Long-term vegetation monitoring in Great Britain – the Countryside Survey 1978–2007 and beyond

. The Countryside Survey (CS) of Great Britain provides a globally unique series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was ﬁrst undertaken in 1978 to monitor ecological and land use change in Britain using standardised pro-cedures for recording ecological data from representative 1 km squares throughout the country. The same sites, with some additional squares, were used for subsequent surveys of vegetation undertaken in 1990, 1998 and 2007, with the intention of future surveys. Other data records include soils, freshwater habitats and invertebrates, and land cover and landscape feature diversity and extents. These data have been recorded in the same locations on analogous dates. However, the present paper describes only the details of the vegetation surveys. The survey design is a series of gridded, stratiﬁed, randomly selected 1 km squares taken as representative of classes derived from a statistical environmental classiﬁcation of Britain. In the 1978 survey, 256 one-kilometre sample squares were recorded, increasing to 506 in 1990, 569 in 1998 and 591 in 2007. Initially each square contained up to 11 dispersed vegetation plots but additional plots were later placed in different features so that eventually up to 36 additional sampling plots


Introduction
The Countryside Survey (CS) of Great Britain was initiated in the late 1970s to monitor ecological and land cover change using quantitative and repeatable methods. The history of the development of the methodology is given by Sheail and Bunce (2003). The survey is based on 1 km squares as a convenient sized unit, which had previously been tested in Cumbria (Bunce and Smith, 1978) and Shetland (Wood and Bunce, 2016) in the years preceding the first survey in 1978. The survey design is based on a series of dispersed, stratified, randomly selected 1 km squares from across Britain, which numbered 256 in 1978Britain, which numbered 256 in , 506 in 1990Britain, which numbered 256 in , 569 in 1998Britain, which numbered 256 in and 591 in 2007. The stratification used was the statistical environmental classification of 1 km squares in Great Britain as described in Bunce et al. (1996b, c), and summarised in Sect. 2.2.
In the first survey, data were recorded from up to 11 vegetation plots of four different types, distributed through each of the squares (which form the main subject of the present paper), along with soil samples and land cover maps using standard classes which were later converted into standard habitat categories . Subsequent surveys including the vegetation component were undertaken in 1990, 1998 and most recently, 2007 (with an additional land use survey in 1984). During this period, additional vegetation plots have been placed in different land cover types and landscape features for policy objectives, eventually giving up to 36 more plots per square. Varying numbers of each vegetation plot type were initially placed in locations across each survey square according to rules outlined in Sect. 3. In subsequent surveys, these plots are repeated in these same fixed positions, except those such as on field margins, which are based on rules applied in the field. Details of the types of plot employed are described below, with an average of 29 plots being completed in each sample square. In addition to the vegetation plots described here, data are also recorded from linear features such as hedgerows, landscape elements such as veteran trees, areal broad habitats (Jackson, 2000) and related key species, soils and aquatic invertebrates (see Carey et al., 2008).
The survey as a whole provides a wealth of globally unique ecological data, consisting of an extensive range of measurements at a national scale, covering a time span of 29 years. From an international perspective, CS was a pioneer in surveys of its type. Although environmental surveys such as forest monitoring programmes were nothing new (e.g. United States Forest Service, http://www.fia.fs.fed.us/; Forest Survey of India, http://fsi.nic.in/), the integrated, systematic national monitoring of vegetation species, soils and landscape features across all land uses provided by CS was a novel concept, preceding programmes in many other countries particularly in Europe (e.g. Dramstad et al., 2002;Hintermann et al., 2002;Ståhl et al., 2011) and beyond (e.g. Burton et al., 2014).

Background
As a result of the earlier work carried out in the 1970s on a regional scale (Wood and Bunce, 2016;Bunce and Smith, 1978), a sample unit of 1 km square was found to be an appropriate size for CS. It also forms the basic unit of the stratification framework described below. A 1 km square is small enough to survey in a reasonable period of time (1 week or less) and yet large enough to contain sufficient environmental features to allow differentiation between squares. Kilometre squares are used as a framework by the British national mapping agency, the Ordnance Survey, thus providing useful basemaps to aid surveyor navigation. A sampling unit of 1 km square is also widely used in other European projects (for example in Spain, Ortega et al., 2013), although in countries with small-scale landscapes, for example Northern Ireland, 0.25 km square has been adopted (Cooper et al., 2009).

Stratification
With over 240 000 of 1 km squares in Great Britain, a sampling approach was essential and a statistical environmental classification was constructed, from which the stratified, random samples of 1 km squares were taken. Due to the limitations of computing power and lack of readily available data at the time, the classification initially only covered a subset of 1212 one-kilometre squares, rather than the whole of Britain, based on a 15 × 15 km grid drawn from the National Grid defined by Britain's national mapping agency, the Ordnance Survey. Altitude, climate, geology, human geography and location variables from each 1 km square were recorded manually for each 1 km square. Because the data were a mixture of variables (for example, altitude) and attributes (for example, geology) the variables were converted into four classes so that the database was suitable for analysis by indicator species analysis (ISA, now TWINSPAN (Hill and Šmilauer, 2005) and stopped at 32 classes. It is recognised that nowadays, with automated data capture, variables can be recorded for millions of 1 km squares, and recent environmental classifications (for example Metzger et al., 2008;Villoslada et al., 2016) have used principal component analysis and clustering. Jones and Bunce (1985) compared classifications of European climate using both methods and concluded that the results were comparable. Bunce et al. (2002) compared classifications for similar regions using different databases and analytical techniques and showed that the basic patterns were sufficiently close that policy makers would be able to have confidence in the results. The many multivariate techniques which are now available will give slightly different boundaries to classes but the core structure will always be identified. Finally, any inefficiencies in stratifications will be re-flected in higher standard errors for the observed independent variables. The independent tests in papers such as Metzger et al. (2008) and Villoslada et al. (2016) are all highly significant and any improvements are likely to be marginal.
The resulting classes were described on the basis of average values of the environmental characteristics of the initial database, for example, altitude and rainfall (Bunce et al., 1996a, c).
A primary objective of the methodology is to reduce variation, as the classification divides the population into discrete strata which are then used to derive samples from which ecological parameters such as vegetation can be recorded. As a statistically robust method is used (i.e. ISA), it is possible to extrapolate the results from the sample sites into land class means, which can then be combined to describe an entire population (for example England, Scotland, Wales or Britain). The principles and development of this procedure are described by (Sheail and Bunce, 2003).
By 1990, all 1 km squares in Great Britain were classified into the same set of strata, which was not considered possible at the start of the 1970s. Known as the "Institute of Terrestrial Ecology (ITE) Land Classification of Great Britain" (Bunce et al., 1990(Bunce et al., , 1996a, it has developed over the 30year period (Sheail and Bunce, 2003). The most recent modifications largely concern the incorporation of the requirement for country level reporting, separating Scotland (in 1998) and Wales (in 2007). The basic stratification still underpins the CS and the latest development of the original Land Classification consists of 45 classes (or strata), and is illustrated in Fig. 1, along with a map of the distribution of sampling locations (Fig. 2).

Site selection
Having constructed this initial stratification, the number of samples to be surveyed in the first (1978) survey was considered. Ideally, this number would depend on the size of the stratum (i.e. how many 1 km squares of the class occurred in Great Britain) and on the ecological variability within the stratum. Preliminary work had suggested that for ecological surveys of this type, at least eight samples per stratum were necessary in order to be representative of that stratum. Eight 1 km squares were therefore selected at random from each of the classes from the grid of classified squares. Thus the final sample for the first Great Britain survey was a gridded, stratified, random sample of 256 × 1 km squares. Surveying commenced in 1977, although the majority of squares were surveyed in 1978. Note that the location of the 1 km sample squares is not disclosed by agreement with land owners; the majority of the land in the sample squares is in private ownership. If the locations of the sites were made available, this would not only threaten future surveys but also prevent and future collaboration with the owners or their descendants. Furthermore, future land use decisions could be influenced and have an effect on the monitoring results. Thus Figure 1. ITE Land Classification, 2007. this policy ensures that the squares do not attract additional research or land management activity that could potentially undermine their status as an unbiased, representative sample of the British countryside. In spite of the restrictions placed on the site locations, the data may be used in a wide range of analyses on a national or regional level, as described more fully in Sect. 7.

Sampling sites and plots
Initially, vegetation and soil data were recorded from five dispersed random ("X") plots in each 1 km square, which were located using a restricted randomisation procedure designed to reduce auto-correlation. Depending on the type of analyses in question, data users may wish to account for spatial auto-correlation, as in Baude et al (2016). However, in the majority of cases this is not an issue, as described by Betts et al. (2009), and as shown by in model checks in, for example, Henrys et al. (2015b). Vegetation was sampled from a further six plots placed along linear features (two hedgerow ("H") plots, two streamside ("S") plots, and two roadside ("R") plots). Plots have never been placed in built-up areas or be- low the mean high-water mark, and are only sited where the landowner has given permission. The types and total numbers of plots have increased over time from 1978 to 2007 along with the total number of CS 1 km squares surveyed. The total number of plots within squares varies depending on the landscape type and range of landscape features. Plots differ in size depending upon their type (Table 1). By 2007, the mean number of plots per square was 29 (min. 2, max. 47). The locations of all plots were mapped, together with measurements to local features, thus allowing them to be found again and re-recorded in the same place. Additional information ensuring the highest degree of accuracy when re-finding plots began to be recorded in the 1990 survey, as described in Sect. 4.2. The same plot locations have been repeated in all subsequent surveys (where appropriate), with additions.

Data collected
The vegetation survey involves recording plant species presence and abundance in different sizes and types of vegetation plot. In each vegetation plot, a complete list of all vascular plants and a selected range of readily identifiable bryophytes and macro-lichens is made (with the exception of D plots, which record woody species only). The field training course held before the surveys covered identification of difficult species, regular visits were made to survey teams by managers, and difficult specimens could be collected and sent to experts for identification. However, predetermined combinations of species may be recorded as aggregates reflecting known difficulties in their separation in the field (refer to Maskell et al., 2008;Barr, 1998). Cover estimates are made to the nearest 5 % for all species reaching at least an estimated 5 % cover. Presence is recorded if cover is less than 5 %. Canopy cover of overhanging trees and shrubs is also noted, even if individuals were not rooted within the plot. Additionally, general information about the plot is recorded to provide supporting information for analytical purposes as well as describing potential habitats such as glades and dead wood.

Plot types X plots -large or main plots
The X plots are large nested plots designed to provide a random sample in proportion to the extent of the different vegetation types in each square and therefore in the wider countryside. X plots were pre-located before going into the field, with one plot being randomly placed into one of 5 equal sectors dividing the 1 km survey square. X plots typically sample the most common vegetation types. The X plot is 200 m 2 (14.14 × 14.14 m); the large size was adopted to obtain the maximum number of species within the plot. The methodol-   ogy was originally produced for woodlands as described by Bunce and Shaw (1973) and was also used and found appropriate for strategic ecological survey, as described by Bunce and Smith (1978). The design of the plot not only aids a systematic search of the vegetation present but is straightforward to set out in the field, and ensures a standard area of the plot is covered on every occasion, making a square plot more advantageous than a circular plot in this case. The plot is set up by using a centre post and four corner posts, with a set of four strings tagged with markers at specified distances. The tagged strings form the diagonals of the square (as shown in Fig. 3). The diagonals should be orientated carefully at right angles and the plot should be orientated with the strings on the north/south, east/west axes. The different nested plots shown in Fig. 4 are marked by different coloured strings on the appropriate position of the diagonal. The design is to ensure that the whole plot is observed consistently and systematically, as unstructured search routines are more likely to lead to species being overlooked, as described as far back as 1940, by Hope-Simpson (1940). Within the plot shown in Fig. 4, the first nest of the plot (2×2 m) is searched first. This procedure is then repeated for each nest of the quadrat, increasing the size each time and only recording additional species discovered in each larger nest (Fig. 4). In the final nest (the whole 200 m 2 plot), the percentage cover (to the nearest 5 %) of each species is also estimated. Estimates of cover for litter, wood, rock and bare ground are also included where present. In 2007, an additional 1 m 2 nest (not shown in Fig. 4) was introduced, in order to allow joint analysis of 1 m 2 plots being recorded in parallel as part of agri-environment scheme monitoring programs. This nest is located in the northernmost corner of the inner 4 m 2 nest (named nest "0"). Vegetation height, aspect and slope are also recorded. Soil samples are also taken at the same time, at the site of these plots; the procedure used for recording soil samples is given by Emmett et al. (2008) and is outside the remit of the present paper.
In arable fields where full access is not possible, for reasons of practicality species records are made from plots taken from an estimated 14 m square, starting 3 m into the crop, which avoids edge effects in most cases and minimises disturbance to the crop. Access is made using drill lines where possible in order to avoid trampling the crop. Overall cover is also estimated as in other land cover types. The relative uniformity of species within crops led to the adoption of this approach and the subsequent changes observed in species numbers in arable fields justified its use.
Y plots -small, targeted or habitat plots These are small (2×2 m) plots located in less common vegetation types, usually of conservation interest, often occurring in small patches not sampled by other plot types. In 2007, additional Y plots also were placed in priority habitat (Maddock, 2008) patches that had also not been sampled by any other plot in the square. The Y plots are therefore important in sampling fragments of semi-natural habitat particularly in lowland landscapes where patches may be small and embedded in a matrix of intensive farmland. These plots are placed randomly by surveyors in suitable patches of vegetation (based on rules described in Maskell et al., 2008). Of all the plots recorded, they are most similar to the approach taken when positioning quadrats during National Vegetation Classification (NVC) (Rodwell, 2006) survey, where the location of the plot is designed to represent a vegetation unit perceived to be floristically distinct and homogenous. However, protocols for locating Y plots from 1998 onwards stipulated random location from within a larger extent of vegetation type in the 1 km square or from a number of patches representing the mapped land cover type. The validity of statistically analysing plots located with a degree of subjectivity is an ongoing matter of debate (see for example Lájer, 2007, andPalmer, 1993, for an illustration of analytical problems but also Ross et al., 2010, for counter-argument and examples of analysing temporal change in subjectively located samples).

U plots -unenclosed plots
These plots were introduced into the CS methodology for the first time in the 1998 survey to characterise the unenclosed broad habitats (Jackson, 2000) -these being calcareous and acid grasslands; bracken; dwarf shrub heath; fen, marsh and swamp; bog; montane; supra-littoral rock and sediment; and inland rock. Up to 10 plots were established in any unenclosed broad habitat types that occurred within the square (proportional to area), again placed randomly by surveyors. The plots are 2 × 2 m in all instances, regardless of the broad habitat in which they are located.

B plots -boundary plots
Boundary linear plots are recorded at a position on the boundary closest to each X plot and on a cardinal axis from it (i.e. north, south, east or west). A boundary is taken to be any linear physical feature that has a length greater than 20 m and which is an interface between the land cover of the 200 m 2 X plot and any other land cover type. This might include a hedge, wall, fence, ditch or embankment. These are linear 10 × 1 m plots.

A plots -arable field margin plots
Arable field margin plots were recorded for the first time in the 1998 survey. The purpose of establishing the plots was to record the arable weed population at the edge of cultivated fields and any subsequent changes. Theses plots relate only to the edge of fields and are quite distinct from the (arable) X (main) plots which are actually in the crop. They contribute an important source of biodiversity not present in the arable main plots, which cover the overall composition of arable crops because as described above, the margin is specifically excluded. The uptake of "conservation headland" options for arable field management under agri-environment schemes may further enhance species diversity in A plots. The plots are 100 m long by 1 m wide and located adjacent to B plots which border arable fields, up to a maximum of five per square. They always sample the first 1 m of cultivated land moving away from the perennial-dominated margin.

M plots -margin plots
M plots are small (2 × 2 m) square plots and were new in the 2007 survey. They are associated with B plots where an A plot is present, and the number depends on the widths of the margins present, with up to three per field. They are designed to record the quality of new arable field margins that form part of the agri-environment agreements on farms and other margins put in without agri-environment support. These margins are additional to the cross-compliance margin (not relevant in Wales), which is a 2 m margin measured from the centre of the hedge. The most common types of margin likely to be encountered are perennial grass margins, with or without supplementary wildflowers. Other rarer types include, uncropped strips, usually cultivated each year (regenerating from the seedbank); wild bird seed cover, e.g. kale, quinoa; and pollen and nectar mixes, usually with a high proportion of legumes.

H plots -hedgerow plots
H plots are linear 10 × 1 m plots running alongside managed woody linear features ("WLFs", hedgerows). Within H plots, species associated with the managed WLFs are recorded. When first recorded in 1978, the plot positions were located as close as possible to the two X plots in each square which were furthest apart.

D plots -hedgerow diversity plots
Hedgerow diversity plots were recorded for the first time in 1998. The overall purpose was to set up a baseline of plots to monitor woody species diversity in WLFs. One D plot is placed on each WLF in the square, up to a maximum of 10 plots. As well as providing information on woody species diversity, the data collected in D plots also help to provide an assessment of the condition of hedgerows and other WLFs by providing vital information about the size of the WLFs, gappiness, levels of disturbance and species composition. Each plot is 30 m long and includes the full width of the WLF.

S/W plots -streamside plots
The term "streamside plot" denotes linear plots which lie alongside running water features (mainly rivers and streams, but also canals and ditches). The S and W prefixes refer to the different origins of the plots: two Streamside (S) plots were established in 256 of the 1 km squares in 1978, located as close as possible to the two X plots in each square which were furthest apart. W plots were up to three additional waterside plots, placed in all squares in 1990 to increase representation of other waterside types. These are linear 10 × 1 m plots.

R/V plots -roadside and verge plots
The term "roadside plot" denotes those linear plots which lie alongside transport routes (mainly roads and tracks). The R and V prefixes refer to the different origins of the plots: two roadside (R) plots were established in 256 of the 1 km squares in 1978, located as close as possible to the two X plots in each square which were furthest apart. V plots are up to three additional verge plots first placed in 1 km squares in 1990 to increase representation of other transport types. These are linear 10 × 1 m plots.

Plot relocation
To analyse change, it is important to relocate the exact same sampling plot locations in successive surveys. The data from the repeated vegetation plots provide a globally unique dataset allowing large-scale yet fine-grained change in overall vegetation and the state or condition of the broad and priority habitats over time to be documented at four points over the last 29 years. There are no other national data sets that cover entire landscapes, including constituent habitats over such a long period of time. In practice, there are actually very few long-term studies of vegetation change. Those existing are usually either opportunistic, because some local recording has given a precise location, for example Dunnett  (Silvertown et al., 2006).
During the surveys, plot locations have been recorded on paper using a sketch map with measurements from distinguishing landscape features (Fig. 5), and by taking at least two photographs (see Fig. 6 for an example), preferably also including key landscape features in proximity to the plot. In addition to these, permanent metal plates or wooden stakes were introduced in the 1990 survey. In 1998, a GPS position was recorded in some remote squares, which assisted locating plots again in 2007. In 2007, the plot locations were recorded via the ruggedised field computers using the inbuilt GPS (where a GPS signal was available). Surveyors are also able to record whether the plots have been re-found adequately or otherwise. Circumstances where a plot may not be repeated might include an area becoming built-up, a feature having been removed or a land owner refusing access to the land containing the plot. Using a combination of metal plates, photographs and sketch maps, plots are estimated to have a precise relocation accuracy of 85-86 % (Prosser and Wallace, 2008). (See Prosser and Wallace, 2008, for further analysis regarding this issue.)

Data quality
Each field survey was carried out by teams of experienced botanical surveyors, and was preceded by an intensive training course, ensuring high standards and consistency of methodology, effort, identification and recording across CS according to criteria laid out in the field handbooks Barr, 1998Barr, , 1990Bunce, 1978). During the surveys, survey teams were initially supervised and later mon- itored by experienced project staff in order to control data quality.
Data were recorded on waterproof paper sheets in 1978, 1990 and 1998 and were consequently transferred from the original field sheets to spreadsheets, using a "double-punch" method to minimise errors in data entry. They were checked using range and format checks, and corrected to produce a final validated copy. In 2007, a new electronic data capture method was developed by the Centre for Ecology & Hydrology and used in CS for the first time. The move to electronic methods created greater efficiency in terms of data entry and also eliminated a potentially significant source of error. Improvements to data quality also resulted from the inclusion of mandatory data entry fields for each plot.
In terms of assessing the actual level of botanical expertise in the field surveys, quality assurance (QA) reports were completed by independent botanists for the surveys in 1990, 1998(Prosser and Wallace, 2008, 1999, 1992. These reports have been a vital tool in assessing and validating the quality of the botanical record in each CS. Paired species records from a subset of plots (the QA plots) have been analysed in a number of ways to measure the consistency of recording effort within each survey. In all three surveys the QA assessors found more species than the CS field teams, yet in both the 1990 and 1998 assessments, the results showed that there was no bias in the species composition of the vegetation recorded, as described by Prosser and Wallace (2008). In 2007, the QA analysis appeared to show a decline in the quality of botanical recording. However, this was possibly due to less comprehensive recording of common bryophytes than in previous surveys, but subsequent analyses determined that the bias was not significant Smart et al., 2008). Users of the data could remove bryophytes from analyses if they were concerned by this feature of the database. Errors attributable to use of the electronic data capture software were minor and not significant (Prosser and Wallace, 2008).

Methodological development
The now established method of CS, using a stratified random series of samples, was developed over two decades by what was then the Institute of Terrestrial Ecology as described by Sheail and Bunce (2003). The first national series of stratified random samples was the 1971 Woodland Survey (Wood et al., 2015) and strategic sampling at the landscape level was subsequently used successfully in defining the range of variation in vegetation in regional surveys in Cumbria and Shetland (Bunce and Smith, 1978;Wood and Bunce, 2016). These methods have now been proven as a successful national vegetation monitoring strategy incorporating four surveys across nearly 30 years. Minor modifications to the methods have more recently been used for a comprehensive ecological survey of Wales (2013-), the Glastir Monitoring and Evaluation Programme (GMEP) (Emmett and GMEP team, 2014).
Since the first survey in 1978, the methods have gradually developed to incorporate contemporary technologies, for example, the introduction of GPS in 1998, and the use of ruggedised field computers with internal GPS to record the location and species composition of the vegetation plots in 2007. Over time, the development of geographical information systems (GISs) has greatly facilitated both the efficiency of storage of ecological spatial data, and also the types of analyses that can be undertaken. It is now possible to perform much wider analyses than previously, using a range of ancillary explanatory datasets, as described in the Integrated Assessment Report for the Countryside Survey (Smart et al., 2010a). The underlying principles of the Countryside Survey methodology provide an ideal framework for the planning of large-scale monitoring, not only in Britain but across Europe and worldwide, as discussed in Wood and Bunce (2016).
It has now been a decade since the last survey, and current funding constraints mean that the traditional cycle of large one-off national surveys taking place roughly 1 year in every decade is likely to need revising. Various options are available for repeating all or parts of the survey. A rolling program over several years is attractive because it spreads the financial load. It also allows inter-annual effects of differences in the weather and variation in recorder effort to be more robustly estimated and separated from long-term trends. A Markov chain approach could be used to examine possible outcomes from the time series of plots (for example, Balzter, 2000). Between 2013 and 2016, CS methods have already been applied in an annual rolling program to monitor the effects of the Glastir agri-environment scheme across Wales (https://gmep.wales/). Plot numbers could be rationalised according to the desired results. Using previous data, it is possible to identify the optimal numbers of plots required by plot type, vegetation type and region in order to provide data on specific criteria, for example, species richness change at Great Britain level by plot type. Less costly options for maximising the use of the existing surveys in future surveillance have been suggested as part of the Future Options review for national monitoring in Wales (Emmett et al., 2016a). However, the feasibility of these options has yet to be determined.

Use of the data
The Countryside Survey provides a unique and well-utilised resource, offering potential for a wide range of analyses at different temporal and spatial scales. A major benefit of the programme is the co-registration of a wide range of recorded ecological variables (i.e. soil, vegetation, land use, freshwater). In parallel to its direct policy application, a vibrant and productive research agenda has used CS vegetation data often in combination with other datasets to produce improved understanding about the significance and causes of largescale but finely resolved ecological change in Britain. Questions can be broadly categorised as "What has changed and where?", "What are the drivers of change?", "Is the change important?" and "Can we use forecast future change?".
As the data from the vegetation plots are intended to be representative of the larger areas in which they are located (i.e. land class), the restrictions on the precise locations of the plots need not restrict potential analyses. For example, the design of the survey is such that the data are intended to be extrapolated to a land class and, ultimately, a national level. Data may also be used in conjunction with other co-located variables (for example, soils data; Emmett et al., 2016c) to examine inter-relationships.

Key findings
Key findings and fundamental questions about the extent and condition of terrestrial broad/priority habitats are addressed in the reporting round to policy makers that has followed each survey (e.g. Haines-Young et al., 2000;Carey et al., 2008;Smart et al., 2009;Norton et al., 2009;Emmett et al., 2010;Bunce, 1979;Barr et al., 1993) and elsewhere (Smart et al., 2003;. Overall changes in plant species richness formed part of a trend in species loss (8 %) across Great Britain between 1978 and 2007 (although this measure is a simple one, it is readily understood and appreciated by policy makers; however, it does need to be supported by the more detailed ecological analyses described in Sect. 7.2). Woody species increased in vegetation associated with landscape boundaries by 14 % between 1998and by nearly 80 % in Great Britain between 1978.
The most commonly recorded species in CS, ryegrass (Lolium perenne), was the same in 2007 as in both 1998 and 1990. The top 10 most commonly recorded species in 2007 also included stinging nettle (Urtica dioica), hawthorn (Crataegus monogyna), and bramble (Rubus fruticosus) all of which increased between 1998 and 2007.
Long-term change in vegetation from 1978 to 2007 has also been assessed using a range of condition measures ( Table 2). In open countryside in Great Britain, between 1998 and 2007 plant species that prefer wetter conditions increased, while those preferring fertile soils and high pH decreased. In the period 1978 to 2007, an increase in species preferring wetter conditions was the most consistent signal in plots sampling different parts of the landscape across all countries.

Wider uses of data to date
After CS in 2007, the data continued to have a substantial impact, contributing to many areas of the UK National Ecosystem Assessment (NEA) (Watson et al., 2011), which articulated ecological status and change in terms of ecosystem services (ESs). This was the first analysis of the UK's natural environment in terms of the benefits it provides to society and continuing economic prosperity. Soils, vegetation, headwater stream and land cover data from Countryside Survey were also jointly analysed with a range of explanatory variable datasets to produce new indicators and analysis of potential ES delivery in the Integrated Assessment project that marked the final phase of reporting after the 2007 survey (Smart et al., 2010a;. Subsequently, CS plot data have been used in conjunction with land cover map data (Morton et al., 2011) and wider environmental datasets as part of a natural capital mapping tool which has been used, alongside other modelling techniques, to produce maps of natural capital for policy makers (https://eip.ceh.ac.uk/naturalengland-ncmaps) and to help in understanding the factors influencing spatial differences in ES delivery (Henrys et al., 2015a;Norton et al., 2016). Analysis demonstrated fundamental trade-offs between ecosystem productivity and soil carbon concentration while a range of biodiversity indicators appeared to peak at intermediate levels of productivity (Maskell et al., 2013). The novel inclusion of dynamic ecosystem model estimates of productivity provided both the foundation and research direction for ongoing work that has sought to develop dynamic models of natural capital and ES delivery (Emmett et al., 2016b;Smart et al., 2017;Rowe et al., 2016).
CS datasets have also made a unique contribution to the development of plant species niche models for ecosystem dominants and many rare species in Britain (Hill et al., 2017;Henrys et al., 2015b, c;Smart et al., 2010b, c). The policy motivation for this originally was detection and modelling of the effects of atmospheric pollutant deposition (De Vries et al., 2010;Stevens et al., 2016).
The statistically robust, national scale of the CS vegetation data makes it ideally placed to detect realistically scaled relationships between global change drivers, such as pollutant deposition (for example van den Berg et al., 2016; Ellenberg (1988) al., 2010; Smart et al., 2005a;Stevens et al., 2009) as well as other drivers of eutrophication and land management change (Smart et al., , 2003(Smart et al., , 2005b(Smart et al., , 2006a. While research into the causes and consequences of eutrophication was a response to clear policy interest, analysis of CS vegetation data has also contributed evidence in response to concerns over the causes and consequences of loss of pollinators in north-west Europe and Britain Carvell et al., 2006;Baude et al., 2016).
Habitat specific studies, such as those relating to woodlands (for example Petit et al., 2004;Kimberley et al., 2013Kimberley et al., , 2016 and hedgerows, McCollin et al., 2000;Garbutt and Sparks, 2002;Critchley et al., 2013) have been facilitated through the use of CS data. Interesting conclusions have been made through use of the data with regard to increasing numbers of non-native invasive species (Chytrý et al., 2008;Maskell et al., 2006).
The results and information derived from CS can often be set into wider contexts, for example, European (Chytrý et al., 2008;Metzger et al., 2013), or in relation to other monitored datasets (Rose et al., 2016;Scott et al., 2010;Carey et al., 2002;Rhodes et al., 2015).
Data from the 1990 survey were used in the development of a statistically based British vegetation classification, termed the Countryside Vegetation Classification (CVS) as described in Bunce et al. (1999). This led to the development of a computer system termed MAVIS (Modular Analysis of Vegetation Information System), enabling classification of any lists of species from plots into the CVS but also into the phytosociological classes of the National Vegetation Classification (Rodwell, 2006). The software is publicly available (Smart and DART Computing, 2017).

Data availability
The datasets have been assigned digital object identifiers and users of the data must reference the data as follows: The datasets are available from the CEH Environmental Information Data Centre Catalogue (https://eip.ceh.ac.uk/data). Datasets are provided under the terms of the Open Government Licence (http://eidchub.ceh.ac.uk/administration-folder/tools/ ceh-standard-licence-texts/ceh-open-government-licence/ plain, http://www.nationalarchives.gov.uk/doc/ open-government-licence/version/3/). The metadata are stored in the ISO 19115 (2003) schema (International Organization for Standardization, 2015) in the UK Gemini 2.1 profile (UK GEMINI, http://www.agi.org.uk/join-us/ agi-groups/standards-committee/uk-gemini). Users of the datasets will find the following documents useful (supplied as supporting documentation with the datasets): the Sampling Strategy for Countryside Survey (Barr and Wood, 2011) and the field survey handbooks (Barr, 1990(Barr, , 1998Bunce, 1978;Maskell et al., 2008).

Conclusions
The vegetation data recorded during the Countryside Survey of Great Britain are an invaluable national resource, which, over the years, has been exploited in a large number of ways. The data are collected in a statistically robust and quality controlled manner, follow standard, repeatable methods and cover wide temporal and spatial scales. As consequence of this, the data present a unique opportunity for inclusion in a wide range of analyses and models. The intention is that a repeat survey will be undertaken in the near future (indeed a sub-sample of plots (the majority being located in Wales) have already been surveyed in the summer of 2016, largely as part of the Glastir Monitoring and Evaluation Programme, Emmett and GMEP team, 2014). As a decade has now passed since the most recent full survey, an addition to this long-term national resource is becoming increasingly timely, particularly in these current times of political, socio-economic and climatic change.
Author contributions. CMW prepared the manuscript with significant contributions from all co-authors, and is the current database manager for the Land Use Research Group at CEH Lancaster. RGHB designed the sampling framework and survey strategy in 1978. RGHB, SMS, LCM, LRN and DCH have all been part of the Countryside Survey co-ordination team for at least one survey, with WAS and PAH contributing statistical support.