# A functional method for classifying European grasslands for use in joint ecological and economic studies

J.G. Hodgson, G. Montserrat-Marti, B. Cerabolini, R.M. Ceriani, M.

Maestro-Martinez, B. Peco, P.J. Wilson, K. Thompson, J.P. Grime, S.R.

Band, et al.

► To cite this version:

J.G. Hodgson, G. Montserrat-Marti, B. Cerabolini, R.M. Ceriani, M. Maestro-Martinez, et al., A functional method for classifying European grasslands for use in joint ecological and economic studies.

Basic and Applied Ecology, 2005, 6, pp.119-131. 10.1016/j.baae.2005.01.006. hal-02674434

HAL Id: hal-02674434

https://hal.inrae.fr/hal-02674434v1

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A functional method for classifying European grasslands for use in joint ecological and economic studies

J.G. Hodgson<sup>a,*,1</sup>, G. Montserrat-Martí<sup>b</sup>, B. Cerabolini<sup>c</sup>, R.M. Ceriani<sup>c</sup>, M. Maestro-Martínez<sup>b</sup>, B. Peco<sup>d</sup>, P.J. Wilson<sup>a</sup>, K. Thompson<sup>a</sup>, J.P. Grime<sup>a</sup>, S.R. Band<sup>a</sup>, A. Bogard<sup>e</sup>, P. Castro-Díez<sup>b</sup>, M. Charles<sup>e</sup>, G. Jones<sup>e</sup>, M.C. Pérez-Rontomé<sup>b</sup>, M. Caccianiga<sup>c</sup>, D. Alard<sup>f</sup>, J.P. Bakker<sup>g</sup>, J.H.C. Cornelissen<sup>h</sup>, T. Dutoit<sup>i</sup>, A.P. Grootjans<sup>g</sup>, J. Guerrero-Campo<sup>b</sup>, P.L. Gupta<sup>a</sup>, A. Hynd<sup>e</sup>, S. Kahmen<sup>j</sup>, P. Poschlod<sup>j</sup>, A. Romo-Díez<sup>k</sup>, I.H. Rorison<sup>a</sup>, E. Rosén<sup>l</sup>, K.-F. Schreiber<sup>m</sup>, J. Tallowin<sup>n</sup>, L. de Torres Espuny<sup>b</sup>, P. Villar-Salvador<sup>b</sup>

<footnote><sup>a</sup>Unit of Comparative Plant Ecology, Department of Animal and Plant Sciences, The University, Sheffield S10 2TN, UK</footnote>

<footnote><sup>b</sup>Department Ecología Funcional y Biodiversidad, Instituto Pirenaico de Ecología (CSIC) Aptdo. 202, E-50080 Zaragoza, Spain</footnote>

<footnote><sup>c</sup>Unità di Analisi e Gestione Biocenosi, Dipartimento di Biologia Strutturale e Funzionale, Università degli Studi dell'Insubria, Via J.H. Dunant, 3-21100 Varese, Italy</footnote>

<footnote><sup>d</sup>Departamento de Ecología, Universidad Autónoma de Madrid, 28049 Madrid, Spain</footnote>

<footnote><sup>e</sup>Department of Archaeology, The University, Sheffield S1 4ET, UK</footnote>

<footnote><sup>f</sup>Université de Rouen, Laboratoire d'Ecologie, UPRES EA 1293 - ECODIV "Etude et Compréhension de la Biodiversité" 76821 Mont Saint Aignan Cedex, France</footnote>

<footnote><sup>g</sup>Laboratory of Plant Ecology, University of Groningen, P.O. Box 14, 9750 AA Haren, The Netherlands</footnote>

<footnote><sup>h</sup>Department of Systems Ecology, Faculty of Biology, Free University, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands</footnote>

<footnote><sup>i</sup>UMR/CNRS 6116 IMEP, case 461, University of Provence, FST Saint Jerome, 13397 Marseille Cedex 20, France</footnote>

<footnote><sup>j</sup>Institute of Botany, University of Regensburg, 93040 Regensburg, Germany</footnote>

<footnote><sup>k</sup>Institut Botànic de Barcelona. Parc Montjuïc. Av. dels Muntanyans s/n, 08038 Barcelona, Spain</footnote>

<footnote><sup>l</sup>Department of Plant Ecology, Evolutionary Biology Center, Uppsala University, Villavägen 14, S-752 36 Uppsala, Sweden</footnote>

<footnote><sup>m</sup>Pröbstingstr. 77, 48147 Münster, Germany</footnote>

<footnote><sup>n</sup>Institute of Grassland and Environmental Research, North Wyke Research Station, Okehampton, Devon EX20 2SB, UK</footnote>
### KEYWORDS

* Land use;
* Fertility;
* Disturbance;
* Abandonment;
* Agri-environment schemes

## Abstract

A simple protocol is presented for a functional classification of European grassland species using attributes that can be quickly and easily measured. These attributes relate to habitat fertility, intensity of grazing and disturbance. As a surrogate for habitat fertility we use leaf nitrogen predicted by multiple regression from three leaf characters, specific leaf area, dry matter content and size. Average maximum canopy height of the component species of our vegetation, weighted by abundance, provides a rough assessment of the intensity of grazing. The percentage of annuals and vernal geophytes assesses disturbance. Functional descriptions of the CLIMB grasslands were produced and trends relating to both ecosystem and economic processes were detected. Most importantly, our estimate of habitat fertility predicts land use change. Within NW Europe the threat to grassland of conservation value from agricultural 'improvement' increases with fertility while in the Mediterranean increased fertility decreases the likelihood of abandonment. These mathematical relationships between an ecological attribute and a perception of economic potential can help us to routinely combine ecological and economic data. This is an important preliminary step as we attempt to reconcile practical economic concerns and conservation objectives within working landscapes.

## Zussammenfassung

Es wird ein einfaches Protokoll für eine funktionelle Klassifikation europäischer Grünlandarten unter Verwendung von einfachen Eigenschaften präsentiert, die schnell und einfach gemessen werden können. Diese Eigenschaften beziehen sich auf die Habitatfruchtbarkeit sowie auf die Intensität der Beweidung und der Störung. Als Surrogat der Habitatfruchtbarkeit nutzen wir den Blattstickstoff, der mit Hilfe einer multiplen Regression aus drei Blattmerkmalen vorhergesagt wird: Spezifische Blattfläche, Trockensubstanz und Größe. Die mittlere maximale Vegetationshöhe der in unserer Vegetation enthaltenen Arten nach der Abundanz gewichtet liefert eine grobe Einschätzung der Beweidungsintensität. Der prozentuale Anteil von Annuellen und Frühlingsgeophyten ergibt eine Einschätzung der Störung. Es wurden funktionelle Beschreibungen der CLIMB Grünländer erstellt und Trends sowohl in Beziehung auf Ökosystem- als auch ökonomische Prozesse erkannt. Besonders wichtig ist, dass unsere Einschätzung der Habitatfruchtbarkeit die Veränderung der Landnutzung vorhersagt. Innerhalb von NW Europa steigt die Gefährdung von Gründländern mit Naturschutzwert durch landwirtschaftliche „Verbesserungen“ mit der Fruchtbarkeit während eine zunehmende Fruchtbarkeit in den Mittelmeergebieten die Wahrscheinlichkeit der Aufgabe verringert. Diese mathematischen Beziehungen zwischen einer ökologischen Eigenschaft und der Wahrnehmung des ökonomischen Potentials können uns helfen, routinemäßig ökologische und ökonomische Daten zu kombinieren. Dies ist ein bedeutender erster Schritt, wenn wir versuchen praktische ökonomische Überlegungen und Naturschutzziele innerhalb von funktionierenden Landschaften in Übereinstimmung zu bringen.

## Introduction

The main objective of the CLIMB network is to find Europe-wide economic solutions to the problems of management of grassland of conservation value: the biodiversity of the landscape must be maintained or enhanced using management that is inexpensive, or better still, economically profitable. Individual case studies are vital to this quest. These both provide potential solutions and highlight unresolved problems. Nevertheless, if these case studies are to be translated into workable

European policies, we must subsequently introduce generality. Separate studies must be integrated to produce an ecological-economic synthesis. If our study sites had been floristically similar the joint analysis of plant species composition and environmental characteristics may have provided an appropriate method for such ecological comparison (Jongman ter Braak & van Tongeren, 1995). This approach has, however, severe limitations where regions have contrasted floras, as is the case here. We, therefore, need an alternative methodology, a new simple basic ecological language, that will (1)
enable ecologists throughout Europe to understand
the functioning of each others very floristically and
environmentally different ecosystems and (2) re-
late ecosystem properties to economic value and
potential.

The first and most basic impediment to an
ecological characterisation of our diverse European
grasslands is that vegetation is generally defined in
terms of its floristic composition. The fundamental
unit utilised in these descriptions is the species and
while taxonomy in its intended use provides
invaluable information as to evolutionary related-
ness, it is not additionally a satisfactory basis for
the ecological classification of plants. True, some
ecological specialisation is taxonomically linked;
families such as Fabaceae (legumes) and Poaceae
(grasses) may be viewed for some purposes as
functional groupings. However, this 'functional
uniformity' is only partial. Take grasses; different
species from the same genus Festuca have very
different ecological distributions even within small
geographical areas (see Grime, Hodgson, & Hunt,
1988). Thus, close to Sheffield in C. England there
are species of Festuca characteristic of shaded
habitats and others of open situations, species of
fertile habitats and those of chronically unproduc-
tive ones, species of calcareous soils and those of
highly acidic ones. Moreover, some species of
Festuca are tall and others are short and some
show extensive vegetative spread while others do
not. Thus, before we can compare vegetation and
the way it functions across the whole of Europe, we
need to find a rather different, and preferably
numerical, ecological classification of plants.

Vegetation processes are complex and the
responses of individual species vary according to
ecological circumstances. For such reasons some
ecologists have strong philosophical objections to
generalising about the way ecosystems function
(e.g. Grubb, 1985). Moreover, there are logistic
problems; values are needed for hundreds or even
thousands of species. This precludes the adoption
of a rigorous but time-consuming experimental
approach to data collection. Nevertheless, much
progress has been made towards a usable ecological
classification of species. Early work was narrowly
focussed upon easily measured attributes such as
growth form (Raunkiaer, 1934) and seed size
(Salisbury, 1942). However, a major breakthrough
came with the work of Ellenberg (1974) and
Ellenberg, Weber, and Dull (1992). He produced
an integrated ecological classification for the flora
of Central Europe encompassing c. 2000 species.
This classification included such characters as soil
reaction (pH), drought, nitrogen, light and climate
based primarily upon the distribution of species in

relation to recognised plant communities. Despite
being to some extent subjective, the values that
Ellenberg generated show very good correspon-
dence with objective attributes independently
measured elsewhere (e.g. Thompson et al., 1993;
Schaffers & Sykora, 2000). Moreover, 'Ellenberg
values' have proved practically useful (e.g. Velve &
Aase, 1980; Persson, 1981). There is, however, a
major problem with the Ellenberg approach. It is
difficult to transfer the method to other regions
with different climates and vegetation types. We,
have therefore, preferred functional measure-
ments, underpinned by mechanistic ecological
theory (e.g. Grime, 1974), that relate to what
species do rather than where they are found. Our
approach has, however, been pragmatic. Where we
can classify species using functional traits, we have
done so; where data are lacking or there are
methodological problems, we have used distribu-
tional data. Thus, our initial classification of 500
species in Grime et al. (1988) included both
functional traits (e.g. the presence or absence of
seed banks) and distributional ones (e.g. associa-
tion with shaded or waterlogged habitats). Methods
are, however, still evolving. An increasing propor-
tion of values can now be derived through direct
measurements of plants (e.g. Hodgson, Wilson,
Hunt, Grime, & Thompson 1999; Cornelissen et al.,
2003) and functional datasets can now be
produced that are capable of identifying key
ecosystem processes operating across continents
(Díaz et al., 2004).

Since CLIMB has both an ecological and an
economic focus, we have concentrated upon the
ecosystem processes that are highly relevant to
pastoral management, to stocking rates of grazing
animals and, very importantly, to economic profit-
ability. Arguably three key ecosystem processes
define both how pastures function as ecosystems
and their economic potential. The most important
relates to fertility: plant growth rate, palatability
to unspecialised herbivores and nutrient recycling
all depend critically on nutrient availability (Díaz et
al., 2004). Moreover, fertility directly impacts upon
maximum sustainable stocking rate and profit
margins. The other two ecosystem processes,
competition and disturbance, relate to sustainabil-
ity. A relaxation of grazing pressure, understocking,
may result in the development of tall swards of
competitive species; overstocking causes distur-
bance. These three variables approximate to the
competitive (C), stress (S) and ruderal (R) axes of
the CSR model of Grime (1974, 2001) that has
already been used as a basis for monitoring land use
change (Hodgson & Colasanti, 1995). Here, how-
ever, the C, S and R axes are treated as
independent variables. Methods for assessing these three ecological attributes and the range of values associated with the CLIMB sites are presented in this paper. The potential of the approach for the future analysis of Pan-European ecological and economic grassland processes is then briefly assessed.

# Methods

## General procedures

The practicalities of compiling a large dataset are enormous. Functional data, and datasets are both in short supply and of variable quality. To circumvent these problems we have used only our own leaf data collected from Central England and Europe generally (Unit of Comparative Plant Ecology and Department of Archaeology, University of Sheffield), NC Spain (Instituto Pirenaico de Ecológía), N. Italy (Unità di Analisi e Gestione Biocenosi, Università degli Studi dell'Insubria) and C. Spain (Departamento de Ecología, Universidad Autónoma de Madrid). We also used our own datasets for leaf nitrogen, one for the English flora (Thompson, Parkinson, Band, & Spencer, 1997), the other from Spain (M. Maestro-Martínez unpublished data). Leaf data were supplemented by measurements of other attributes abstracted from Grime et al. (1988) or measured specifically for the project. Standardised protocols were used throughout. Nomenclature follows Tutin et al. (1964–80).

## Habitat fertility

In unproductive ecosystems, species have inherently low relative growth rates, low leaf nutrient concentrations, low litter decomposition rates and low palatability to generalist herbivores while in productive ecosystems rapid growth rates, high foliar nutrient concentrations, high palatability tend to prevail (Díaz et al., 2004). These relationships are the consequence of a fundamental trade-off in leaves between rapid acquisition of nutrients and conservation of resources within well-protected tissues (see Reich, Walters & Ellsworth, 1992; Garnier & Laurent, 1994; Wright et al., 2004) and these species attributes are all strongly functionally related to ecosystem function. Species of fertile habitats generally possess soft mesomorphic leaves and those of infertile habitats tough, fibrous ones (see Díaz et al., 2004). However, the species attribute most universally recognised as having an important impact upon

ecosystem function is plant nitrogen content (Field & Mooney, 1986; Poorter & Bergkotte, 1992; Reich et al., 1992). We have therefore decided to develop a predictor for leaf nitrogen concentration. To ensure a wide European relevance, we carried out separate studies of a NW European flora, Sheffield, Central England (see Hodgson, Grime, Wilson, Thompson & Band, 2005) and a Mediterranean one, Zaragoza, NC Spain (Braun Blanquet & Bolós, 1953). Insufficient data were available to include soil nitrogen in the analysis.

Prior to measurement leaves were enclosed within a moistened paper towel and kept refrigerated overnight in a sealed polythene bag to ensure that they are fully imbibed. Subsequently, leaf area (using a leaf area machine), leaf fresh weight, leaf dry weight and intervenal leaf thickness (to the nearest 0.01 mm, using a dial thickness gauge) were measured. The procedures used have been tested both for their reproducibility and their ecological value in the floras of the Sheffield and Zaragoza regions. They are described in detail in Charles, Jones and Hodgson (1997) and also conform to the general recommendations of Garnier, Shipley, Roumet and Laurent (2001). Where possible, three ecologically contrasted populations were sampled and leaf values from the replicate populations averaged. We then attempted to derive a predictor regression equation for leaf nitrogen (mg g⁻¹ dry leaf mass) using these easily measured leaf attributes [leaf size (mm²), abbreviated to LA; specific leaf area (leaf area (mm²)/leaf mass (mg)), SLA; leaf thickness (mm), and leaf dry matter content (100 × dry mass of leaf/saturated mass of leaf), DMC]. The values for leaf nitrogen are independent of our other leaf measurements, having generally been collected from different field sites and frequently in different years.

After checking the statistical properties of the variables for suitability for multiple regression analysis, it was found necessary to transform each, including leaf nitrogen, to log₁₀. The one exception was leaf dry matter content, which has been transformed to its square root. Predictor regression equations defining foliar N in terms of leaf characters were calculated for each flora and for the two datasets combined. The regressions were then used to estimate log₁₀ foliar nitrogen for species associated with CLIMB grasslands.

## Competition and canopy height

Competitive interactions between species, both above and below ground, are important determinants of vegetation composition and operate at
greatest intensity under conditions of low disturbance and high nutrient status (Grime, 2001). A competitive index has been devised for the flora of C. England (Hodgson et al., 1999). It is a function of canopy height, leaf size and lateral vegetative spread. No comparable competitive index has yet been calculated within S. Europe. Moreover, we have insufficient data on lateral vegetative spread to widely apply the English regression equation. We have, therefore, been unable to include an estimate of the competitive ability in these analyses. However, species of intensively grazed pastures tend to be inherently low growing even when grazing is removed while species characteristic of lightly grazed swards tend to be naturally taller (see Grime et al., 1988). We have, therefore, used maximum canopy height of perennial species as a rough estimate of both the level of grazing pressure and the severity of competitive interactions between pastural species.

The height of the canopy and that of the flowering stem were measured in the field or assessed by reference to floras and/or herbarium sheets. Maximum plant height was abstracted from Tutin et al. (1964–80). Assuming that the canopy accounts for a relatively constant proportion of the total height of the plant (including flowers) a maximum European canopy height can be roughly estimated as follows:

$$ \text{Maximum canopy height} = \frac{\text{measured canopy height}}{\text{measured plant height}} \times \text{maximum plant height.} $$

Species were subsequently assigned to one of the following canopy height classes $1 \le 50$ mm; $2 = 51–100$ mm; $3 = 101–300$ mm; $4 = 301–600$ mm; $5 = 601–1000$ mm; $6 = 1001–3000$ mm; $7 \ge 3000$ mm.

Canopy height does not always provide a reliable guide as to grazing intensity. Some tall species can survive in otherwise tightly grazed swards because they are unpalatable (e.g. *Deschampsia cespitosa*, *Juncus effusus* and *Pteridium aquilinum*). Moreover, in dry habitats and climates (e.g. the Mediterranean region) tall shrubs and trees may be important components of grazed systems. Because of their deep root systems these woody species are often able to utilise subsoil moisture unavailable to herbaceous plants. They can, as a result, maintain large canopies throughout the summer and represent an important source of forage (and wood) and provide shade for herbaceous plants and for grazing animals (see Peco, de Pablos, Traba & Levassor, 2005). Data on canopy height must, therefore, be interpreted with caution.

## Disturbance

Here we follow Grime (2001) and include as disturbance all events that result in the destruction of plant biomass. There are three groups of species particularly characteristic of disturbed habitats (Hodgson, Pearce & Booth, 1993; Grime, 2001):

1. *Annuals*. These are fast-growing, early flowering, short-lived plants. They are particularly characteristic of sites prone to mechanical disturbance (e.g. arable land) and droughted habitats (e.g. rock outcrops and Mediterranean grasslands). Typical species include *Stellaria media*.

2. *Vernal geophytes*. These perennial species are characterised by rapid expansion growth in spring, or less typically autumn, using reserves from underground storage organs. Leaves die back in summer. Most flower in spring and set seed early but a few flower instead in autumn. Vernal geophytes are found in habitats subject to seasonally predictable disturbance events. They include *Hyacinthoides non-scripta* which exploits deciduous woodland during the light phase before the onset of dense summer shade associated with leaf expansion and *Poa bulbosa*, a common, bulbous, early flowering, drought-avoiding grass of dry sandy habitats, particularly in the Mediterranean.

3. *Species that frequently regenerate from shoot or root fragments*. These plants are characteristic of disturbed, mesic, productive habitats. They are particularly well represented in wetland habitats subject to flooding. For example, several taxa are widespread along river systems in C. England despite being sterile (e.g. *Glyceria* × *pedicellata* and *Rorippa* × *sterilis*). This grouping also includes a number of pernicious arable and garden weeds (e.g. *Elytrigia repens* and *Aegopodium podagraria*). A few species are also found in moist grasslands, which may suffer poaching (trampling into holes and mud) by grazing animals (e.g. *Agrostis stolonifera*) or as a weed in lawns (e.g. *Veronica filiformis*).

Whether species are annuals (or short-lived perennials) or are vernal geophytes was assessed by field observation and by reference to floras. Unfortunately, we are unable to categorise species in terms of their capacity to regenerate from plant fragments. We have unpublished evidence that within C. England species that regenerate freely from plant fragments are short, fast-growing and with marked lateral vegetative spread. However,
studies are needed to confirm that this relationship holds equally in S. Europe. Moreover, we have no European-wide data for lateral spread, one of the attributes needed to assess capacity to establish from plant fragments. Therefore, disturbance has only been assessed by reference to the abundance of annuals and vernal geophytes and may be slightly underestimated in the most productive and wettest of the CLIMB grasslands. There is also another interpretational problem relating to our estimate of disturbance. As noted above, disturbance does not result solely from the activities of grazing stock: other seasonal events (e.g. summer drought, flooding and the onset of shade in woodland) also contribute. Thus, high scores for disturbance may indicate overgrazing or the impact of seasonal events or a combination of the two. This ambiguity highlights the need for an improved estimate of disturbance that specifically targets the impacts of grazing. Unfortunately, at the moment this is not available.

## Calculating mean functional traits for vegetation

Habitat change will have major impacts on floristic composition and to understand these impacts it is important to study two groups of species of contrasted abundance. The most abundant species will generally have the greatest biomass and will also tend to be monopolising key resources of the ecosystem. Equally important is which species are being lost and which are colonising the habitat. If colonists tend to have one particular characteristic (e.g. they are tall), while those that are becoming extinct show the opposite trend (i.e. they are short), this gives us insights into the direction, if any, of vegetation change.

If we calculate mean functional traits using presence absence/data, the most abundant species contributes little to the mean functional value for the vegetation. If we use percentage cover (or rooted or shoot frequency), it is only the most abundant species that contribute significantly to the value. This problem is made worse by the fact that high values of cover are difficult to estimate accurately yet at the same time they have a major impact upon the functional value attributed to the vegetation. For each relevé we have, therefore, chosen to calculate the mean value for each functional trait as follows:

$$ \frac{\sum ((\sqrt{\text{species cover}}) \times (\text{value for functional trait}))}{\sum (\sqrt{\text{species cover}})} $$

The use of the square root of cover, rather than the cover value itself, has accentuated the impact

of the less abundant species. Nevertheless it provides the most abundant species with a high score but the value is 'damped down' a little to compensate for the difficulties in estimating cover. This methodology was subsequently used to define functional characteristics of the CLIMB grasslands.

Ideally all the CLIMB grasslands would have been described using the same sampling protocols. Most of the data had, however, already been collected for other purposes and did not conform to a standard format. Sample size and number differed between sites as did the method of defining species abundance. In most of the datasets abundance was expressed as percentage cover but data from Sheffield (Hodgson et al., 2005) and La Crau (C. Römermann, unpublished data) relate to rooted and shoot frequency respectively and those for organic farming in the Netherlands (Bakker & ter Heerdt, 2005) are presence/absence data. Nevertheless, trial analyses comparing quantitative and presence/absence data gave similar results and for the purposes of this exploratory investigation we have incorporated all datasets into the analyses.

## Identifying ecological and economic patterns

The functional data were used primarily to classify the European grasslands. Nevertheless, if this approach has potential within a future Pan-European synthesis of ecological and economic data, it should be possible to identify patterns even within this limited dataset that relate to ecological and economic processes. Accordingly we have looked for ecological relevance by investigating interactions between the three ecological variables. Since land use change is primarily driven by economic factors we have also considered the current status of CLIMB grasslands in relation to land use. Using local published and unpublished sources the level of abandonment, afforestation and destruction by agricultural improvement was, where possible, roughly assessed (0 = little change; 1≤25% changed; 2 = 26–50%; 3 = 51–75%; 4≥75%; 5 = virtually all destroyed). The relationship between changed status and fertility was then examined. Enclosed pastures in Central England, which consist of agriculturally improved grassland of little conservation interest, have been excluded from the analysis.

## Results

### Habitat fertility

A highly statistically significant multiple regression was obtained defining leaf nitrogen as a
function of specific leaf area, dry matter content and maximum leaf size (Table 1). Moreover, there appears to be a common basic relationship between leaf structure and foliar nitrogen concentration for both NW and Mediterranean Europe. The equations were similar for both the English and Spanish floras and the regression terms derived from the two datasets were not statistically significantly different from each other (Table 1). Predicted values do not, however, show an exact correspondence with measured values (Fig. 1). Nevertheless, the relationship is statistically robust, and we have subsequently used the regression equation from the combined dataset to produce estimates of leaf nitrogen for our CLIMB grasslands.

## Functional characterisation of CLIMB sites

The CLIMB sites vary widely in their functional characteristics (Table 2). They can be roughly

subdivided in terms of fertility (log₁₀(estimated leaf nitrogen)) ≤ 0.30 = 'infertile'; 0.31–0.39 = 'intermediate'; ≥ 0.40 = 'fertile'). Both Mediterranean grasslands and their more northern counterparts included variants with a high estimate for leaf nitrogen and those with a low value but most of our Mediterranean grasslands are 'infertile' or 'intermediate' whereas nearly all those from North West Europe are 'intermediate' or 'fertile'. Mediterranean grasslands also tend to have taller canopies (Table 2). Moreover, these differences in estimated leaf nitrogen and canopy height class are statistically significant (although our sample of CLIMB grasslands may not be representative of the two regions). The other differences in Table 2 relate to life history. In Mediterranean grasslands, where drought often restricts summer plant growth, annuals and vernal geophytes are often a large and important functional grouping within the vegetation; in wetter NW Europe they are less well represented. Because there are also a number of

<table><caption>Table 1. Predictor equations for log₁₀ foliar N (mg g⁻¹).</caption><thead><tr><th rowspan="2"></th><th colspan="2">England+Spain</th><th colspan="2">England</th><th colspan="2">Spain</th></tr><tr><th>Mean</th><th>SE</th><th>Mean</th><th>SE</th><th>Mean</th><th>SE</th></tr></thead><tbody><tr><td>Constant</td><td>0.225</td><td>0.112</td><td>-0.079</td><td>0.146</td><td>0.291</td><td>0.165</td></tr><tr><td>log SLA</td><td>0.246</td><td>0.050</td><td>0.380</td><td>0.068</td><td>0.324</td><td>0.079</td></tr><tr><td>sqrtDMC</td><td>-0.049</td><td>0.012</td><td>-0.032</td><td>0.015</td><td>-0.069</td><td>0.018</td></tr><tr><td>log LA</td><td>0.033</td><td>0.010</td><td>0.039</td><td>0.012</td><td>0.039</td><td>0.016</td></tr><tr><td>R<sup>2</sup></td><td>0.452</td><td></td><td>0.356</td><td></td><td>0.644</td><td></td></tr><tr><td>P</td><td>2 × 10<sup>-27</sup></td><td></td><td>1 × 10<sup>-13</sup></td><td></td><td>2 × 10<sup>-16</sup></td><td></td></tr><tr><td>N</td><td>215</td><td></td><td>148</td><td></td><td>77</td><td></td></tr></tbody></table>

As described more fully in the Methods section, log LA relates to transformed values of maximum leaf size, logSLA to specific leaf area and sqrtDMC to leaf dry matter content. The other attribute included in the analysis, leaf thickness, did not contribute significantly to any of the regression equations.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** The relationship between measurements of foliar N concentration and estimates derived from leaf characters by multiple regression. For all species direct measurements of foliar nitrogen are plotted against predicted values from the 'England+Spain' regression equation in Table 1. Data from Spain (■) and for England (×) are identified by different symbols.</figcaption></figure>
<table><caption>Table 2. Ecological characteristics of CLIMB sites</caption><thead><tr><th rowspan="2">Site</th><th rowspan="2">Phytosociological Alliance</th><th rowspan="2">Current status</th><th colspan="3">Mean log<sub>10</sub> N</th><th colspan="3">Life history (%)</th><th rowspan="2">Mean height class (perennials)</th><th colspan="2">Average representation for N estimates (%)</th></tr><tr><th>Perennials</th><th>All species</th><th>Seasonal variation</th><th>Perennials</th><th>Biennials+ short-lived perennials</th><th>Annuals+ Geophytes</th><th>Species</th><th>√abundance</th></tr></thead><tbody><tr><td colspan="11"><strong>Mediterranean</strong></td></tr><tr><td colspan="11"><em>Average value—all sites</em></td></tr><tr><td colspan="11"><strong>(a) Low fertility</strong></td></tr><tr><td>Zaragoza, N. C. Spain (dry, shrubby pasture)</td><td>Rosmarino-Ericion</td><td>Now little grazed</td><td>0.20</td><td>0.20</td><td>Low</td><td>99.7</td><td>0.3</td><td>0.1</td><td>4.3</td><td>70</td><td>89</td></tr><tr><td>Zaragoza, N. C. Spain (dry, shrubby pasture on gypsophilous soils)</td><td>Gypsophilon</td><td>Now little grazed</td><td>0.22</td><td>0.22</td><td>Low</td><td>99.5</td><td>0.4</td><td>0.1</td><td>4.3</td><td>86</td><td>92</td></tr><tr><td>Zaragoza, N. C. Spain (dry pasture with annuals)</td><td>Eremopyro-Lygeion, Thero-Brachypodion</td><td>Now little grazed</td><td>0.23</td><td>0.26</td><td>High</td><td>74.2</td><td>0.1</td><td>25.8</td><td>4.2</td><td>51</td><td>68</td></tr><tr><td colspan="11"><strong>(b) Intermediate fertility</strong></td></tr><tr><td>Zaragoza, N. C. Spain (dry, nitrophilous communities with annuals)</td><td>Salsolo-Peganon</td><td>Destroyed for arable agriculture</td><td>0.30</td><td>0.33</td><td>High</td><td>66.4</td><td>0.0</td><td>33.6</td><td>4.7</td><td>67</td><td>76</td></tr><tr><td>La Crau, S. France (dry, calcareous pasture with annuals)</td><td>Helianthemion</td><td>Extensive grazing</td><td>0.31</td><td>0.33</td><td>High</td><td>27.9</td><td>3.1</td><td>69.1</td><td>3.2</td><td>52</td><td>60</td></tr><tr><td>Madrid, C. Spain (dry pasture with annuals; dehesa)</td><td>Helianthemion-guttati/Trifolio-Periballion</td><td>Extensive grazing; some abandonment</td><td>0.32</td><td>0.36</td><td>High</td><td>23.9</td><td>5.6</td><td>70.5</td><td>3.7</td><td>70</td><td>84</td></tr><tr><td colspan="11"><strong>(c) High fertility</strong></td></tr><tr><td>Zaragoza, N. C. Spain (moist pasture)</td><td>Trifolieto-Cynodontion</td><td>Destroyed for arable agriculture</td><td>0.39</td><td>0.40</td><td>Low</td><td>84.9</td><td>7.6</td><td>7.5</td><td>2.9</td><td>87</td><td>97</td></tr><tr><td colspan="11"><strong>N. W. Europe</strong></td></tr><tr><td colspan="11"><em>Average value—all sites</em></td></tr><tr><td colspan="11"><strong>(a) Low fertility</strong></td></tr><tr><td>Sheffield, C. England (upland, acidic pasture and moorland)</td><td>Calluno-Genistion, Junceion squarrosi, Nardion</td><td>Extensive grazing</td><td>0.30</td><td>0.30</td><td>Low</td><td>99.5</td><td>0.2</td><td>0.3</td><td>3.3</td><td>100</td><td>100</td></tr><tr><td colspan="11"><strong>(b) Intermediate fertility</strong></td></tr><tr><td>Stora Alvaret, Sweden (dry, calcareous grassland)</td><td>Mesobromion</td><td>Extensive grazing; abandoned land being reclaimed</td><td>0.32</td><td>0.33</td><td>Low</td><td>94.1</td><td>0.8</td><td>5.1</td><td>2.6</td><td>86</td><td>90</td></tr><tr><td>Baden-Württemberg, SW Germany (base-rich pasture)</td><td>Violion</td><td>Destroyed for arable agriculture; remaining fragments mostly ungrazed</td><td>0.36</td><td>0.36</td><td>Low</td><td>96.2</td><td>0.0</td><td>3.8</td><td>2.8</td><td>87</td><td>92</td></tr><tr><td>Rouen, N. France (calcareous grassland)</td><td>Mesobromion</td><td>Abandoned; some areas now grazed for conservation</td><td>0.36</td><td>0.36</td><td>Low</td><td>89.4</td><td>6.0</td><td>4.7</td><td>2.9</td><td>94</td><td>97</td></tr></tbody></table>
<table><tbody><tr><td>Baden-Württemberg, SW Germany (calcareous pasture)</td><td>Mesobromion</td><td> DESTROYED FOR arable agriculture; remaining fragments mostly ungrazed</td><td>0.36</td><td>0.37</td><td>Low</td><td>95.1</td><td>0.0</td><td>4.9</td><td>2.4</td><td>94</td><td>98</td></tr><tr><td>Sheffield, C. England (lowland calcareous pasture)</td><td>Mesobromion</td><td> DESTROYED FOR arable agriculture; remaining fragments mostly ungrazed</td><td>0.37</td><td>0.37</td><td>Low</td><td>91.5</td><td>5.6</td><td>2.8</td><td>3.0</td><td>97</td><td>100</td></tr><tr><td>Sheffield, C. England (upland calcareous pasture)</td><td>Mesobromion</td><td> EXTENSIVE GRAZING; some abandonment</td><td>0.37</td><td>0.37</td><td>Low</td><td>92.6</td><td>3.3</td><td>4.1</td><td>3.0</td><td>98</td><td>100</td></tr><tr><td colspan="12">(c) High fertility</td></tr><tr><td>Anderen, Drentse Aa, Netherlands (wet meadow)</td><td>Calthion</td><td> Intensive grazing</td><td>0.40</td><td>0.40</td><td>Low</td><td>98.3</td><td>0.6</td><td>1.1</td><td>3.3</td><td>87</td><td>92</td></tr><tr><td>Somerset Levels, S. W. England (wet meadow)</td><td>Calthion, Cynosurion</td><td> Intensive grazing</td><td>0.40</td><td>0.40</td><td>Low</td><td>97.3</td><td>0.5</td><td>2.3</td><td>3.9</td><td>92</td><td>95</td></tr><tr><td>Sheffield, C. England ('improved', lowland pasture)</td><td>Cynosurion</td><td> Intensive grazing; some losses due to arabisation</td><td>0.42</td><td>0.42</td><td>Low</td><td>93.9</td><td>1.0</td><td>5.2</td><td>3.1</td><td>100</td><td>100</td></tr><tr><td>Sheffield, C. England ('improved', upland pasture)</td><td>Cynosurion</td><td> Intensive grazing</td><td>0.42</td><td>0.42</td><td>Low</td><td>93.3</td><td>0.6</td><td>6.1</td><td>2.9</td><td>100</td><td>100</td></tr><tr><td>Baden-Württemberg, SW Germany ('improved' pasture)</td><td>Cynosurion</td><td> Intensive grazing</td><td>0.42</td><td>0.42</td><td>Low</td><td>95.4</td><td>1.8</td><td>2.8</td><td>2.7</td><td>86</td><td>92</td></tr><tr><td>Netherlands ('improved' pasture; organic farming)</td><td>Cynosurion</td><td> Intensive grazing</td><td>0.43</td><td>0.43</td><td>Low</td><td>91.2</td><td>0.0</td><td>8.8</td><td>3.2</td><td>96</td><td>-</td></tr><tr><td>Rouen, N. France (flood plain grassland)</td><td>Arrhenatherion, Cynosurion, Bromion racemosi</td><td> Intensive grazing, mowing</td><td>0.43</td><td>0.43</td><td>Low</td><td>91.0</td><td>2.6</td><td>6.5</td><td>3.9</td><td>98</td><td>98</td></tr><tr><td>Baden-Württemberg, SW Germany (meadow)</td><td>Arrhenatherion</td><td> Intensive grazing, mowing</td><td>0.43</td><td>0.44</td><td>Low</td><td>94.2</td><td>2.2</td><td>3.7</td><td>3.1</td><td>83</td><td>97</td></tr></tbody></table>

Values for all attributes take into account the abundance of species (see 'Methods'). Log₁₀ nitrogen was estimated using the multiple regression equation in Table 1. The average percentage of species and of √cover, or equivalent, for which nitrogen data were available in each relevé is appended at the end of the table. (We have a complete dataset for life history and canopy height, the other two attributes.) Sites are arbitrarily subdivided into fertility classes on the basis of the mean log₁₀ N value for all species ('low' ≤0.30, 'intermediate' 0.31–0.39, 'high' ≥0.40). The annuals and geophytes in our dataset had generally relatively high values for estimated leaf nitrogen. As a result vegetation with many winter annuals and geophytes will show seasonal variation in mean estimated leaf nitrogen. The difference between the estimate for summer (perennials only) and for the cooler part of the year (all species) gives an estimate of seasonal differences in the nutritional quality, and perhaps quantity, of the vegetation. Where the two estimates of nitrogen differ by ≤0.01, vegetation is considered to have 'low' seasonal variation. Values of ≥0.02 are considered 'high'. The two geographical groups of CLIMB grasslands are not necessarily representative of their regions. Nevertheless, Mann Whitney U-tests have been carried out and statistically significantly differences (P<0.05) are given in bold.
Mediterranean grasslands without annuals and vernals, this difference between the two regions, though extremely ecologically important, is not statistically significant.

## Ecological trends within European pastures

Habitat fertility, as estimated by leaf nitrogen, has a major impact upon the characteristics of the vegetation and its potential agricultural

value. Ecological theory leads us to expect unproductive ecosystems to recover slowly if damaged (Leps, Osbornova-Kosinova & Rejmanek, 1982). In such fragile ecosystems stocking rates of grazing animals might be expected to be unusually low so as to minimise both irreversible damage to the vegetation and soil erosion. Consistent with this, unproductive pastures tend to have taller vegetation than their more productive counterparts (Fig. 2A). Only wet, fertile pastures, where stocking rates are traditionally kept low to reduce

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

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

**Figure 2.** The relationship between estimated leaf nitrogen and (A) mean height class of vegetation and (B) abundance of annuals+geophytes for the CLIMB grasslands. In (A) wetland (□) and dryland sites (×) are separated. For all sites $r = -0.53$, $n = 22$, $P = 0.01$; excluding wetland sites $r = -0.67$, $n = 19$, $P = 0.002$. In (B) the data are separated into droughted Mediterranean (×, $r = 0.92$, $n = 6$, $P = 0.009$), moist Mediterranean (+, $n = 1$) and North West European sites (□, $r = -0.43$, $n = 15$, n.s.).
trampling damage, appear not to conform to this relationship.

Annuals exploit the cool, moist conditions of autumn to spring and set seed before the onset of summer drought. Geophytes have a similarly dynamic phenology. Because of their rapid growth and high nutrient requirements, we would expect these two groups of species to be more abundant in more fertile but seasonally dry habitats. This prediction is confirmed. Wet Mediterranean grasslands and those from NW Europe have uniformly low values for annuals+geophytes. In contrast, the cover of annuals+geophytes within dry Mediterranean grasslands is positively correlated with the leaf nitrogen value for the perennial component of the vegetation (Fig. 2B). This relationship is

also shown by winter annuals alone but not by geophytes, which are a more minor and less frequent component (for brevity data are not shown).

## Impacts of land use

Reductions in area through afforestation show no clear pattern possibly because woods include both coniferous plantings, primarily in infertile habitats, and poplar plantations, on wet more fertile sites (Table 3). Habitat fertility, as assessed by estimated leaf nitrogen, does, however, predict other impacts of land use change to CLIMB grassland sites (Table 3). For sites in the Mediterranean there is a negative correlation between fertility

<table><caption>**Table 3.** The relationship between estimated leaf nitrogen and the current status of CLIMB grasslands of nature conservation value</caption><thead><tr><th></th><th>Log<sub>10</sub> leaf nitrogen</th><th>Abandoned</th><th>Agriculturally 'improved'</th><th>Afforested</th></tr></thead><tbody><tr><td colspan="5"><strong>Mediterranean (n = 7)</strong></td></tr><tr><td>Zaragoza, N. C. Spain (dry, shrubby pasture)</td><td>0.20</td><td>4</td><td>0</td><td>1</td></tr><tr><td>Zaragoza, N. C. Spain (dry, shrubby pasture on gypsophilous soils)</td><td>0.22</td><td>4</td><td>0</td><td>1</td></tr><tr><td>Zaragoza, N. C. Spain (dry pasture with annuals)</td><td>0.26</td><td>2</td><td>3</td><td>1</td></tr><tr><td>Zaragoza, N. C. Spain (dry, nitrophilous communities with annuals)</td><td>0.33</td><td>0</td><td>5</td><td>0</td></tr><tr><td>La Crau, S. France (dry, calcareous pasture with annuals)</td><td>0.33</td><td>0</td><td>2</td><td>0</td></tr><tr><td>Madrid, C Spain (dry pasture with annuals; dehesa)</td><td>0.36</td><td>2</td><td>2</td><td>1</td></tr><tr><td>Zaragoza, N. C. Spain (moist pasture)</td><td>0.40</td><td>0</td><td>1</td><td>2</td></tr><tr><td>Spearman's r</td><td></td><td>-0.76</td><td>0.33</td><td>0.17</td></tr><tr><td>P</td><td></td><td>0.049</td><td>n.s.</td><td>n.s.</td></tr><tr><td colspan="5"><strong>NW Europe (n = 8)</strong></td></tr><tr><td>Sheffield, C. England (upland, acidic pasture and moorland)</td><td>0.30</td><td>1</td><td>2</td><td>1</td></tr><tr><td>Stora Alvaret, Sweden (dry, calcareous grassland)</td><td>0.33</td><td>1</td><td>0</td><td>0</td></tr><tr><td>Rouen, N. France (calcareous grassland)</td><td>0.36</td><td>4</td><td>0</td><td>0</td></tr><tr><td>Baden-Württemberg, SW Germany (calcareous pasture)</td><td>0.37</td><td>2</td><td>1</td><td>1</td></tr><tr><td>Sheffield, C. England (lowland, calcareous pasture)</td><td>0.37</td><td>1</td><td>4</td><td>0</td></tr><tr><td>Sheffield, C. England (upland, calcareous pasture)</td><td>0.37</td><td>2</td><td>3</td><td>0</td></tr><tr><td>Somerset Levels, S. W. England (wet meadow)</td><td>0.40</td><td>0</td><td>4</td><td>0</td></tr><tr><td>Rouen, N. France (flood plain grassland)</td><td>0.43</td><td>2</td><td>3</td><td>1</td></tr><tr><td>Spearman's r</td><td></td><td>0.03</td><td>0.71</td><td>-0.07</td></tr><tr><td>P</td><td></td><td>n.s.</td><td>0.048</td><td>n.s.</td></tr></tbody></table>

The level of abandonment, afforestation and destruction by agricultural 'improvement' on a six point scale (0 = little change; 1≤25% changed; 2 = 26-50%; 3 = 51-75%; 4≥ 75%; 5 = virtually all destroyed) was assessed from local published and unpublished sources for most grasslands in Table 2. Enclosed pastures in Central England, which are of little conservation interest, have been excluded from the analysis.
and abandonment. Within NW Europe, on the other hand, there is a positive correlation between fertility and losses through agricultural 'improvement' (conversion to either more productive grassland or arable land).

## Discussion

The paper presents a simple functional description of the CLIMB grasslands. Of the methodological problems, those relating to the indices themselves are discussed in the 'Methods' section. The two other more far-reaching ones are as follows:

(1) Mediterranean countries have much larger floras, and much higher levels of endemism, than those of N. Europe (Webb, 1978). Inevitably, our dataset for the Mediterranean region is much less complete than that for NW Europe (see Table 2). The impact of this problem is, however, probably relatively minor. Most of the species for which we lack data are minor components of the vegetation.

(2) The CLIMB grasslands from the two regions do not have exactly the same range of fertility values. Those from NW Europe are biased towards high fertility and those from the Mediterranean towards low fertility. This is probably a fair reflection of the actual distribution of fertility in the grazing lands of the two regions but it does make data interpretation more difficult. For example, additional data from infertile sites from NW Europe are needed to verify the relationship between canopy height and fertility in Fig. 2A. Tall species may have greater access to subsoil moisture (see 'Methods') and are particularly associated with Mediterranean sites that are both infertile and droughted. Similarly, we are uncertain whether impacts of land use recorded in Table 3 are fundamentally different in the two regions. If both regions had shared a similar distribution of fertility values, abandonment at extremely low fertility (at least when coupled with major environment impediments to improvement) and extensive losses of fertile grassland to agricultural 'improvement' might have recorded for both regions.

Despite these problems the prospects of this approach being practically useful are essentially positive. In particular, our methodology appears particularly robust for estimates of leaf nitrogen (Fig. 1) and has detected important relationships.

There is a positive association between fertility and the cover of annuals and geophytes in dry Mediterranean grazing lands (Fig. 2B). Moreover, as the exploitation of this seasonal flush of growth has long been an important part of traditional pasture management (e.g. transhumance; see Braun Blanquet & Bolós, 1953; Peco et al., 2005), the relationship has also considerable economic significance. Most important, however, has been the demonstration that our estimate of fertility is negatively correlated with abandonment in the Mediterranean and positively correlated with loss through agricultural improvement in NW Europe. These results illustrate that the 'innate' fertility of ecosystems and their perceived economic value (deduced from changed management status) coincide. We need to reverse current trends of habitat destruction and to combine economic prosperity with the conservation of European biodiversity. Our estimate of leaf nitrogen may provide a crucial mathematical bridge between ecology and economics that helps to facilitate this goal.

## Acknowledgements

We thank all past members of UCPE and Bryan Wheeler. This work has been supported by the European Science Foundation, Darwin Initiative (DEFRA-UK), the British Council, NERC, and Comisión Interministerial de Ciencia y Tecnología.

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