# Patterns of secondary succession in calcareous grasslands: can we distinguish the influence of former land uses from present vegetation data?

Didier Alard, Olivier Chabrerie, Thierry Dutoit, Philip K Roche, Estelle

Langlois

► To cite this version:

Didier Alard, Olivier Chabrerie, Thierry Dutoit, Philip K Roche, Estelle Langlois. Patterns of secondary succession in calcareous grasslands: can we distinguish the influence of former land uses from present vegetation data?. Basic and Applied Ecology, 2005, 6 (2), pp.161-173. 10.1016/j.baae.2005.01.010. hal-02417162

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<figure><img src="image_1.png" /></figure>
# Patterns of secondary succession in calcareous grasslands: can we distinguish the influence of former land uses from present vegetation data?

Didier Alard<sup>a,*</sup>, Olivier Chabrerie<sup>b</sup>, Thierry Dutoit<sup>c</sup>, Philip Roche<sup>d</sup>, Estelle Langlois<sup>b</sup>

## KEYWORDS

Chalk grassland;
Historical ecology;
Plant community;
Conservation;
Restoration;
Ecosystem of reference;
Normandy;
France

## Summary

We performed a comparative analysis of two neighbouring chalk hillsides, constituted of mosaics of patches of different secondary successional ages but differing with respect to recent land use history (stable vs. variable). A survey on plant communities was performed with similar design for each site. Multivariate analyses were performed to study the relationships between vegetation and environmental variables so as to interpret the present vegetation variability. Co-inertia analyses show that, in both sites, (1) secondary succession dominates the general pattern of composition gradients in plant communities and (2) succession gradient is strongly connected with soil depth. While soil features are linked to natural variables (altitude) in one site, such link is absent in the other site, suggesting the potential role of past land uses. In order to test this last hypothesis, we performed a partial canonical correspondence analysis (CCA) on an additional data set. Two issues are discussed in the context of a conservation LIFE program: (1) the importance of historical data for the definition of ecosystems of reference, seen as target for restoration and (2) the importance of assessing the long-term influence of past land uses for the definition of conservation management plans.

<footnote><sup>a</sup>INRA, Centre de Toulouse, DynaFor, UMR 1201 INRA-INP/ENSAT, BP 27, F-31326 Castanet Tolosan Cedex, France</footnote>

<footnote><sup>b</sup>Laboratoire d'Ecologie, Ecodiv, Université de Rouen, UPRES EA 1293, UFR Sciences, F-76821 Mont-Saint-Aignan Cedex, France</footnote>

<footnote><sup>c</sup>Ecologie des Invertébrés, Université d'Avignon, UMR INRA-UAPV 406, Site Agroparc, Domaine Saint-Paul, F-84914 Avignon, Cedex 9, France</footnote>

<footnote><sup>d</sup>Institut Méditerranéen d'Ecologie et de Paléoécologie, Université Paul Cézanne, UMR-CNRS 6116 IMEP, FST Saint-Jérôme, case 461, F-13397 Marseille Cedex 20, France</footnote>
Introduction

Calcareous grasslands are of major interest for the conservation of biodiversity in north-western Europe as they support many rare and protected species (Van Swaay, 2002; WalliesDeVries, Poschlod, & Willems, 2002). These ecosystems, originating from traditional agriculture and grazing, are nowadays threatened by habitat fragmentation (Fischer & Stöcklin, 1997) and habitat degradation due to agricultural or atmospheric inputs (Bobbink, 1991; Willems, Peet, & Bik, 1993; Willems & Van Nieuwstadt, 1996; Jacquemyn, Brys, & Hermy, 2003) or due to secondary succession occurring after land abandonment (Bobbink & Willems, 1987; Hillier, Walton, & Wells, 1990; Hurst & John, 1999). In Normandy, calcareous grasslands were abandoned in the first half of the 20th century and have been the focus of restoration projects since the 1990s (Alard, 2002).

Experiences in restoration of calcareous grass-
lands were developed during the last decades
(Willems, 2001; Kahmen, Poschlod, & Schreiber,
2002). Recent researches have dealt with the
mechanisms of species recovering during restora-
tion experiments (e.g. Zobel, Suurkask, Rosén, &
Pärtel, 1996; Dzwonko & Loster, 1998; Burnside,
Smith, & Waite, 2002) and the potential influence
of past land uses (Poschlod & WalliesDeVries, 2002).
Indeed, this focus on historical variability is
crucial to understand the functioning of present

ecosystems (Landres, Morgan, & Swanson, 1999; Swetnam, Allen, & Betancourt, 1999) and to define the "ecosystems of reference", a key concept in ecological restoration (Aronson, Floret, Le Floc'h, Ovalle, & Pontanier, 1993; Muller, Dutoit, Alard, & Grevilliot, 1998).

Historical studies on calcareous grasslands have shown that contrast may persist for long periods between former arable lands and ancient grasslands (Wells, Sheail, Ball, & Ward, 1976; Gibson & Brown, 1991), suggesting that the restoration of "typical" grasslands may be a rather slow process. They have also shown that former arable lands may be considered as alternative references for restoration, as they may harbour declining arable weeds (Dutoit, Buisson, Roche, & Alard, 2003).

In this paper, we compared two sites, chosen
for their geographical proximity and the differ-
ences in their land use history during the 19th
century. Because both sites were almost abandoned
in the first half of the 20th century, they exhibit
similar present vegetation mosaic, constituted of
different successional stages from open sward to
encroached scrubs in tall grasslands. Our study of
past land uses aims at understanding to which
extent contrasted histories between both sites (1)
still influence present plant communities and
the course of secondary succession, as suggested
for one of these sites (Dutoit & Alard, 1995),
and (2) imply that specific restoration objectives
for each site.
# Material and methods

## The sites

The two Nature Reserves of Henouville (NRH: 0°56'E, 49°28'N) and Saint Adrien (NRSA: 1°7'E, 49°26'N) are distant by about 15 km in the river Seine valley. Both sites are situated on Coniacian and Santonian chalk. Mean annual temperature is 12 °C for NRH and 10.1 °C for NRSA. The area of the sites is 26 ha for NRH and 32 ha for NRSA, respectively. Orientation, generally south-west oriented, is more diverse in NRSA, due to a small adjacent valley and the effect of recent fluvial erosion while NRH is a regular, slightly concave hillside, which was more affected by periglacial erosion (D. Lefebvre, pers. comm.). The two sites have been used as vineyards in the Middle Ages, then grazed or cultivated for cereals in modern times until the early 19th century. The major differences lie in the past land uses in the 19th and the beginning of the 20th century. In the NRH, ancient cadastre did not mention ploughed parcels since the 19th century. On the other hand, contrast of land uses has been maintained in the NRSA until the WWI (Dutoit & Alard, 1995) where parcels were still used for cereals or vine. Chalk grasslands in the NRH may thus be considered as ancient grasslands (≈100 years old at least) while in the NRSA, there are both ancient grasslands and more recent grasslands on former arable land.

In both sites, vegetation belongs to *Mesobromion* communities, namely *Pulsatilla vulgaris-Sesleria albicans* and *Festuca lemanii-Sesleria* phytosociological associations (De Foucault & Frileux, 1988).

## Data sampling

In 2000, vegetation data were recorded from permanent quadrats of 4 m × 4 m, distributed along seven transects in NRH and six transects in NRSA. These transects were positioned parallel to the slope, each transect being approximately 100 m long, depending on site configuration. For each transect, we positioned 5-8 quadrats with a minimum interval of 15 m between quadrats. The 15 m distance was inferred from previous studies on the NRH site (Chabrerie, Alard, & Touzard, 2002; Chabrerie & Alard, 2005) in which spatially explicit sampling and geostatistics methods (i.e. semivariograms) were performed on vegetation data in order to determine the minimum range for spatially independent quadrats. Beyond this limit, samples are likely to be considered as real replications, without spatial auto-correlation due for example to

species aggregation. We therefore consider the quadrats as spatially and statistically independent samples. The minimum distance between two adjacent transects is at least 50 m, and hence, above the 15 m threshold. In each quadrat, relative abundances of species were recorded within 16 small quadrats of 0.25 cm × 0.25 cm regularly positioned in the centre of each square meter of the quadrat. Other species occurring in the quadrat (but outside the 16 small quadrats) were registered. This sampling design was performed for both sites. Fifty-five and forty-eight quadrats were realized, respectively in NRH and NRSA.

A set of environmental variables was collected for each quadrat in order to analyse the relationship between vegetation and local environment. These variables account for local biotic and abiotic conditions and management or landscape contexts (Appendix B).

Historical data were investigated to account for the role of past land uses. Unlike the survey based on quadrat analysis and performed at a rather fine scale, past land uses can only be collected at the cadastral parcel scale. A parcel is an agricultural management unit, mapped in present and past cadastres. Its size may range from one to a few hectares. A parcel can be stable over time or it can be grouped with other parcels, for example after exchanges between farms, to constitute a bigger parcel. For the site of NRH, the long history of grazing (at least 100 years) suggests that this site is homogeneous for historical variables, as there is no variability in past land uses between parcels. For the NRSA, we revisited a sampling procedure performed in 1994 in order to account for historical variables on the 23 land parcels located on cadastral historical maps. We measured seven environmental variables (slope, elevation, exposition, Ca, $CaCO_3$, C:N ratio, $P_2O_5$) and used six historical variables (colonization by trees in 1952, 1973 and 1991 from aerial photographs; past land uses in 1824, 1914 and 1991 from land survey maps). Details on data sampling and coding are given in Dutoit and Alard (1995). On the parcels mapped, we also adapted the collection of vegetation data, which have to fit this macro-scale grain. For each parcel, we recorded species occurrence and abundance following a phytosociological scale.

## Data analysis

Data reduction was performed by the mean of ordination methods with ADE-4 software (Thiou-louse, Chessel, Doledec, & Olivier, 1997). Multi-variate analyses were preferred to other statistical
methods because they allow, through indirect correspondence analysis (CA) or direct canonical correspondence analysis (CCA) gradient analysis, the establishment of a hierarchy between explanatory variables. This step was crucial for our study, as we not only attempted to assess vegetation-environment relationships, but we also aimed at identifying the main relevant variables for explaining the structure and dynamics of plant communities.

For the study of quadrats, the relationships between vegetation composition and environmental factors in the NRH and NRSA were analysed using co-inertia analyses (Dolédec & Chessel, 1994), which are the best way to couple two data tables (records × plant species) and (records × environmental variables), when the number of floristic and environmental variables is high (Dolédec & Chessel, 1994). The first step in co-inertia analysis is to conduct a separate analysis on the vegetation data set, analysed with a CA, and on the environmental data set, analysed with a principal component analysis (PCA). Then, simultaneously, new axes are isolated in both of the corresponding multidimensional spaces (one representing the vegetation data and the other, the environmental data). A factorial plane is created, deforming as little as possible the structure of each data set and enabling a simultaneous ordination of them. The factor map corresponding to environmental variables is then drawn and interpreted.

For the study at the parcel scale in the specific survey of the NRSA, we performed a partial canonical correspondence analysis pCCA (Ter Braak, 1987) using three matrices: first, the vegetation matrix (23 parcels × 75 species), second, the environmental variables matrix (23 parcels × 7 variables) and third the historical variables matrix (23 parcels × 6 variables). This analysis determines the relationship between a species composition matrix and the matrix of environmental variables once the impact of some other variables (third matrix = historical variables) has been statistically removed. Then it determines the relationship between a species composition matrix and the third matrix once the impact of the environmental variables has been statistically removed (McIntyre & Lavorel, 1994). This method allowed us to study the partial influence of environmental variables (recorded at the parcel scale) and historical variables on the composition of the above-ground vegetation (Ter Braak & Prentice, 1988; Økland & Eilertsen, 1994; Yoccoz & Chessel, 1988). The first eigenvalues of the analyses were compared in order to decompose the total variability into 'historical' effect, 'environmental'

effect and combined effect (historical+environmental effects). We thus performed four pCCAs: (pCCA1) analysis of the simple effects of environmental variables and historical variables; (pCCA2) analysis of the combined effects of environmental variables and historical variables; (pCCA3) analysis of the partial effect of environmental variables without historical variables and (pCCA4) analysis of the partial effect of historical variables without environmental variables (McIntyre & Lavorel, 1994). Statistical significance of each effect or combination of effects was tested using a Monte-Carlo permutation test with 2000 permutations.

## Results

### The analysis of vegetation-environment relationships

The co-inertia analysis performed in the NRH (Fig. 1A-C) exhibits a major gradient along axis 1 (eigenvalues of the two first axis, respectively, 37.5% and 15.0%) which reflects the species turnover along a secondary succession, i.e. open grasslands species (Teucrium montanum, Epipactis atrorubens, Briza media) versus scrub (Cornus sanguinea, Clematis vitalba, Rubus sp.) and tall herbs (Daucus carota, Echium vulgare). Soil depth (Pmax, Pmoy, Pmin) is strongly related to this gradient, and it is clearly linked to the topographical feature of the site, shown by the "Alti" variable (thin soils at the top of the hillside vs. deep soils at the bottom). The variables related to the successional process (Ntree, %tree, Htree) and litter accumulation (%necr) also contribute to the first axis.

Variables associated with axis 2 correspond to management (%bov) and landscape feature (%pras vs. %brous). This axis separates tall grassland records grazed by cattle to other records situated in grasslands encroached by scrubs.

A similar although less dominating gradient can be identified along axis 1 (eigenvalues of 33.7% and 18.0% for axes 1 and 2, respectively) for the analysis of NRSA (Fig. 2A-C). The main floristical gradient opposes open grassland species (Hippocrepis comosa, Bellis perennis, Euphorbia peplus,) to colonizing woody species (Quercus robur, Fagus sylvatica, Prunus spinosa, Rubus sp.) although there are exceptions to this trend (Evonymus europaeus, Genista tinctoria vs. Ranunculus repens, Agrimonia eupatoria). Again, soil depth (Pmin, Pmoy, Pmax) and variables related to
<figure><img src="image_2.png" /></figure>

**Figure 1.** (A–C): Distribution of the 16 m² vegetation quadrats (1A) and of species (1B) and distribution of the environmental variables (1C) in the axis1–axis2 plan of the co-inertia analysis performed on the vegetation and the environmental data matrix of Henouville NRH. Coding for quadrats is based on transect number (I–VII, IX)/quadrat number (1, ..., n) on the transect. Coding for species is given in Appendix A. Coding for environmental variables is given in Appendix B.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

**Figure 2.** (A–C): Distribution of the 16 m² vegetation quadrats (2A) and of species (2B) and distribution of the environmental variables (2C) in the axis1–axis2 plan of the co-inertia analysis performed on the vegetation and the environmental data matrix of the nature reserve of Saint Adrien (NRSA). Coding for quadrats is based on transect number (I–VI)/quadrat number (1, ..., n) on the transect. Coding for species is given in Appendix A.

successional process (%brous, %necr, Ntree, Htree) are linked to this gradient. The “Alti” variable is here associated with other abiotic factors (Orient, Slope) and with axis 2, which opposes species found in low nutrient and stressful soil conditions (Bupleurum falcatum, Teucrium montanum, Ononis natrix, Linum catharticum) to species characterising more productive conditions (Euphorbia amygdaloides, Sonchus asper, Dactylis glomerata).

## The historical analysis of Saint Adrien Nature Reserve

From the CCA (pCCA1) it could be shown that 30.45% (*p* < 0.05) of the above-ground vegetation

matrix was explained by the historical variables alone, and 42.42% (*p* < 0.05) by environmental variables alone. The second partial CCA (pCCA2) performed on combined effects (historical+environmental variables) explained 66.56% (*p* < 0.05). The third partial CCA (pCCA3) performed on the environmental variables without the historical variables explained 36.11% and this result was not significant according to the results of Monte-Carlo permutation as were the results obtained for pCCA4 performed on the historical variables without the environmental variables (24.15%). Historical and environmental variables could not be considered as independent variables and the CCA was performed on the combined variables (environmental+historical variables) to study the species-environment relationships.
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 3.** (A and B): Canonical correspondence analysis (CCA) of the data matrix on the vegetation relevés in the parcels of the Nature Reserve of Saint Adrien. (A) Relevés and environmental-historical data: expo:exposure, alti:altitude, pasture, crop, heath: historical land uses, P: soil P₂O₅(g/kg), C: soil carbon (g/kg), CaCO₃ (g/kg), Col 52, 73, 91: % of shrub colonization in the plot at the different dates from aerial photographs. (B) Species distribution in the F1–F2 plan. Coding for species is given in Appendix A.</figcaption></figure>

The CCA performed in the NRSA clearly separates (axis 1) old-field, which showed the higher contents in P₂O₅, associated with species like *Bromus erectus*, *Arrhenatherum elatius*, *Seseli libanotis* and *Genista tinctoria* from old pastures situated on the steepest slopes and on shallow soils, rich in CaCO₃, associated with species like *H. comosa*, *Thymus praecox*, *Leontodon hyoseroides*, *Anthericum ramosum* and *Briza media* (Fig. 3A and B). Axis 2 of the CCA separated land parcels which are colonized by scrubs and tree species, e.g. *Crataegus monogyna*, *Rosa canina*, *Cornus sanguinea* and *Prunus avium* from parcels still dominated by open vegetation. Neither environmental nor historical variables are linked to this successional axis. Additional information is given (Table 1) to identify the species statistically linked to historical variables only (e.g. *Senecio erucifolius*, *Trisetum flavescens*, *Tragopogon pratense*), environmental variables only (*Sesleria albicans*, *Leontodon hyoseroides*) or both, and the species likely to be independent of these variables in the data set.

## Discussion

The similar history during the last 50 years (i.e. grazing abandonment) has lead, in both sites, to a mosaic of patches at different successional stages

<table><caption>**Table 1.** Contribution to total variance (× 10⁻³) of species in the canonical correspondence analysis (CCA) in the NRSA</caption><thead><tr><th>Species</th><th>History</th><th>Environment</th></tr></thead><tbody><tr><td>Eseg</td><td>1718</td><td>7794</td></tr><tr><td>Salb</td><td>1027</td><td>7067</td></tr><tr><td>Lhyo</td><td>2041</td><td>6895</td></tr><tr><td>Pavi</td><td>1999</td><td>6452</td></tr><tr><td>Jcom</td><td>2578</td><td>6099</td></tr><tr><td>Gtin</td><td>4293</td><td>6075</td></tr><tr><td>Pcal</td><td>2692</td><td>6005</td></tr><tr><td>Opur</td><td>3906</td><td>5596</td></tr><tr><td>Bpub</td><td>1435</td><td>5352</td></tr><tr><td>Smon</td><td>3301</td><td>5239</td></tr><tr><td>Csca</td><td>2154</td><td>5073</td></tr><tr><td>Psax</td><td>2359</td><td>5069</td></tr><tr><td>Hper</td><td>2936</td><td>4985</td></tr><tr><td>Lcat</td><td>418</td><td>4961</td></tr><tr><td>Pvul</td><td>2746</td><td>4957</td></tr><tr><td>Smin</td><td>1813</td><td>4877</td></tr><tr><td>Flem</td><td>3435</td><td>4873</td></tr><tr><td>Slat</td><td>4533</td><td>4820</td></tr><tr><td>Tsco</td><td>1804</td><td>4804</td></tr><tr><td>Dlut</td><td>2490</td><td>4756</td></tr><tr><td>Aela</td><td>2271</td><td>4724</td></tr><tr><td>Slib</td><td>523</td><td>4671</td></tr><tr><td>Gpum</td><td>3359</td><td>4574</td></tr><tr><td>Apse</td><td>2878</td><td>4436</td></tr><tr><td>Lcor</td><td>3123</td><td>4410</td></tr><tr><td>Ofus</td><td>2610</td><td>4393</td></tr><tr><td>Bmed</td><td>3001</td><td>4362</td></tr></tbody></table>
<table><caption>Table 1. (continued)</caption><thead><tr><th>Species</th><th>History</th><th>Environment</th></tr></thead><tbody><tr><td>Ospi</td><td>1594</td><td>4356</td></tr><tr><td>Pspi</td><td>1917</td><td>4351</td></tr><tr><td>Hpil</td><td>1740</td><td>4271</td></tr><tr><td>Pver</td><td>1182</td><td>4097</td></tr><tr><td>Bfal</td><td>2418</td><td>4036</td></tr><tr><td>Fsyl</td><td>2899</td><td>3993</td></tr><tr><td>Avul</td><td>3162</td><td>3942</td></tr><tr><td>Cfla</td><td>1973</td><td>3888</td></tr><tr><td>Qrob</td><td>3140</td><td>3831</td></tr><tr><td>Cave</td><td>2541</td><td>3814</td></tr><tr><td>Cmon</td><td>741</td><td>3740</td></tr><tr><td>Aram</td><td>3231</td><td>3737</td></tr><tr><td>Gcon</td><td>7</td><td>3698</td></tr><tr><td>Vhir</td><td>2410</td><td>3677</td></tr><tr><td>Karv</td><td>1596</td><td>3534</td></tr><tr><td>Ppra</td><td>4071</td><td>3515</td></tr><tr><td>Vhir</td><td>1083</td><td>3466</td></tr><tr><td>Pchl</td><td>1043</td><td>3447</td></tr><tr><td>Apyr</td><td>1405</td><td>3430</td></tr><tr><td>Hcom</td><td>4227</td><td>3343</td></tr><tr><td>Hmur</td><td>2255</td><td>3285</td></tr><tr><td>Cjac</td><td>1022</td><td>3227</td></tr><tr><td>Tpra</td><td>2554</td><td>3194</td></tr><tr><td>Eatr</td><td>1736</td><td>3192</td></tr><tr><td>Caca</td><td>670</td><td>3134</td></tr><tr><td>Vlan</td><td>2567</td><td>3095</td></tr><tr><td>Amil</td><td>4150</td><td>3080</td></tr><tr><td>Tfla</td><td>4638</td><td>3075</td></tr><tr><td>Kmac</td><td>424</td><td>2846</td></tr><tr><td>Bpin</td><td>2843</td><td>2779</td></tr><tr><td>Lvul</td><td>3393</td><td>2770</td></tr><tr><td>Lhis</td><td>629</td><td>2749</td></tr><tr><td>Tpra</td><td>4838</td><td>2746</td></tr><tr><td>Oapi</td><td>2226</td><td>2666</td></tr><tr><td>Cvit</td><td>2386</td><td>2568</td></tr><tr><td>Fexc</td><td>2064</td><td>2537</td></tr><tr><td>Ogra</td><td>904</td><td>2124</td></tr><tr><td>Ecam</td><td>2133</td><td>1999</td></tr><tr><td>Tcha</td><td>4270</td><td>1993</td></tr><tr><td>Srec</td><td>1451</td><td>1957</td></tr><tr><td>Csan</td><td>1920</td><td>1944</td></tr><tr><td>Svir</td><td>3292</td><td>1758</td></tr><tr><td>Rcan</td><td>2386</td><td>1527</td></tr><tr><td>Ovul</td><td>2712</td><td>1514</td></tr><tr><td>Bper</td><td>2725</td><td>1427</td></tr><tr><td>Bere</td><td>2416</td><td>1403</td></tr><tr><td>Seru</td><td>5322</td><td>1399</td></tr><tr><td>Hnum</td><td>1909</td><td>1149</td></tr></tbody></table>

Species in bold are species significantly linked to a given set of variables (environment or history). Coding for species are given in Appendix A.

and a dominant pattern of plant communities' composition detected in the analyses of quadrats. Similarly, soil depth at the quadrat scale is a

strong explanatory variable for this gradient as late-successional stages occur on the deepest soils. Better availability of water and soil fertility, associated with soil depth (Dutoit & Alard, 1995), could explain the enhancement of the growth of shrubs (Pärtel, Mändla & Zobel, 1999). However, such soil conditions may also influence the competitive hierarchy between species (Fuhlendorf & Smeins, 1998) and increase the herbaceous density which may slow down the course of succession by competitive exclusion of trees (but see Castro, Zamora, & Hodar, 2002; Brown & Archer, 1999). Instead of reflecting successional age (i.e. time since relaxation of grazing), successional stages are more likely to reflect successional rate (for example determined by soil fertility). This may explain why, at NRSA, grassland species of fertile soils such as *Ranunculus repens* are associated with the wooded quadrats.

Although soil depth is strongly correlated with altitude in the NRH analyse, this link is absent from the NRSA analyse of quadrats. We assume this major difference is the result of the different histories of each site. In the chalk hills of the Seine valley, such as in NRH, soil *catenas* occur naturally along the slopes due to accumulation of eroded material at the bottom. Parcels formerly ploughed may also exhibit deep soils, independently of the topography, as in NRSA. Therefore, soil depth variability and the resulting macro-scale heterogeneity (i.e. at the scale of parcels) are likely to result from anthropogenic influence in NRSA. Past land uses may emphasize heterogeneity between parcels with different histories although they should have a long-term homogenizing effect on soil properties within parcels. Considering this dual effect on macro- and micro-heterogeneity, grassland fragmentation may follow different spatial patterns in each site. Even with a time-lag, species are likely to respond to this fragmentation process (Eriksson, Cousins, & Bruun, 2002) but evidence should only be revealed by the study of fine-grain spatial structures (Dutoit & Alard, 1995).

There is no speeding up of the succession process at the land parcel scale in the NRSA: old fields are not more colonized by woody species than old pasture. This could be seen as a contradiction of the result at the plot scale. However, soil heterogeneity appears to have a finer grain than the mean size of the parcels. This explains that colonization is not homogenous in the parcel and that old-fields and old pastures are both colonized in NRSA. Furthermore, our data suggest that invading tree species are likely to be the same in both former arable and ancient grasslands. Therefore, the greatest differences between these two
types are to be found between grassland vegetation.

Ancient grasslands are distinct from grasslands on former arable fields by different species assemblages, as generally noticed by authors (Wells et al., 1976; Gibson & Brown, 1991 but see Pärtel et al., 1999). The species identified as specific to ancient grasslands or to old-fields are similar in our work and these studies: *Teucrium chamaedrys* and *H. comosa* are linked with ancient grasslands while *Trisetum flavescens* and *Poa pratensis* are linked with former arable lands. Furthermore, *H. comosa* is present in almost 90% of the quadrats in the NRH while it is absent from almost 90% of the quadrats of the NRSA (although present in a few parcels). This confirms the continuous period of grazing for the past century in NRH. When separating the effect of history to that of present environment, we found that *Pulsatilla vulgaris*, *Juniperus communis* and *S. albicans*, although mostly recorded in ancient grasslands in the NRSA, are probably more limited by current soil conditions. However, because abiotic heterogeneity has probably greatly influenced history, past land uses and soil conditions are non-independent and finer details will probably remain out of reach.

Considering past land uses of the two sites, the period of divergence is probably limited to a short time lapse (≈100 years) with both cultivation and grazing in the NRSA and continuous grazing in the NRH, although is difficult to limit exactly this period of continuity. Before the period under study (>200 years), vineyards and cultivation were common to the history of both sites. Thus present vegetation mosaics are likely to reflect past land

uses in both sites, but following two major trends: mature grasslands at NRH, as a result of grazing continuity; a broader range of grasslands at NRSA due to differences in parcel land uses. Although history is important in both sites, its footprint is different as regards the range of pre-existing ecosystems and the current vegetation patterns.

The concept of "ecosystem of reference" (Aronson et al., 1993) is still debated, mostly because changing environment appears to be a common history of many ecosystems (Sprugel, 1991). Therefore, "natural variability" (Landres et al., 1999) and non-equilibrium mechanism probably come closer to historical conditions than does a static, even if desired, ecosystem type (Swetnam et al., 1999). Among the multiple criteria proposed for the selection of an ecosystem of reference, the concept of "naturalness" was considered the foundation for ecological integrity and a major issue for ecological restoration (Angermeier, 1998). But is this concept relevant for management decision in our case study and what could be, within past trajectories of calcareous grasslands, designated as the "natural range of variation"?

In the chalk hillside of the Seine valley, conditions free of human influence are probably to be found only in the chalk scree remnants considered as relict habitats of high patrimonial value because they host protected endemic species (*Viola hispida*, *Biscutella neustriaca*). These species are probably the very few ones that have maintained populations in the open habitats during the last Ice-Age in that region (Stott, 1975). Conversely, chalk grasslands are definitively linked to human activities, and ecosystem naturalness is probably not of great help

<figure><img src="image_10.png" /><figcaption>**Figure 4.** The selection of one or more ecosystem(s) of references lies in (1) the analysis of historical trajectories and (2) management options, which are based on a single historical time lapse (left) or historical variability (right).</figcaption></figure>
if we exclude human disturbances from the range of natural variations.

Environmental heterogeneity is also a crucial issue for biodiversity maintenance (Wilson, Sykes, & Peet, 1995). Historical variability is part of this heterogeneity. Therefore, restoration objectives should not only focus on one given idealized state of past ecosystem, but also enhance temporal variability in the range of possible references. Management options for chalk grasslands should follow an alternative, which is not based on natural vs. anthropogenic drivers, but on static vs. dynamic targets (Fig. 4). The restoration of "typical" chalk grassland implies a reference to ancient grasslands, their associated species as well as their historical stability. The NRH is obviously a good candidate for such an option. However, restoring typicalness is a long-term issue for grassland on former arable fields (Gibson & Brown, 1991), as in NRSA. Historical variability and macro-scale heterogeneity, allowing threatened weeds to occur, should be considered as a tangible reference for the restoration in this site (Dutoit, Hill, Mahieu, & Gerbaud, 1997). The restoration of particular disturbance for such species may be realized at the lowest cost (low frequency, small parcels) in a general context of grazing management. This would result in early successional grasslands, not far, considering floristic composition, from the mid-successional grassland currently recorded in former arable lands, alternating with occasional arable fields with their associated species. This coexistence of ecosystems, at a spatial level (i.e. macro-heterogeneity at the broad scale) or a temporal one (maintenance of non-equilibrium mechanisms) is probably a neglected dimension of restoration management, which could be considered, especially when typicalness is likely to be unreachable and naturalness is likely to be irrelevant.

## Acknowledgements

Financial support was provided through the LIFE-Nature program "Espèces prioritaires, pelouses et éboulis du bassin aval de la Seine", headed by the "Conservatoire Regional des Sites de Haute Normandie". We thank two anonymous reviewers for their valuable comments on a previous version. Peter Poschlod and Jan Bakker are kindly acknowledged for their advices and their invitation to present our results in the context of the network "Changing Land Use and its Impact on Biodiversity" (CLIMB) supported by the European Science Foundation.

## Appendix A. Coding for species in co-inertia analyses (Figs. 1 and 2) and CCA (Fig. 3)

<table><thead><tr><td>Species</td><td>Code</td></tr></thead><tbody><tr><td>Acer campestre</td><td>Acam</td></tr><tr><td>Acer pseudoplatanus</td><td>Apse</td></tr><tr><td>Achillea millefolium</td><td>Amil</td></tr><tr><td>Agrimonia eupatoria</td><td>Aeup</td></tr><tr><td>Anacamptis pyramidalis</td><td>Apyr</td></tr><tr><td>Anthericum ramosum</td><td>Aram</td></tr><tr><td>Anthoxanthum odorarum</td><td>Aodo</td></tr><tr><td>Anthyllis vulneraria</td><td>Avul</td></tr><tr><td>Arabidopsis thaliana</td><td>Atha</td></tr><tr><td>Arrhenatherum elatius</td><td>Aela</td></tr><tr><td>Asperula cynanchica</td><td>Acyn</td></tr><tr><td>Avenula pubescens</td><td>Apub</td></tr><tr><td>Bellis perennis</td><td>Bper</td></tr><tr><td>Betula pubescens</td><td>Bpub</td></tr><tr><td>Blackstonia perfoliata</td><td>Bperf</td></tr><tr><td>Brachypodium pinnatum</td><td>Bpin</td></tr><tr><td>Briza media</td><td>Bmed</td></tr><tr><td>Bromus erectus</td><td>Bere</td></tr><tr><td>Bupleurum falcatum</td><td>Bfal</td></tr><tr><td>Campanula rotundifolia</td><td>Crot</td></tr><tr><td>Cardamine hirsuta</td><td>Chir</td></tr><tr><td>Carex caryophyllea</td><td>Ccar</td></tr><tr><td>Carex flacca</td><td>Cfla</td></tr><tr><td>Carex muricata</td><td>Cmur</td></tr><tr><td>Carlina vulgaris</td><td>Cvul</td></tr><tr><td>Centaurea jacea</td><td>Cjac</td></tr><tr><td>Centaurea nigra</td><td>Cnig</td></tr><tr><td>Centaurea scabiosa</td><td>Csca</td></tr><tr><td>Cerastium fontanum</td><td>Cfon</td></tr><tr><td>Cirsium acaule</td><td>Caca</td></tr><tr><td>Clematis vitalba</td><td>Cvit</td></tr><tr><td>Clinopodium vulgare</td><td>Clvu</td></tr><tr><td>Convolvulus sepium</td><td>Csep</td></tr><tr><td>Cornus sanguinea</td><td>Csan</td></tr><tr><td>Corylus avellana</td><td>Cave</td></tr><tr><td>Crataegus monogyna</td><td>Cmon</td></tr><tr><td>Crepis biennis</td><td>Cbie</td></tr><tr><td>Dactylis glomerata</td><td>Dglo</td></tr><tr><td>Daucus carota</td><td>Dcar</td></tr><tr><td>Digitalis lutea</td><td>Dlut</td></tr><tr><td>Echium vulgare</td><td>Evul</td></tr><tr><td>Epipactis atrorubens</td><td>Eatr</td></tr><tr><td>Eryngium campestre</td><td>Ecam</td></tr><tr><td>Eupatorium cannabinum</td><td>Ecan</td></tr><tr><td>Euphorbia amygdaloides</td><td>Eamy</td></tr><tr><td>Euphorbia cyparissias</td><td>Ecyp</td></tr><tr><td>Euphorbia seguieriana</td><td>Eseg</td></tr><tr><td>Euphrasia stricta</td><td>Estr</td></tr></tbody></table>
Evonymus europaeus
Fagus sylvatica
Festuca lemanii
Fraxinus excelsior
Galium aparine
Galium mollugo
Galium pumilum
Galium verum
Genista tinctoria
Geranium robertianum
Gymnadenia conopsea
Hedera helix
Helianthemum apenninum
Helianthemum nummularium
Helleborus foetidus
Hieracium murorum
Hieracium pilosella
Himantoglossum hircinum
Hippocrepis comosa
Hypericum perforatum
Hypochoeris radicata
Juniperus communis
Knautia arvensis
Koeleria pyramidata
Leontodon hispidus
Leontodon hyoseroides
Leucanthenum vulgare
Ligustrum vulgare
Linum catharticum
Lotus corniculatus
Luzula campestris
Medicago lupulina
Ononis natrix
Ononis repens
Ophrys apifera
Ophrys fusciflora
Orchis purpurea
Origanum vulgare
Orobanche gracilis
Picris hyoseroides
Pimpinella saxifraga
Pimpinella seselfifolia
Plantago lanceolata
Planthantera chlorantha
Poa pratensis
Polygala calcarea
Polygala vulgaris
Potentilla reptans
Potentilla sterilis
Primula veris
Prunella vulgaris
Prunus avium
Prunus spinosa
Pulsatilla vulgaris
Quercus robur
Ranunculus bulbosus

Eeur
Fsyl
Flem
Fexc
Gapa
Gmol
Gpum
Gver
Gtin
Grob
Gcon
Hhel
Hape
Hnum
Hfoe
Hmur
Hpil
Hhir
Hcom
Hper
Hrad
Jcom
Karv
Kpyr
Lhis
Lhyo
Lvul
Livul
Lcat
Lcor
Lcam
Mlup
Onat
Orep
Oapi
Ofus
Opur
Ovul
Ogra
Phyo
Psax
Pses
Plan
Pchl
Ppra
Pcal
Povu
Prep
Pste
Pver
Prvu
Pavi
Pspi
Pvul
Qrob
Rbul
Ranunculus repens
Rosa canina
Rubus sp.
Sambucus nigra
Sanguisorba minor
Scabiosa columbaria
Senecio erucifolius
Seseli libanotis
Seseli montanum
Sesleria albicans
Solidago virgaurea
Sonchus asper
Sorbus latifolia
Stachys recta
Succisa pratensis
Taraxacum officinale
Teucrium chamaedrys
Teucrium montanum
Teucrium scorodonia
Thymus praecox
Thymus pulegioides
Tragopogon pratense
Trisetum flavescens
Ulmus minor
Verbascum thapsus
Veronica chamaedrys
Viburnum lantana
Vincetoxicum hirundinaria
Viola hirta
Rrep
Rcan
Rsp.
Snig
Smin
Scol
Seru
Slib
Smon
Salb
Svir
Sasp
Slat
Srec
Spra
Toff
Tcha
Tmon
Tsco
Tpra
Tpul
Trpr
Tfla
Umin
Vtha
Vcha
Vlan
Vhir
Vhirt

Appendix B. Coding for variables in the co-inertia analysis (Figs. 1 and 2)

| Variable names | Variable |
|---|---|
| Soil variables | |
| Pmoy | Mean soil depth |
| Pmax | Maximum soil depth |
| Pmin | Minimum soil depth |
| Pvar | Soil depth variability |
| PAmoy | Mean depth of horizon A |
| PAmax | Maximum depth of horizon A |
| PAmin | Minimum depth of horizon A |
| NetA-C | Sharpness of the transition between A and C (1) |
| %Bl AC | % blocks in horizon A–C |
| %Bl 30!cm | % blocks in the first 30 cm of the soil |
| %Ca A | % gravel in horizon A (2) |
| %Ca AC | % gravel in horizon A–C |
| %Ca 30 cm | % gravel in the first 30 cm of the soil |
| %Gr A | % fine gravel in horizon A (2) |
<table><tr><td>%Gr AC</td><td>% fine gravel in horizon A-C</td></tr><tr><td>%Gr 30 cm</td><td>% fine gravel in the first 30 cm of the soil</td></tr><tr><td>%Si endo</td><td>% endogenous flint (not decayed) in the soil</td></tr><tr><td>%Si exo</td><td>% exogenous flint (decayed) in the soil</td></tr><tr><td>%Eltot</td><td>Total % particules > 0.2 cm in the soil</td></tr><tr><td>PH</td><td>pH value in H₂O (1:2.5)</td></tr><tr><td>% Corg</td><td>% organic matter</td></tr><tr><td>Alt Roc</td><td>Alteration of the bed rock (3)</td></tr></table>

### Local abiotic conditions

<table><tr><td>Orient</td><td>Aspect</td></tr><tr><td>Osud</td><td>Aspect to the South (4)</td></tr><tr><td>Dhaut</td><td>Proximity to the bottom of the slope</td></tr><tr><td>Alti</td><td>Elevation</td></tr><tr><td>Slope</td><td>Slope</td></tr><tr><td>RegSol</td><td>Regularity of the surface of the soil (5)</td></tr><tr><td>%sol nu</td><td>% bare soil</td></tr><tr><td>%roc</td><td>% stone cover</td></tr></table>

### Local biotic conditions

<table><tr><td>%mous</td><td>% moss</td></tr><tr><td>%lich</td><td>% lichen</td></tr><tr><td>%necr</td><td>% necromass (standing dead)</td></tr><tr><td>%tree</td><td>% tree cover</td></tr><tr><td>Ntree</td><td>Number of tree</td></tr><tr><td>Hgrass</td><td>Maximum herbaceous vegetation height</td></tr><tr><td>Htree</td><td>Maximum tree height</td></tr><tr><td>Dtree</td><td>Distance to the nearest tree</td></tr></table>

### Landscape and biogeographic variables

<table><tr><td>%for</td><td>% woodland</td></tr><tr><td>%brous</td><td>% scrub</td></tr><tr><td>%phaut</td><td>% tall grassland</td></tr><tr><td>%pras</td><td>% short grassland</td></tr><tr><td>Surf</td><td>Area of chalk grassland in each site</td></tr><tr><td>Lcot</td><td>Length of chalk grassland</td></tr><tr><td>Prmoy</td><td>Annual rainfall</td></tr><tr><td>Tmoy</td><td>Mean annual temperature</td></tr><tr><td>Tmax</td><td>Maximum annual temperature</td></tr><tr><td>Dsea</td><td>Distance to the sea (6)</td></tr><tr><td>AgeRoc</td><td>Age of geologic substratum</td></tr></table>

### Management variables

<table><tr><td>%ov</td><td>% sheep</td></tr><tr><td>%cap</td><td>% goat</td></tr><tr><td>%bov</td><td>% cattle</td></tr><tr><td>%aban</td><td>% abandoned grassland</td></tr><tr><td>%lap</td><td>Presence of rabbits (% feces on the soil)</td></tr><tr><td>Ipat</td><td>Grazing intensity (7)</td></tr></table>

(1): NetA-C = DSmax-DSmin; (2): blocks = particles > 20 cm, gravels = 20 cm = particles < 2 cm, fine gravels = 0.2 cm = particles < 2 cm; (3): 1 = original bed rock without crack, 2 = cracked chalk, 3 = chalky particles dissociated into the soil, 4 = soil developing on colluviums, 5 = very deep soil with few or no particles > 0.5 cm, bed rock at several metres under the soil surface; (4): ASouth = 180°-abs(Aspect-180°); (5): 1 = flat soil, 2 = lightly irregular soil, 3 = very irregular soil (scratched soil), 4 = soil turned over; (6): Distance measured from each site to the sea; (7): 0 = none, 1 = light, 2 = moderate, 3 = heavy.

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