ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-445-2017Long-term vegetation monitoring in Great Britain – the Countryside Survey
1978–2007 and beyondWoodClaire M.https://orcid.org/0000-0002-0394-2998SmartSimon M.BunceRobert G. H.NortonLisa R.MaskellLindsay C.HowardDavid C.ScottW. AndrewHenrysPeter A.Centre for Ecology and Hydrology, Lancaster Environment Centre,
Bailrigg, Lancaster, LA1 4AP, UKEstonian University of Life Sciences, Kreuzwaldi 5, 51014 Tartu, EstoniaC. M. Wood (clamw@ceh.ac.uk)20July20179244545924February20176March201713June201715June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/445/2017/essd-9-445-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/445/2017/essd-9-445-2017.pdf
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 procedures 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 were recorded, all of
which can be relocated where possible (unless the plot has been lost, for
example as a consequence of building work), providing a total of 16 992
plots by 2007. Plots are estimated to have a precise relocation accuracy of
85 %. A range of plots located in different land cover types and
landscape features (for example, field boundaries) are included.
Although a range of analyses have already been carried out, with changes in
the vegetation being related to a range of drivers at local and national
scales, there is major potential for further analyses, for example in
relation to climate change. Although the precise locations of the plots are
restricted, largely for reasons of landowner confidentiality, sample sites
are intended to be representative of larger areas, and many potential
opportunities for further analyses remain.
Data from each of the survey years (1978, 1990, 1998, 2007) are available via
the following DOIs: Countryside Survey 1978 vegetation plot data
(10.5285/67bbfabb-d981-4ced-b7e7-225205de9c96), Countryside Survey
1990 vegetation plot data
(10.5285/26e79792-5ffc-4116-9ac7-72193dd7f191), Countryside Survey
1998 vegetation plot data
(10.5285/07896bb2-7078-468c-b56d-fb8b41d47065), Countryside Survey
2007 vegetation plot data
(10.5285/57f97915-8ff1-473b-8c77-2564cbd747bc).
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
1978, 506 in 1990, 569 in 1998 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 (Wood et al., 2012). 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).
Survey design: background, stratification and site selectionBackground
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 reflected 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, 1996a, b), it has developed over the 30-year
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).
ITE Land Classification, 2007.
Map of Countryside Survey sampling locations across Britain.
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 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 below 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.
Summary of vegetation plot types, sizes and numbers.
CodeNameOther namesWhereSizeNo. per squareAreal plotsX1Large“Wally plot”;Random points in open polygons200 m25mainY2+4SmallTargetedUncommon vegetation types and in 2007,4 m2Up to 5habitatPriority habitatsU3UnenclosedUnenclosed broad habitats4 m2Up to 10Linear plotsB2BoundaryAdjacent to field boundaries and paired with X plots10 × 1 m5A3ArableArable field edges centred on each B plot100 × 1 mUp to 5M4+5MarginField margins2 × 2 mUp to 15H1HedgerowAlongside hedgerows10 × 1 m2D3Hedgerow diversityHedgerows/woody linear features30 × 1 mUp to 10S1/W2StreamsideAlongside water courses10 × 1 m5R1/V2RoadsideAlongside roads and tracks10 × 1 m5
1 First recorded in 1978; 2 first recorded in 1990; 3 first
recorded in 1998; 4 first recorded in 2007; 5 if there are five A
plots in a square and wide margins.
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 plot construction.
Layout of vegetation X plot.
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 m2 (14.14×14.14 m); the large size was adopted to obtain the maximum number of
species within the plot. The methodology 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 m2 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 m2
nest (not shown in Fig. 4) was introduced, in order to allow joint analysis
of 1 m2 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 m2 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, and Palmer, 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 m2 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 et al. (1998) on a roadside
verge in Bibury in Gloucestershire, or pertain to specific habitats, such as
the Park Grass Experiment at Rothamsted (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 in-built 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.)
Example of a plot sketch map.
Example of a plot photograph.
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
(Maskell et al., 2008; Barr, 1998, 1990; Bunce, 1978). During the surveys,
survey teams were initially supervised and later monitored 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 and 2007 (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 (Scott et al., 2008; 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 large-scale 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;
Norton, 2012). 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 1998 and 2007 and by nearly 80 % in Great Britain between 1978
and 2007.
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.
Change in the characteristics of all types of vegetation in
200 m2 main plots in Great Britain between 1978 and 2007. Arrows denote
significant change (p<0.05) in the direction shown.
Mean values Direction of significantchanges 1978–2007Vegetation condition measures1978199019982007GBSpecies richness (no. of species)17.116.516.215.7↓Light score6.986.956.956.95Fertility score4.534.644.614.55Ellenberg pH score*5.075.175.145.09Moisture score5.755.715.775.82↑
* Ellenberg (1988)
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; Norton et al., 2012).
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; Maskell et al., 2010; Smart et al., 2005a; Stevens et al., 2009)
as well as other drivers of eutrophication and land management change (Smart
et al., 2012, 2002, 2003, 2005b, 2006a, b). 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 (Smart et al., 2000; 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., 2013, 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).
The datasets have been assigned digital object identifiers
and users of the data must reference the data as follows:
Barr, C. J., Bunce, R. G. H., Smart, S. M., and Whittaker, H. A.:
Countryside Survey 1978 vegetation plot data, NERC Environmental Information
Data Centre, 10.5285/67bbfabb-d981-4ced-b7e7-225205de9c96, 2014.
Barr, C. J., Bunce, R. G. H., Gillespie, M. K., Hallam, C. J., Howard, D.
C., Maskell, L. C., Ness, M. J., Norton, L. R., Scott, R. J., Smart, S. M.,
Stuart, R. C., and Wood, C. M.: Countryside Survey 1990 vegetation plot data,
NERC Environmental Information Data Centre,
10.5285/26e79792-5ffc-4116-9ac7-72193dd7f191, 2014.
Barr, C. J., Bunce, R. G. H., Gillespie, M. K., Howard, D. C., Maskell, L.
C., Norton, L. R., Scott, R. J., Shield, E. R., Smart, S. M., Stuart, R. C.,
Watkins, J. W., and Wood, C. M.: Countryside Survey 1998 vegetation plot
data, NERC Environmental Information Data Centre,
10.5285/07896bb2-7078-468c-b56d-fb8b41d47065, 2014.
Bunce, R. G. H., Carey, P. D., Maskell, L. C., Norton, L. R., Scott, R. J., Smart,
S. M., and Wood, C. M.: Countryside Survey 2007 vegetation plot data, NERC
Environmental Information Data Centre,
10.5285/57f97915-8ff1-473b-8c77-2564cbd747bc, 2014.
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, 1998; Bunce, 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.
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.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank all the land owners who kindly gave permission to survey their
holdings in the survey sample squares in 1978, 1990, 1998 and 2007. Without
their co-operation and assistance the Countryside Survey would not exist. We
also acknowledge and thank all the field surveyors involved in each field
campaign. We are grateful to two anonymous reviewers and the editor,
Falk Huettmann, whose comments improved the manuscript considerably.
The most recent Countryside Survey was funded largely by the Natural
Environment Research Council, the Department for Environment Food and Rural
Affairs and the Scottish and Welsh Governments. Edited by: Falk Huettmann Reviewed by: two
anonymous referees
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herbaceous plant species richness across Great Britain, Landsc. Ecol., 19,
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Centre of Ecology & Hydrology, Lancaster, 2008.Rhodes, C. J., Henrys, P., Siriwardena, G. M., Whittingham, M. J., and
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biodiversity assessment, Methods Ecol. Evol., 6, 772–781,
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Rodwell, J. S.: National vegetation classification: Users' handbook, Joint
Nature Conservation Committee, Peterborough, 2006.Rose, R., Monteith, D. T., Henrys, P., Smart, S., Wood, C., Morecroft, M.,
Andrews, C., Beaumont, D., Benham, S., and Bowmaker, V.: Evidence for
increases in vegetation species richness across UK Environmental Change
Network sites linked to changes in air pollution and weather patterns, Ecol.
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How important is plot relocation accuracy when interpreting re-visitation
studies of vegetation change?, Plant. Ecol. Divers, 3, 1–8,
10.1080/17550871003706233, 2010.Rowe, E. C., Ford, A. E. S., Smart, S. M., Henrys, P. A., and Ashmore, M. R.:
Using qualitative and quantitative methods to choose a habitat quality metric
for air pollution policy evaluation, PLOS ONE, 11, e0161085,
10.1371/journal.pone.0161085, 2016.
Scott, W., Morecroft, M., Taylor, M., and Smart, S.: Countryside
Survey-Environmental Change Network link, Centre for Ecology and Hydrology,
Wallingford, UK, 2010.
Scott, W. A., Smart, S. M., and Clarke, R.: Quality Assurance Report: QA and
bias in vegetation recording, Centre for Ecology and Hydrology, Lancaster,
2008.Sheail, J. and Bunce, R. G. H.: The development and scientific principles of
an environmental classification for strategic ecological survey in the United
Kingdom, Environ. Conserv., 30, 147–159, 10.1017/S0376892903000134,
2003.Silvertown, J., Poulton, P., Johnston, E., Edwards, G., Heard, M., and Biss,
P. M.: The Park Grass Experiment 1856–2006: its contribution to ecology, J.
Ecol., 94, 801–814, 10.1111/j.1365-2745.2006.01145.x, 2006.Smart, S., Clarke, R., Van De Poll, H., Robertson, E., Shield, E., Bunce, R.,
and Maskell, L.: National-scale vegetation change across Britain; an analysis
of sample-based surveillance data from the Countryside Surveys of 1990 and
1998, J. Environ. Manage., 67, 239–254, 10.1016/S0301-4797(02)00177-9,
2003.Smart, S., Ashmore, M., Hornung, M., Scott, W., Fowler, D., Dragosits, U.,
Howard, D., Sutton, M., and Famulari, D.: Detecting the signal of atmospheric
N deposition in recent national-scale vegetation change across Britain, Water
Air Soil Poll., 4, 269–278, 10.1007/s11267-005-3037-5, 2005a.Smart, S., Bunce, R., Marrs, R., LeDuc, M., Firbank, L., Maskell, L., Scott,
W., Thompson, K., and Walker, K.: Large-scale changes in the abundance of
common higher plant species across Britain between 1978, 1990 and 1998 as a
consequence of human activity: tests of hypothesised changes in trait
representation, Biol. Conserv., 124, 355–371,
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Smart, S., Maskell, L., Dunbar, M., Emmett, B., Marks, S., Norton, L., Rose,
P., and Simpson, I.: An Integrated Assessment of Countryside Survey data to
investigate Ecosystem Services in Great Britain. Countryside Survey Technical
Report No. 10/07, NERC Centre for Ecology and Hydrology, Lancaster, 2010a.Smart, S., Henrys, P. A., Scott, W. A., Hall, J. R., Evans, C. D., Crowe, A.,
Rowe, E. C., Dragosits, U., Page, T., Whyatt, J. D., Sowerby, A., and Clark,
J. M.: Impacts of pollution and climate change on ombrotrophic Sphagnum
species in the UK: analysis of uncertainties in two empirical niche models,
Clim. Res., 45, 163–177, 10.3354/cr00969 2010b.Smart, S., Scott, W. A., Whitaker, J., Hill, M. O., Roy, D. B., Critchley, C.
N., Marina, L., Evans, C., Emmett, B. A., Rowe, E. C., Crowe, A., and Marrs,
R. H.: Empirical realised niche models for British higher and lower plants –
development and preliminary testing, J. Veg. Sci., 21, 643–656,
10.1111/j.1654-1103.2010.01173.x, 2010c.Smart, S. M. and DART Computing: Modular Analysis of Vegetation Information
System (MAVIS), Centre for Ecology and Hydrology,
http://www.ceh.ac.uk/services/modular-analysis-vegetation-information-system-mavis,
last access: 16 November, 2017.Smart, S. M., Firbank, L. G., Bunce, R. G. H., and Watkins, J. W.:
Quantifying changes in abundance of food plants for butterfly larvae and
farmland birds, J. Appl. Ecol., 37, 398–414,
10.1046/j.1365-2664.2000.00508.x, 2000.Smart, S. M., Bunce, R. G. H., Firbank, L. G., and Coward, P.: Do field
boundaries act as refugia for grassland plant species diversity in
intensively managed agricultural landscapes in Britain?, Agr. Ecosyst.
Environ., 91, 73–87, 10.1016/S0167-8809(01)00259-6, 2002.Smart, S. M., Marrs, R. H., Le Duc, M. G., Thompson, K., Bunce, R. G.,
Firbank, L. G., and Rossall, M. J.: Spatial relationships between intensive
land cover and residual plant species diversity in temperate farmed
landscapes, J. Appl. Ecol., 43, 1128–1137,
10.1111/j.1365-2664.2006.01231.x, 2006a.Smart, S. M., Thompson, K., Marrs, R. H., Le Duc, M. G., Maskell, L. C., and
Firbank, L. G.: Biotic homogenization and changes in species diversity across
human-modified ecosystems, P. Roy. Soc. B, 273, 2659–2665,
10.1098/rspb.2006.3630, 2006b.
Smart, S. M., Scott, A., and Clarke, R.: Quality Assurance Report – Analysis
of plant species recording bias in Countryside Survey terrestrial vegetation
plots – summary report, Centre for Ecology & Hydrology, Lancaster, 2008.
Smart, S. M., Allen, D., Murphy, J., Carey, P. D., Emmett, B. A., Reynolds,
B., Simpson, I. C., Evans, R. A., Skates, J., Scott, W. A., Maskell, L. C.,
Norton, L. R., Rossall, M. J., and Wood, C.: Countryside Survey: Wales
Results from 2007, NERC/Centre for Ecology & Hydrology, Wallingford, UK,
94, 2009.Smart, S. M., Henrys, P. A., Purse, B. V., Murphy, J. M., Bailey, M. J., and
Marrs, R. H.: Clarity or confusion?: problems in attributing large-scale
ecological changes to anthropogenic drivers, Ecol. Indic., 20, 51–56,
10.1016/j.ecolind.2012.01.022, 2012.Smart, S. M., Glanville, H. C., Blanes, M. d. C., Mercado, L. M., Emmett, B.
A., Jones, D. L., Cosby, B. J., Marrs, R. H., Butler, A., Marshall, M. R.,
Reinsch, S., Herrero-Jáuregui, C., and Hodgson, J. G.: Leaf dry matter
content is better at predicting above-ground net primary production than
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Ståhl, G., Allard, A., Esseen, P.-A., Glimskär, A., Ringvall, A.,
Svensson, J., Sundquist, S., Christensen, P., Torell, Å. G., and
Högström, M.: National Inventory of Landscapes in Sweden
(NILS)—scope, design, and experiences from establishing a multiscale
biodiversity monitoring system, Environ. Monit. Assess., 173, 579–595, 2011.Stevens, C., Maskell, L., Smart, S., Caporn, S., Dise, N., and Gowing, D.:
Identifying indicators of atmospheric nitrogen deposition impacts in acid
grasslands, Biol. Conserv., 142, 2069–2075,
10.1016/j.biocon.2009.04.002, 2009.Stevens, C. J., Payne, R. J., Kimberley, A., and Smart, S. M.: How will the
semi-natural vegetation of the UK have changed by 2030 given likely changes
in nitrogen deposition?, Environ. Pollut., 208, 879–889,
10.1016/j.envpol.2015.09.013, 2016.van den Berg, L. J., Jones, L., Sheppard, L. J., Smart, S. M., Bobbink, R.,
Dise, N. B., and Ashmore, M. R.: Evidence for differential effects of reduced
and oxidised nitrogen deposition on vegetation independent of nitrogen load,
Environ. Pollut., 208, 890–897, 10.1016/j.envpol.2015.09.017, 2016.
Villoslada, M., Bunce, R. G. H., Sepp, K., Jongman, R. H. G., Metzger, M. J.,
Kull, T., Raet, J., Kuusemets, V., Kull, A., and Leito, A.: A framework for
habitat monitoring and climate change modelling: construction and validation
of the Environmental Stratification of Estonia, Reg. Environ. Change, 17,
1–15, 10.1007/s10113-016-1002-7, 2016.
Watson, R., Albon, S., Aspinall, R., Austen, M., Bardgett, B., Bateman, I.,
Berry, P., Bird, W., Bradbury, R., and Brown, C.: UK National Ecosystem
Assessment: Technical Report, United Nations Environment Programme World
Conservation Monitoring Centre, Cambridge, 2011.Wood, C. M. and Bunce, R. G. H.: Survey of the terrestrial habitats and
vegetation of Shetland, 1974 – a framework for long-term ecological
monitoring, Earth Syst. Sci. Data, 8, 89–103, 10.5194/essd-8-89-2016,
2016.
Wood, C. M., Howard, D. C., Henrys, P. A., Bunce, R. G. H., and Smart, S. M.:
Countryside Survey: measuring habitat change over 30 years: 1978 data rescue
– final report, Centre for Ecology & Hydrology, Lancaster, 2012.Wood, C. M., Smart, S. M., and Bunce, R. G. H.: Woodland Survey of Great
Britain 1971–2001, Earth Syst. Sci. Data, 7, 203–214,
10.5194/essd-7-203-2015, 2015.