Olivier Bonnet
email: odbonnet@hotmail.fr

Hervé Fritz

Jacques Gignoux

Michel Meuret

Challenges of foraging on a high-quality but unpredictable food source: the dynamics of grass production and consumption in savanna grazing lawns

Keywords: climatic variability, continuous stocking, grazing systems, intensively managed grasslands, large herbivores, primary production, rainfall, white rhino

Grazing lawns are short grassland areas where intense grazing maintains grass in an early growth stage. These areas represent a source of high-quality forage for herbivores. However, as herbivores continually remove nearly all the newly accumulated biomass, instantaneous resource availability depends on the dynamics of grass growth.

2.

In this paper, we investigate how production and consumption inside grazing lawns are synchronized. We then explore how that synchronization affects the ability of large herbivores to use these lawns. We also provide a critical comparison between grazing lawns and intensively managed grasslands in livestock farms.

3.

We investigated vegetation production and herbivore grazing activity during a wet and a dry season using clipping experiments and direct observation in two grazing lawns in a South African savanna.

4.

Weekly total grazing activity by unit area was strongly and positively related to short-term primary production. This indicates a close synchronization between these two processes. In contrast, grazing activity was poorly related to standing biomass. Primary production had a threshold response to the weekly pattern of rainfall, implying a stochastic dynamics of grass growth.

5.

The dynamics of grass production and consumption of grazing lawns is similar to the one of continuously stocked grazing systems from intensively managed grasslands. But the mechanisms regulating the two systems lead to different equilibrium points between production and consumption. The two systems also have opposed nutritional functions within the animal diet.

6.

Synthesis. The close synchronization between resource production and consumption inside grazing lawns indicates that instantaneous resource availability is a direct function of the short-term rate of grass growth. In tropical savannas, the main source of variability of lawn

Introduction

Sustainable food resource use is critical in plant-herbivore systems supporting intense levels of consumption. At a regional scale, vertebrate herbivores from most native terrestrial ecosystems only remove a limited proportion (10-20% on average) of the annual net aboveground primary production (NAPP) [START_REF] Wiegert | Investigations of secondary productivity in grasslands. Secondary productivity of terrestrial ecosystems, principles and methods[END_REF]. At a local scale, however, levels of consumption can be much higher. By enhancing local resource attractiveness, repeated grazing can lead to the formation of intensively grazed, short-cropped sward areas called grazing lawns [START_REF] Mcnaughton | Grazing lawns: animals in herds, plant form, and coevolution[END_REF]. Grazing lawns exist in a large variety of terrestrial ecosystems, including North-American tall or mixed grass prairies [START_REF] Coppock | Plant-herbivore interactions in a North-American mixed-grass prairie .2. Responses of bison to modification of vegetation by prairie dogs[END_REF][START_REF] Knapp | The keystone role of bison in north American tallgrass prairie[END_REF], European grasslands [START_REF] Menard | Comparative foraging and nutrition of horses and cattle in European wetlands[END_REF], alpine tundra [START_REF] Mcintire | Grazing history versus current grazing: leaf demography and compensatory growth of three alpine plants in response to a native herbivore (Ochotona collaris)[END_REF], subarctic salt marshes (Person et al. 2003) and African savannas [START_REF] Vesey-Fitzgerald | The utilization of natural pasture by wild animals in the Rukwa valley, Tanganyika[END_REF][START_REF] Mcnaughton | Grazing lawns: animals in herds, plant form, and coevolution[END_REF]. In these lawns, herbivores can consume up to 80-95% of the annual NAPP (e.g. [START_REF] Mcnaughton | Ecology of a grazing ecosystem: the Serengeti[END_REF]Person et al. 1998). Such intense levels of consumption involve a dynamics of resource production and consumption completely different from that in less intensively grazed areas. Understanding this dynamics is of critical importance for evaluating the potential interest of large herbivores in grazing lawns as a food resource.

Grazing lawns are generally reported to be highly profitable grazing areas (e.g. [START_REF] Mcnaughton | Grazing lawns: animals in herds, plant form, and coevolution[END_REF]Person et al. 2003;[START_REF] Verweij | Grazing lawns contribute to the subsistence of mesoherbivores on dystrophic savannas[END_REF]. Maintaining a short-cropped sward allows herbivores to keep grass tissues in a phenologically young state of high nutritional value [START_REF] Waite | Botanical and chemical changes in maturing grass and their effect on its digestibility[END_REF]. In addition, primary production remains at remarkably high levels, sometimes exceeding 10 tonnes dry matter ha -1 year -1 in tropical areas [START_REF] Mcnaughton | Ecology of a grazing ecosystem: the Serengeti[END_REF]. However, continually removing nearly all NAPP has a strong impact on instantaneous food availability: it does not allow the build up of standing crop. As a result, the ability of herbivores to use grazing lawns at any one time is probably strongly dependent on the short-term rate of primary production of these areas.

Results from past studies have emphasized the importance of temporal changes in primary production on the pattern in which herbivores use grazing lawns. For instance, in the Serengeti Plains (Tanzania), the localized distribution of rainfall leads to levels of primary production that are highly stochastic in space and time [START_REF] Mcnaughton | Ecology of a grazing ecosystem: the Serengeti[END_REF]. Nomadic populations of Thomson's gazelle (Gazella thomsoni) and wildebeest (Connochaetes taurinus) closely track these changes in the large-scale spatial distribution of lawn grass production (Wilmshurst et al. 1999;[START_REF] Fryxell | Predictive models of movement by Serengeti grazers[END_REF]). But at a more local scale, the temporal link between primary production and grass consumption in grazing lawns remains poorly documented.

Continuously stocked grazing systems, optimized for livestock production in intensively managed grasslands, have a dynamics of grass production and consumption remarkably similar to the one of grazing lawns. Indeed, they were designed to maintain a relatively constant short sward canopy by letting domestic herbivores continually remove newly accumulated biomass [START_REF] Spedding | The physiological basis of grazing management[END_REF]. Whereas the two systems remain fundamentally different in their overall functioning (i.e. continuously stocked grazing systems are closed and carefully managed by farmers, whereas grazing lawns are open, with herbivores free to move in and out), established fine-scale processes on the dynamics of grass production and consumption in intensively managed grasslands (e.g. [START_REF] Bircham | The influence of sward condition on rates of herbage growth and senescence in mixed swards under continuous stocking management[END_REF]Parsons et al. 1983;[START_REF] Johnson | A theoretical analysis of grass growth under grazing[END_REF] can usefully enlighten patterns observed in grazing lawns.

The objective of this study was first to determine the strength of the temporal link between primary production and grass consumption inside grazing lawns. Secondly, it was to evaluate how this link affects the ability of large herbivores to use grazing lawns as a food resource. Specifically, we determined for grazing lawns the relationships at a weekly time Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript 6 scale between: (i) primary production and rainfall; (ii) large herbivore grazing activity and primary production and (iii) large herbivore grazing activity and standing biomass. We interpret and discuss our results relative to the principles of grass production and consumption established in continuously stocked grazing systems in farms and provide a critical analysis of this comparison. We then discuss implications of our results in a context of highly stochastic distribution of rainfall.

Materials and methods

STUDY SITE

We conducted the study in the Hluhluwe section (hereafter Hluhluwe) of the Hluhluwe-iMfolozi Park (28º10' S, 32º00' E), an enclosed c. 90 000-ha mesic savanna in South Africa.

Mean annual rainfall ranges between 750 and 950 mm, depending on altitude. Inter-annual variations in monthly rainfall are high during the wet season (CV from 55 to 75 % mm) and even higher during the dry season (CV from 74 to 154 %).

The vegetation comprises open grasslands, savannas, broad-leaved thickets and Acacia woodlands (see [START_REF] Whateley | The woody vegetation communities of the Hluhluwe-Corridor-Umfolozi Game Reserve Complex[END_REF] for a detailed description). Grazing lawns occur as small patches (mostly < 8 ha) in a taller bunch-grass matrix and cover less than 10% of the total area of the Park [START_REF] Archibald | Shaping the landscape: fire-grazer interactions in an African savanna[END_REF]. Grass species in grazing lawns include, in order of abundance, Digitaria longiflora, Dactylocterium australe, Sporobolus nitens, Digitaria eylesii and Eustachys paspaloides. Indigenous large grazers include white rhino (Ceratotherium simum), African buffalo (Syncerus caffer), zebra (Equus burchelli), blue wildebeest (Connochaetes taurinus), warthog (Phacochoerus africanus) and impala (Aepyceros melampus). Populations are resident. Herbivore biomass in Hluhluwe (c. 11 000 kg live weight km -2 , Ezemvelo KwaZulu-Natal Wildlife data) is high in comparison with other African conservation areas with the same range of rainfall [START_REF] Fritz | On the carrying-capacity for large ungulates of african savanna ecosystems[END_REF].

White rhinos (c. 480 individuals in 2006) account for around 30% of this biomass and are the main actors of the maintenance of grazing lawns [START_REF] Waldram | Ecological engineering by a mega-grazer: white rhino impacts on a South African savanna[END_REF].

We conducted the study in two distinct grazing lawns (Site 1: 2.5 ha; Site 2: 6.6 ha), 7 km apart from each other, during both the dry (July -August 2005) and wet (January -March 2006) seasons. Daily rainfall data come from four automatic meteorological stations located nearby our study sites. We used net biomass accumulation (NBA, g m -2 d -1 ) instead of NAPP as a measure of primary production. NBA corresponds to NAPP minus the rate of tissue death and provides better estimates of newly produced biomass available to herbivores. We monitored short-term NBA of lawn grass by repeatedly clipping grass inside small exclosures. Four exclosure replicates of 2 × 2 m were randomly placed in each of the two sites, and six square 0.5-m 2 plots were arranged evenly in each of them. Each exclosure had a balanced factorial design with two levels of clipping height (2 and 6 cm) and three levels of clipping frequency (weekly, every 1.5 weeks, and every 3 weeks). Treatments (two clipping heights × three clipping frequencies) were randomly arranged in each exclosure. This design allowed the simulation of a variety of grazing intensities, as well as monitoring the dynamics of lawn grass production at different time scales (from 1 to 3 weeks). We chose clipping heights of 2 and 6 cm to simulate the mean cropping height of white rhino and zebra, respectively. As sward height was extremely short (1-2 cm) and without any growth at the beginning of the dry season, we limited our clipping to the 2 cm clipping treatment thought this season.

Clipped samples were oven-dried at 60 ºC for 8 h to obtain dry mass.

STANDING BIOMASS

We divided each site into 12 areas of relatively homogeneous size and vegetation aspect (sward height, presence of bunch grasses or shrubs). During the dry season, three locations were randomly selected in each area and for each sampling event (three by site). Sampling consisted of clipping all standing material within a 60 × 60 cm quadrat. During the wet season, grazing lawn swards were much more heterogeneous. Thus, we used transects (dimensions 0.1 × 10 m) rather than quadrats to sample the vegetation during this period. One location was randomly selected inside each area and for each sampling event (three for site 1, Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript two for site 2). Due to safety problems (e.g. presence of lions and elephants), we were not able to complete a third sampling event on site 2 at the end of the study. Transect were chosen along a random direction from the selected location. Because of the change of sampling procedure between the dry and the wet season, we did not try to compare standing biomass between the two seasons. It was clear, however, that standing biomass was higher during the wet than during the dry season. Vegetation samples were sorted by species and tissue type (i.e. green leaves, dead leaves and steams) before they were oven-dried.

GRAZING ACTIVITY

We monitored grazing activity of all herbivore species present on the lawns by direct observation during daylight hours (6:00 -18:00). We conducted observations from hides installed in a tree at the centre of each site. This insured minimal disturbance of the feeding behaviour of observed herbivores. Every 5 min, we counted the number of individuals present for each species, and classified them according by age, sex and activity (young or adult, male or female, grazing or not). Grazing activity (min kg 0.75 ha -1 ) was calculated as the product of the total time spent grazing by each individual and its metabolic body mass (BM 0.75 ), divided by the area of the site. This was done to allow interspecific and intersite comparisons. We made a distinction in the mean body mass of age-sex classes for white rhinos (adult males: 2200 kg, adult females: 1700 kg, young: 400 kg, from Owen-Smith 1988) and warthogs (adult males: 90 kg, adult females: 70 kg, young: 10 kg, from [START_REF] Brooks | The Hluhluwe Umfolozi Reserve: an ecological case history[END_REF] only, as these differences were significant for these two species. We used a unique body mass value for other species: zebra, 230 kg; wildebeest, 220 kg; impala, 50 kg (from [START_REF] Brooks | The Hluhluwe Umfolozi Reserve: an ecological case history[END_REF].

During the dry season, we conducted 12 observation hours a week at each site over seven consecutive weeks. Observations were divided into three sessions of four hours (a morning, a mid-day and an afternoon) conducted at random during the week. During the wet season, grazing activity was much more intense. As a result, we conducted continuous observations (12 hours every day for two or three consecutive weeks). The two sites were observed alternately in the following sequence: site 1 (two weeks), site 2 (two weeks) and site 1 (three weeks). We were not able to complete additional observations on site 2 at the end of the study the due to safety problems.

DATA ANALYSIS

The clipping experiment used to monitor NBA had a hierarchical sampling design (exclosures were nested within sites) and temporal pseudo-replication between successive clipping events.

We used mixed generalized linear models to test covariates and treatments affecting NBA. As values for NBA were strictly positive with a variance increasing roughly proportionally with the mean, we used a quasi-Poisson distribution in the models, as suggested by [START_REF] Fisher | A biological assay of tuberculins[END_REF].

We used two different models to test effects on NBA under the weekly clipping treatment.

The first model used data from the two seasons, with cumulative rainfall over one week (CRain1) and season (Season) as fixed effects. The second model was restricted to data from the wet season. Fixed effects were CRain1, time in days since the beginning of drier conditions, i.e. since the last big rainfall of January (Drier, see Fig. 1b) and clipping height (Ch). We used a third model, restricted to the wet season, to test effects on NBA at a time scale of three weeks. This allowed us to test the effect of clipping frequency (Cf) by pooling over three weeks data from plots with shorter clipping intervals. Fixed effects for this last model were cumulative rainfall over three weeks (CRain3), Ch and Cf. All three models contained two additive terms as random effects. One for the nested hierarchical design with exclosure nested within site, and the other with the number of successive clipping events.

Non-significant interactions were systematically removed during the model simplification procedure. We tested for significant differences in standing biomass by using linear models with log-transformed biomass data. Finally, we used a Spearman rank correlation test to investigate correlations of grazing activity with NBA and standing biomass. All analyses were conducted on R 2.7.0 GUI 1.24 free online software (http://www.R-project.org).

Results

PRIMARY PRODUCTION

Net biomass accumulation (NBA) closely matched the rainfall pattern during both the wet and the dry seasons (Figs 1a to 1f). Over the two seasons, the one-week cumulative rainfall positively influenced NBA from the weekly clipping treatment (Table 1, model 1, estimate = 0.165 ± 0.016). This relationship had a marked threshold shape (Fig. 2). Weekly NBA remained close to zero for cumulative rainfall lower than 14 mm, whereas it increased linearly for higher rainfall values. Season did not have a significant effect on this relationship (Table 1, model 1). During the wet season, the number of days since the beginning of drier conditions (i.e. since the last big rain of January) had an additional negative effect on weekly NBA (Table 1, model 2, estimate = -0.056 ± 0.007). On the other hand, clipping height did not have any significant effects (Table 1, model 2).

Considering a time scale unit of three weeks, the relationship between NBA (from the three clipping frequencies) and cumulative rainfall was no longer significant (Table 1,model 3); neither was clipping height nor clipping frequency (Table 1, model 3). The absence of effect of clipping frequency indicates that NBA was a strictly additive process between one and three weeks. A time scale of one week is therefore likely to better capture the effect of a heterogeneous rainfall distribution on NBA.

STANDING BIOMASS

The biomass of green leaves was generally low during the dry season (Figs 1c and1e). However, it significantly increased at the third sampling event (site 1: F 2,73 = 7.77, P < 0.001, contrast: t = 2.67, P = 0.009; site 2: F 2,55 = 15.45, P < 0.001, contrast: t = 5.17, P < 0.001), after the heavy rains of mid-August. During the wet season, (Figs 1d and 1f) total biomass (F 1,54 = 15.24, P < 0.001) and green leaf biomass (F 1,54 = 14.15, P < 0.001) were significantly higher on site 2 than on site 1. For site 1, total biomass significantly decreased between the three sampling events (F 2,26 = 3.69, P = 0.038), but green leaf biomass remained constant (F 2,26 = 1.08, P = 0.35). For site 2, neither total (F 1,18 = 0.12, P = 0.73) nor green leaf biomass (F 1,18 = 0.18, P = 0.68) significantly changed between the two sampling events.

GRAZING ACTIVITY

Grazing activity closely matched variations of weekly NBA over time (Fig 1). During the dry season (Fig. 1g), herbivores rarely used grazing lawns prior to the heavy rains of August.

After these rains, primary production increased and green leaves re-appeared. As a result, herbivores moved back onto these lawns and total grazing activity reached its highest levels.

During the wet season (Fig. 1h), both total grazing activity and weekly NBA declined over time, whereas standing green biomass remained constant.

Considering weekly NBA over the week of and before the grazing observations, weekly total grazing activity and NBA were strongly correlated for the 2-cm clipping treatment (Fig. 3a, Table 2). Correlation was not significant for the 6-cm clipping treatment (Table 2), probably because of the low number of replicates. It followed, however, the same trend as the 2-cm clipping treatment (Fig. 3a). Considering each herbivore species independently, the relationship at the 2-cm clipping height was significant for white rhino (see Fig. 3b), wildebeest and warthog, nearly significant for impala and not significant for zebra (Table 2).

Total grazing activity over one week was positively correlated to standing green biomass during the dry seasons (r s = 0.94, P = 0.017, N = 6), but not during the wet season (r s = 0.3, P = 0.68, N = 5).

Discussion

Many studies have reported that grazing lawns are important areas of highly nutritive food for grazing herbivores (e.g. [START_REF] Mcnaughton | Grazing lawns: animals in herds, plant form, and coevolution[END_REF]Person et al. 2003;[START_REF] Verweij | Grazing lawns contribute to the subsistence of mesoherbivores on dystrophic savannas[END_REF]).

However, our results show that grazing activity inside lawns is strongly dependent on the short-term rate of primary production of these areas. As a result, the ability of herbivores to use these lawns depends on the sustainability of grass growth. We also found that primary production of grazing lawns had a threshold response to weekly rainfall. This dependence of grazing lawns' primary production on a process as stochastic as short-term rainfall indicates that grazing lawns are, in arid to mesic areas, highly unpredictable resources in time. This makes their exploitation by herbivores unsustainable, particularly for resident populations who cannot migrate to track grass productivity.

DETERMINANTS OF GRAZING ACTIVITY IN LAWNS

Weekly total grazing activity was strongly and positively related to short-term NBA (Fig. 3a).

On the other hand, it was poorly related to standing green biomass. Indeed, both NBA and total grazing activity significantly decreased during the wet season, whereas standing green biomass remained constant (Fig. 1). During the dry season, August rainfall resulted in a strong increase of both NBA and total grazing activity. In spite of lower standing green biomass, NBA and grazing activity reached values near the ones observed at the beginning of the wet season (Fig. 1). It is, however, possible that this last pattern was due to the attractiveness of fresh, green leaves being greater during the dry season than during the wet season.

We found a strong positive correlation between grazing activity and standing green biomass during the dry season. This result does not contradict our finding that grazing activity was poorly related to standing green biomass. It only reflects the contrast between conditions in early dry season, when standing green biomass was too low to support grazing activity, and conditions at the end of the dry season, when an increase in standing green biomass allowed grazing activity. As soon as there was enough biomass to support grazing, grazing activity and standing green biomass were no longer related.

By adjusting their grazing activity to primary production, large herbivores maintain grazing lawns in a dynamic equilibrium where newly produced biomass is continually consumed, and where standing biomass remains relatively constant. Studies from salt marshes in northern Netherlands (Ydenberg & Prins 1981) reported similar results. On a range of lawns supporting widely different rates of primary production, barnacle geese (Branta leucopsis) were able to maintain an equal standing biomass by grazing more on sites that were more productive.

These findings strongly suggest that the ability of large herbivores to use grazing lawns is driven by short-term primary production rather than standing biomass. The recrudescence of grazing activity after the large rainfall event observed during the dry season confirms that primary production is the determinant of grazing activity, and not grazing activity the determinant of primary production. As a result, environmental conditions affecting the pattern of primary production over time will be the ultimate drivers of the ability of herbivores to use grazing lawns.

DEPENDENCE OF PRIMARY PRODUCTION ON THE PATTERN OF RAINFALL

In accordance with the vast majority of studies from grasslands or savanna (e.g. Rosenzweig 1968;Sala et al. 1988;[START_REF] O'connor | Influence of precipitation and species composition on phytomass of a semi-arid African grassland[END_REF], we found a strong relationship between rainfall and primary production. Perhaps more interesting is that this relationship was highly significant at the time scale of one week but not when we looked over three weeks. The threshold response of NBA to weekly rainfall (Fig. 2) can explain this discrepancy. When summed over three weeks, rainfalls lower than this threshold (no production) counteract rainfalls higher than the threshold (high production), blurring the relationship.

The close synchronization between rainfall and primary production, and the threshold shape of this relationship have important implications. Ultimately, it indicates that grazing lawns are productive areas only when weekly rainfall exceeds the threshold value. Short-term productivity is thus not sustained over time because of high rainfall stochasticity. Using the threshold value, we can predict from historical rainfall data the expected temporal pattern of lawn grass production over the year in Hluhluwe (i.e. when weekly rainfall exceeded 14 mm).

In 2002, a particularly dry year, weekly cumulative rainfall exceeded 14 mm for only 71 days, compared to 138 days in 2005, which was an average rainfall year (Fig. 4). We can compare this difference in the number of days of potential lawn grass production (i.e. ×2) to the difference in the annual rainfall between 2002 (480 mm) and in 2005 (763 mm) (i.e. ×1.6).

The intra-seasonal distribution of rainfall events may therefore have an effect on primary production as important as the annual amount of precipitation, a result already pointed out in recent studies [START_REF] Knapp | Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland[END_REF][START_REF] Swemmer | Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands[END_REF]). Another implication is that during the dry season, a single but sufficient rainfall amount is able to initiate growth in grazing lawns. The resulting green flush of young leaves can be of primary importance for herbivores during this period of limited food quality [START_REF] Owen-Smith | Correlates of survival rates for 10 African ungulate populations: density, rainfall and predation[END_REF][START_REF] Valeix | Fluctuations in abundance of large herbivore populations: insights into the influence of dry season rainfall and elephant numbers from long-term data[END_REF].

Surveying primary production at a monthly time scale in the Serengeti, McNaughton (1985) reported that a minimum rainfall of 25 mm month -1 was required for significant productivity of lawn grass areas. He also emphasized that the relevant time scale between the two processes probably ranged from a few days to one week. When we convert our data to a monthly time scale, we find that a minimum rainfall of 26 mm month -1 is needed to reach the weekly threshold of 14 mm week -1 at least once a month. This result closely matches McNaughton's observations. It indicates that the primary production of grazing lawns It is surprising that we did not find any significant effect of clipping frequency or clipping height on the relationship between rainfall and primary production. A large number of previous experiments on lawn grass species in the field (e.g. [START_REF] Hik | Effects of the timing of multiple grazing by geese on net aboveground primary production of swards of Puccinellia-phryganodes[END_REF][START_REF] Green | Defoliation-induced enhancement of total aboveground nitrogen yield of grasses[END_REF] or in a greenhouse (e.g. [START_REF] Mcnaughton | Plant adaptation in an ecosystem context: effects of defoliation, nitrogen, and water on growth of an African C-4 sedge[END_REF][START_REF] Georgiadis | Ecological conditions that determine when grazing stimulates grass production[END_REF] reported such effects. This result is probably explained by the predominant effect of rainfall, which overshadowed the effects of the clipping treatments we applied.

GRAZING LAWNS AND INTENSIVELY MANAGED GRASSLANDS

The use of grazing lawns by large herbivores raises the practical question of how sustainable foraging is under repeated grazing? This is a common question in intensively managed grasslands, which has led to the development of a large diversity of grazing systems for farm animals (Parsons 1988;[START_REF] Clark | Grazing management systems for dairy cattle[END_REF]. 'Continuous variable stocking' systems are a form of continuous stocking (see [START_REF] Spedding | The physiological basis of grazing management[END_REF]Parsons 1984) with a dynamics of grass production and consumption dynamics close to the one we observed in savanna grazing lawns. In grazing lawns, we showed that large herbivores maintained a sward of nearly constant standing biomass by quickly adjusting their grazing activity to the primary production. Similarly, in continuous variable stocking systems, farmers maintain a desired, typically short sward canopy height by frequently and carefully adjusting the stocking density of animals to primary production.

In continuous variable stocking systems, which are mainly used with lactating ewes and dairy cattle, maintaining a short sward canopy height does not constrain herbivores' intake levels to low values. Under continuous grazing, maintaining a short sward reduces the net photosynthetic rate of plants. But because of the rapid tissue turnover in grasses, it also increases the efficiency of forage use by improving the proportion of forage consumed to that which senesces and dies without having been eaten (see Parsons et al. 1983). When the balance between net photosynthesis, forage intake and tissues death is optimized, continuous variable stocking systems allow maximizing herbivores' intake levels by unit of grassland area [START_REF] Ernst | Animal and sward production under rotational and continuous grazing management: a critical appraisal[END_REF][START_REF] Grant | Comparison of herbage production under continuous stocking and intermittent grazing[END_REF]Parsons et al. 1988;Pulido & Leaver 2003). For example, in temperate areas, when domestic herbivores are grazed on Lolium perenne L. swards, such maximum levels of intake are reached for a sward canopy height kept between 3 and 6 cm for sheep [START_REF] Bircham | The influence of sward condition on rates of herbage growth and senescence in mixed swards under continuous stocking management[END_REF][START_REF] Grant | Sward management, lamina turnover and tiller population-density in continuously stocked Lolium Perennedominated swards[END_REF]) and between 7 and 9 cm for dairy cattle [START_REF] Ernst | Animal and sward production under rotational and continuous grazing management: a critical appraisal[END_REF]. This highlights a key feature of short grasslands in that despite a low standing biomass, herbivores can maintain large levels of intake by using the sward more efficiently. This feature probably explains how grazing lawns support such high levels of intake, as long as rainfall is sufficient.

In both continuous variable stocking systems and grazing lawns, continuous grazing does not allow the build up of a consistent standing crop. As a result, instantaneous resource availability depends on the short-term rate of primary production. In natural systems, the diversity of the landscape allows herbivores to use other areas when primary production ceases inside grazing lawns. But in intensively managed grasslands, the risk of pasture shortage constrains farmers to maintain areas devoted to forage conservation (i.e. buffer areas), or to conserve a stock of hay or silage [START_REF] Mayne | Grassland management under grazing and animal response. Grass. Its Production and Utilisation[END_REF]. The predominance of this farming practice emphasizes the general problem of resource unpredictability in continuously, intensively grazed areas.

Despite their dynamics being similar, grazing lawns and continuous variable stocking systems remain fundamentally different in their overall functioning. In intensively managed grasslands, farmers carefully adjust stocking densities as a function of their pasture management planning. As a result, they have control over sward canopy height. In grazing lawns, it is herbivores who adjust their own grazing activity as they are free to move in and out of these areas. Because grazing lawns are highly attractive resources, grazing activity increases in proportion to primary production. As a result, sward height remains short and herbivores have to decrease their consumption rates as soon as production slows down or ceases. Grazing lawn systems do not have the flexibility that farmers have in the range of sward height that can be maintained under intensively managed grassland systems. In some cases, it is possible that sward height of grazing lawns may approach a value that allows maximum levels of intake per unit area similar to that aimed for in intensively managed grasslands. Most of the time, however, sward height in lawns is probably lower than the ones that allow for these maxima.

Another important difference between the two systems lies in the nutritional role they play in the individual animals' diet. In intensively managed systems, the grazed forage forms the bulk of the animals diet. To increase the production per animal, or to remedy any deficiencies in nutritional quality or quantity per animal, higher-quality feeds are usually provided in the form of supplements / concentrates. In contrast, although grazing lawns may represent the highest quality part of herbivores diet, the quantity consumed on a daily basis of this material may be limited because of the lawns' low standing biomass. Ultimately, these lawns may play a critical role in the herbivores nutrition. However, the bulk of the animal's diet, particularly over the seasonal time frame, is generally obtained in other, taller grassland or savanna areas that are of lower nutritional value.

Continuously stocked grazing systems and grazing lawns are similar with regard to their dynamics, but contrast with regard to their nutritional role in the herbivores' diet. This highlights the artificial nature of intensively managed grazing systems, which is not surprising as artificiality is part of the definition of intensive management [START_REF] Fgtc | Terminology for grazing lands and grazing animals[END_REF].

Conclusion

By concentrating their grazing activity inside grazing lawns, large herbivores are able to maintain a short-cropped, highly nutritive grass. This can be of high nutritional interest, particularly in environments where the forage resource is often of poor nutritive value. Large herbivores also maintain high levels of intake as short, repeated cropping maximizes the proportion of newly accumulated biomass consumed relative to that which senesces.

However, maintaining a short-cropped sward means that instantaneous resource availability is a function of its short-term rate of growth. This is a general limitation on the use of short grass, in grazing lawns, but also in intensively, continuously stocked grazing systems in farms. In mesic to sub-arid environments, short-term primary production of lawn grass is driven by the weekly pattern of rainfall, a highly stochastic process. As a result, resource availability inside grazing lawns is highly unpredictable in time. It is therefore difficult for herbivores, in the elaboration of their foraging strategy, to develop a perfect knowledge of the resource available inside their habitat. Many models in foraging theory assume perfect knowledge of the environment [START_REF] Stephens | Foraging Theory[END_REF], but this assumption becomes quickly unreliable with grazing lawns. Grazing lawns are predictable in space, but unpredictable in time, and particularly vulnerable to changes in climatic conditions, such as changes in the frequency and intensity of droughts we face in the present context of climate change. Table 1. Analysis of variance from mixed generalized linear models for effects of environmental conditions and treatments on net biomass accumulation. Covariates (fixed effects) -CRain1: cumulative rainfall over one week, CRain3: cumulative rainfall over three weeks, Drier: number of days since the beginning of drier conditions (i.e. since the last big rain of January). Factors (fixed effects) -Season: studying period (wet or dry season), Ch: clipping height (2 or 6 cm), Cf: clipping frequency (weekly, every one week and half, and every three weeks)

Tables

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  Version définitive du manuscrit publié dans / Final version of the manuscript published in : Journal ofEcology, 2010, in advance of print, DOI: 10.1111/j.1365-2745.2010.01663.x Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript 25 Parsons, A.J. (1984) Guidelines for management of continuously grazed swards. Grass Farmer, 17, 5-9. Parsons, A.J. (1988) The effects of season and management on the growth of grass swards. The Grass Crop -The Physioloy Basis of Production (eds M.B. Jones & A. Lazenby), pp. 129-177. Chapman & Hall Ltd, London. Parsons, A.J., Johnson, I.R. & Harvey, A. (1988) Use of a model to optimize the interaction between frequency and severity of intermittent defoliation and to provide a fundamental comparison of the continuous and intermittent defoliation of grass. Grass and Forage Science, 43, 49-59. Parsons, A.J., Leafe, E.L., Collett, B., Penning, P.D. & Lewis, J. (1983) The physiology of grass production under grazing .2. Photosynthesis, crop growth and animal intake of continuously-grazed swards. Journal of Applied Ecology, 20, 127-139. Person, B.T., Babcock, C.A. & Ruess, R.W. (1998) Forage variation in brood-rearing areas used by Pacific black brant geese on the Yukon-Kuskokwim delta, Alaska. Journal of Ecology, 86, 243-259. Person, B.T., Herzog, M.P., Ruess, R.W., Sedinger, J.S., Anthony, R.M. & Babcock, C.A. (2003) Feedback dynamics of grazing lawns: coupling vegetation change with animal growth. Oecologia, 135, 583-592. Pulido, R.G. & Leaver, J.D. (2003) Continuous and rotational grazing of dairy cows -the interactions of grazing system with level of milk yield, sward height and concentrate level. Grass and Forage Science, 58, 265-275. Rosenzweig, M.L. (1968) Net primary productivity of terrestrial communities: prediction from climatological data. American Naturalist, 102, 67-74. Sala, O.E., Parton, W.J., Joyce, L.A. & Lauenroth, W.K. (1988) Primary production of the central grassland region of the United-States. Ecology, 69, 40-45. Wilmshurst, J.F., Fryxell, J.M., Farm, B.P., Sinclair, A.R.E. & Henschel, C.P. (1999) Spatial distribution of Serengeti wildebeest in relation to resources. Canadian Journal of Zoology-Revue Canadienne de Zoologie, 77, 1223-1232. Ydenberg, R.C. & Prins, H.H.T. (1981) Spring grazing and the manipulation of food quality by barnacle geese. Journal of Applied Ecology, 18, 443-453.

FiguresFig. 1 .Fig. 2 .Fig. 3 .

 123 Figures

Fig. 4 .

 4 Fig. 4. Daily variation of cumulative rainfall (mm) over seven preceding days in Hluhluwe

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Table 2 .

 2 Spearman rank correlation test between grazing activity over one week and weekly net biomass accumulation over the week of and before the grazing observations. Clipping height is the clipping treatment used for the measurements of net biomass accumulation

	Source of	d.f.	Deviance	χ 2	P
	variation
	Model 1: two seasons, weekly clipping treatment only
	CRain1	1	30.53	20.08	< 0.001
	Season	1	0.01	0.01	0.92
	Model 2: wet seasons, weekly clipping treatment only
	CRain1	1	14.79	7.07	0.008
	Drier	1	9.56	11.13	< 0.001
	Ch	1	0.01	0.01	0.93
	Model 3: wet seasons, all three clipping treatments
	CRain3	1	0.70	0.62	0.43
	Ch	1	0.71	0.74	0.39
	Cf	2	0.07	0.07	0.96
	Herbivore	Clipping	N Total observed	r s	P
	species	height		grazing time
		(cm)		(min)
	All	2	17	15 565	0.86	< 0.001
	All	6	6	15 565	0.77	0.10
	White rhino	2	17	4 015	0.89	< 0.001
	Wildebeest	2	17	1 655	0.71	0.001
	Warthog	2	17	6 135	0.54	0.024
	Impala	2	17	2 710	0.46	0.057
	Zebra	2	17	1 050	0.37	0.147

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Acknowledgements

This work was supported by the French ANR BioFun project ANR-05-BDIV-013-01 and by the French and South African international research agreement between CNRS and NRF: GDRI -Biodiversity in Southern Africa. Authors are really thankful to all the staff team of HiP for reception and fieldwork help, as well as to the Ezemvelo KwaZulu-Natal Wildlife for fieldwork authorisations. We thank the Zululand Grass Project directed by William Bond for logistical help and sharing data as well as students for help on field and laboratory work: Maud Bernard Verdier, Sara Ferry, Natalie Lebon, Audrey Boisron and Julie Marsaud.

Finally, we thank René Ecochard for help on statistical analyses, Nicole Hagenah, Adrian Shrader and Hallam Payne for help in improving earlier versions of the manuscript and two anonymous reviewers for their insightful contributions to this manuscript.


Loic L Le Cunff
email: lecunff@supagro.inra.fr

Alexandre Fournier Level

Valerie V Laucou
email: laucou@supagro.inra.fr

Silvia Vezzulli
email: silvia.vezzulli@iasma.it

Thierry Lacombe
email: lacombe@supagro.inra.fr

Anne-Francoise A.-F Adam-Blondon

Jean-Michel Boursiquot
email: boursiqu@supagro.inra.fr

Loïc Le Cunff

Alexandre Fournier-Level

Valérie Laucou

Anne-Françoise Adam-Blondon

Patrice This
email: this@supagro.inra.fr

Construction of nested genetic core collections to optimize the

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Background

The study of natural allelic diversity has proved fruitful in understanding the genetic basis of complex traits [START_REF] Roses | A model for susceptibility polymorphisms for complex diseases: apolipoprotein E and Alzheimer disease[END_REF][START_REF] Hugot | Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease[END_REF][START_REF] Ogura | A frameshift mutation in NOD2 associated with susceptibility to Crohn's disease[END_REF][START_REF] Maloof | Natural variation in light sensitivity of Arabidopsis[END_REF][START_REF] Thornsberry | Dwarf8 polymorphisms associate with variation in flowering time[END_REF][START_REF] Szalma | Association analysis of candidate genes for maysin and chlorogenic acid accumulation in maize silks[END_REF].

However, exploiting it successfully through association genetics requires basic knowledge of the extent of allelic variation within a species. One of the most interesting ways to achieve this goal consists of developing high-density diversity maps like the those developed in human and chicken, which allow the identification of causal polymorphisms for important traits [START_REF]The international HapMap Project[END_REF][START_REF]International Chicken Polymorphism Map Consortium: A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms[END_REF][START_REF] Glinsky | Integration of HapMap-based SNP pattern analysis and gene expression profiling reveals common SNP profiles for cancer therapy outcome predictor genes[END_REF][START_REF] Pal | Tagging SNPs in the kallikrein genes 3 and 2 on 19q13 and their associations with prostate cancer in men of European origin[END_REF]. The recent publication of the first high quality draft of the grapevine genome sequence opens the way to building such a diversity map [START_REF] Jaillon | French-Italian Public Consortium for Grapevine Genome Characterization: The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla[END_REF]. Like in animals or in other perennial plant species where genetic approaches based on the study of segregating populations are hampered by a long biological cycle, association genetics is of particular interest in grapevine.

The development of diversity map relies on the discovery of sequence polymorphisms in the genome in a small set of genotypes that are as representative as possible of available genetic diversity. Such a concept was first proposed by Frankel and Brown under the name of core collection [START_REF] Frankel | Plant genetic resources today: a critical appraisal[END_REF]. Core collections can be built using different types of markers. For example, molecular markers were used for rice, wheat and potato, while for yam a core collection was built using the origin of cultivars, eating quality, tuber shape, tuber flesh colour, and morphotype [START_REF] Zhang | Genetic structure and phylogeography of rice landraces in Yunnan, China, revealed by SSR[END_REF][START_REF] Hao | Genetic diversity and core collection evaluations in common wheat germplasm from the Northwestern Spring Wheat Region in China[END_REF][START_REF] Ghislain | Genetic analysis of the cultivated potato Solanum tuberosum L. Phureja Group using RAPDs and nuclear SSRs[END_REF][START_REF] Lebot | Physico-chemical characterisation of yam (Dioscorea alata L.) tubers from Vanuatu[END_REF]. Different strategies have been proposed to assist the construction of core collections including the M-Method developed by Schoen and Brown and implemented in the software MSTRAT [START_REF] Wang | Investigation of Combining Plant Genotypic Values and Molecular Marker Information for Constructing Core Subsets[END_REF][START_REF] Jansen | Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce[END_REF][START_REF] Schoen | Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers[END_REF][START_REF] Gouesnard | MSTRAT: an algorithm for building germplasm core collections by maximizing allelic or phenotypic richness[END_REF]. This strategy has been successfully used for the construction of core collections in Arabidopsis thaliana and Medicago truncatula and was also proposed as a preliminary step in association genetics [START_REF] Mckhann | Nested core collections maximizing genetic diversity in Arabidopsis thaliana[END_REF][START_REF] Ellwood | SSR analysis of the Medicago truncatula SARDI core collection reveals substantial diversity and unusual genotype dispersal throughout the Mediterranean basin[END_REF][START_REF] Ronfort | Microsatellite diversity and broad scale geographic structure in a model legume: building a set of nested core collection for studying naturally occurring variation in Medicago truncatula[END_REF][START_REF] Whitt | Using natural allelic diversity to evaluate gene function[END_REF].

Large collections of genetic resources are available for grapevine especially in Europe [25]. The largest one is held by INRA in France at the domain of Vassal: this collection contains 7000 accessions of Vitis genus of worldwide origin [26]. The genotyping of the whole collection using 20 well-scattered SSR markers is complete Laucou et al. (in prep). The cultivated compartment (V. vinifera L. subsp. sativa) is represented by 3900 accessions corresponding to 2262 unique genotypes (Laucou et al, in prep), from 38 different countries. It represents about a half of the known grapevine cultivars [START_REF] This | Historical origins and genetic diversity of wine grapes[END_REF]. The Vassal collection was highly diverse for V. vinifera L. subsp. sativa, exhibiting a total of 326 alleles for the 20 SSRs markers with an average of 16.3 SSR alleles per locus (Laucou et al, in prep). Moreover a large proportion of these alleles (17%) were present at very low frequency (freq < 0.05%).

A first core collection (M-core) in grape was recently developed based on 50 morphological traits on 1759 accessions from the Vassal collection [START_REF] Barnaud | Linkage disequilibrium in cultivated grapevine, Vitis vinifera L[END_REF]. It was used for a preliminary study of the extent of linkage disequilibrium (LD) in V. vinifera L. as well as for association studies [START_REF] Barnaud | Linkage disequilibrium in cultivated grapevine, Vitis vinifera L[END_REF][START_REF] This | Wine grape (Vitis vinifera L.) color associates with allelic variation in the domestication gene VvmybA1[END_REF]. However, the size of the M-core (141 individuals) limits its use for the analysis of wide sequence diversity.

Here we present the use of the data set obtained by Laucou et al. (in prep) to develop four nested genetic core collections (G-cores) suitable for the search for allelic diversity. The ability of retaining the SSR genetic diversity using different sample sizes was studied and compared to the SSR diversity present in the M-core and in the Vassal collection. Finally, the allelic diversity captured at the sequence level in the different sub-cores was analysed by sequencing three gene fragments. This work provides the foundation required for the development of a detailed map of haplotypic diversity in grapevine.

Results

Construction of nested core collections representing the available germplasm diversity of cultivated V. vinifera L

We first determined the optimal size of a core collection by retaining the 271 alleles showing a frequency above 0.05%. Forty-eight cultivars were necessary to capture 100% of the 271 alleles (Figure 1A). Within this core collection of 48 cultivars (G-48), we then determined the two most diverse samples of 12 (G-12) and 24 (G-24) cultivars (Table 1). In order to assess the robustness of these nested core collections, we calculated the percentage of identical varieties among the G-12, G-24 and G-48 core collections obtained in the second run using the same process, which corresponded to 83.3% [START_REF] Pal | Tagging SNPs in the kallikrein genes 3 and 2 on 19q13 and their associations with prostate cancer in men of European origin[END_REF] of the varieties selected in the two G-12 to 83.3% (20) of the varieties selected in the two G-24 and to 60.24% (29) of the varieties selected in the two G-48. Among these two sets of samples, the G-48 core collection presenting the highest Nei's index was chosen as the reference core collection (Table 2).

The G-48 core was used as a core to build the final core collection retaining the 326 alleles found in the cultivated compartment of Vitis vinifera L. represented in the Vassal collection by Laucou et al. (in prep). The optimal size of this final core collection was 92 individuals (Fig. 1B). The cultivars added at this step contained only rare alleles (freq < 0.05%, present on less than 3 copies), which corresponded to less choice for the selection of varieties. Indeed, only two alternative samples were proposed by MSTRAT, with only one individual differing between the two samples: Rich baba rose faux versus Kizil. Again, we selected the G-92 presenting the highest value for the Nei's index as the reference core collection for the cultivated compartment of V. vinifera L; the resulting final core collection is listed in Table 2.

In order to estimate the gain of SSR allelic diversity, we compared the number of alleles captured in samples obtained by the M-method and by random sampling. In each case, when using the M-method, we observed a gain (Table 3), the greatest of which being obtained for the selection of the G-48.

Analysis of the diversity retained in the nested core collections using different descriptors

The reference nested core collections for the cultivated compartment of Vitis vinifera were described for several characteristics and compared to the Vassal collection and to the M-core collection (141 individuals) defined by Barnaud et al. [START_REF] Barnaud | Linkage disequilibrium in cultivated grapevine, Vitis vinifera L[END_REF].

SSR diversity

The nested core collections represented 58% to 100% of the total SSR diversity of the Vassal collection and 70% to 100% of the restricted SSR diversity of the Vassal collection (only considering alleles with frequencies higher than 0.05%) (Table 1). All the SSR alleles with a frequency of more than 5% within the Vassal collection are present in the G-12 core and all those with a frequency of more than 3.5% within the Vassal collection are present in the G-24. The values of the unbiased Nei's diversity index and the level of unbiased observed heterozygosity for the G-12 core, G-24 core and G-48 core collection were quite similar and slightly higher than those calculated for the G-92 core collection. These values were slightly higher than those of the Vassal collection and of the M-core (Table 1). We also compared allele frequencies of the SSR markers in the three G-cores and in the M-core with the frequencies observed in the Vassal collection: the best correlation was obtained between the Vassal collection and the M-core (r2 = 0.98) and the G-92 (r2 = 0.92) core collections (Table 1).

Geographic origin and final uses

The definition of the true geographical origin of grapevine cultivars is sometimes difficult due to many migration events with humans [START_REF] Mc Govern | Ancient Wine: the search for the origins of viniculture Princeton[END_REF]. Based on current knowledge, the cultivars held in the Vassal collection originated from 38 countries, with about half of them from Western Europe (France, Iberian Peninsula and Italy). The cultivars selected in the nested core collection originated from 27 different countries (Table 2). However 10 varieties of the G-92 sample could not be assigned to a precise geographical origin. Among them, 9 varieties were recent crosses between varieties from different countries (indicated by * in the Table 2) and one have an unknown origin (Moscatel de Oeiras faux). Moscatel de Oeiras faux microsatellite data seemed to indicate a Western Europe origin when compared to the whole collection. The origins of the 82 remaining varieties were well distributed (Figure 2): 21 (25%) came from the Caspian region (Dagestan, Georgia, Armenia and Azerbaijan) and the Middle East (Iran) which corresponds to the center of domestication, and 35 (42%) came from Western Europe and North Africa (Iberian Peninsula, Morocco, Algeria, Tunisia, Italy and France) (Table 4). Interestingly, five varieties (6% of the G-92) originated from Central Asia and Asia despite their very limited representation in the whole collection (less than 2%).

No differences were observed between the M-core (22 countries) and the Vassal collection (Table 4) whereas all the G-cores differed from the Vassal collection. Indeed the number of cultivars from Western Europe and the center of domestication were more balanced in the G-92 core with a very good representation of the whole set of geographic origins. The same trend was observed in the different sub-cores (Table 4).

We also compared the G-92 core collection, the M-core and the Vassal collection with respect to the final use of the cultivars: wine making (wine cultivars), fruit consumption (table cultivars) or both (wine/table cultivars). The M-core and the different G-cores all resembled the Vassal collection (Table 4).

Evaluation of the capture of unlinked diversity in the nested core collections

Next, we assessed the ability of the nested G-core samples to capture diversity unlinked to the SSR markers used to build the nested core collection. Barnaud Redundancy curves obtained using MSTRAT software Figure 1 Redundancy curves obtained using MSTRAT software. Redundancy curves with standard deviation obtained using MSTRAT software (five independent samplings). Determination of the optimal size allowed the capture of all alleles of the original sample. A. For the 271 alleles of the restricted Vassal collection using the M-method (blue dot) and random sampling method (pink dot). B. For the 326 alleles of the Vassal collection using the G-48 core as core using the M-method (blue dot) and random sampling method (pink dot). Estimating the unlinked diversity within the whole Vassal collection (2262 cultivars) would have been very fastidious. Consequently we compared the capture of unlinked diversity in the nested core collections and in the M-core developed only on morphological traits. The total number of SNPs in the M-core (25 SNPs; Table 5) was smaller than in any of the nested G-core samples, even the G-12 core (32 SNPs; Table 5). Moreover, none of the SNPs observed in the M-core was new compared to those found in the nested core collections.

Discussion

In the present work, we developed a set of nested core collections from the cultivated compartment of the Vassal collection, using the M-method and SSR diversity data obtained on 2262 unique genotypes. However, in this way we did not take into account the somatic variants present within V. vinifera L. cultivated germplasm. The usefulness of core collections is due to their ability to capture the diversity of the whole species. Even the smallest nested core collections were more efficient in capturing allelic diversity than the M-core with its 141 accessions. Probable geographic origin of the varieties contained in the nested genetic core collections Figure 2 Probable geographic origin of the varieties contained in the nested genetic core collections. Each triangle corresponds to one variety, red triangles correspond to the first sub-sample of the nested genetic core collection (G-12), yellow triangles to the second sub-sample (G-24), black triangles to the third sub-sample (G-48) and green triangles to the fourth subsample (G-92). Ten varieties belonging to the Core G-92 did not have a precise geographical origin and are not shown on this map.

China China

The Vassal collection, which formed the basis of this work, includes around 3900 cultivars which correspond to 2262 unique SSR genotypes from 38 countries, including from the main domestication area. This represents more than half the varieties found world wide [START_REF] This | Historical origins and genetic diversity of wine grapes[END_REF]. A core collection developed from Vassal collection is thus of major interest for the scientific community, and thanks to the vegetative propagation ability of grape, could be easily multiplied and distributed.

Construction of the core collections

The first result of our work is the fact that only a small number of cultivars (92 individuals, 4% of the Vassal collection) are needed to represent the whole diversity and an even smaller number of cultivars are needed to capture all the most frequent alleles (48 individuals, 2.1%). The comparison with other models is not easy, as they have different biological characteristics, the original collection did not reach the same global diversity of the species, and the analyses are seldom performed in the same way. Nevertheless, the core collections developed for A. thaliana (18%) or M. truncatula (31%) using the same method required higher percentages of individuals selected to represent all the genetic diversity [START_REF] Mckhann | Nested core collections maximizing genetic diversity in Arabidopsis thaliana[END_REF][START_REF] Ellwood | SSR analysis of the Medicago truncatula SARDI core collection reveals substantial diversity and unusual genotype dispersal throughout the Mediterranean basin[END_REF]. In our study we only considered the cultivated compartment which tends to be less diverse than wild compartments [START_REF] Bartsch | Impact of gene flow from cultivated beet on genetic diversity of wild sea beet populations[END_REF]. But the high level of heterozygosity of the grapevine is probably also one of the factors that allow a lower number of individuals than homozygous species like the two plant species mentioned above. Finally, the small number of individuals needed to represent the genetic diversity of the cultivated grapevine also pinpointed the high redundancy of the Vassal collection where many kingroups were highlighted and the interest in using such core collections to optimize the study of the phenotypic and genetic diversity in grapevine [START_REF] Bowers | Historical Genetics: The Parentage of Chardonnay, Gamay, and Other Wine Grapes of Northeastern France[END_REF][START_REF] Staraz | Genetic Structuring and Parentage Analysis for Evolutionary Studies in Grapevine: Kin Group and Origin of the Cultivar Sangiovese Revealed[END_REF].

Nested core collections are of great interest for identifying the sequence diversity that exists in the cultivated compartment of the V. vinifera species

The total genetic diversity revealed in the sequences of three gene fragments (2010 bp) in the G-92 core was quite high with 41 SNPs, i.e. one SNP for every 49 nucleotides. [START_REF] Salmaso | Genome diversity and gene haplotypes in the grapevine (Vitis vinifera L.) as revealed by single nucleotide polymorphisms[END_REF][START_REF] Lijavetzky | High throughput SNP discovery and genotyping in grapevine (Vitis vinifera L.) by combining a re-sequencing approach and SNPlex technology[END_REF]. This comparison thus emphasises the interest of such a core collection for the discovery of genetic diversity.

Among cultivated species, polymorphism in grape is relatively high compared to Zea mays (one SNP every 100 nucleotides), Pinus pinaster (one SNP for every 102 nucleotides) and Hordeum vulgare (one SNP for every 78 nucleotides), while it is relatively low compared to wild species such as A. thaliana (one SNP for every 32 nucleotides) [START_REF] Mckhann | Nested core collections maximizing genetic diversity in Arabidopsis thaliana[END_REF][START_REF] Ching | SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines[END_REF][START_REF] Dantec | Automated SNP detection in expressed sequence tags: statistical considerations and application to maritime pine sequences[END_REF][START_REF] Russell | A comparison of sequence-based polymorphism and haplotype content in transcribed and anonymous regions of the barley genome[END_REF].

G-48 core is highly diverse and non-redundant

The G-92 core was built taking into account extremely rare alleles. Considering the rapid evolution of SSR markers, we assumed that the alleles present in two cultivars or less in the collection did not adequately represent gene diversity and they were thus removed when we built the G-48 core [START_REF] Thuillet | Direct estimation of mutation rate for 10 microsatellite loci in durum wheat, Triticum turgidum (L.) Thell. ssp durum desf[END_REF][START_REF] Donnelly | The coalescent and microsatellite variability[END_REF][START_REF] Valdes | Allele frequencies at microsatellite loci: the stepwise mutation model revisited[END_REF]. Indeed, only one additional SNP was revealed in the G-92 sample (present in two cultivars and not in the M-core) compared to the G-48, thus validating our assumption. On one hand, the gain in the unlinked diversity was high in the G-48, probably due to the decrease in redundancy compared to the Vassal collection (revealed by the number of kingroups). On the other hand, when compared to a random sampling, the gain was much higher using the M-method. The final G-48 core is highly non-redundant and highly diverse. Moreover the G-48 core optimized the unlinked diversity in the three different regions sequenced compared to the Mcore, whose individuals coming from Vassal collection were not selected based on their genotypes, by consequent they could be consider as a less optimized sampling within the Vassal collection. The G-12 and G-24 cores already include respectively 78% and 88% of the SNPs markers present in the G-92 core (80% and 90% of the G-48 core). They also include 58% and 73% of all the SSRs markers identified within the Vassal collection, representing a gain of 6% to 8% compared to random sampling from the G-48 or G-24 core. From a technical point of view, the size of the G-12 and G-24 cores is better suited for high throughput genomic studies and consequently highly suitable for ambitious projects of SNP discovery.

Geographic origin and final uses of the varieties within the G-core

Interestingly the nested core collections constructed in the present work reflect the distribution of grapes in Europe and around the Mediterranean Sea but with over-representation of the cultivars originating from the Caspian region and Middle East, and under-representation of the cultivars from Western Europe (Iberian peninsula, France and Italy) compared to the Vassal collection. We compared the SSR allele frequencies of the nested core collections and of the Vassal collection and found low correlations. This result further emphasizes the decrease in redundancy in the core collections compared with the Vassal collection, but also reflected the relative high number of cultivars originating from Western Europe in the Vassal collection, whereas the main domestication center is the Middle East [START_REF] Mc Govern | Ancient Wine: the search for the origins of viniculture Princeton[END_REF]. These two regions may thus represent important sources of genetic diversity for the V. vinifera L. species. They represent the cradle of viticulture and the first migration of cultivars by Greeks and Etruscans, and a second domestication center in Western Mediterranean region [START_REF] Arroyo-García | Multiple origins of cultivated grapevine (Vitis vinifera L. ssp. sativa) based on chloroplast DNA polymorphisms[END_REF]. Finally, despite their low representation in the Vassal collection, the presence of cultivars from Asia and Central Asia in the nested core collections could also indicate an underexploited center of diversification worthy of prospection and analysis.

The proportion of table varieties, wine varieties and table/ wine varieties was very well conserved in the nested core collections compared to the M-core and to the Vassal collection. The distinction between these three categories of cultivars is based on morphological traits such as berry size, bunch size and compacity but also on other traits such as the sugar/acid balance at maturity [START_REF] Boursiquot | Distribution of the Main Phenological, Agronomical and Technological Characters of Vitis-Vinifera L[END_REF]. Previous studies have shown that there is strong genetic differentiation between these three groups of varieties that may be due either to divergent selection based on the same gene pool or to the use of specific gene pools for the development of the three types of varieties [START_REF] Aradhya | Genetic structure and differentiation in cultivated grape, Vitis vinifera L[END_REF][START_REF] This | Historical origins and genetic diversity of wine grapes[END_REF].

As a consequence, if the samples are well suited for analysis of allelic diversity, other uses can also be proposed for the cores, for example, the nested core collection could help understand the evolution of grape. Both G-12 and G-24 cores contained more frequent alleles representing ancient alleles while G-48 and G-92 may constitute subsequent diversification of cultivars in recent periods.

Conclusion

In the present work, we developed a set of robust nested core collections of V. vinifera L. (cultivated compartment) that will facilitate the discovery of allelic diversity by the scientific community. Moreover, this is an important basic tool for the development of projects of association mapping in grapevine. In conclusion, even if these nested core collections are statistically too small to study correlations between phenotype and nucleotide diversity, their use for preliminary tests of hypothesis will speed up the selection of suitable candidates (for example by discarding unsuitable candidates) and for SNP discovery. Due to the perennial nature of grape and the ease of vegetative propagation, these nested core collections could easily be disseminated worldwide for analyses (by simple request at request-vassal@supagro.inra.fr).

Methods

Plant material and DNA extraction

For each genotype of the four nested core collections, an accession of the Vassal collection (Domain de Vassal, Herault, France) was selected (Table 1) and a batch of young leaves was collected and lyophilized for long-term conservation. Lyophilized leaves were ground twice for 1 min at 20 Hz using a Qiagen-Retsch MM300 crusher. DNA was extracted using the Qiagen DNeasy Plant mini kit (Qiagen) following the manufacturer's instructions with minor modifications: addition of 1% w/v of PVP-40 to the AP1 solution, addition of 180 µl AP2 instead of 130 µl and an additional step of 10 minutes centrifugation at 6000 rpm after incubation on ice, which enabled the majority of the cellular remains and aggregates formed after the addition of AP2 to be pelleted.

Methods for the construction of the core collection

The dataset obtained by Laucou et al. (in prep) on the 2262 unique genotypes from the Vassal collection was used. The M-method proposed by Schoen and Brown and implemented in the MSTRAT software by Gouesnard at al. was used to generate the nested genetic core collections that maximize the number of observed alleles in the SSR data set [START_REF] Schoen | Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers[END_REF][START_REF] Gouesnard | MSTRAT: an algorithm for building germplasm core collections by maximizing allelic or phenotypic richness[END_REF]. The efficiency of the sampling strategy was assessed by comparing the total number of alleles captured using MSTRAT in samples of increasing size with the number of alleles captured in randomly chosen collections of the same size (five independent samplings). After having determined the optimal size of the nested core collections, 200 core collections were generated independently for each sample size. Putative core collections exhibiting the same allelic richness (determined by the total number of alleles represented) were ranked using Nei's index as the second criterion [START_REF] Nei | Molecular Evolutionary Genetics[END_REF].

PCR primer design

The gene sequences that were analysed were derived from three genes located on three separate chromosomes (Table 6). Two were involved in the anthocyanin metabolic pathway: the dihydroflavonol 4-reductase (DFR, gi 499017) present in one copy in the genome of V. vinifera L. and the leucoanthocyanidin dioxygenase (L-DOX gi 22010674) present at least in three copies in the genome of V. vinifera L. based on the NCBI database. The third gene codes for a BURP domain protein presenting a differential expression in a natural mutant of berry development compared to the wild type (VvBURP1; gi 22014825) [START_REF] Boss | Expression of anthocyanin biosynthesis pathway in red and white grapes[END_REF][START_REF] Gollop | Expression of the grape dihydroflavonol reductase gene and analysis of its promoter region[END_REF][START_REF] Fernandez | Identification of genes associated with flesh morphogenesis during grapevine fruit development[END_REF]. Specific PCR primers (Table 2) were designed for the amplification of fragments of these three genes using Primer3 software and tested for amplification on the genomic DNA of the 12 individuals of the core G-12 [START_REF] Rozen | Primer3 on the WWW for general users and for biologist programmers[END_REF].

PCR amplification, sequencing, sequence analysis and SNP detection

The 25 µl PCR reaction mixtures contained 20 ng of genomic DNA, 50 mM KCl, 10 mM TRIS-HCl (pH 8.3), 0.4 mM of each primer, 125 µM of each dNTP, 1.5 mM MgCl2 and 2.5 U of Taq polymerase (Qiagen). PCR amplifications were performed in a MJ Research PTC 100 Thermal Cycler programmed as follows: 5 min denaturation at 94°C, 35 cycles of 94°C for 30 s, 52°C for 45 s, and 72°C for 1 min, followed by an extension step at 72°C for 8 min. The PCR products were purified using the Agencourt AMPure method (Beckman Coulter) and directly sequenced in the two ways using the Big Dye Sequencing kit according to the manufacturer's specifications (Applied Biosystems Inc.). The sequence products were purified using the Agencourt CleanSEQ method (Beckman Coulter) and loaded onto an ABI PRISM ® 3130 XL (Applera) capillary sequencer. The DNA sequences were analysed using the Staden Package [START_REF] Bonfield | new DNA sequence assembly program[END_REF]. Heterozygous SNPs were identified as double pics on the chromatograms and coded according to international codes (nucleotide codes of the International Union of Biochemistry). Insertion/Deletions were easily identified by overlapping sequences. Sequencing both strands enable to deal with such events. Only SNPs present on both forward and reverse sequences were validated.

Statistical analysis

Different indices were used in this study. The selection of reference core collections among those constructed using MSTRAT and exhibiting the same allelic richness (determined by the total number of alleles represented) was performed using Nei's index [START_REF] Nei | Molecular Evolutionary Genetics[END_REF] as the second criterion. Nei's index is given for one locus by: I Neij = 1-∑p ij 2 where pij represents the i allele frequency of the j locus. The Nei diversity index for all the loci is the sum of indices for each locus given by I Nei = ∑ j I Neij 2 . The more the allelic frequencies are equilibrated within a sample, the higher the value of Nei's index As the samples compared were of different size (M-core, nested core collections and the whole collection) the comparison was performed using the unbiased observed heterozygosity and the unbiased Nei's index [START_REF] Nei | Molecular Evolutionary Genetics[END_REF]. The unbiased Nei's index for the locus j is given by: H Neij = (2n/ 2n-1) (1-∑p ij 2 ) where n represents the number of individuals and where pij represents the i allele frequency of the j locus, the unbiased Nei diversity index for all the loci studied is given by H Nei = (1/C) ∑ j H Neij 2 where C is the number of loci studied. The more the allelic frequencies are equilibrated within a sample, the higher the value of the unbiased Nei's index. The observed heterozygosity for the j locus is given by H obsj = 1-∑x ij 2 where x ij represents the homozygote frequency for i allele of the j locus. The unbiased observed heterozygosity for the j locus is: H unobsj = (2n/2n-1) (1-∑x ij 2 ) >where n represents the number of individuals and the unbiased observed heterozygosity for all loci studied is H unobs = (1/C) ∑ j H unobsj 2 where C is the number of loci studied.

We compared the SSR frequencies found in the M-core and the nested G core-collections with those of the Vassal collection using the R 2 correlation coefficient. R 2 is given by R 2 = (Cov ij /σiσj) 2 where Covij is the covariance between the two samples compared and σi and σj are the variance of samples i and j respectively.

  Estimation of the ability to capture unlinked diversity of the G-24 core and G-12 core was performed by comparing their SNP diversity with SNP diversity in five random samples of 24 individuals in the G-48 core and 12 individuals in the G-24 core. The number of SNPs in the different random samples varied from 35 to 37 SNPs for the five random samples of 24 individuals and from 30 to 34 SNP for the five random samples of 12 individuals. In order to compare SNP distribution, we also calculated the unbiased Nei's index, which varied from 0.24 to 0.25 for the five random samples of 24 individuals and from 0.30 to 0.32 for the five random samples of 12 individuals. The unbiased Nei's index of the G-24 and G-12 cores was respectively 0.28 and 0.33.

Table 1 : SSR diversity within each sample of the G-core compared to the Vassal collection with and without the rare allele (Restricted Vassal collection).

 1

	Sample Name	Size	Number	Nei's indices	Observed	Percentage of total	Percentage of	Correlation of SSR
			of alleles		heterozygosity	SSR diversity	restricted SSR	frequency with Vassal
							diversity	collection (R 2 )
	Vassal collection 2262	326	0.76	0.75	100%	100%
	Restricted Vassal	2262	271	0.76	0.75	83%	100%	1
	collection
	G-12 core	12	191	0.83	0.80	58%	70%	0.77
	G-24 core	24	239	0.83	0.81	73%	88%	0.85
	G-48 core	48	271	0.82	0.80	83%	100%	0.92
	G-92 core	92	326	0.81	0.78	100%	100%	0.94
	M-core	141	227	0.76	0.75	70%	81%	0.98

Table 2 : Nested genetic core collection of 12 to 92 varieties.* Varieties bred from cultivars of different geographical origin: the countries listed are breeding locations.

 2

	Size	Variety name

Table 5

 5

	of

). The total polymorphism is thus one SNP for 49 nucleotides. The number of SNPs per base also varied between the three gene fragments: one SNP for every 58 nucleotides for DFR, one SNP for every 42 nucleotides for L-DOX and one SNP for every 50 nucleotides for BURP. The difference of genetic diversity between coding and non coding region of the sequences was estimated only for the DFR sequence which has a quite similar length of the two types G-24 than in the G-48 cores was due to two genotypes: Yapincack with three additional SNPs in the DFR gene fragment and Kisilowy with one additional SNP in the L-DOX gene fragment.

Table 3 : Gain obtained using the M-method at each step of the construction of the nested core collection versus random sampling.

 3

	Original collection	Sample size	M-method	Random sampling	Gain using
			(mean number of alleles for 5 runs)	(mean number of alleles for 5 runs)	M-method
	Vassal with G-48 used as core	92 individuals	326	278.2 (+/-1.3)	15%
	Restricted Vassal collection	48 individuals	269.8 (+/-1.6)	185.2 (+/-5.7)	31%
	(without rare alleles freq < 0.05%)
	G-48 (without rare alleles freq < 0.05%)	24 individuals	238.2 (+/-0.4)	218.8 (+/-6.5)	8%
	G-24 (without rare alleles freq < 0.05%)	12 individuals	190.8 (+/-0.4)	177.8 (+/-1.6)	6%

Table 4 : Distribution of the geographical origin and the final use of the cultivars in the different samples

 4

	Region or	Western Europe	Center of	Asia and	Other area Wine cultivars Table cultivars	Wine and table
	Final uses	and North Africa	domestication	central Asia				cultivars
	Vassal collection	56%	3%	1.6%	39.4%	55%	36%	9%
	M-core	58%	7.2%	0.9%	33.9%	63%	30%	7%
	G-12 core	33%	33%	8%	26%	67%	33%	0%
	G-24 core	33%	33%	12.5%	21.5%	58.5%	37.5%	4%
	G-48 core	37.5%	23%	6.25%	33.25%	56%	31%	12.5%
	G-92 core	42%	25%	6%	27%	56%	32%	12%

  This is substantially higher than the level of genetic diversity observed in the M-core on the same gene set. Moreover, it is higher that the level of genetic diversity observed on an other set of 25 gene fragments totalling 12 kilobases sequenced on seven cultivated individuals (one SNP for every 118 nucleotides) bySalmaso et al. and on a set of 230 gene fragments, what represents the analysis of over 1 Mb of grape DNA sequence 11 grape genotypes (one SNP for every 64 nucleotides) by Lijavetzky et al.

Table 5 : Number of polymorphic bases (SNP or insertion deletions found in the DNA fragments)

 5

	Core collection studied

Table 6 : Localisation of the genes chosen for partial re-sequencing, specific PCR primers used and size of the gene fragment re- sequenced

 6

	DNA fragment (GenbanK)	LG located	Size	Primer forward (5'→3' sequence)	Primer reverse (5'→3' sequence)
	DFR (X75964)	18	810 nt	CAAGCTGCATGGAAGTATGC	TTGGGCCATTCCGTTTTATTA
	L-DOX (BQ795708)	8	500 nt	TTGAGCCCAATCATATTAGTTCC	GTGGCATGACCATTCTCCTC
	BURP (BQ799859)	3	700 nt	CGAAAAGGGACACACAGAG	GTTCAGAGTAGGCCTCGGAA
	Total		2010 nt

(page number not for citation purposes)

Table 2: Nested genetic core collection of 12 to 92 varieties.* Varieties bred from cultivars of different geographical origin: the countries listed are breeding locations. (Continued)

Acknowledgements

This project (Trilateral project TRI017 CoreGrapeGen) was funded by Genoplante, the French Ministry of Research, the INRA Genetic and Breeding Department, and the region of Languedoc-Roussillon. We thank D. Vares and the technical staff of the Vassal domain for plant management and also A. Doligez and T. Bataillon for critical reading of the manuscript. We acknowledge Daphne Goodfellow for improving the English.

Authors' contributions

LLC carried out the sequence, participated in the sequence alignment, performed the statistical analysis and drafted the manuscript. AF-L carried out the sequence and participated in the sequence alignment. VL carried out the SSR analysis. SV carried out the sequence. TL participated in the design of the study and performed the ampelographic analysis. A-FA participated in the design and coordination of the study and helped to draft the manuscript. J-MB participated in the design of the study and performed the ampelographic analysis. PT conceived of the study, participated in the design and coordination of the study and helped to draft the manuscript. All authors read and approved the final manuscript