-and shade-intolerant species A multivariate ANOVA applied to the two groups of variables revealed by the PCA, V cmax , J max , R d and thickness for the first axis (P = 0.97) and LMA, N a and N m for the second (P = 0.11), confirmed that no difference was observed among the three Favrichon groups. A complementary one-way ANOVA of the effect of the ecological groups on the parameters, applied to each studied variable, confirmed this observation. Furthermore, no significant difference was observed between the three groups for all the studied parameters, even when the pioneer Cecropia obtusa was removed from the analysis (data not shown).

Discussion

Interspecific variability in leaf traits

Large differences were found in leaf traits among seedlings from 14 tropical rain forest tree species in French Guiana. Leaf structure varied significantly and, in particular, leaf density and thickness displayed a wide range of values. No correlation was found between leaf density and thickness, in agreement with previous studies (Niinemets 1999a[START_REF] Wilson | Specific leaf area and leaf dry matter content as alternative predictors of plant strategies[END_REF].

The two variables seem to be totally independent even among species from a common biome, indicating that control over leaf density and thickness is exerted by different mechanisms (Niinemets 1999a). Similarly, N m displayed a wide range of values that fell within the ranges reported by others (Niinemets 1999a[START_REF] Bonal | Interspecific variability of ∆ 13 C among trees in rainforests of French Guiana: functional groups and canopy integration[END_REF]. Mature trees generally exhibit higher LMA and leaf N than seedlings (Thomas andWinner 2002, Mediavilla and[START_REF] Mediavilla | Mature trees versus seedlings: differences in leaf traits and gas exchange patterns in three co-occurring Mediterranean oaks[END_REF] and this could explain why our N m values ranked within the lower range of those presented by Niinemets (1999a). We found that N m was independent of leaf density and thickness, which contrasts with the observations of Niinemets (1999a) and the general view that species with a low N m usually maximize LMA through increased leaf density or thickness, thereby maximizing N a . The interspecific variability in C m was also high and similar to that recorded by D. Bonal (personal communication) for a wider range of French Guiana forest tropical species. We found that C m was tightly correlated with density: high leaf density may be partly associated with the presence of sclerenchymatous tissues with high lignin contents (Niinemets andKull 1998, Niinemets 1999b). However, although dense leaves had higher C m , neither leaf density nor C m was correlated with LMA. Leaf mass per area is often tightly correlated with leaf life span [START_REF] Reich | From tropics to tundra: global convergence in plant functioning[END_REF][START_REF] Wright | Relationships between leaf lifespan and structural defences in a low-nutrient, sclerophyll flora[END_REF][START_REF] Wright | The worldwide leaf economics spectrum[END_REF]) and a high LMA is supposed to contribute to enhanced leaf resistance to herbivory [START_REF] Walters | Low-light carbon balance and shade tolerance in the seedlings of woody plants: do winter deciduous and broad-leaved evergreen species differ?[END_REF][START_REF] Rijkers | The effect of tree height and light availability on photosynthetic leaf traits of four neotropical species differing in shade tolerance[END_REF][START_REF] Hanba | The effect of growth irradiance on leaf anatomy and photosynthesis in Acer species differing in light demand[END_REF]. However, there is little information on how leaf density or thickness influences leaf life span in tree species. In a study of 17 perennial dicots, [START_REF] Wright | Relationships between leaf lifespan and structural defences in a low-nutrient, sclerophyll flora[END_REF] found that LMA and leaf thickness each explained approximately 30-40% of the variation in leaf life span, whereas leaf toughness (generally correlated with density) explained only 25%. A trade-off between leaf N investment in photosynthesis and leaf life span has frequently been detected [START_REF] Reich | Leaf life-span as a determinant of leaf structure and function among 23 Amazonian tree species[END_REF][START_REF] Reich | From tropics to tundra: global convergence in plant functioning[END_REF]. In our study, dense leaves displayed low N allocation to photosynthesis (low values of P c and P b ), but we did not determine if this was associated with long life span. These large differences in leaf structure and leaf N concentration were paralleled by significant variability (twofold range) in photosynthetic capacity as measured by V cmax and J max . As expected from earlier findings (Niinemets et al. 1998), V cmax and J max were tightly correlated, and no species-specific feature could be detected between these components of photosynthetic capacity. The residual (nonsignificant) variability may be associated with the method used to estimate the two parameters. For example, the estimate of V cmax may be affected by internal resistance to CO 2 transfer, because V cmax is adjusted on the CO 2 -limited fraction of the curve, and substomatal CO 2 mole fraction (not chloroplastic CO 2 ) was used for the computation. A significant underestimation of V cmax may result [START_REF] Ethier | On the need to incorporate sensitivity to CO 2 transfer conductance into the Farquhar-von Caemmerer-Berry leaf photosynthesis model[END_REF]. The estimate of J max derives from the CO 2 -saturated segment of the curve and is therefore less prone to underestimation. To date, no information is available about the importance of internal resistance to CO 2 transfer in the tested species, and we have no idea whether significant interspecific variability affects this resistance in tropical trees, although significant diversity has been recorded among evergreen trees [START_REF] Warren | Evergreen trees do not maximize instantaneous photosynthesis[END_REF]. From recent investigations, it may be concluded that the internal resistance is of the same magnitude as stomatal resistance [START_REF] Piel | Effect of local irradiance on CO 2 transfer conductance of mesophyll in walnut[END_REF]. Non-photorespiratory respiration was low and displayed large variation due to measurement errors. A significant interspecific diversity in R d , related to thickness, density and N m , is predicted, but would require more precise estimates with an adapted chamber.

The values of V cmax and J max recorded in our study were within the range of those reported by Niinemets (1999a) for a large number of species from a wide range of earth biomes with woody vegetation, and close to values recorded in the canopy of rain forest species in central Amazonia [START_REF] Carswell | Photosynthetic capacity in a central Amazonian rain forest[END_REF], but they were lower than those reported by [START_REF] Dreyer | Temperature response of leaf photosynthetic capacity in seedlings from seven temperate tree species[END_REF] for temperate forest tree seedlings (63 to 90.5 µmol m -2 s -1 ). This difference can be explained on the basis of the following considerations: (1) tropical rain forest trees operate at temperatures close to optimum for photosynthesis and with ample water supply almost year-round, whereas temperate species usually operate at less than optimum temperatures during a large fraction of the growth season; and (2) leaf life span is usually, but not always, longer in tropical species than in deciduous, temperate broad-leaved species. As a consequence, higher photosynthetic capacities are required in temperate trees to approach the net CO 2 assimilation rates detected in tropical species.

Leaf structure and photosynthetic capacity are plastic features that depend on the irradiance received during growth (Niinemets and Kull 1998[START_REF] Lee | Effects of irradiance and spectral quality on leaf structure and function in seedlings of two southeast Asian Hopea (Dipterocarpaceae) species[END_REF][START_REF] Evans | Photosynthetic acclimation of plants to growth irradiance: the relative importance of specific leaf area and nitrogen partitioning in maximizing carbon gain[END_REF]. Our data were recorded under common conditions and at a comparatively high irradiance (30%) relative to that generally found at the forest floor (usually below 5%; [START_REF] Bazzaz | Physiological ecology of tropical succession: a comparative review[END_REF][START_REF] Clark | Landscape-scale evaluation of understory light and canopy structure: methods and application in a neotropical lowland rainforest[END_REF][START_REF] Poorter | Growth responses of 15 rainforest tree species to a light gradient: the relative importance of morphological and physiological traits[END_REF]. Only the pioneer species Cecropia obtusa generally grows in large gaps. Because the plastic responses of leaf traits are poorly known in these species (Rijkers et al. 2000, Kitajima and[START_REF] Kitajima | Increases of chlorophyll a/b ratios during acclimation of tropical woody seedlings to nitrogen limitation and high light[END_REF], we can only speculate that, based on the results obtained with temperate species (P. Montpied et al., INRA-UHP, Champenoux, France, personal communication), the interspecific diversity we recorded at 30% of full sunlight would probably be maintained at the much lower irradiances on the forest floor. Additional data are needed to document such irradiance × genotype interactions.

Juvenility effects are known to occur for leaf anatomy and photosynthetic capacity, and adult trees often display rather different leaves (Thomas andWinner 2002, Mediavilla and[START_REF] Mediavilla | Mature trees versus seedlings: differences in leaf traits and gas exchange patterns in three co-occurring Mediterranean oaks[END_REF]. For example, significant changes in leaf structure and function occur during successive architectural stages in Dicorynia guianensis Amsh. (Caesalpiniaceae) [START_REF] Roggy | Links between tree structure, and functional leaf traits in the tropical forest tree Dicorynia guianensis Amshoff (Caesalpiniaceae)[END_REF]. Nevertheless, even though the absolute values of leaf traits may vary, the ranking of species and the degree of diversity is likely to remain independent of tree age; however, this assumption needs to be verified.

Relationships between leaf structure, leaf nitrogen concentration and photosynthetic capacity

Despite the tight correlation observed on a broad scale between leaf N and photosynthetic capacity (Reich and Walters 1994, Reich et al. 1994, 1999, Niinemets 1999a[START_REF] Wright | The worldwide leaf economics spectrum[END_REF], there is significant variability in PNUE among co-occurring species [START_REF] Pons | Nitrogen reallocation and photosynthetic acclimation in response to partial shading in soybean plants[END_REF]. This was confirmed in our data set, where there was no correlation between leaf N concentration and photosynthetic capacity when expressed on a leaf area basis, whereas weak correlations were detected when these variables were expressed on a mass basis. This difference was a result of the covariation of leaf N and LMA; moreover, the relationships were negative, i.e., increasing N and density both resulted in a slight decrease in photosynthetic capacity. No other leaf trait was significantly correlated to photosynthetic capacity expressed on a leaf area basis. These findings led to the conclusion that the diversity of photosynthetic capacity among species was explained by differences in the relative allocation of N to photosynthetic functions. In other words, there was large diversity in PNUE among species.

To document this conclusion, we computed an index for P c and P b [START_REF] Niinemets | A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum[END_REF]. Because of the tight correlation between J max and V cmax , P c and P b were also tightly correlated, and so for simplicity, we will concentrate the discussion on P c , which is tightly correlated with PNUE. The computation of this allocation may yield comparative results, provided that several assumptions are fulfilled: Rubisco specific activity is stable among species, and Rubisco in the leaves is fully activated-which is probably not always the case [START_REF] Warren | Photosynthesis-Rubisco relationships in foliage of Pinus sylvestris in response to nitrogen supply and the proposed role of Rubisco and amino acids as nitrogen stores[END_REF][START_REF] Warren | Evergreen trees do not maximize instantaneous photosynthesis[END_REF]. The specific activity used in our computation was derived from in vivo data produced by [START_REF] Jordan | The CO 2 /O 2 specificity of ribulose-1,5-bisphosphate carboxylase/oxygenase. Dependence on ribulose bisphosphate concentration, pH and temperature[END_REF] and is smaller than that produced by von [START_REF] Von Caemmerer | The kinetics of ribulose-1,5-bisphosphate carboxylase/oxygenase in vivo inferred from measurements of photosynthesis in leaves of transgenic tobacco[END_REF] with transgenic tobacco, and as a result, the computed N investment was probably an overestimate of the real values. We nevertheless kept these values to facilitate comparisons with published results based on the same parameters and computed values of PNUE. Our computations resulted in a large range of values for P c , with highest values (above 40%) in the pioneer Cecropia obtusa and lowest values (around 15%) in Vouacapoua americana and Eperua grandiflora. In parallel, P b ranked from 3 to 8%. The P c values computed by Niinemets (1999a) ranged from 4 to 17% for adult trees. Similarly, our PNUE values (61-187 µmol mol -1 N s -1 ) were large compared with the literature values [START_REF] Field | The photosynthesis-nitrogen relationship in wild plants[END_REF]Mooney 1986, Reich et al. 1991). Our data therefore point to large variability in N investment in photosynthesis (i.e., PNUE) among our study species, and this factor was probably the main source of variation in photosynthetic capacity among the tested species.

Do ecological groups of rain forest trees differ in leaf traits?

Based on our data set and the studied parameters, it was difficult to detect a clear-cut segregation among the groups defined by [START_REF] Favrichon | Classification des espèces arborées en groupes fonctionnels en vue de la réalisation d'un modèle de dynamique de peuplement en forêt guyanaise[END_REF], although photosynthetic capacities were weakly discriminating. The pioneer and fast-growing Cecropia obtusa differed from the other shade-intolerant species by having a larger photosynthetic capacity, whereas Bagassa guyanensis and Carapa procera displayed low photosynthetic capacities, comparable with those of the shade-tolerant species. Similarly, the deeply shade-tolerant Vouacapoua americana had a much lower photosynthetic capacity than the other shade-tolerant species. Although some of our shade-intolerant species tended to display higher PNUE and lower LMA and N a than the shade-tolerant species, as observed by others (Reich and Walters 1994, Reich et al. 1994[START_REF] Ellsworth | Photosynthesis and leaf nitrogen in five Amazonian tree species during early secondary succession[END_REF][START_REF] Hikosaka | Photosynthetic nitrogen-use efficiency in evergreen broad-leaved woody species coexisting in a warm-temperate forest[END_REF][START_REF] Poorter | Leaf optical properties in Venezuelan cloud forest trees[END_REF], no significant inter-group difference was detected. This was well illustrated by the absence of any grouping in the PCA. The large within-group variability observed for most of the variables makes Favrichon's classification questionable. Favrichon's classification was based on the vegetative development of adult and sub-adult trees, and no criteria relative to seedlings were taken into account [START_REF] Favrichon | Classification des espèces arborées en groupes fonctionnels en vue de la réalisation d'un modèle de dynamique de peuplement en forêt guyanaise[END_REF]. The only pioneer species in our experiment, Cecropia obtusa, did not break established classification rules; it had a high photosynthetic capacity, allocated a large fraction of leaf N to photosynthesis and had a low LMA and N a . However, non-pioneer species like Tabebuia insignis, Virola surinamensis, Hymenaea courbaril and Symphonia globulifera had photosynthetic capacities comparable with that of Cecropia obtusa. Distribution of the species in the factorial plan confirmed the lack of any potential grouping based on these criteria.

Many studies have predicted that leaves with a high LMA are better defended against herbivore attack than their low LMA counterparts (Grime et al. 1996, Hanley and[START_REF] Hanley | Relationships between physical and chemical attributes of congeneric seedlings: how important is seedling defence?[END_REF] and exhibit longer life spans [START_REF] Reich | Leaf life-span as a determinant of leaf structure and function among 23 Amazonian tree species[END_REF], 1992[START_REF] Niinemets | Role of foliar nitrogen in light harvesting and shade tolerance of four temperate deciduous woody species[END_REF], 1999[START_REF] Wright | The worldwide leaf economics spectrum[END_REF]). We found no correlation between LMA (and low N m ) and leaf density or C m (which may be associated with a larger investment of carbon in defence compounds), and the ecological groups did not differ in this respect.

We conclude that photosynthetic features and functional leaf traits, although they differ greatly among co-occurring rain forest species, are inadequate for characterizing ecological groups. Other traits related to seedling carbon budget, like construction costs of leafy shoots and leaf life spans, probably need to be taken into account. The question of whether tree species can be assigned to clear ecological groups based on their growth and carbon economy at the seedling stage, or whether there is a continuum of growth habits between the extremes, remains a matter of debate.

Figure 2 .

 2 Figure2. Relationship between the maximum electron transport rate (J max ) and the maximum carboxylation velocity (V cmax ) in seedlings of 14 species of the tropical rainforest of French Guiana. Each value represents the mean ± 95% confidence interval for a given species.

Figure 3 .

 3 Figure 3. Box and whisker plots of leaf (A) mass per area (LMA), (B) density and (C) thickness, (D) total carbon content per unit leaf dry mass (C m ), total nitrogen content (E) per unit leaf dry mass (N m ), and (F) per unit leaf area (N a ), (G) photosynthetic nitrogen-use efficiency (PNUE), and relative investment of total leaf nitrogen in (H) Rubisco (P c ) and (I) bioenergetics (P b ) for 14 species (abbreviations as in Table 1). The upper and the lower edges of each box indicate the 75th and 25th percentiles, respectively. The horizontal line within each box is the median and the vertical bars indicate the 10th to the 90th percentile ranges. A value of P < 0.05 indicates a significant difference among species according to a one-way ANOVA. Shade-intolerant species are on the left, mid-tolerant species in the center and shade-tolerant species on the right of the x-axis.

Figure 4 .

 4 Figure 4. Relationships between leaf mass per area ratio (LMA), leaf thickness or leaf density, and total nitrogen (N m ) or carbon concentration (C m ) per unit leaf dry mass. Simple linear regressions were applied to species means (± 95% confidence intervals) of the variables.

Figure 5 .

 5 Photosynthetic capacity expressed on a leaf mass basis (nmol g -1 s -1 ) N m (mg g -1 ) 0.55 * 0.55 * 0.55 * 0.54 ns C m (mg g -1 ) -0.32 ns -0.35 ns -0.39 ns -0.14 ns LMA (g m -2 ) -0.67 ** -0.70 ** -0.67 ** -0.69 ** Leaf thickness (µm) 0.08 ns 0.03 ns 0.09 ns -0.19 ns Leaf density (g cm -3 ) -0.68 ** -0.71 ** -0.73 ** -0.44 ns PNUE (µmol mol -1 s -1 ) 0.9 **** 0.87 **** 0.88 **** 0.66 ** P c 0.81 *** 0.85 **** 0.83 *** 0.63 * P b 0.80 *** 0.82 *** 0.86 **** 0.54 *

Figure 6 .

 6 Figure6. Principal component analysis of the studied species, ordinated according to eight leaf traits (in bold): maximum rate of carboxylation (V cmax ); maximum rate of electron transport (J max ); dark respiration (R d ); leaf thickness; leaf mass to area ratio (LMA); total carbon (C m ) and nitrogen concentrations per unit leaf dry mass (N m ); and nitrogen content per unit leaf area (N a ). Additional variables (with asterisks) were light-saturated net photosynthesis (A max ), leaf density, photosynthetic nitrogen-use efficiency (PNUE) and the relative investment of leaf nitrogen in carboxylation (P c ) and bioenergetics (P b ). Projection of the variables on the factor-planes (1 × 2) and projection of the species on the same plane are shown. Circles envelop points from species belonging to the three ecological groups: shade-intolerant (᭺), mid-tolerant (᭹) and shade-tolerant (᭡) species[START_REF] Favrichon | Classification des espèces arborées en groupes fonctionnels en vue de la réalisation d'un modèle de dynamique de peuplement en forêt guyanaise[END_REF].

Table 3 .

 3 Pearson's correlation coefficients between photosynthetic capacity expressed on a leaf area or on a mass basis (light-saturated net photosynthesis rate (A max ), maximal carboxylation velocity (V cmax ), maximum electron transport rate (J max ) and dark respiration (R d )) and studied leaf characteristics: total carbon content per unit leaf dry mass (C m ); total nitrogen content per unit leaf dry mass (N m ) and per unit leaf area (N a ); leaf mass per area (LMA); leaf density and thickness; photosynthetic nitrogen-use efficiency (PNUE); and relative nitrogen allocation to Rubisco (P c ) and to bioenergetics (P b ). Simple linear regressions were applied using species means for each variable. Significance levels: ns , P > 0.05; *, P < 0.05; **, P = 0.01; ***, P = 0.001; and ****, P = 0.0001.

		A max	V cmax	J max	R d
	Photosynthetic capacity expressed on a leaf area basis (µmol m -2 s -1 )
	N a (mg m -2 )	0.04 ns	0.05 ns	0.05 ns	0.07 ns
	LMA (g m -2 )	-0.04 ns	0.02 ns	0.01 ns	-0.01 ns
	Leaf thickness (µm)	0.42 ns	0.39 ns	0.42 ns	-0.001 ns
	Leaf density (g cm -3 )	-0.46 ns	-0.45 ns	-0.43 ns	0.01 ns
	PNUE (µmol mol -1 s -1 )	0.83 ***	0.79 ***	0.75 **	0.39 ns
	P c	0.71 **	0.77 ***	0.7 **	0.41 ns
	P b	0.77 ***	0.8 ***	0.81 **	0.31 ns
	Photosynthetic capacity expressed on a leaf mass basis (nmol g -1 s -1 )
	N m (mg g -1 )	0.55 *	0.55 *	0.55 *	0.54 ns
	C m (mg g -1 )	-0.32 ns	-0.35 ns	-0.39 ns	-0.14 ns
	LMA (g m -2 )	-0.67 **	-0.70 **	-0.67 **	-0.69 **
	Leaf thickness (µm)	0.08 ns	0.03 ns	0.09 ns	-0.

TREE PHYSIOLOGY ONLINE at http://heronpublishing.com DIVERSITY OF LEAF TRAITS IN RAIN FOREST TREES 1133

TreePhysiology 25, 1127-1137 © 2005 Heron Publishing-Victoria, Canada

Shade tolerance ranking according to[START_REF] Béna | Essences forestières de Guyane[END_REF].

Mean value of ∆H a from seven temperate tree species(Dreyer et al.

2001).

Acknowledgments

The authors thank Jacqueline Marchand for carbon and nitrogen analyses, Jean-Yves Goret and Elli Lentilus for their help throughout the experiment, and Christopher Baraloto for the supply of seedlings. Useful discussions with Pierre Montpied, Daniel Epron and Eric Marcon are gratefully acknowledged. We also thank Stéphane Ponton, Patrick Heuret and Christopher Baraloto for constructive comments on earlier drafts of the manuscript.


domain: sdv sde shs

text:
O Cerdan
email: cerdan@orleans.inra.fr

Y Le Bissonnais

V Souch Ère

P Martin

V Lecomte

SEDIMENT CONCENTRATION IN INTERRILL FLOW: INTERACTIONS BETWEEN SOIL SURFACE CONDITIONS, VEGETATION AND RAINFALL

Keywords: interrill erosion, sediment concentration, sealing, crop cover, roughness

A database composed of 673 natural rainfall events with sediment concentration measurements at the field or plot scale was analysed. Measurements were conducted on similar soil type (loess soils prone to sealing phenomenon) to apprehend the variability and complexity involved in interrill erosion processes attributable to soil surface conditions. The effects of the dominant controlling factors are not described by means of equations; rather, we established a classification of potential sediment concentration domain according to combination of the dominant parameters. Thereby, significant differences and evolution trends of mean sediment concentration between the different parameter categories are identified. Further, when parameter influences interact, it allows us to discern the relative effects of factors according to their respective degree of expression. It was shown that crop cover had a major influence on mean sediment concentration, particularly when soil surface roughness is low and when maximum 6-min intensity of rainfall events exceeds 10 mm h 1 : mean sediment concentration decreases from 8Ð93 g l 1 for 0-20 per cent of coverage to 0Ð97 g l 1 for 21-60 per cent of coverage. The established classification also indicates that the increase of the maximum 6-min intensity of the rainfall factor leads to a linear increase of mean sediment concentration for crop cover over 21 per cent (e.g. from 2Ð96 g l 1 to 14Ð44 g l 1 for the 1-5 cm roughness class) and to an exponential increase for low crop cover (e.g. from 3Ð92 g l 1 to 58Ð76 g l 1 for the 1-5 cm roughness class). The implication of this work may bring perspective for erosion prediction modelling and give references for the development of interrill erosion equation.

INTRODUCTION

Process-based erosion prediction models that apply on agricultural land most often rely on the rill-interrill concept [START_REF] Foster | Mathematical simulation of upland erosion by fundamental erosion mechanics[END_REF][START_REF] Meyer | Source of soil eroded by water from upland slopes[END_REF] and develop different formalism for each of these domains. Interrill erosion includes processes of detachment by raindrop impact and transport by shallow sheet flow. General interrill erosion model framework, as the first version of the WEPP model [START_REF] Nearing | A process based soil erosion model for USDA-Water Erosion Prediction Project technology[END_REF], was expressed under the form:

D i D f K i , I, S
where D i is the interrill sediment delivery per unit area per unit time, K i is interrill erodibility parameter related to soil properties, I is the rainfall intensity and S is a slope factor. Kinnell demonstrated the advantage of including a transport component in the interrill model by adding a runoff term [START_REF] Kinnell | Isolating erosivity and erodibility components in erosion by rain impacted flow[END_REF]Kinnell and[START_REF] Kinnell | Soil/slope gradient interactions in erosion by rain-impacted flow[END_REF]:

D i D f K i , I, q, S
where q is the excess surface water factor. [START_REF] Zhang | Modeling interrill sediment delivery[END_REF], after a factorial rainfall simulation experiment, found that neither runoff nor rainfall alone can adequately simulate interrill sediment delivery. [START_REF] Huang | Empirical analysis of slope and runoff for sediment delivery from interrill areas[END_REF] found that sediment delivery related well to either runoff rate or slope steepness in a quadratic model. Further, for the successful modelling of interrill delivery several authors have emphasized the necessity to separate and understand both detachment and transport and the factors controlling their behaviour [START_REF] Foster | Process-based modelling of soil erosion by water on agricultural land[END_REF][START_REF] Nearing | Modelling soil erosion[END_REF][START_REF] Huang | Sediment regimes under different slope and surface hydrologic conditions[END_REF]. As found by [START_REF] Meyer | How rain intensity affects interrill erosion[END_REF] using rainfall simulator data, the process of soil detachment by raindrop impact can be expressed by the equation:

D D KI 2
Foster (1982) and [START_REF] Foster | Beyond the USLE: Advancements in soil erosion prediction[END_REF] expressed detachment on interrill areas by raindrop impact integrating directly the effect of crop cover:

P i D K i I 2 C i C g C s
where P i is the detachment rate on interrill areas, C i is the factor for the effect of canopy, C g is the factor for the effect of ground cover and C s is the factor for the effect of soil biomass and soil disturbance.

Although the relationships between soil properties and interrill erosion processes have been addressed [START_REF] Bradford | Interrill soil erosion processes: Relationship of splash detachment to soil properties[END_REF][START_REF] Bissonnais | Interactions between soil properties and moisture content in crust formation, runoff and interrill erosion from tilled loess soils[END_REF], the development of an equation predicting the soil factor K i as a function of measurable soil properties has not yet been achieved [START_REF] Agassi | Methodologies for interrill soil erosion studies[END_REF]. In addition, erodibility (³ K i ) is not constant for a given soil type and may be highly variable in the field depending on other factors such as surface conditions. Most interrill soil erosion studies are based on laboratory or field rainfall simulation on specific soil samples, which on the one hand permits control of all the variables in order to study in detail one process, but on the other hand, restricts the range of possible applications of the results as erosion processes are known to be dependent upon methodologies. Further, numerous studies investigated the processes of wash erosion and soil particle splash, using laboratory or field rainfall simulators. Various methodologies may lead to various results which are not necessarily related to the specific effect of the variables under study on erosion and infiltration processes [START_REF] Agassi | Methodologies for interrill soil erosion studies[END_REF].

The objective of this study is to explicitly express potential sediment concentration in interrill flow as a function of surface condition factors on the basis of long-term field experiments. The effects of the dominant controlling factors are accounted for by establishing a classification of uniform potential sediment concentration domain according to a combination of the dominant soil surface parameters.

MATERIAL AND METHOD

Description of the database

A total of 673 rainfall-runoff events with sediment concentration measurements originating from different experiments realized in the Normandie region since 1992 (Tables I andII) were gathered in a common data set. The studied area is covered by silt loam soils developed on the loess Quaternary deposit and containing at least 60 per cent silt in the surface horizons. Soils are classified as Neoluvisol in the French Classification and are described as 'excessively drained' by the USDA (1951) soil drainage classification. Such soils are very sensitive to soil sealing because of their low clay content (130 to 170 g kg 1 ) and low organic matter content (10 to 20 g kg 1 ). When in arable use, large areas are left bare and open to rainfall during most of the cultural season which, combined with the sensitivity to sealing, renders them vulnerable to runoff and water erosion phenomena. The topography is relatively smooth with slope gradients ranging from 1 to 4 per cent on the plateau and 4 to 10 per cent on valley sides. The soil characteristics show very little variability within the studied area. In addition to rainfall, runoff and sediment delivery monitoring, overland flow patterns, surface roughness, soil sealing stages and rate of soil coverage by vegetation were measured semi-quantitatively twice a month during the period of study [START_REF] Auzet | Concentrated flow erosion in cultivated catchments: influence of soil surface state[END_REF][START_REF] Ludwig | Hydrological structure and erosion damage caused by concentrated flow in cultivated catchments[END_REF][START_REF] Bissonnais | Crusting, runoff and sheet erosion on silty loamy soils at various scales and upscaling from m 2 to small catchments[END_REF].

Variables present in the database are: area (m 2 ), slope (per cent), land use, roughness (in five classes: 0-1 cm, 1-2 cm, 2-5 cm, 5-10 cm, >10 cm), soil surface degradation stage (four stages: (i) non-sealed: initial fragmentary structure with all fragments clearly distinguishable; (ii) structural seal: altered fragmentary state with local structural seal; (iii) transitional seal: generalized structural seal with local appearance of depositional seal; (iv) sedimentary seal: continuous state with depositional seal), crop cover (exact percentage of coverage leaves, litter and debris; or notation in three classes: C1, 0-20 per cent, C2, 21-60 per cent, C3, 61-100 per cent), runoff volume (l), rainfall amount (mm), rainfall events mean and 6-min maximum intensity (mm h 1 ), 48-hour antecedent rainfall amount (mm), and sediment concentration (SC) in the flow (g l 1 ). Repartition of sediment concentration values is shown in Figure 1.

Representativeness of the data base

In order to test the representativeness of the erosion measurements data set in terms of soil surface conditions, field observations have been carried out once every two months up to twice a month on two experimental catchments: Blosseville catchment (89 ha) between [START_REF] Kinnell | Runoff as a factor influencing experimentally determined interrill erodibilities[END_REF]and 1998and Bourville catchment (1100 ha) ha) between 1996 and 1998. The two catchments have different characteristics (physical and land use) and together are representative of the region [START_REF] Lecomte | Transfert de produits phytosanitaires par le ruissellement et l'érosion de la parcelle au bassin versant -Processus, déterminisme et modélisation spatiale[END_REF]. Forty dates of observation (11 in autumn, 12 in winter, 13 in spring and 4 in summer) have been selected so as to represent the different land uses and evolution of soil Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P., [START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth Surface Processes andLandforms, 27 (2), 193-205 Sediment concentration (g/l) . The experimental measurements of SC which constituted the database (Figure 3) are also mainly situated on soils with degraded surface condition (sedimentary seal class). From the 36 combinations (we only consider combinations which account for at least 0Ð2 per cent of the total field observation area) that constitute the field observations (Figure 2), 22 are present in the measurement data set which represents 83Ð8 per cent of the total field observation area. Combinations that are not represented in the SC measurement data set therefore stand for the remaining 16Ð2 per cent of the total field observation area from which 9Ð9 per cent are situations with non-degraded soil surface (non-sealed class) associated with well marked roughness (>5 cm). The lack of SC measurements under such conditions can be explained by the facts that these situations do not often produce runoff, and also because not being key situations, few experiments have been specifically carried out. This database is made of natural rainfall events in field conditions with a long enough time span and with a sufficient number of different measurements to be able to isolate adequately the effects of the various controlling factors.

Number of measurements

RESULTS AND DISCUSSION

Sediment delivery response according to the measured soil surface condition parameters

For each combination of the parameters describing the soil surface conditions, the number of measurements, the mean value of SC (Figure 4) together with the standard deviation have been calculated. Results show that the classification that has been defined for the data collection and which has already been used in a runoff prediction model for the calculation of potential field infiltration rates [START_REF] Cerdan | Incorporating soil surface crusting processes in an expert-based runoff and erosion model STREAM (Sealing Transfer Runoff Erosion Agricultural Modification)[END_REF] clear trend when passing from one class to the next and the standard deviation values are not satisfactory as most of the time they are of the same order or even higher than the average value, particularly for combinations where roughness is high or where the percentage of crop cover is low. Figure 5 shows that a simpler classification based on only two vegetation cover classes (0-20 per cent 21-60 per cent) and on four roughness classes (0-1 cm, 1-5 cm, 5-10 cm and >10 cm), gives a better classification of the SC values. Table III describes the effect of roughness. It contains average sediment concentration measured with increasing orientated roughness values for different crop cover and sealing stages. An increase in roughness values leads to an increase of sediment concentrations for all the situations. The increasing trends are similar, independent of crop cover class; only the magnitude of the response varies. An effect which is hidden in Table III but which is visible on Figure 4, where three crop cover classes are distinguished, is that the increase in sediment concentrations with roughness height is less remarkable when percentage of crop cover exceeds 60 per cent. Another effect of roughness which is not shown in Table III is that even if an increase in roughness was seen to increase average soil suspended particle concentration in the flow, it was seen to decrease total soil loss because it also significantly decreased runoff volume. This remark, already observed by other authors [START_REF] Johnson | Influence of surface roughness and clod size and stability on soil and water losses[END_REF][START_REF] Zobeck | Tillage and rainfall effects on random roughness: a review[END_REF] is particularly true for minor rainfall-runoff events, but can be reversed for extreme events. In our observations there are no noticeable interactions between sealing stages and roughness classes.

The effect of crop cover is displayed in of plants as well as residues, on the ground surface. Continuous evolution could only be compared on sealed soils, as the degradation of the soil surface structure most often occurred before plants had time to develop. An increase in crop cover leads to a decrease in sediment delivery rates, with values three times lower whatever the roughness class when passing from 0-20 per cent to 21-100 per cent of coverage. The data set also confirms earlier observations that cover is very effective against rain erosion, and that small amounts have a more than proportional effect [START_REF] Wischmeier | Rainfall energy and its relationship to soil loss[END_REF][START_REF] Freebairn | A study of soil erosion on vertisols of the Eastern Darling Downs, Queensland. II The effect of soil, rainfall and flow conditions on suspended sediment losses[END_REF]: measurements where we have the exact percentage of coverage indicate a dramatic influence up to 20 per cent, and after 40 per cent augmentation in crop cover is less effective. However, even if it is possible to draw general tendencies from this classification, the standard deviation values (Table V) are acceptable only for combinations where percentage of crop cover and roughness are low on partly sealed soils (structural seal) and where percentage of crop cover is high and roughness is low on sealed soils (sedimentary seal). For all the other combinations standard deviation values indicate that the parameters taken into account do not fully described sediment concentration response.

Erodibility and erosivity

A principal component analysis (PCA) was applied to the matrix composed by eight variables measured on the 673 samples that constituted the database. Since the measurements were on several kinds of measurement scale the analysis was performed on standardized data. The results of the PCA are shown in Table VI and Figure 6. The first component is dominated by orientated and random roughness, sealing stages and percentage of crop cover in descending order of importance and therefore can be defined as the field erodibility axis.

Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P., [START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth Surface Processes and Landforms, 27 ( 2 The second component is explained by rainfall event mean intensity and 6-min maximum intensity and, to a lesser extent, by the percentage of crop cover. This component can consequently be defined as the rain erosivity axis which stands for the potential of the rain to erode. The main indication of PCA is that for our data set, the description of sediment concentration in the flow could be enhanced by the introduction of a rainfall erosivity factor through rainfall mean intensity or rainfall maximum 6-min intensity. The importance of rainfall characteristics in interrill erosion has long been recognized in literature and various indices have been used to account for these processes. However, there is still no general agreement on which rainfall properties actually best describe the mass of sediment detached [START_REF] Salles | Rain properties controlling soil splash detachment[END_REF]. The rainfall erosivity parameter most commonly used is certainly the rainfall intensity. In the WEPP erosion model the square of the average rainfall intensity integrated over the duration of rainfall excess is used to express the combined effect of splash detachment and sheet flow [START_REF] Nearing | A process based soil erosion model for USDA-Water Erosion Prediction Project technology[END_REF]. Similarly, various authors have reported an erosivity factor under the form of I p [START_REF] Meyer | How rain intensity affects interrill erosion[END_REF][START_REF] Kinnell | Laboratory studies on the effect of drop size on splash erosion[END_REF][START_REF] Rose | A mathematical model of soil erosion and deposition processes: I. Theory for a plane land element[END_REF][START_REF] Govers | Spatial and temporal variations in splash detachment: a field study[END_REF]. In the EUROSEM model [START_REF] Morgan | The European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments[END_REF], the calculation of the amount of soil detached by the rainfall impact is related to the kinetic energy. [START_REF] Wischmeier | Predicting Rainfall Erosion Losses -a Guide to Conservation Planning[END_REF] developed a relation between soil loss and the total storm energy multiplied by the maximum 30-min intensity (EI30). [START_REF] Rose | Soil detachment caused by rainfall[END_REF] and [START_REF] William | Prediction of rainfall splash erosion in the seasonally wet tropics[END_REF] have found best correlation using the rainfall momentum. In most of the studies, rate of soil detachment is related to functions of rainfall intensity, kinetic energy, rainfall momentum or combinations of

Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P., [START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth Surface Processes and Landforms, 27 ( 2 these according to the specificity of the experimental designs, i.e. experiments on silty or clayey soils or on sand, previously dry or wet, with very high or low intensity simulated rainfall obtained with different kinds of simulator devices. For all the combinations where standard deviations are high, we try both the mean intensity and the maximum 6-min intensity parameters to enhance the classification. The mean intensity did not bring any Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P., [START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth improvement. One reason could be attributed to problems with the definition of a rainfall event: to calculate the mean intensity when using a rainfall simulator is rather straightforward, when dealing with a natural rainfall events it implies definition of the temporal limits of the events. In our context of intermittent rainfall regime it is difficult to distinguish between contiguous rainfall events with thresholds just based on rainfall characteristics, that is to say which do not rely on the measurement methods (size of the plots) or upon the nature of the studied variables (e.g. if time with no rainfall exceeds the time for the upslope rainfall to flow to the outlet if runoff or sediment yield is the variable of interest).

Table VII shows the variance explained by the maximum 6-min intensity parameter, according to the coefficient of determination, for the combinations where the mean sediment concentrations were not representative. For combinations where crop cover is superior to 21 per cent and where roughness exceeds 5 cm (except Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P., [START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth in the case of structural seal) the correlation is significant even though only 30 per cent of the variance is explained. For the other combinations we obtain good correlation, with R 2 ranging from 0Ð70 up to 0Ð91. We therefore used this parameter to further classify the sediment concentrations response. We have defined three classes of maximum 6-min intensity: 0-10 mm h 1 , 10-40 mm h 1 and ½40 mm h 1 . The classification obtained is shown on Figure 7. Sediment concentration evolution trends are clearly noticeable according to the degree of expression of the different chosen parameters. The most remarkable effect which dominates is certainly the effect of crop cover. Whatever the surface structural state, roughness class or maximum 6-min intensity class, there is a decrease of mean sediment concentration when crop cover goes from 0-20 per cent to 21-100 per cent. This effect is attenuated when roughness height exceeds 5 cm, when moving towards non-degraded surface condition or when maximum 6-min intensity is inferior to 10 mm h 1 , that is to say when rain erosivity controlling influence on particle detachment is less effective and is challenged by wash processes. Roughness influence is well marked when it is superior to 1 cm and when it is not in the sedimentary seal class, whatever the maximum 6-min intensity class or crop cover class. Roughness influence which diminishes when crop cover exceeds 60 per cent is not visible here because of the crop cover threshold retained in the final classification. The maximum 6-min intensity effect is general: it is exponential when crop cover is low and tends to be linear when crop cover is superior to 21 per cent (except the combination of roughness class 5-10 cm with sedimentary seals). It is not feasible here to determine the influence of surface structural stage according to the other parameters as most of the observations are in the same class.

CONCLUSION

A database composed of 673 natural rainfall events with sediment concentration measurements along with notation of percentage of crop cover, roughness height, surface sealing stage and rainfall characteristics was analysed. The results of this study show the complexity of processes involved in interrill erosion even though all the observations were conducted on similar soil types. However, significant differences and evolution trends of mean sediment concentration between the different defined parameter categories were identified. Further, when parameter influences interact, it permits factors having a dominant effect to be discerned according to their respective degree of expression. Thereby it was shown that crop cover had a major influence on mean sediment concentration unless roughness exceeded 5 cm or in the case of rainfall with low maximum 6-min intensity. The established classification also indicates that the increase of the maximum 6-min intensity factor leads to a linear increase of mean sediment concentration for crop cover superior to 21 per cent and to an exponential increase for low crop cover.

The implication of this work may bring perspective for erosion prediction modelling and give references for the development of an interrill erosion equation. It also pinpoints further areas of investigation. Although crop cover is expressed in three classes, for most of the experiments we have measured the exact percentage of area covered by plant leaf and/or plant debris. It would be interesting to separate the two as they do not have the same effect on particle detachment processes. All the experiments have been carried out on slopes with similar gradient (4 per cent). It would also be interesting to study the relationship between mean sediment concentration and slope gradient although soil loss is probably detachment-limited for the range of slope Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P., [START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth gradients observed in the study area since the interrill erosion rate decreases with increasing runoff volume.

All the measurements contained in the database were conducted on loess soils prone to sealing phenomena, extension of these experiments for other soil types is also desirable.

Figure 1 .

 1 Figure 1. Histogram of the value of sediment concentration for the 673 measurements that constitute the database

Figure 3 .

 3 Figure 3. Percentage of sediment concentration measurements per situation contained in the database (n D 673). Non-sealed: initial fragmentary structure, all particles are clearly distinguishable; structural seal: altered fragmentary state with structural seal; transitional seal: local appearance of depositional seal; sedimentary seal: continuous state with depositional seal

Figure 4 .

 4 Figure 4. Mean value of sediment concentration (n D 673). Non-sealed: initial fragmentary structure, all particles are clearly distinguishable; structural seal: altered fragmentary state with structural crusts; transitional seal: local appearance of depositional seal; sedimentary seal: continuous state with depositional seal. Error bars represent the standard deviations

Figure 5 .

 5 Figure 5. Mean value of sediment concentration (n D 673). Non-sealed: initial fragmentary structure, all particles are clearly distinguishable; structural seal: altered fragmentary state with structural seal; transitional seal: local appearance of depositional seal; sedimentary seal: continuous state with depositional seal. Error bars represent the standard deviations

Figure 7 .

 7 Figure 7. Mean sediment concentration classification. Error bars represent the standard deviations

Table I .

 I Characteristics of the experimental sites which constituted the erosion measurement data set

		SEDIMENT CONCENTRATION IN INTERRILL FLOW		195
	Date	Location	Area (m 2 )	Crop type; land use	References
	1993-1994	St-Valery-en-Caux	20	Intercrops (no till,	Martin (1997); Martin
				mouldboard ploughing;	et al. (1999)
				superficial tillage,
				mustard)
	1993-1994	Blosseville	20; 500	Mustard,	Gallien et al. (1995)
				clover-ray-grass,
				mouldboard and
				superficial tillage, mulch
				ploughed and seedbed
				preparation
	1994-1995	Blosseville	20	Intercrops (no till,	Martin (1997); Martin
				mouldboard ploughing;	et al. (1999)
				superficial tillage,
				mustard)
	1996-1997	Englesqueville	100	Wheat	Lecomte (1999)
	1997-1998	Englesqueville	100	Wheat	Lecomte (1999)
		Table II. Characteristics of the rainfall events with erosion measurements
			Mean	Median	Standard deviation	Minimum	Maximum
	Slope (%)		2Ð93	2Ð92	0Ð65	1Ð59	4Ð00
	Rainfall (mm)		14Ð72	12Ð50	9Ð99	1	60Ð00
	Mean intensity (mm h 1 )	1 Ð63	1Ð11	1Ð69	0Ð05	11Ð92
	Max I/6 m n (mm h 1 )	1 3 Ð63	9Ð00	14Ð74	1Ð20	99Ð60
	48 h antecedent rainfall amount (mm)	8Ð63	3Ð39	13Ð24	0	57Ð44
	Runoff ratio (%)		6Ð80	2Ð71	9Ð40	0	69Ð29
	Sediment concentration (g l 1 )	9 Ð20	3Ð00	16Ð39	0	104Ð27

Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P.,

[START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]

. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth Surface Processes and Landforms, 27 (2), 193-205. DOI : 10.1002/esp.314

  is not the most relevant in terms of sediment particle concentration classification. SC values do not reveal any Figure 2. Percentage of observations per class for a total of 5464 field annotations (40 dates of observation at regular time intervals during several cultural seasons for two catchments of 87 and 1100 ha). Non-sealed: initial fragmentary structure, all particles are clearly distinguishable; structural seal: altered fragmentary state with structural seal; transitional seal: local appearance of depositional seal; sedimentary seal: Continuous state with depositional seal

	SEDIMENT CONCENTRATION IN INTERRILL FLOW	197
	20.00
	18.00			non sealed
	16.00			Structural seal Transitional seal
	14.00			Sedimentary seal
	12.00
	10.00
	8.00
	6.00
	4.00
	2.00
	0.00
	Ro ugh nes s 0-1 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s 1-2 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s 2-5 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s 5-1 0 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s >10 cm Cro p cov er 0-2 0% 61-100 % 21-60%

Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P.,

[START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]

. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth Surface Processes and Landforms, 27 (2), 193-205. DOI : 10.1002/esp.314

Table IV .

 IV As mentioned in the Introduction, crop cover is expressed by the percentage of the area, determined by the vertical projection of the cover, i.e. basal and aerial parts

	20.00
	18.00			non sealed
				Structural seal
	16.00			Transitional seal
	14.00			Sedimentary seal
	12.00
	10.00
	8.00
	6.00
	4.00
	2.00
	0.00
	Ro ugh nes s 0-1 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s 1-2 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s 2-5 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s 5-1 0 cm Cro p cov er 0-2 0% 61-100 % 21-60%	Ro ugh nes s >10 cm Cro p cov er 0-2 0% 61-100 % 21-60%

Comment citer ce document : Cerdan, O., Le Bissonnais, Y., Souchère, V., Martin, P.,

[START_REF] Cerdan | Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall[END_REF]

. Sediment concentration in interrill flow: interactions between soil surface conditions, vegetation and rainfall.

Earth Surface Processes and Landforms, 27 (2), 193-205. DOI : 10.1002/esp.314 198 O. CERDAN ET AL.

Table III .

 III Effect of roughness on sediment concentration (SC)

	Sealing stage	Crop cover (%)	Roughness (cm)	Number of	Average SC	Standard deviation
				observations	(g l 1 )
	All sealed stages mixed	0-20	>10	9	30Ð28	9Ð02
			5-10	82	22Ð5	2 4 Ð92
			1-5	206	9Ð59	14Ð07
	Sedimentary seal	21-100	5-10	52	9Ð69	14Ð92
			1-5	185	4Ð04	6Ð22
			0-1	83	1Ð15	2Ð44
		0-20	5-10	62	25Ð37	27Ð53
			1-5	166	11Ð04	15Ð49
			0-1	26	5Ð99	12Ð95

Table IV .

 IV Effect of crop cover on sediment concentration (SC)

		SEDIMENT CONCENTRATION IN INTERRILL FLOW		201
	Sealing stage	Roughness (cm)	Crop cover (%)	Number of	Average SC	Standard deviation
				observations	(g l 1 )
	Sedimentary seal	5-10	21-100	52	9Ð69	14Ð92
			0-20	62	25Ð37	27Ð53
		1-5	21-100	185	4Ð04	6Ð22
			0-20	166	11Ð04	15Ð49
		0-1	21-100	83	1Ð15	2Ð44
			0-20	26	5Ð99	12Ð95
	Table V. Average sediment concentration (SC) for combinations of soil surface factors with more than 10
			measurements
	Crop cover	Sealing	Roughness	Number of		Average	Standard
	(%)	stage	(cm)	observations	SC(g/l)	deviation
	21-100	Sedimentary seal	5-10	52		9Ð69	14Ð92
			1-5	185		4Ð04	6Ð22
			0-1	83		1Ð15	2Ð44
	0-20	Sedimentary seal	5-10	62		25Ð37	27Ð53
			1-5	166		11Ð04	15Ð49
			0-1	26		5Ð99	12Ð95
		Transitional seal	1-5	39		5Ð29	6Ð70
		Structural seal	5-10	11		22Ð59	15Ð69
			1-5	12		3Ð65	1Ð98
		Table VI. Relative contributions of the variables to the two first
		components (/10000) in the principal component analysis
		Variable	Component 1	Component 2
		Orientated roughness	6751	475
		Random roughness	5620	2536
		Sealing		3498	210
		Crop cover		2486	1753
		Mean intensity		1930	5457
		6-min maximum intensity	2852	4202
		Runoff ratio		79	100

Surface Processes and Landforms, 27 (2), 193-205. DOI : 10.1002/esp.314

  Surface Processes and Landforms, 27 (2), 193-205. DOI : 10.1002/esp.314

	202	O. CERDAN ET AL.
		Mean intensity	-1	1 -1	1
			Maximum
	Crop cover		intensity
			Sediment
			concentration
	Sealing	Runoff coefficient	Random
			roughness
		Orientated
		roughness
	Figure 6. Principal component analysis (first and second components)

Table VII .

 VII Situations where the maximum 6-min intensity factor (max 6-min I) explain the variance of sediment concentration (SC)

	Crop cover	Orientated roughness	Sealing	Number of	Average SC	Standard	R 2 max
	(%)	(cm)	stage	observations	(g/l) 1	deviation	6-min I
	21-60	1-5	Sedimentary seal	88	3Ð39	4Ð10	0,320
	0-20	5-10	Sedimentary seal	62	25Ð37	27Ð53	0,369
			Structural seal	11	22Ð59	15Ð69	0,702
		1-5	Sedimentary seal	166	11Ð04	15Ð49	0,811
			Intermediate seal	39	5Ð29	6Ð70	0,752
		0-1	Sedimentary seal	26	5Ð99	12Ð95	0,908

Copyright  2002 John Wiley & Sons, Ltd.Earth Surf.Process. Landforms 27, 193-205 (2002)