The effect of landscape-scale environmental drivers on the vegetation composition of British woodlands

BIOLOGICAL CONSERVATION

Biological Conservation 120 (2004) 491–505 www.elsevier.com/locate/biocon

The effect of landscape-scale environmental drivers on the vegetation composition of British woodlands P.M. Corney a

a,*

, M.G. Le Duc a, S.M. Smart b, K.J. Kirby c, R.G.H. Bunce R.H. Marrs a

b,1

,

Applied Vegetation Dynamics Laboratory, School of Biological Sciences, University of Liverpool, P.O. Box 147, Liverpool L69 3GS, UK b Merlewood Research Station, Centre for Ecology and Hydrology, Grange-over-sands, Cumbria LA11 6JU, UK c English Nature, Northminster House, Peterborough PE1 1UA, UK Received 4 September 2003; received in revised form 16 March 2004; accepted 26 March 2004

Abstract Assessment of factors influencing woodland vegetation composition across Britain was made using multivariate techniques to analyse data gathered during the 1971 National Woodland Survey. Indirect gradient analysis (unconstrained ordination using detrended correspondence analysis) suggested a gradient strongly associated with nutrient availability and pH. Direct gradient analysis (constrained ordination using canonical correspondence analysis) and variation partitioning were used with over 250 ecophysiologically relevant variables, including climatic, geographical, soil and herbivore data, to model the response of woodland vegetation. Although there was a high degree of multicollinearity between environmental variables, analysis revealed the vegetation composition of surveyed woodlands to be primarily structured by geographical, climatic and soil gradients, in particular rainfall, soil pH and accumulated temperature. The woods have recently been resurveyed. The results of this analysis therefore provide a baseline against which species dynamics can be assessed under a series of conservation threats, such as land use and climate change. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: National Woodland Survey; Environmental factors; Vegetation analysis; Canonical correspondence analysis; Variation partitioning

1. Introduction Successful implementation of local and national woodland conservation management goals requires an understanding of the way in which key environmental factors influence the ability of species to persist. Extant classifications for woodlands in Britain (e.g. Bunce, 1982, 1989; Rodwell et al., 1991; Peterken, 1993; Rackham, 2003) provide useful information regarding species composition, history and management, while

*

Corresponding author. Tel.: +44-151-794-4775; fax: +44-151-7944940. E-mail address: [email protected] (P.M. Corney). 1 Present address: Alterra, Landscape and Spatial Planning Section, P.O. Box 47, 6700AA Wageningen, The Netherlands. 0006-3207/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2004.03.022

Bunce and Shaw (1975) and Bunce (1981) assess the principal gradients behind woodland vegetation in Britain. However, little has been done to quantify the relative contribution of key environmental variables to the determination of woodland species composition. In this paper, we explore the relative impact of climate (e.g. temperature, precipitation, atmospheric pollution), site factors (e.g. altitude, edaphic factors) and land use on woodland composition, using vegetation data collected in 1971. We thus provide a baseline against which to assess changes under a series of conservation threats, such as land use change, global warming and exotic species invasion. In fragmented landscapes, woodlands may act as refugia, harbouring rare woodland plant communities, which can act as a source of propagules for establishment of new communities in the wider landscape. Since both persistence of species and dispersal ability

492

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

are significantly affected by environmental conditions, it is important for the purposes of conservation science that the effects of biotic and abiotic factors be quantified. Current factors affecting woodland composition and distribution are diverse and their interactions complex. They range from factors such as climate (Cannell, 2002; Williams et al., 2002) and topography (Modrzynski and Eriksson, 2002), soil parent material and time of soil development (Jenny, 1980), to management (Brunet et al., 1996) and grazing by native ungulates, such as deer (Kirby, 2001). Moreover, it is becoming apparent that anthropogenic activity leading to climate change (e.g. Bakkenes et al., 2002; Houghton et al., 2001; Lasch et al., 2002; Mitchell and Karoly, 2001), nitrogen deposition and acidification (Bobbink et al., 1998; Brunet et al., 1998; Hofmeister et al., 2002) may alter plant distribution, as species climatic envelopes begin to shift geographically. Modification of local climatic optima has particular significance for conservation of woodland habitats in Britain, as it has been suggested that a large proportion (30%) of woodland plant species may be unable to colonise suitable target habitats in fragmented areas after a period of 30–40 years (Honnay et al., 2002). Thus, although management can profoundly effect woodland vegetation distribution, assessing the true impact of such can be problematic, as continental, regional, countrywide and local scale environmental factors also exert a considerable influence on the performance of both higher and lower plants (Furness and Grime, 1982; Redfern and Hendry, 2002; Yeo and Blackstock, 2002). Accordingly, this paper reports a study which aimed to partition the range of variation within a sample of semi-natural woodlands in Britain in terms of factors that operate at a range of scales (site to countrywide) and through a variety of mechanisms (natural to anthropogenic, direct to diffuse), and are likely to be important in driving the distribution and composition of native woodlands. The analysis presented here is based upon data collected during the National Woodland Survey (NWS), carried out by the Nature Conservancy of Britain in the summer of 1971 using a standardised methodology (Bunce and Shaw, 1973; Smart et al., 2001). Whilst this project was primarily about classification, the objectives written in 1969 also specified examining principal underlying environmental factors. Environmental data not collected during the original survey but likely to be important in controlling woodland species composition were obtained from a variety of sources at the site scale. Multivariate analyses were used to describe variation in the dataset in relation to selected significant environmental variables and relative contribution of selected groups of environmental variables was assessed using variation partitioning.

2. Methods 2.1. National Woodland Survey site selection In order to cover the range of variation within seminatural woodlands, selection of sites in 1971 was made from a survey of 2463 woodland sites (>4.05 ha) carried out for the Nature Conservation Review (Ratcliffe, 1977). This site series represented approximately a 10% sample of the population of semi-natural woodlands in Britain (Bunce and Shaw, 1972, 1973). Full historical background is given by Sheail and Bunce (2003). Association analysis (Williams and Lambert, 1959) performed on this dataset divided the 2463 sites into 103 groups defined by their similarity of plant species composition (Hill et al., 1975). Numerical analysis of topographical and climatic data was then used to select a representative site from each group (Bunce and Shaw, 1973). These 103 sites were surveyed for NWS 1971. Site locations are shown in Fig. 1, grouped into Countryside Vegetation System (CVS) classes (Barr et al., 1993; Bunce et al., 1999) generated from site species data using the Modular Analysis of Vegetation Information System (MAVIS) Plot Analyser, version 1.0 (Smart, 2000). 2.2. NWS methodology For each of the 103 sites, a sample size of 16 points was fixed and marked on 1:25,000 Ordnance Survey (OS) site maps prior to survey within the delineated woodland area, representing the location of survey plots (Bunce and Shaw, 1973). Survey of all 1648 14.14
14.14 m (200 m2 ) plots was carried out between June and October 1971 by separate teams of trained surveyors (Bunce and Shaw, 1973; Hill et al., 1975). Estimates of cover (5% classes) were made across each 200 m2 plot, for individual ground flora species, along with six additional ground cover categories; bryophyte cover, litter, dead wood material, rock, bare ground and standing water (Smart et al., 2001). Woody species (trees, saplings and shrubs) and bryophyte species present within each plot were also assessed. Plot slope and aspect were recorded, along with plot- and site-scale descriptions, including signs of seven groups of herbivorous mammals (sheep, red deer, other deer species, cattle, horses/ponies, rabbits, squirrels) and boundary types such as intact walls, ditches and derelict fences. A single soil sample was taken from each plot for assessment of pH and loss-on-ignition. Diagnostic soil profile details were recorded in the field (Bunce and Shaw, 1973) and soil subgroup classes were assigned to each soil sample using Avery’s (1980) classification system. For further description of NWS methods, see Bunce and Shaw (1973). Table 1 gives a breakdown of those variables available for, and used in, the present analysis.

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

493

Fig. 1. Distribution of sites surveyed in the 1971 National Woodland Survey of Britain. Sites are illustrated according to Countryside Vegetation System (Barr et al., 1993; Bunce et al., 1999) classes.

2.3. Use of NWS botanical data To facilitate examination of between-site variation, within-site variation was explicitly omitted by aggregating botanical data for all 16 plots per site. Although cover data were available for field layer species, data for the complete vegetation complement (bryophytes, field, sapling, shrub and tree layer species) were only available as presence/absence. Analysis therefore proceeded on

the basis of full botanical complement of species present within each site as present or absent only. 2.4. Potential environmental drivers of woodland composition One hundred eighty six additional environmental variables likely to affect woodland species composition were assembled and amalgamated into groups

494

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

Table 1 Description of the nature and transformation of variable sets obtained from National Woodland Survey 1971 data and used in the analysis presented here Variable set

Description of original data

T

Description of data used in analysis

V

No.

Plot level presence or absence (sight, signs, sounds) of seven herbivorous mammal species Site boundary type Site level presence or absence of different boundary types Date Month during which woodland was surveyed Edaphic Plot level soil pH; LOI (%); depth to each soil horizon (A00, litter layer; A0, organic layer; A1, mixed mineral/organic layer; A2, leached or eluviated layer; B, weathered mineral layer) present (cm) Geographic, National Grid easting and northing (km) regionality of site datum Geographic, Plot level slope, the steepest gradient passing micro-climatic through plot centre (deg) and aspect (deg)

s

Intensity of herbivore grazing pressure by site

d

7

u

Site level presence or absence of different boundary types Month surveyed. Used as five dummy variables. Site level soil pH; Site level LOI; Site level cumulative depth to the bottom of five soil horizons (cm)

b

17

b

1 (5)

c

7

Animals

u a g, t a

u

Easting and northing (km) of site datum

c

2

a g

Average slope of site (deg); Transformation (s) of aspect (a, degrees, the site-wise mean), s ¼ sinða
p=180Þ=2, giving a site level southerly aspect; Transformation (w) of aspect (a, degrees, the site-wise mean), w ¼ j sinðða
p=180Þ  p=2Þ=2Þj, giving a site level westerly aspect Site level estimate of six ground cover types (bryophyte cover, litter, dead wood material, rock, bare ground and standing water) Arithmetic average of proportion of subgroups present in plots, giving the proportion of 26 soil groups present within each site; As above, giving the proportion of seven major soil groups present within each site

c

3

c

6

c

33

g

Ground cover categories

Plot level cover (%) of ground cover estimated within 14.14
14.14 m quadrats

g, t

Soil classification

Soil sub groups present in each plot. Where more than one subgroup occurred in each plot, the mosaic was assumed to comprise equal amounts of each constituent subgroup

a, t

a, t

T – transformation used: a, arithmetic average of plot data by site; g, geometric average of plot data by site; s, sum of plot data by site; t, arcsine transformation of data (Sokal and Rohlf, 1995); u, untransformed site level data. V– variable type: b, binary; c, continuous; d, discrete. No. – number of variables per set used in this analysis.

containing landscape, spatial, anthropogenic and climatic variables. Data for 33 variables, the Centre for Ecology and Hydrology (CEH) satellite Land Cover Map 2000 (LCM 2000) classes, woodland spatial variables, nitrogenous deposition data and distance to the sea, were only available at the site level. However for other variables (e.g. altitude), plot level data were obtained using plot OS grid references generated using the software package ERDAS IMAGINE version 8.5 (ERDAS, 2001). These results were then aggregated for each site, to produce an appropriate mean site value for each variable. Using a full series of 103 digital site map images, ERDAS IMAGINE was also employed to calculate length of delineated woodland perimeter and area. These measures of woodland perimeter (Pw ) (km) and area (Aw ) (ha) were then used to generate two woodland shape indices, a perimeter index, after Hinsley et al. (1995), and an area index. The perimeter index was

calculated as Pw Pc , where Pc was the perimeter of a hypothetical circular site of the same area. Thus the perimeter index of a wood is high for woods with scalloped or jagged edges. The area index was described using an index calculated as Aw =Pw . This second index contrasts circular woodlands with those that tend towards a more ellipsoidal shape. Presence and effect of possible spatial autocorrelation was assessed using seven terms of quadratic and cubic trend surface, derived from site geographical co-ordinates (e.g. Yeo and Blackstock, 2002). Distance to the sea, calculated as the minimum distance from site OS datum point to the nearest coast was computed with MINDIST2, a FORTRAN77 program using a set of digitised UK coastal co-ordinates, comprising 1550 points (Le Duc et al., 2000). Plot altitude was extracted from Land-Form PANORAMA Digital Terrain Model contour maps held on the OS DIGIMAP service (EDINA, 2002), using OS

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

plot co-ordinates generated as outlined above, MAP MANAGER version 6.2 (ESRI, 2001), ARCVIEW GIS version 3.2a (ESRI, 2000), and the Avenue Script GETGRIDVALUE (Elmquist and Davies, 1999). Climatic data were drawn from two sources: the Meteorological Office and the Forestry Commission. Monthly and yearly Meteorological Office 5 km
5 km grid baseline data were acquired for generated plot coordinates from long term average (LTA) datasets for the period 1961 to 1990 and 1961 to 2000, respectively, produced in association with the UK Climate Impacts Programme, UKCIP (Meteorological Office, 2003). This

495

information is drawn from interpolation models created from a large network of weather stations across the country (approximately 3500 stations for rainfall and 500 stations for most other parameters). Data for six yearly LTA variables (1961–2000) were used, including number of growing degree days and maximum number of consecutive dry days in a year, along with data for eight monthly variables (1961 to 1990), such as mean daily minimum temperature and days of ground frost, giving 96 variables. Where data for specific LTA variables were available in such monthly blocks, these were also accumulated to give a further 32 seasonal and eight

Table 2 Description of the 186 additional environmental variables generated or collected for the present study Variable set

T

Description

Site form, shape

u

Spatial

u

Geographic Surrounding landscape

u a u

Pollution

u

Woodland site conditions 1961–1990 LTA data

a

Climatic monthly 1961–1990 LTA data

a

Climatic seasonal 1961–1990 LTA data (accumulated monthly data)

a, s

Climatic annual 1961–1990 LTA data (Accumulated monthly data)

a, y

Climatic yearly 1961–2000 LTA data

a

4 Woodland area (ha); woodland perimeter (m); shape indices, woodland area index (Aw =Pw ); woodland perimeter index (Pw =Pc ) Site (East)2 ; (East)3 ; (North)2 ; (North)3 ; East
North; (East)2
North; 7 (North)2
East Distance from site to nearest coast (km) 2 Altitude (m) Area (ha) of broad habitat classes (e.g. broadleaved woodland, 25 set-aside land, inland rock, etc) surrounding site datum Site level deposition of NOx (kg N ha1 y1 ); deposition of NHx (kg N ha1 y1 ); 3 total N deposition (kg N ha1 y1 ) Forest wind climate, calculated using tatter flags and digital elevation models, 100 m 3 resolution; annual accumulated temperature (degree days/30), 10 km grid squares; moisture deficit (accumulated excess of evaporation minus rainfall), 10 km grid squares Mean monthly daily maximum temperature (°C); mean monthly daily minimum 96 temperature (°C); days of ground frost; mean monthly cloud cover (Oktas converted to %); total monthly bright sunshine (h); number of days per month having a rainfall P1 mm (rain days); number of days per month having a rainfall P10 mm (wet days); total monthly precipitation (mm) Mean seasonal daily maximum temperature (°C); mean seasonal daily minimum 32 temperature (°C); days of ground frost; mean seasonal cloud cover (Oktas converted to %); total seasonal bright sunshine (h); number of days per month having a rainfall P1 mm (rain days); number of days per month having a rainfall P10 mm (wet days); total seasonal precipitation (mm) Mean yearly daily maximum temperature (°C); mean yearly daily minimum 8 temperature (°C); days of ground frost; mean yearly cloud cover (Oktas converted to %); total yearly bright sunshine (h); number of days per month having a rainfall P1 mm (rain days); number of days per month having a rainfall P10 mm (wet days); total yearly precipitation (mm) 6 Annual extreme temperature range (highest daily maximum – lowest daily maximum) (°C); number of growing degree days, sum of (daily mean temperature – 4), ignoring negatives, over the summer months; growing season length, bounded by daily mean temperature being >5 °C for >five consecutive days and <5 °C for >five consecutive days; maximum number of consecutive dry days (days with less than 1 mm of rain) in a year; greatest five day precipitation total in a year (mm); mean rainfall amount (mm) on rain days (total rain on rain days/number of rain days)

No.

All variables are continuous. Nitrogen deposition data extracted from deposition maps compiled by CEH Edinburgh. Woodland site condition data obtained from ESC decision support system (Ray, 2001). All other climatic data acquired from the UK Meteorological Office 5 km
5 km grid baseline datasets for the period 1961–2000 produced in association with UK Climate Impact Programme. T – transformation used: a, arithmetic average of plot data by site; s, arithmetic average of monthly data to generate 32 seasonal variables (winter ¼ Dec, Jan, Feb; spring ¼ Mar, Apr, May; summer ¼ Jun, Jul, Aug; autumn ¼ Sep, Oct, Nov); y, arithmetic average of monthly data to generate a further eight annual variables; u, untransformed site level data. No. – number of variables in set.

496

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

annual values. Woodland site condition data were obtained from the Ecological Site Classification (ESC) decision support system published by the Forestry Commission (Ray, 2001), comprising a windiness score, accumulated temperature and moisture deficit data for the 30-year period 1961–1990. Oxidised (NOx) and reduced (NHx) nitrogen (N) deposition data (sum of wet, dry and cloud deposition) for surveyed woodlands were extracted from 5
5 km resolution national deposition maps (1995–1997) compiled by CEH Edinburgh and derived using methods described in NEGTAP (2001). NOx and NHx were summed to generate total N deposition entered as a third pollutant variable in analyses. Although a measure of total N deposition generated in this way is unlikely to be independent of its constituent parts, this facilitated an examination of possible responses to total N load. Current landscape-scale habitat data constructed from satellite images were obtained from CEH LCM 2000 (Fuller et al., 2002). LCM subclass level-two habitats found within a circular area of radius of 1500 m surrounding each site OS datum were selected. Table 2 gives a summary of additional environmental variables used.

 variables (Ter Braak and Smilauer, 2002). As a result, the effects of different environmental variables on community composition cannot be separated out and consequently, canonical coefficients are unstable and do not merit interpretation (Ter Braak, 1986). Therefore, variables exhibiting high inflation values coupled with low or non-significance were removed and analysis re-run, until a set of significant variables was obtained, with inflation factors of <15. The final model contained 20 variables. Significant variables selected were divided into three clearly defined and ecologically meaningful sets; biotic factors, soil properties and geographical with climatic (geo-climatic) variables. Variation partitioning was carried out using a standardised procedure (e.g. Borcard et al., 1992; Marrs and Le Duc, 2000; Yeo and Blackstock, 2002), variance in the data being partitioned between these sets and expressed as a percentage of the total variation explained.

2.5. Statistical analyses

Site summary data are presented in Table 3 according to CVS vegetation classes (Barr et al., 1993; Bunce et al., 1999; Smart, 2000). The sites covered an extremely wide geographic range (Fig. 1) and showed considerable physiographic variability (Table 3), while the wide range of vegetation types encountered reflects the high b-diversity within the dataset.

All statistical analyses were carried out using CA NOCO for Windows version 4.5 (Ter Braak and Smilauer, 2002), using default settings and untransformed species data, unless otherwise specified. Detrended Correspondence Analysis (DCA) was used to obtain estimates of gradient lengths in standard deviation (SD) units of species turnover (Ter Braak and  Smilauer, 2002), thereby assisting in the decision of whether to use a linear or unimodal approach to the data. Principle Components Analysis (PCA) was employed with both original survey variables (Table 1) and additional variables (Table 2) for preliminary exploration of environmental factors only. The full range of variables was then used, either as environmental variables or covariables, in different combinations, along with botanical data, to assess the influence of environmental factors on vegetation variation, using the CANOCO Canonical Correspondence Analysis (CCA) forward selection procedure. Forward selection was used to select significant variables, the Monte Carlo test being used to assess significance, with 499 permutations for exploratory analyses and 9999 for final results (Legendre and Legendre, 1998). In all permutation tests, an unrestricted permutation structure was used. This process was combined with an examination of inflation values, to remove those variables that were highly multicollinear. If an inflation factor is large, the variable is likely to be strongly correlated with other

3. Results 3.1. Description of survey sites

3.2. Range of variation in native semi-natural woodland communities Preliminary DCA produced an ordination with a first axis gradient length of 3.266 SD units, demonstrating high b-diversity and suggesting that a unimodal model such as CCA would adequately describe the relationship between species and environmental variables (Ter Braak  and Prentice, 1988; Ter Braak and Smilauer, 2002). Nevertheless, initial CCA results described using  CANODRAW (Ter Braak and Smilauer, 2002) revealed an apparent distortion. Subsequent data exploration revealed the distortion to be due to a large number (151) of species, each of which occurred only once within the dataset (25% of all species recorded) and one outlying site. As CCA is sensitive to deviant sites when they are outliers with regard to both species composition and environment, the 151 species were made supplementary in further analysis, as recommended by Ter Braak and Prentice (1988). Species made passive in this way do not influence ordination axes, but are added to ordination diagrams so that their relationship to other species can be examined. Rare species were also down-weighted,

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

497

Table 3 Summary of site attributes CVS class number

CVS class name

No. of sites

Altitude (m)

Slope (°)

pH

Summer rainfalla (days)

24 25 35 42 46 48 50 61 62 63 68 69 76

Dry base-rich lowland woodland Shaded grassland/hedges Lowland base-rich woods/hedges Lowland woodland on heavy soils Diverse upland wooded streamsides Marsh/streamsides Neutral/acidic upland woodland Species-rich acid grassland/moorland Upland woodland on podzolic soils Herb-rich streamsides/acid grassland Upland oak/birch woodland Upland open woodland/heath Acid moorland streamsides/flushes

1 1 44 7 8 2 27 1 1 3 2 5 1

90 103 43–226 60–192 9–172 80–154 43–204 216 107 108–297 119–148 71–189 259

8 1 0–31 2–8 3–19 10–31 3–32 28 3 6–28 16–25 9–32 13

7 8 4–7 4 4–6 5 4–6 4 4 4 4 4–5 4

9 8 8–13 8–12 10–13 11–15 8–13 13 8 9–13 12–13 12–16 16

a

Data are presented according to CVS vegetation classes. Average monthly number of days having rainfall P1 mm over the summer months (June–August).

using the appropriate option in CANOCO. However, there remained a single outlying site (076; Coille Coire Chuilc, a moorland habitat in the Grampian mountains). With the exclusion of this site from analyses, in addition to down-weighting of rare species and a subset of species made passive, ordination distortion was removed and analysis therefore proceeded with these factors set as default. As a consequence, the final DCA produced eigenvalues (k) of 0.241, 0.116, 0.102 and 0.075 for the first four axes, with gradient lengths of 2.573, 1.894, 1.684 and 1.430 SD units, respectively. The scatter of species in the DCA biplot (Fig. 2) illustrates both regional differentiation and environmental components. The first axis is characterised by a gradient broadly associated with edaphic variables such as pH and nutrient availability; from fertile Fraxinus/ Corylus woodlands on alkaline to neutral soils in the lower left hand area of the diagram, to more open Betula/Sorbus communities on acid soils, in the upper right hand area. There is also a complex orthogonal gradient associated with both climate and grazing intensity that appears to be less apparent in upland/ base-poor woodlands. Species indicative of ungrazed woodland interiors in warmer, dryer, base-rich areas include Hyacinthoides non-scripta, Rubus fruticosus and Mercurialis perennis, whereas heavily grazed or woodland edges in cooler, wetter areas feature Cirsium palustre, Geranium robertianum, Dactylis glomerata and Galium aparine. 3.3. Relationship between species composition and environmental factors Prior to constrained analysis, PCA was used to model the relationship between the responses of each variable and ordination axes. However, when constrained ordi-

nation with species data was subsequently performed, variables found to be important in PCA (e.g. northing and perimeter length) were no longer significant. This was probably because they were replaced by more ecologically meaningful edaphic, climatic and shape variables using forward selection. Although final DCA gradient lengths were reduced somewhat from those estimated initially, the high bdiversity (demonstrated by the range of sites, from upland open woods and heaths to lowland woodlands on heavy soils, e.g. Table 3) indicated that species exhibited unimodal responses to environmental gradients and thus further analyses were made using CCA unimodal response models. CCA with forward selection was initially employed as an iterative process to explore correlation within the environmental dataset, prior to final selection of significant variables. In order to reduce multicollinearity of plot level NWS soil subgroup data, these were combined into two sets of variables corresponding to Avery’s (1980) higher categories; soil group and major soil group. Aggregated by site, these two variable sets therefore represented the proportion of (i) each soil group and (ii) each major soil group present within each site. CCA models were also run using yearly and monthly LTA climatic variables, along with the seasonal and annual variables created by averaging monthly data, both separately and in combination. However, since monthly variables were found to significantly increase multicollinearity with other variables, relative to runs using only seasonal, annual and yearly LTA data, these were excluded from further analyses. Survey date variables were entered into the final model as covariables, to remove the effect of a staggered survey from June to October. Forward selection using CCA indicates the ranking of environmental variables in their importance for

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

2.5

498

Betusp+Luzupilo Betusp++ Ilexaqui Holcmoll Dicrhete

Quer 1.2 Quersp++

Acerpseu

Pteraqui

Mniuhorn

Acerps++

Junceffu Agrocapi

Polysp.

Sorbaucu

Plagdent Hyacnon-

Loniperi

Eurhprae

Axis 2

Fraxex++

Dryofili

Fraxexce

Mercpere

Ajugrept

Coryav-+ Hedeheli

Viol 3.4

Cratmono

Caresylv

Dryodila

Rubufrut

Urtidioi

Holclana

Desccesp

Ranurepe

Cratmo++ Bracsylv

Cirspalu Gerarobe

Poa 1.2

Stacsylv Agrostol

Fragvesc Dactglom Veromont

Galiapar

0.0

Lysinemo

Thuitama

Geumurba

Eurhstri

Athyfili

Epilmont

Circlute Fraxex+-

Oxalacet Lophbide

Pmniundu

Plagaspl

0.0

Axis 1

3.0

Fig. 2. DCA species scatter plot. Ordination of those 59 species with a high level of occurrence in the dataset (species present in 65 or more of the 102 sites). Species found in the ground flora component (including bryophytes) listed by first four letters of genus and species name. Woody species listed by first four letters of genus, first two letters of species and suffixed appropriately. Suffixes ++ and +) refer to tree and sapling growth stages. The suffix )+ refers to shrub species. The species are: Acerps++, Acer pseudoplatanus, Acerpseu, A. pseudoplatanus, Agrocapi, Agrostis capillaris, Agrostol, A. stolonifera, Ajugrept, Ajuga reptans, Athyfili, Athyrium filix-femina, Betusp+), Betula sp., Betusp++, Betula sp., Bracsylv, Brachypodium sylvaticum, Caresylv, Carex sylvatica, Circlute, Circaea lutetiana, Cirspalu, Cirsium palustre, Coryav)+, Corylus avellana, Cratmo++, Crataegus monogyna, Cratmono, C. monogyna, Dactglom, Dactylis glomerata, Desccesp, Deschampsia cespitosa, Dicrhete, Dicranella heteromalla, Dryodila, Dryopteris dilatata, Dryofili, D. filix-mas, Epilmont, Epilobium montanum, Eurhprae, Eurhynchium praelongum, Eurhstri, E. striatum, Fragvesc, Fragaria vesca, Fraxex+), Fraxinus excelsior, Fraxex++, F. excelsior, Fraxexce, F. excelsior, Galiapar, Galium aparine, Gerarobe, Geranium robertianum, Geumurba, Geum urbanum, Hedeheli, Hedera helix, Holclana, Holcus lanatus, Holcmoll, H. mollis, Hyacnon-, Hyacinthoides non-scripta, Ilexaqui, Ilex aquifolium, Junceffu, Juncus effusus,Loniperi, Lonicera periclymenum, Lophbide, Lophocolea bidentata, Luzupilo, Luzula pilosa, Lysinemo, Lysimachia nemorum, Mercpere, Mercurialis perennis, Mniuhorn, Mnium hornum, Oxalacet, Oxalis acetosella, Plagaspl, Plagiochila asplenioides, Plagdent, P. denticulatum, Pmniundu, Plagiomnium undulatum, Polysp., Polytrichum sp., Pteraqui, Pteridium aquilinum, Quersp++, Quercus sp., Ranurepe, Ranunculus repens, Rubufrut, Rubus fruticosus, Sorbaucu, Sorbus aucuparia, Stacsylv, Stachys sylvatica, Thuitama, Thuidium tamariscinum, Urtidioi, Urtica dioica, Veromont, Veronica montana. Codes Poa 1.2, Quer 1.2 and Viol 3.4 refer to the species couplets Poa nemoralis and P.trivialis, Quercus petraea and Q. robur (seedlings) and Viola reichenbachiana and V. riviniana, respectively.

 predicting the species data (Ter Braak and Smilauer, 2002). Although analysis performed on NWS 1971 data included variables that were at least partly the result of woodland processes, such as dead wood material, litter and depth of AO horizon, these variables were included because they in turn also affect woodland vegetation composition. The final constrained model (CCA) examined species response to the 20 significant environmental variables defined by forward selection. Eigenvalues for axes 1 and 2 were 0.149 and 0.061, respectively, and the model was significant according to the Monte Carlo test (f ¼ 2:115, p 6 0:001, 9999 permutations). Site and species ordinations are given in Fig. 3. The

positions of those environmental variables used in the final model are illustrated using a biplot (Fig. 3(a)), along with the response to these of all species in the analysis. The biplot is redrawn (Fig. 3(b)) to show the positions of sites, classified into CVS classes (Barr et al., 1993; Bunce et al., 1999) using MAVIS (Smart, 2000). Fig. 3(c) gives response to the 20 environmental variables of 59 most common species, defined as those present in 65 or more survey sites. The difference between the species ordinations (Figs. 3(a) and (c)) is a feature of scaling and dependent on the focus of study. In Fig. 3(c), for example, the vectors have been extended so that the biplot shows only the most common species.

499

1.0

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

Litter

Winter cloud cover Brown sands

Axis 2

Depth, bottom A0

Sea distance Dead wood

Pararendzinas

Slope

Summer rain days

Easting

Area index

Altitude Intact walls

Sheep

Accumulated temp

Depth, bottom A1H Rendzinas

Bare ground Brown calcareous earths

-1.0

Soil pH

(a)

-1.0

Axis 1

1.0

Fig. 3. CANODRAW CCA biplots. (a) Full species complement and environmental variables.

The first two axes of the constrained ordination (Fig. 3(a)) reflect the major gradients highlighted by DCA, suggesting that the environmental variables shown account for a good proportion of species trends. The first axis is characterised principally by two climatic variables, temperature and rainfall. The axis illustrates a climatic gradient from woodlands surveyed in cooler, wetter and more western areas of the country (interset correlations of the number of summer rain days, accumulated temperature and eastings, r ¼ 0:7296, r ¼ 0:6189 and r ¼ 0:5290, respectively) to warmer and dryer NWS woodlands in the east. The anthropogenic variable related to sheep grazing intensity in open upland woodlands and heaths is also a key element of this axis (r ¼ 0:4450), as evidenced by the position of woodlands belonging to CVS class 69 (Upland open woodland/heath) in Fig. 3(b), sites that are located primarily in the north of Britain (see Fig. 1). The second axis broadly represents a soil gradient from higher pH (r ¼ 0:6484) rendzinas (r ¼ 0:3757) at the negative end, to more acidic soils with a concomitant rise in quantity of litter (r ¼ 0:6554). This is reflected in Fig. 3(b) by the position of sites falling into CVS classes 35 (Lowland base-rich woods/hedges) and 50 (Neutral/acidic upland woodland). The association of eastings with distance from the coast (Fig. 3(a)) is interpreted as the effect of more con-

tinental conditions prevailing on sites located in eastern Britain, contrasting with more oceanic conditions found in sites located towards the west coast. The warmer and drier end of the climatic gradient is associated with species such as Circaea lutetiana, Deschampsia cespitosa, R. fruticosus, Viola reichenbachiana and Viola riviniana found in communities dominated by Fraxinus excelsior and Acer pseudoplatanus in NWS sites located towards the south and east of the country (Fig. 3(c)). At the opposite end of this gradient, e.g. sites located in cooler and wetter areas, are plants of open woodland and upland streamsides (Fig. 3(b)), such as C. palustre. The absence in the biplot of species at the lower end of this climatic gradient is an artefact of the selection criteria (species present in 65 or more of the 102 sites) used to select species for presentation in the figure. Shade tolerant species Dryopteris filix-mas and Dryopteris dilatata, along with palatable woody species Lonicera periclymenum and R. fruticosus, appear to respond negatively to open woodland habitats created or maintained by sheep grazing. The second axis is characterised by variation along an edaphic gradient, from acidic, heathy, open Betula NWS woodlands of the western coasts and uplands to baserich communities of sites surveyed mainly in the southern and eastern lowlands (Fig. 3(b)). Species

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

-1.0

Axis 2

1.0

500

(b)

-1.0

Axis 1

1.0

Fig. 3 (continued). (b) Environmental variables and sites, given in CVS classes. CVS class symbols: 61 Species-rich acid grassland/moorland, ; 25 Shaded grassland/hedges, ; 63 Herb-rich streamsides/acid grassland, ; 46 Diverse upland wooded streamsides, N; 48 Marsh/streamsides, ; 68 Upland oak/birch woodland, j; 62 Upland woodland on podzolic soils, ; 69 Upland open woodland/heath, ; 50 Neutral/acidic upland woodland,
; 24 Dry base-rich lowland woodland, s; 42 Lowland woodland on heavy soils, d; 35 Lowland base-rich woodland/hedges, .

typical of more shallow, acid or lighter soils at the negative end of this gradient (Fig. 3(c)) include Polytrichum sp., often found on wet heaths, moorland and streamsides in woodland, along with Luzula pilosa, common among heather, leaf-litter, or moss-dominated sites, and under Sorbus aucuparia. These communities are also often found at higher altitudes and exhibit a more open character associated with higher field layer species diversity. In contrast, at the positive end of this gradient are Geum urbanum, M. perennis, Ajuga reptans and Plagiomnium undulatum, species found in woodland on deep, fertile, neutral to base-rich soils which are more common in the lowlands where dense stands and pronounced shrub layers are associated with greater amounts of bare ground. The Quercus species, Pteridium aquilinum and D. dilatata, all species that contribute significantly to woodland litter production, are found to be strongly associated with this variable (Fig. 3(c)). 3.4. Assessing the relative contribution of environmental factors to species composition The 20 significant environmental variables were divided into three sets (biotic factors, geo-climatic vari-

ables and soil properties), each with similar numbers of variables (Table 4). Total inertia was 1.748, estimated total variation explained (Borcard et al., 1992) was 0.587 and total variation in the data explained by all three variable sets was 33.6%. At this level, the variables selected can be considered to be strongly associated with the structure of the woodland communities sampled, with regard to between-site species coincidence. The results of variation partitioning indicating the relative importance of sets of variables in determining broadscale woodland vegetation patterns are illustrated (Fig. 4). There was very little overlap in the variation accounted for by each of the sets in this model, suggesting only modest interaction between the variable groups used. Of the total variation in the dataset explained, 36.6% (Table 5) can be attributed to biotic factors on their own, of which anthropogenic influences are a part. When soil and geo-climatic sets are included, 20.6% is attributable to biotic variables, the remainder being as likely to be explained by the other variable sets, because of their inter-correlation. Variation partitioning therefore suggests a moderate impact of

501

1.0

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

Betusp+-

Luzupilo

Dicrhete Ilexaqui

Polysp.

Betusp++

Agrocapi Junceffu

Quer 1.2

Holcmoll Oxalacet Athyfili

Axis 2

Hyacnon-

Pteraqui

Sorbaucu

Lysinemo

Mniuhorn Loniperi

Quersp++

Hedeheli Acerps++ Dryodila Acerpseu Rubufrut Holclana Caresylv Dryofili Fraxex++ Eurhprae Plagdent Coryav-+ Circlute Fraxex+Ranurepe Desccesp Veromont Agrostol Cratmono Poa 1.2 Viol 3.4 Mercpere Fraxexce Epilmont Gerarobe Urtidioi Ajugrept Cratmo++ Fragvesc Thuitama Eurhstri Geumurba Cirspalu Dactglom Galiapar Lophbide

Plagaspl Stacsylv

Bracsylv

-1.0

Pmniundu

(c) -1.0

Axis 1

1.0

Fig. 3 (continued). (c) Response of 59 species with a high level of occurrence in the dataset (those present in 65 or more of the 102 sites) to key environmental variables. Species abbreviations as Fig. 2.

significant biotic factors (those derived using forward selection) on vegetation in sampled NWS woodlands. However, biotic factors available for use in this study were limited both by NWS methodology and additional data availability. Moreover, biotic factors as a group, including the effect of management, may be less likely to be detected at the coarse scale of between-site frequency. Of explained variation, 45.0% (Table 5) is attributable to soil properties and the amount that can be explained by this set, independent of the other two sets, is 28.1%. However, it is not possible to quantify to what extent the proportion of explained variance due to pedological variables reflects response of species to changing edaphic conditions, or the integral role soil plays in the cycling of nutrients and water in woodland ecosystems. The parameter set containing climatic and geographical variables accounts for the final portion of variance explained by the model. Variation attributable to this set is 45.0% (Table 5), while the variance explained by this set apart from that also attributable to either biotic or soil factors is 28.3%. In addition, this set contains the climatic variable, summer rain days, which

was consistently selected first in CCA runs with forward selection and which, along with accumulated temperature, exhibited the strongest interset correlations with axis 1. This illustrates the crucial influence of climate on the distribution of woodland species in the UK. The amount of unexplained variation (66.4%) attributable to non-measured environmental determinants as opposed to stochastic variation is unknown.

4. Discussion 4.1. Description of woodland vegetation in Britain DCA analysis illustrated that in 1971, vegetation composition of sampled semi-natural woodlands was characterised by several key environmental factors and demonstrated the way in which native species align themselves along these gradients. Shade-tolerant and a greater proportion of shrub layer species were found to be associated with woodlands on deep fertile base-rich soils, while less fertile woodlands on acid soils were associated with species of relatively open, nutrient poor environments. The upland woodlands surveyed, more

502

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

Table 4 Sets of explanatory variables used in variation partitioning of woodland survey data; set allocations are shown along with the f -values of an unrestricted Monte Carlo test with 499 permutations conducted at the final automatic forward selection stage Subset

Variable name

Order selected

f

G G G G G G G

Summer rain days Accumulated temperature Easting Distance to sea Winter cloud cover Slope Altitude

1 3 6 8 10 12 18

6.34** 2.35** 1.82** 1.61** 1.53** 1.43** 1.41**

S S S S S S S

Soil pH 2 Depth, bottom of A0 horizon 4 Rendzinas 5 Depth, bottom of A1 horizon 7 Brown sands 11 Pararendzinas 14 Brown calcareous earths 20

4.42** 1.88** 1.86** 1.75** 1.49** 1.45** 1.3*

B B B B B B

Dead wood Litter Area index Sheep grazing Bare ground Intact walls

1.58** 1.49** 1.41** 1.35** 1.43** 1.3*

9 13 15 16 17 19

Significance of the f -test: *p < 0:05; **p < 0:01. Subsets: B, biotic factors; G, geo-climatic variables; S, soil properties.

Soil

Geo-climatic 7.2%

28.1%

28.3%

3.2% 6.3%

6.3%

Biotic 20.6%

Fig. 4. Venn diagram showing the proportion of the total variance explained by the CCA model in woodland plant community composition in Britain, attributable to the sets of environmental variables.

likely to be grazed by sheep and coincident with geomorphological factors such as rocky or shallow soils, were found to support fewer deep-rooted herbs and shrub species and were not found to express response to

the grazing/woodland edge gradient more apparent in lowland woodlands. 4.2. Relative contribution of environmental factors to species composition At the between-site level, the vegetation composition of sampled semi-natural woodlands appears to be structured mainly by climatic, geographical and soil variables and to a slightly lesser extent, by biotic factors. This reflects the qualitative interpretation of the main divisions within the National Vegetation Classification (Rodwell et al., 1991); for example between north-west and south-east oak woods and ash woods and within the south-east types, the split between baserich, mesotrophic and acidic communities. There was a relatively small amount of overlap in variation accounted for by each of the sets in this model, indicating that, in sampled woodlands, interactions between groups of geo-climatic, soil and biotic variables are weak in relation to the effects of the variables themselves. 4.3. Effects of biotic factors Of biotic variables found to be significant using forward selection, only two were primarily causal factors (sheep grazing intensity and area index) and these were of principally anthropogenic influence. High grazing pressure was found to be associated with species typical of western uplands; grassland, moorland mosaics and open woodland, whilst low pressures in the south and east allowed palatable species such as L. periclymenum and R. fruticosus to thrive. Similar results have been found in the Atlantic deciduous woodlands of the western Pyrenees where sheep actively select woodlands containing an abundant herbaceous understorey and preferentially browse Rubus, Ilex, Vaccinium, forbs and Festuca-like graminoids, while preferentially rejecting Brachypodium and Luzula sp. (Garin et al., 2000). Conversely, no significant botanical response to presence of deer or other herbivorous mammal species recorded during NWS 1971 was found in this study. Management of the wider countryside, defined as LCM 2000 broad habitats, did not correlate with species distribution, although this may be expected with the non-contemporaneous nature of these data. 4.4. Effects of soil properties This analysis also indicates the extent to which soil type determines vegetation composition of sampled woodlands. As expected, a strong environmental gradient related to soil pH was found, from upland communities on acid soils with low biotic activity, to

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

503

Table 5 Variation partitioned between sets of environmental parameters, showing the proportion of explained variance in woodland community composition at the between-site level explained by each set and grouping of parameters P Variable set Notation canonical kn Variation accounted for by variable set from total variation explained (%) All variable sets Biotic set Soil set Geo-climatic set Biotic without effect of soil and geo-climatic Soil without effect of biotic and geo-climatic Geo-climatic without effect of biotic and soil Interaction of biotic and geo-climatic Interaction of soil and biotic Interaction of geo-climatic and soil Interaction of all variable sets

{B[S[G} {B} {S} {G} {B\S0 \G0 } {S\B0 \G0 } {G\B0 \S0 } {B\S0 } {S\G0 } {G\B0 } {B\S\G}

species-rich lowland forests on more base-rich soils featuring a more rapid decomposition of plant material. Increasing N availability, and consequent soil acidification has been shown to affect woodland ground vegetation composition in continental oak forests even at relatively low N loads (e.g. Brunet et al., 1998; Tybirk and Strandberg, 1999). Nitrogen enrichment can lead to competitive exclusion of woodland species by more nitrophilous species, while acidification favours calcifuge species (Bobbink et al., 1998; Brunet et al., 1998; Hofmeister et al., 2002; Ling, 2003). However, although no significant species response to N deposition was found in this analysis, the finding is limited by the time lapse between NWS 1971 and the compilation of national deposition maps.

0.587 0.215 0.264 0.264 0.121 0.165 0.166 0.037 0.037 0.042 0.019

100.0 36.6 45.0 45.0 20.6 28.1 28.3 6.3 6.3 7.2 3.2

of migration required to colonise new areas, wide ranging review of conservation policy may become necessary. 4.6. Limitations of this analysis Analysis of landscape-scale environmental factors affecting vegetation of woodlands surveyed across Britain must necessarily be an analysis at the site level, where much plot level variation driven by micro-climatic factors is lost. Nevertheless, analysing vegetation at the between-site scale has enabled that part of the variation pertaining to the geographical range of species sampled in woodlands to be specifically targeted and will both balance and inform subsequent analysis of plot level variation.

4.5. Effects of climatic and geographical factors 4.7. Relevance for future woodland management Summer rainfall and growing degree-days (accumulated temperature) were found to be the most important factors driving vegetation composition in woodlands sampled. This effect is well documented; temperature is one of the primary factors controlling distribution of Tilia cordata (Pigott and Huntley, 1978), whilst the east– west oceanic-continental rainfall gradient determines native fern and bryophyte distribution, which is highest in western oak woods but declines towards the southeast. However, countrywide influence of both temperature and rainfall gradients on woodlands in Britain may be changing as a result of global warming. Over the last century, average global surface temperatures have risen by ca. 0.6° (Houghton et al., 2001), while precipitation and cloud cover have both increased by between 0.5% and 2% over land in the Northern Hemisphere (Nicholls et al., 1996; Folland and Karl, 2001; Houghton et al., 2001). Moreover, it has been predicted that these trends will continue over the next 50–100 years (Cubasch and Meehl, 2001). If climate change does alter plant distribution considerably and species do not achieve the speed

This study has provided a baseline assessment of a stratified sample of native semi-natural woodlands in Britain with particular reference to environmental factors that control species distribution at the between-site level. Original NWS sites have recently been resurveyed (2002/3) and the analysis presented in this paper will provide an opportunity for change to be gauged in a range of ways. Resurvey will facilitate an assessment of the way in which plant communities have developed over time, along with an examination of whether environmental variables that were selected as important in 1971 have remained so in 2003. It will also allow investigation of whether variables such as land use change, N deposition and deer grazing are becoming more important in the context of semi-natural woodland conservation. Irrespective of future benefits, this initial analysis provides a useful conservation tool, and a methodology for describing and assessing the important factors that are likely to affect native semi-natural woodlands in Britain.

504

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505

Acknowledgements The authors thank Jane Hall (CEH Monks Wood), Dr. David Howard (CEH Merlewood), Duncan Ray (Forest Research), Mary Thorp and Dr. Hugh McAllister (University of Liverpool) for technical support. Financial support for Philip Corney was provided by EN and CEH in the form of a CASE research studentship. We are grateful to all who read previous drafts and supplied useful comments, in particular the valuable reviews of two anonymous referees which were greatly appreciated in the final preparation of this manuscript. References Avery, B.W., 1980. Soil classification for England and Wales [Higher categories]. Soil Survey Technical Monograph No.14, Rothamsted Experimental Station, Harpenden. Bakkenes, M., Alkemade, J.R.M., Ihle, F., Leemans, R., Latour, J.B., 2002. Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Global Change Biology 8, 390–407. Barr, C.J., Bunce, R.G.H., Clarke, R.T., Fuller, R.M., Furse, M.T., Gillespie, M.K., Groom, G.B., Hallam, C.J., Hornung, M., Howard, D.C., Ness, M., 1993. Countryside Survey 1990: main report. Department of the Environment, London. Bobbink, R., Hornung, M., Roelofs, J.G.M., 1998. The effects of airborne nitrogen pollutants on species diversity in natural and seminatural European vegetation. Journal of Ecology 86, 717–738. Borcard, D., Legendre, P., Drapeau, P., 1992. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055. Brunet, J., Diekmann, M., Falkengren-Grerup, U., 1998. Effects of nitrogen deposition on field layer vegetation in south Swedish oak forests. Environmental Pollution 102, 35–40. Brunet, J., FalkengrenGrerup, U., Tyler, G., 1996. Herb layer vegetation of south Swedish beech and oak forests – effects of management and soil acidity during one decade. Forest Ecology and Management 88, 259–272. Bunce, R.G.H., 1981. British woodlands in an European context. In: Last, F.T., Gardiner, A.S. (Eds.), Institute of Terrestrial Ecology Symposium No. 8. Nature of Tree and Woodland Resources. Natural Environment Research Council. Bunce, R.G.H., 1982. A Field Key for Classifying British Woodland Vegetation. Part 1. Institute of Terrestrial Ecology, Cambridge. Bunce, R.G.H., 1989. A Field Key for Classifying British Woodland Vegetation. Part 2. Institute of Terrestrial Ecology, Cambridge. Bunce, R.G.H., Barr, C.J., Gillespie, M.K., Howard, D.C., Scott, W.A., Smart, S.M., van de Poll, H.M., Watkins, J.W., 1999. Vegetation of the British countryside – the Countryside Vegetation System. ECOFACT Volume 1. Department of Environment, Transport and the Regions, London. Bunce, R.G.H., Shaw, M.W., 1972. Classifying woodland for conservation. Forestry and Home Grown Timber 1, 23–25. Bunce, R.G.H., Shaw, M.W., 1973. A standardized procedure for ecological survey. Journal of Environmental Management 1, 239– 258. Bunce, R.G.H., Shaw, M.W., 1975. Classification of semi-natural woodlands in Britain. Institute of Terrestrial Ecology Annual Report 1974. Natural Environmental Research Council, p. 43. Cannell, M., 2002. Impacts of climate change on forest growth. In: Broadmeadow, M. (Ed.), Climate Change: Impacts on UK forests. Forestry Commission, Edinburgh, pp. 141–149.

Cubasch, U., Meehl, G.A., 2001. Projections of future climate change. In: Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A. (Eds.), Climate Change 2001; The Scientific Basis. Cambridge University Press, Cambridge, pp. 525–582. EDINA, 2002. Digimap online mapping service (http://edina.ac.uk/ digimap). Ó Crown Copyright. An EDINA Digimap/JISC supplied service. Elmquist, M., Davies, J., 1999. Getgridvalue ArcView Avenue Script. ERDAS, 2001. ERDAS IMAGINE, version 8.5. ERDAS, Inc., Atlanta, GA. ESRI, 2000. ArcView GIS, version 3.2a. Environmental Systems Research Institute, Redlands, CA. ESRI, 2001. Map Manager version 6.2. ESRI, Redlands, CA. Folland, C.K., Karl, T.R., 2001. Observed climate variability and change. In: Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A. (Eds.), Climate Change 2001; The Scientific Basis. Cambridge University Press, Cambridge, pp. 99–181. Fuller, R.M., Smith, G.M., Sanderson, R.A., Hill, R.A., Thomson, A.G., 2002. The UK Land Cover Map 2000: Construction of a parcel-based vector map from satellite images. Cartographic Journal 39, 15–25. Furness, S.B., Grime, J.P., 1982. Growth rate and temperature responses in bryophytes. II. A comparative study of species of contrasted ecology. Journal of Ecology 70, 525–536. Garin, I., Aldezabal, A., Herrero, J., Garcia-Serrano, A., 2000. Understorey foraging and habitat selection by sheep in mixed Atlantic woodland. Journal of Vegetation Science 11, 863–870. Hill, M.O., Bunce, R.G.H., Shaw, M.W., 1975. Indicator species analysis, a divisive polythetic method of classification and its application to a survey of native pinewoods in Scotland. Journal of Ecology 63, 597–613. Hinsley, S.A., Bellamy, P.E., Newton, I., 1995. Bird species turnover and stochastic extinction in woodland fragments. Ecography 18, 41–50. Hofmeister, J., Mihaljevic, M., Hosek, J., Sadlo, J., 2002. Eutrophication of deciduous forests in the Bohemian Karst (Czech Republic): the role of nitrogen and phosphorus. Forest Ecology and Management 169, 213–230. Honnay, O., Verheyen, K., Butaye, J., Jacquemyn, H., Bossuyt, B., Hermy, M., 2002. Possible effects of habitat fragmentation and climate change on the range of forest plant species. Ecology Letters 5, 525–530. Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A., 2001. Climate Change 2001; The Scientific Basis. Contribution of Working Group I to the Third assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. Jenny, H., 1980. The Soil Resource. Springer, Berlin. Kirby, K.J., 2001. The impact of deer on the ground flora of British broadleaved woodland. Forestry 74, 219–229. Lasch, P., Lindner, M., Erhard, M., Suckow, F., Wenzel, A., 2002. Regional impact assessment on forest structure and functions under climate change – the Brandenburg case study. Forest Ecology and Management 162, 73–86. Le Duc, M.G., Pakeman, R.J., Marrs, R.H., 2000. Vegetation development on upland and marginal land treated with herbicide, for bracken (Pteridium aquilinum) control, in Great Britain. Journal of Environmental Management 58, 147–160. Legendre, P., Legendre, L., 1998. Numerical Ecology. Elsevier, Amsterdam. Ling, K.A., 2003. Using environmental and growth characteristics of plants to detect long-term changes in response to atmospheric pollution: some examples from British beech woods. Science of the Total Environment 310, 203–210.

P.M. Corney et al. / Biological Conservation 120 (2004) 491–505 Marrs, R.H., Le Duc, M.G., 2000. Factors controlling vegetation change in long-term experiments designed to restore heathland in Breckland, UK. Applied Vegetation Science 3, 135–146. Meteorological Office, 2003. UK 5 km
5 km grid baseline datasets for the period 1961 to 2000. Available from: (http://www.metoffice.com/index.html). Mitchell, J.F.B., Karoly, D.J., 2001. Detection of climate change and attribution of causes. In: Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A. (Eds.), Climate Change 2001; The Scientific Basis. Cambridge University Press, Cambridge, pp. 695–738. Modrzynski, J., Eriksson, G., 2002. Response of Picea abies populations from elevational transects in the Polish Sudety and Carpathian mountains to simulated drought stress. Forest Ecology and Management 165, 105–116. NEGTAP (National Expert Group on Transboundary Air Pollution), 2001. Transboundary Air Pollution: Acidification, Eutrophication and Ground-Level Ozone in the UK. Report of the National Expert Group on Transboundary Air Pollution (NEGTAP) for the UK Department for Environment, Food and Rural Affairs, Scottish Executive, The National Assembly for Wales/Cynulliad Cenedlaethol Cymru and the Department of the Environment for Northern Ireland. CEH, Edinburgh. Nicholls, N., Gruza, G.V., Jouzel, J., Karl, T.R., Ogallo, L.A., Parker, D.E., 1996. Observed climate variability and change. In: Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., Maskell, K. (Eds.), Climate Change 1995; The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 133–192. Peterken, G.F., 1993. Woodland Conservation and Management. Chapman & Hall, London. Pigott, C.D., Huntley, J.P., 1978. Factors controlling the distribution of Tilia cordata at the northern limits of its geographical range. New Phytologist 81, 429–441. Rackham, O., 2003. Ancient Woodland, New ed. Castlepoint Press, Dalbeattie. Ratcliffe, D.A., 1977. A nature conservation review: the selection of biological sites of national importance to nature conservation in Britain. Cambridge University Press, Cambridge. Ray, D., 2001. Ecological Site Classification, version 1.7 A PC-based Decision Support System for British Forests. Forestry Commission, Edinburgh.

505

Redfern, D., Hendry, S., 2002. Climate change and damage to trees caused by extremes of temperature. In: Broadmeadow, M. (Ed.), Climate Change: Impacts on UK forests. Forestry Commission, Edinburgh, pp. 29–39. Rodwell, J., Pigott, C.D., Ratcliffe, D.A., Malloch, A.J.C., Birks, H.J.B., Proctor, M.C.F., Shimwell, D.W., Huntley, J.P., Radford, E., Wigginton, M.J., Wilkins, P., 1991. British Plant Communities, vol. 1. Woodlands and Scrub. Cambridge University Press, Cambridge. Sheail, J., Bunce, R.G.H., 2003. The development and scientific principles of an environmental classification for strategic ecological survey in the united Kingdom. Environmental Conservation 30, 147–159. Smart, S.M., 2000. Modular Analysis of Vegetation Information System (MAVIS) Plot Analyser, version 1.00. CEH, Merlewood, Cumbria. Smart, S.M., Bunce, R.G.H., Black, H.I.J., Ray, N., Bunce, F., Kirby, K.J., Watson, R., Singleton, D., 2001. Measuring long term change in Biodiversity in British woodlands (1971–2000) – a pilot re-survey of 14 sites from the 1971 Nature Conservancy ‘Bunce’ woodland survey and two from the 1971 Native Pinewood Survey. Centre for Ecology and Hydrology, Merlewood, Cumbria. Sokal, R.R., Rohlf, F.J., 1995. Biometry, third ed. Freeman, New York. Ter Braak, C.J.F., 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179. Ter Braak, C.J.F., Prentice, C.I., 1988. A theory of gradient analysis. Advances in Ecological Research 18, 271–313.  Ter Braak, C.J.F., Smilauer, P., 2002. CANOCO Reference Manual and CANODRAW for Windows User’s Guide version 4.5. Microcomputer Power, Ithaca, NY. Tybirk, K., Strandberg, B., 1999. Oak forest development as a result of historical land-use patterns and present nitrogen deposition. Forest Ecology and Management 114, 97–106. Williams, J.W., Post, D.M., Cwynar, L.C., Lotter, A.F., Levesque, A.J., 2002. Rapid and widespread vegetation responses to past climate change in the North Atlantic region. Geology 30, 971–974. Williams, W.T., Lambert, J.M., 1959. Multivariate methods in plant ecology. Association analysis in plant communities. Journal of Ecology 47, 83. Yeo, M.J.M., Blackstock, T.H., 2002. A vegetation analysis of the pastoral landscapes of upland Wales, UK. Journal of Vegetation Science 13, 803–816.