VEGETATION CHANGE FROM 1978 TO 1998 PATTERN AND PROCESS IN ECOLOGICAL SURVEILLANCE DATA ACROSS GB

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VEGETATION CHANGE FROM 1978 TO 1998: TWENTY YEARS OF PATTERN AND PROCESS IN NATIONALSCALE ECOLOGICAL SURVEILLANCE DATA ACROSS GB S. M. SMART, R. G. H. BUNCE, E. J. SHIELD, J. R. ROBERTSON, C. J. BARR, H. M. VAN DE POLL and L. C. MASKELL “SIR. Reference your article “Red Squirrel Concern” (Gazette, May 17). I have known the Foulshaw Moss area for well over 50 years; indeed in the 1940s we as boys regularly went gull egging there. In those days this unique area was a wildlife gem…We came across willow warbler, chiff chaff, meadow pipit, skylark, grasshopper warbler, short-eared owl and cuckoos, great spotted woodpeckers, jays and nightjars. As well as bird life, roe deer were plentiful, many butterflies of numerous varieties could be seen there, adders and of course the lesser black-backed gullery. We later saw the Forestry Commission cutting many deep drainage channels then planting began, after which the gullery soon disappeared. Therefore I think I am in a good position to pass comment on the regeneration of Foulshaw Moss, having known it in its past glory.”

P. H. Woods. Letter to the Westmoreland Gazette, May 21, 2002. Reproduced with permission.

The Countryside Survey of Great Britain: Why? When? How? The letter reproduced above illustrates one important aspect of the value of long-term ecological surveillance: in the face of environmental change and an ever dwindling fund of social memories it can faithfully represent past states of nature. The excerpt was a contribution to a recent debate about objectives for the restoration of a peatland site in south Cumbria. While a range of strongly held opinions about the future restoration of the site were exchanged in the letters page of the Westmoreland Gazette, this letter stood out because it threw light on the past condition of the site prior to modification. The impression given of 1940s Foulshaw Moss offered a reference point in time against which the appropriateness of current management objectives could be assessed. Although inevitably incomplete and anecdotal this first hand account added realism to the debate over the merits of competing and even conflicting visions for the restoration of the site.

Recording the type of information contained in the above extract in a quantitative form that could be measured again without bias in the same location at various times in the future requires the design of an appropriate ecological surveillance scheme. If the data is used to measure change against pre-defined standards then, using Rowell’s (1993) definition, the activity becomes monitoring.

At a very basic level, information on change in the amount and condition of land-cover and ecosystems at the national scale is essential if the consequences of past and future human activities are to be evaluated. This need is reflected by policies such as the UK Biodiversity Action Plan and instruments that include the UK Indicators of Sustainable Development. There is also an increasing recognition that ecological change at smaller spatial scales, for example on land managed under agri-environment schemes, can be more properly evaluated by comparisons with regional or national ‘control’ data (Carey et al., 2002).

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Since 1978, the recording of national change in the extent and condition of common habitats in Britain has been carried out by the Countryside Surveys (CS) of Great Britain (GB). The first survey had three objectives that in retrospect are still very relevant nearly a quarter of a century later. The first was to derive an objective baseline description of ‘common’ vegetation types.

The emphasis on common vegetation types was an acknowledgement that not all types of plant community nor all plant species could be repeatedly recorded. Since the 1978 survey was supposed to be the first of a series of decadal GB-wide surveys then there had to be a trade-off between the intensity of recording versus the diminishing return gained from increasing survey effort. The emphasis of the survey was on the most common habitats and plants that shape the appearance and composition of the wider countryside.

Second, the intention was to collect ecological surveillance data that would allow integration at the landscape scale between land-cover, vegetation, soil and running waters. Integration refers to the benefits that come from recording these ecosystem attributes at the same times and in the same places. As well as minimising random variation in the data, such recording makes it possible to tease out the inter-dependencies between land management, the species composition of the habitats present and abiotic factors such as soil type and pH or the pollution level of associated catchment waters. Indeed subsequent surveys have increased the number of components recorded. For example, the 1998 survey saw sampling of the soil biota as well as baseline recording of the presence of breeding birds. The potential benefit of recording a range of attributes is that insights can be gained into the likely effect of future natural and human-induced change on different ecosystem components. Hence, the third objective was to establish a recording framework for long-term surveillance to yield data for model building and scenario testing. An integrated perspective makes it possible to identify changes in land-cover, soils and landscape structure that are correlated with the change in, for example, the abundance of individual plant species. Identifying these relationships increases our chances of estimating the processes and even causes of observed change (Firbank et al., 2000).

In summary, CS provide data that is used to answer four questions; two are relatively easy – 1) How much of habitat or species x was present at time y? 2) How much change has occurred since time y? The other two questions are harder – 3) Does the change matter? 4) What has caused the change? This paper focuses on some of the patterns and implied processes of change in common plants and plant communities revealed by the Countryside Surveys of Great Britain between 1978 and 1998.

A brief history of the Countryside Survey The 1978 survey was preceded by a number of other similarly designed surveys that acted as test beds for the surveillance methods used (Table 1).

The earliest activities were centred at Monkswood and Merlewood Research Stations, then part of the Nature Conservancy, now part of the Centre for Ecology and Hydrology (CEH) and having been ITE in the interim.

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Table 1. Large-scale surveillance surveys carried out by CEH. Year Survey 1971 1971 1974 1978 1984 1990 1994 1998

GB-wide broadleaved woodland survey Native Pinewood Survey of Scotland Survey of Cumbria Countryside Survey (vegetation + land-cover) Countryside Survey (land-cover) Countryside Survey (vegetation + land-cover) GB Key Habitats – upland, coastal, lowland heath, calcareous grassland Countryside Survey (vegetation + land-cover) (Haines-Young et al.

2000) 2000 Woodland survey (pilot re-survey of 1971 sites) 2002 Woodland survey (full re-survey of 1971 sites) A characteristic feature of all these surveys was that they were based on population sampling and therefore used exactly the same principles as opinion polls designed to produce accurate estimates of popular consensus by sampling only a relatively small number of people. Estimates of stock and change in ecosystem features were coined as average values qualified by estimates of the precision of the average i.e. standard errors and more recently bootstrapped confidence intervals (Barr et al., 1993; Howard et al., in press; Haines-Young et al., 2000). The narrower these intervals of uncertainty the better and since their width is influenced by the amount of variability in each sample, efforts were needed to divide up the total GB sampling domain into more homogenous units. This led firstly to the to the development of the Cumbria land classification and then to the ITE land classification that allocated every 1 km square in GB to 32 classes defined on the basis of unvarying properties such as geology, altitude and climate (Bunce et al., 1996). This subdivision of GB paved the way for the national stratified sampling that underpins the Countryside Surveys. The rationale was that the land classification should provide a general-purpose means of minimising variation within each class and maximising variation between classes in terms of those attributes to be measured during each field survey. This relied on the fact that features such as land-cover and plant species composition would be correlated with the environmental attributes used to define each land class.

Following the Countryside Surveys of 1990 and 1998, the ITE land classes were aggregated to form fewer and larger regions within which analyses of change in land-cover and species diversity were carried out. In the 1990 analysis GB was divided into two lowland and two upland regions (Barr et al., 1993). In 1998 devolution of Wales and Scotland meant that an additional subdivision of the Welsh land classes was necessary while a series of six environmental zones were newly defined based on a complete separation of Scottish 1 km squares from those in England and Wales (Haines-Young et al., 2000; Haines-Young et al., in press).

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The sampling design of the Countryside Survey The results of the Countryside Surveys are based entirely on information recorded from within a random sample of 1 km squares stratified by each of the ITE land classes. The number of 1 km squares has steadily increased from 256 in 1978, to 508 in 1990 and 569 in 1998. Within each 1 km square two basic types of information are recorded. Firstly, a complete land-cover map is made of the whole square and secondly, plant species composition is recorded from a range of fixed vegetation sampling plots (Figure 1, Table 2). These two streams of data are used to generate statistics on stock and change in area of land-cover types and change in condition of the plant communities making up each land-cover type (Barr et al., 1993; Haines-Young et al., 2000).

Table 2. Countryside Survey plot types. The number of plots has increased over time. Analyses of change between the 1978 and 1998 surveys are based on a maximum of 1572 repeat plots while change between 1990 and 1998 is represented by 9596 repeat plots.

Plot type Road verge Additional verge Stream/riverside Additional streamside Field boundaries Hedges Arable field margin Hedgerow woody species Targeted plots Fields/unenclosed land Unenclosed broad habitats

Feature

Years of survey

Dimensions

Linear network " " " " " " " Small biotopes Areas "

78, 90, 98 90, 98 78, 90, 98 90, 98 90, 98 78, 90, 98 98 98 90, 98 78, 90, 98 98

1×10m " " " " " 1×100m Hedge width × 30m 4m2 200m2 4m2

The design of the Countryside Survey means that analyses can be carried out at a variety of scales using all the different combinations of classification levels available. For example, it is possible to analyse botanical change on road verge plots in Scotland only (e.g. MacGowan et al., 2002), or all plots in GB irrespective of plot type. In addition, the most recent analyses of CS data reported by the broad habitats of the UK Biodiversity Action Plan (Jackson, 2000; Haines-Young et al., 2000). This meant that plots were also grouped by the broad habitat in which each was located in 1990 or 1998 (no broad habitat allocations were possible for the 1978 survey). The key, as with all population sampling, is that the results apply with varying levels of uncertainty to the entirety of each broad habitat or to all Scottish road verges, even though only a small sample may have actually been recorded.

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Aggregating 1 km squares by their land class membership allows estimates of stock and change to be made by country and by environmental zones within countries.

A land cover map covering the entire square is made during each survey. Separate maps are prepared for five themed categories of land-cover, forestry, agriculture & semi-natural habitats, buildings & communication, physiography and boundary features.

10m

In 1998 up to 52 vegetation recording plots were recorded per 1 km square.

These are grouped into types reflecting the particular feature sampled. The number of plot types has increased steadily since the 1978 survey.

During repeat surveys, all plots were as far as possible recorded in exactly the same place. Relocation was achieved using photographs, paced distances and compass bearings and, in 1998, by locating buried metal plates with a metal detector.

Figure 1. Countryside Survey sampling and mapping scheme. GB is sampled by a random selection of 1 km squares stratified by the ITE Land Classification and additionally arranged on a 15 km grid to avoid clumping. Within each 1 km square all land cover and linear features are mapped. In addition an average of 27 fixed vegetation sampling plots are used to record plant species composition. The same 1 km squares and same plot locations are visited at each time point.

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Figure 2. How CS plot types sample the vegetation of the wider countryside. The diagram represents a random 1 km square in GB most of which (white area) is not designated as nature reserve although it may well fall under other designations such as AONB or National Park. The landscape consists of areal features such as fields (sampled by the plots marked X) and unenclosed upland (sampled by the plot marked U). In lowland Britain in particular, areal features are separated by a dense network of linear features that include watercourses (S plot), roads (verge sampled by the R plot) and field boundaries (sampled by the B plot). In addition, it is often possible to identify small fragments of habitat not typical of the majority of the square (stippled areas).

Because of their rarity they are often missed by the other randomly located plot types and so are sampled by targeted plots (marked as Y). Note that plots can, by chance, fall within land under some form of funded conservation management (grey areas).

A unique feature of the CS is the way in which plant species recording is carried out in a range of plot types in each 1 km square (Table 2). These different plot types ensure that botanical data properly reflect the way in which the British countryside consists not just of areal habitat patches, such as fields, woodlands, unenclosed upland, lowland heath and wetland, but also comprises a diverse network of linear features including hedges, field boundaries, watercourses and road verges. In addition, the 1990 survey saw the first recording of a series of targeted plots originally funded by the Nature Conservancy Council. These were designed to record the vegetation of scarce semi-natural habitats missed by the other plots in each square. The targeted plots would therefore represent small fragments of woodland, fen or species rich grassland that, in a lowland square, might be restricted to a steep slope or a field corner. They are broadly equivalent to the ‘small biotopes’ targeted in the Danish monitoring scheme (Agger & Brandt, 1988).

The range of CS plot types therefore represents the British landscape as being composed of areas of common habitat (those most likely to be sampled by the random areal plots in each 1 km square), the linear network and patches of habitat not typical of the surrounding landscape. Since the 1 km squares are

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randomly selected, the plots recorded within them can be used to represent plant communities associated with these landscape components across the wider countryside within which the nature reserve network as well as agrienvironment agreement land will be embedded; what Franklin (1993) has called the ‘unreserved matrix’ (Figure 2). Although CS 1 km squares will include some areas of designated land, particularly more widespread designations such as AONB and County Wildlife Sites, one of the key strengths of the CS sampling framework is that it can provide a large-scale analysis of the ecological context in which designated sites function. The range of plot types also allows landscape-scale questions to be addressed that complement regional studies focussing on specific habitats. For example, it is well known that lowland mesotrophic grasslands have declined in extent over the last 50 years (Jefferson & Roberston, 1994; Blackstock et al., 1999) but to what extent can road verges or field boundaries function as refuges for characteristic plant species whose dispersal back into nearby fields might be encouraged via appropriate management?

Strengths and weaknesses of Countryside Survey for botanical recording The strengths and weaknesses of the CS for measuring botanical change reflect the spatial and temporal scales at which recording is carried out.

Because CS is sample based and vegetation plots only actually cover a very small proportion of the total area of GB, a range of relatively scarce plant species are absent or very rare in CS vegetation plots. The CS was designed to quantify change in abundance of the most common plant species and not as a recording scheme for scarce plants, so it is not surprising that CS is, for example, weak at estimating the abundance of red data book or nationally scarce species. Also, because the CS is optimised to produce results for the most common land-cover types in Britain, it also has few records for restricted but locally highly abundant species such as Sesleria albicans and Ammophila arenaria. These constraints result from the trade-off between sample size and number needed to ensure sensitivity to small-scale changes in common plants in the wider British countryside. For example, a species such as Arrhenatherum elatius would need to undergo a huge change in local abundance to register an absence from most 1 km or even Âź km squares in lowland GB. Therefore smaller sampling scales are needed if anything other than very large changes are to be detected (cf. Pearman, 1999; Critchley & Poulton, 1996).

Since resources are not infinite a compromise has to be made on the total number of plots to record. The result is a sampling scheme optimised for relatively common plant species. Hence, two further constraints on the use of CS botanical data are that they cannot be used to census plant biodiversity while estimates of plant species abundance become increasingly imprecise with reduction in size of the region of interest.

Why measure changes in abundance of common plants across Britain?

Common plants naturally include species that dominate widespread plant communities. They might be crops such as Picea sitchensis, Brassica napus or Lolium perenne or co-dominants in more natural situations such as Molinia caerulea, Erica tetralix, Arrhenatherum elatius or Mercurialis perennis. These

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species convey information about the environmental and management context of the stand; whether wet or dry, basic or acidic, semi-natural or highly managed. In addition, natural or management induced changes in their abundance also influence opportunities for persistence or establishment of less common species that typically occupy lower positions in the dominance hierarchy of each assemblage (Grime, 1998). Hence changes in abundance of common plants can provide insights into the causes of vegetation change including shifts in abundance of the rarities (Hodgson, 1991; Firbank et al., 2000). As well as stand dominants, species that occupy subordinate positions in the plant community but are geographically widespread also have value as indicators of changing ecological conditions (Smart et al., in press; Bunce et al., 1999). Common plants can also be valued as the building blocks of increasingly uncommon plant communities despite the fact that the individual species remain relatively widespread (Bunce et al., 1999; Pickering, 2000).

A final reason for measuring change in common plant species is that they include the majority of the most important food plants for butterflies and lowland farmland birds (Quinn et al., 1998; Wilson et al., 1996; Smart et al., 2000).

Changes in frequency of common plants species 1978-1998 Methods Significance tests of change were carried out on all species with at least six recorded occurrences in either 1978 or 1998 since below this level statistically significant change in frequency was not possible. Although only statistically significant changes are considered, small sample sizes often resulted in very uncertain estimates of the size of the change at the GB level. Magnitudes of change and their confidence intervals will be reported elsewhere.

Changes in frequency were analysed for each species based on all plots and then by grouping records into those from linear plots (road verge, streamside and hedgerow) and those from area plots (enclosed and unenclosed land).

Results and discussion A total pool of 568 higher plant species were recorded in CS plots in 1978 and 1998 of which 170 species showed significant change in frequency, 22% being increases and 78% decreases.

The extent to which these changes represent change in abundance of the building blocks of common British plant communities can be gained by conveying the number of increases and decreases as a proportion of the total number of diagnostic species for the most common community groups of the National Vegetation Classification (NVC) (Figure 3). In all cases decreases of diagnostic species outweighed increases with the largest net decreases seen on linear features for all community groups. The largest net decrease was seen for mesotrophic grassland species on linear features. This is consistent with the high starting species richness of many linear plots, especially streamsides, in 1978 (Bunce et al., 1999; Haines-Young et al., 2000) and the likelihood that mid-successional vegetation on linear features has suffered the joint impacts of eutrophication and reduced biomass removal over the twenty year period (Firbank et al., 2000; Smart et al., in press).

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% of diag nos tic plant s pecies 0 Heaths : " Mires "

5

10

15

20

25

30

35

: Linear : A rea : Linear : A rea

Mes o gras s land: Linear " Oth er v eg "

: A rea :Linear : A rea

Upland & acid : Linear "

: A rea

W o od & s crub: Linear

"

: A rea

+

Figure 3. Statistically significant changes (+ or -) in frequency of individual plant species in Countryside Survey fixed plots between 1978 and 1998. The graph includes only species that are diagnostic of sub-community and community level units of the National Vegetation Classification (Rodwell, 1991 et seq.). Changes in frequency are grouped by NVC major habitat type and expressed as the number of species that changed as a percentage of the total number of diagnostic species across all tabled units in each habitat type. Diagnostic species were defined as all plant species (excluding bryophytes) that occurred at a constancy value of IV or V in any community or subcommunity constancy profile. NVC habitat types that are known to be underrepresented in CS data have been excluded. These are saltmarsh, maritime cliff, sand dune, swamp, aquatic and calcareous grassland.

Many of the plant species that declined in frequency between 1978 and 1998 are characteristic of semi-natural plant communities in Britain (Table 3).

Although relatively widespread they tend not to be found in infertile or less disturbed habitats. The list includes community dominants such as Calluna vulgaris, Hyacinthoides non-scripta and Nardus stricta as well as subordinates that vary in their within-habitat abundance but rarely achieve dominance such as Sanicula europaea, Pedicularis palustris and Lotus corniculatus.

Much interest has been focussed on changes in occurrence of species considered not to be native in Britain or if present for long periods then here as

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Table 3. Species characteristic of semi-natural habitats that declined in frequency between 1978 and 1998. L = linear plot types that sampled the vegetation of hedges, road verges and the banks of streams, ditches and rivers.

A = area plots that sampled fields and unenclosed land. … = reduction only detected when plot records were analysed together irrespective of plot type. A/ L indicates that the species declined in linear plots and area plots when these groups were tested separately.

Species

Plot Species type

Plot type

Achillea ptarmica Ajuga reptans Anemone nemorosa Carex echinata Carex pallescens Centaurea nigra Dactylorhiza maculata Erica tetralix Festuca ovina Geranium sylvaticum Hyacinthoides non-scripta Leucanthemum vulgare Lotus corniculatus Moehringia trinervia Pedicularis palustris Sanicula europaea Typha latifolia Veronica montana Viola riviniana/reichenbachiana

L …

L A …

A/L A/L A/L A/L L L A A/L …

A …

L L A

A/L A L A A L …

A A/L L …

A/L …

L A/L …

L L A/L

Calluna vulgaris Carex pilulifera Crepis paludosa Erica cinerea Galium saxatile Geum rivale Helictotrichon pubescens Hypericum pulchrum Lathyrus pratensis Lysimachia nemorum Molinia caerulea Nardus stricta Potamogeton natans P. polygonifolius Primula veris Rhinanthus minor Rorippa nasturtium-aquaticum Stellaria graminea Trifolium pratense

a likely result of human activity. However, CS is generally not good at recording changes in these species since, with the exception of crop plants, many are not widespread even though they may be highly abundant where they do occur. Apart from arable crops, changes in only three non-native species were detected between 1978 and 1998. These are Epilobium brunnescens, Aegopodium podagraria and Picea sitchensis: all three increased in frequency.

Epilobium brunnescens has spread rapidly throughout upland Britain since its first recorded occurrence in 1904 and was considered widespread and abundant by 1960. Recent infilling within its range still seems to have occurred between 1978 and 1998.

Opinions differ regarding the residency of Aegopodium podagraria in our flora. Stace (1997) regarded it as “probably always introduced” reflecting its long association with human settlement and cultivation as a medicinal and pot

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Table 4. Changes in plant species on land under cultivation in 1978 and 1998.

Species in bold are important in the diet of a number of lowland farmland birds known to have declined from the mid-70s onwards (Wilson et al., 1996; Campbell et al., 1997; Smart et al., 2000).

INCREASING PLANTS Anisantha sterilis Brassica napus

Triticum aestivum

DECREASING PLANTS Cirsium arvense Hordeum distichon Convolvulus arvensis Dactylis glomerata Ranunculus repens Polygonum aviculare

Chenopodium album/polyspermum Matricaria discoidea Persicaria maculosa Elytrigia repens Poa annua Stellaria media

herb while Clapham (1953) regarded it as a native component of the ground flora of Oxfordshire ash-elm woodland. Its recent increase has been most marked on road verges in Britain and has probably been favoured by reduced full-width cutting and increases in substrate fertility resulting from lack of biomass removal. The increase in Picea sitchensis reflects the widespread afforestation that occurred in upland Britain particularly in the 1980s (Barr et al., 1993; DEFRA, 2000).

A number of plant species changed in frequency in CS plots located on land that was cultivated for arable crops in both 1978 and 1998 (Table 4).

Although sample size was small, results were consistent with known changes in management intensity and in the popularity of different crops. For example, increased frequency of both Wheat and Rape reflects known increases in their national hectarage (North, 2000; Chamberlain et al., 2000; DEFRA, 2001a).

The increase in Anisantha sterilis is also consistent with the rise of minimum tillage over the recording period. This practice involves reduced soil turnover during sowing and so favours establishment and spread of the grass, most of whose propagules lack dormancy and germinate quickly after seed shed (Peters et al., 1993).

Decreasing plant species on arable land included Barley whose reduced national hectarage is also recorded by agricultural statistics over the past 25 years (North, 2000; DEFRA, 2001b). A number of non-crop species also declined in frequency (Table 4). This probably indicates the increased efficiency of herbicides and their application during the period (Ewald & Aebischer, 2000; Griffiths, 2000). Crucially, the declining group includes key food plants for lowland farmland birds many of which have severely declined since the mid-1970s (Table 4).

Overall, evidence from CS data indicates that the magnitude of floristic change has been less in upland than in lowland Britain. However, a number of species not generally typical of upland habitats do appear to have increased in

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Table 5. Lowland mesophytes that increased in frequency in area plots in upland vegetation types between 1978 and 1998.

Agrostis stolonifera Cynosurus cristatus1 Lolium perenne1 Poa trivialis

Festuca rubra1 Holcus lanatus1 Ranunculus repens Rumex acetosa

1

Species confined to heather moorland that had received fertiliser and was adjacent to reseeded swards in the experimental site series of Welch (1995).

upland Britain over the twenty year period although their frequency and mean cover remains low (Table 5). These species are more usually found in improved or semi-improved grasslands, which in an upland setting includes the more fertile, lower altitude in-bye. Teasing out the drivers behind these increases is problematic though because the 1978-1998 interval saw parallel changes that could all be implicated. For example, unimproved mesotrophic grassland continued to be lost to agricultural improvement (Jefferson & Robertson 1994), increases in sheep numbers occurred in upland GB, particularly in England and Wales (Fuller 1999), while total atmospheric N deposition also peaked during the period (NEGTAP 2001).

Common plant species as indicators of change in environmental conditions Results from analysing change in individual plants can be assessed in terms of their importance from a conservation perspective and can also offer insights into the processes that might be involved. However, it is possible to go further and to summarise plant species data from CS plots as a series of indicators of change in ecological conditions. This step relies on applying classifications which estimate the range of conditions most commonly associated with each plant species. One of the most widely used schemes is the Ellenberg number system that denotes the optimal position of individual plant species along key environmental gradients including fertility, soil pH, light and soil moisture (Ellenberg et al., 1991). Following recalibration and completion for the British flora (Hill et al., 1999), it is possible to calculate a mean Ellenberg value for a vegetation plot at any point in time and to interpret changes in these means as indirect indicators of changing conditions (Hill & Carey, 1997; Smart, 2000).

Using this method, CS data has shown three general trends in British vegetation between 1978 and 1998. Firstly, an increase in fertility scores in less-fertile vegetation types, secondly, decreasing light scores on linear features and small biotope patches in lowland Britain (i.e. increased shade) and lastly increased light scores in upland heath and bog vegetation (i.e. increased disturbance and therefore less shade) (Bunce et al., 1999; Smart et al., in press).

Other classifications have also been applied to the CS data in an attempt to address questions about the significance and cause of vegetation change.

These include the triangular CSR classification of Grime (1979) which divides plant species into ruderals, stress-tolerators and competitors. By grouping together changes in all the non-competitor species, this classification has been

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Figure 4. Proportion of CS plots from the 1990 survey that contained at least one recorded occurrence of a mesotrophic grassland indicator species from a list drawn up by the England Field Unit (Nature Conservancy Council, unpublished data). Data are shown for two environmental zones of GB; an east and south eastern area comprising the arable dominated lowlands of England and Scotland, and largely grassland dominated west and south western lowlands of Britain (see Bunce et al., 1999 for further details).

used to identify species correlated with increases or reductions in richness of those plants most vulnerable to increases in fertility combined with reduced disturbance (Table 6). The results are interesting although perhaps not surprising. The two positive indicator species reflect the widespread distributions of Trifolium pratense and Lotus corniculatus and their tendency to decline in cover alongside decreases in local richness of assemblages vulnerable to increased fertility and shade. On the other hand, Acer pseudoplatanus and Urtica dioica appear to be good examples of species favoured by reduced disturbance and increased fertility and whose expansion may actually result in suppression of those plants intolerant of their competitive effect. The identification of U. dioica as a large-scale indicator is particularly interesting in light of its recent documented increases in parts of lowland Britain (Oliver, 1995).

The refuge role of the linear network – opportunities as well as threats An advantage of the CS sampling design is that it allows the species composition and diversity of different landscape components to be compared within the same 1 km squares but at the national scale. Hence, it is possible to quantify the extent to which linear features, such as road verges and field boundaries, support larger or smaller populations of target species relative to their abundance in adjacent fields.

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Table 6. Large scale indicators of increases or decreases in richness of ruderals and stress-tolerators (sensu Grime, 1979) between 1978 and 1990. Analysis was carried out using Fisher exact tests of the association between change in individual plant species cover and change in the number of ruderals and stresstolerators in each plot (Smart, unpublished data). Results are shown for those species that showed the strongest association. The odds ratio is interpreted as follows. Richness of vulnerable species was 17 times more likely to decrease than increase following an increase in cover of Acer pseudoplatanus.

Positive association with reductions in richness Species

Stratum in CS data

Lotus corniculatus Trifolium pratense

Whole of GB "

Odds ratio 10 8

Negative association with reductions in richness Species

Stratum in CS data

Acer pseudoplatanus Urtica dioica

Southern and western lowlands Unimproved, species-rich grassland throughout GB

1/Odds ratio 17 7

An obvious target group consists of plant species characteristic of unimproved grasslands in lowland Britain. This reflects the marked reduction in extent of unimproved grasslands since WWII largely as a result of changes in agricultural practice (e.g. Hopkins et al., 2000). Analyses of the relative abundance of lowland grassland indicators on linear features versus adjacent fields have indeed shown that there is a greater likelihood of locating these species on the linear network. Figure 4 shows the proportions of different CS plots that were occupied by Nature Conservancy Council mesotrophic grassland indicator species in 1990. Streamside plots ranked highest just outstripping the small biotope plots even though these would be expected to have high numbers because they were targeted on semi-natural habitat patches.

Interestingly, plots located in the grass-dominated west and south west consistently exceeded plots from the east and south eastern arable-dominated zone. This suggests that larger grassland species pools persist in the western British lowlands, a finding also supported by recent work on non-rotational set-aside (Critchley & Fowbert, 2000). Further work on the relationship between richness and species composition of grassland plants between field boundaries and field centres has also shown that field boundaries lose grassland species at a slower rate than the adjacent field as field productivity increases (Smart et al., in press). However, at the lowest field productivity levels, fields generally exceeded their boundaries in grassland species richness, hence boundaries are no substitute for intact field assemblages.

Taken together these analyses highlight the continued importance of conserving remaining areas of unimproved grassland but also suggest that some characteristic species may linger on the linear network despite having been greatly reduced in field situations.

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The partial refuge function of the linear network could provide the starting point for novel agri-environment tiers; for example if it were possible to broadcast road verge cuttings into adjacent field margins in appropriate locations, this could help arrest the build-up of soil fertility on the verge, while also introducing propagules of grassland plants back into a field situation to be perhaps encouraged by appropriate management. However, the opportunities offered by linear refugia are also likely to diminish if the changes that have been observed over the 1978–1998 interval continue unchecked. Although linear features must have been relatively less impacted by changing land-use than adjacent fields, the marked signals of eutrophication and succession that have been seen particularly on the banks of lowland ditches and streams since 1978, suggest that many remnant populations must be small and vulnerable to further change. Indeed the field versus field boundary analysis shows that even in optimal situations, linear features are generally less species dense than adjacent fields.

The results of the analysis of the changes in individual plant species frequency between 1978 and 1998 can be re-examined to determine which characteristics make a plant species a ‘winner’ or a ‘loser’ on linear features that supported unimproved, species-rich grassland in 1978. This exercise should tell us whether a continuation of recent change is likely to have positive or negative implications for the status of linear features as refuges for grassland plants. The analysis was carried out by comparing the representation of a number of plant traits among those species that increased or decreased, with their representation among 1000 random draws of the same number of species from the total pool recorded from linear, unimproved grassland plots in 1978 (Figure 5 a & b). The results indicate that ‘losers’ have tended to be shorter plants than the average while winners tend to be species favoured by high soil fertility. The implications are clear. Despite a significantly higher probability of occurrence of lowland grassland indicators on linear features, a continuation of recent trends will provide increasingly unfavourable conditions for their persistence.

Looking to the future No dataset exists that can foretell the future with 100% accuracy. What can be done however, is to test the potential impacts of possible futures on landcover, soils and biodiversity using models based on a good, quantitative understanding of current and past relationships between human decisionmaking and ecosystem dynamics. The next decade should therefore see increasing use of the spatially integrated data recorded by CS combined with other datasets to develop better models for answering ‘what if’ questions about our ecological future. This is important because, as a society, we need to have the best possible information about the ecological consequences of the economic behaviour we collectively sanction (e.g. Wackernagel & Rees, 1996).

What is certain is that the next fifty years will see change in the vegetation and flora of Britain. The list of potential drivers of these changes in the short term, is well established and includes increased woodland cover in England, ongoing progress toward the large-scale conservation of broad and priority

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habitats, potential large-scale environmental benefits from cross-compliance as a condition for agricultural subsidy, increased pressure on land for transport and development and the wider uptake of both organic farming and potential cultivation of GM crops. Regarding progress toward large-scale conservation and restoration objectives, a future constraint may well be the apparent unresponsiveness of targeted areas even though appropriate management conditions have been put in place. For example, a recent study of Swedish acid woodlands showed that even 47 years after an N fertilization treatment was stopped there were still differences in species composition between formerly treated and control plots (Strengbom et al., 2001). Crucially, all these drivers of vegetation change are likely to operate within the context of ongoing climate change.

Estimating the combined effects of all these potential drivers will require surveillance at spatial and temporal scales in addition to those used by CS. For example, changes in distribution of rare species at the edge of their geographic range is best achieved by B.S.B.I. hectad or tetrad recording. In addition, measuring change in plant species abundance based on infrequent but largescale recording as carried out by Countryside Survey, needs to take account of the possible effects of the weather in each survey year (Leach, 1977). This maybe partly achieved by careful choice of indicators but a stronger basis will come from factoring in annual variation in species performance from more frequent recording programs such as the Environmental Change Network (www.ecn.ac.uk) and the newly initiated Plantlife Common Plants Survey (www.plantlife.org.uk). The next few years will hopefully see greater integration across a variety of schemes and recording scales enabling more confident estimation of the causes and consequences of past and future change in British vegetation.

Acknowledgements Thanks go firstly to the all the landowners who allowed access to their holdings during all the Countryside Surveys. Thanks to Simon Leach for comments on parts of the manuscript. Finally thanks go to Chris Preston for suggesting this lecture be given to the conference. The Countryside Surveys have been funded jointly by DEFRA, NERC, the Scottish Executive and the National Assembly of Wales.

Further information about Countryside Survey can be found at www.cs2000.org.uk.

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Figure 5. Graph a) compares the average value for canopy height between the 21 species that declined between 1978 and 1998 on linear plots in unimproved species-rich grassland with the distribution of average canopy heights for 1000 random draws of 21 plants. The random draws were made from the total pool of plant species only recorded from linear, unimproved grassland plots in 1978. Graph b) compares the average value for Ellenberg fertility between the 3 species that increased between 1978 and 1998 on linear plots in unimproved species-rich grassland and the distribution of averages for 1000 random draws as above.

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