do regional gradients in land-use influence richness, composition and turnover of bird assemblages...

BIOLOGICAL

CONSERVATION

Biological Conservation 119 (2004) 191–206

www.elsevier.com/locate/biocon

Do regional gradients in land-use influence richness, compositionand turnover of bird assemblages in small woods?

A.F. Bennett a,*, S.A. Hinsley b, P.E. Bellamy b, R.D. Swetnam b, R. Mac Nally c

a Landscape Ecology Research Group, School of Ecology and Environment, Deakin University, 221 Burwood Highway, Burwood,

Victoria 3125, Australiab Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Huntingdon, Cambridgeshire PE28 2LS, UK

c Australian Centre for Biodiversity: Analysis, Policy and Management, School of Biological Sciences, Monash University, Melbourne,

Victoria 3800, Australia

Received 28 July 2003; received in revised form 11 November 2003; accepted 12 November 2003

Abstract

Small patches of natural or semi-natural habitat have an important role in the conservation of biodiversity in human-dominated

environments. The values of such areas are determined by attributes of the patch as well as its context in the surrounding land

mosaic. There is a need for better understanding of the ways in which assemblages are influenced by patch context and the scale over

which this occurs. Here we examine the influence of regional environmental gradients on the richness, annual turnover and com-

position of breeding bird species in small woods in south-eastern England. Regional gradients were defined independently of woods

by an ordination of attributes for 5 km� 5 km landscape units across a 2100 km2 region. Patch-level attributes, particularly area,

were the most important predictors for most bird variables. For woodland migrants and woodland-dependent species, variables

representing the context of each wood, either at a local or regional scale, explained significant additional variance in species richness

after accounting for wood area, but did not do so for species turnover. Significant context effects for woodland-dependent species

related to the extent of hedges and woodland cover in the local vicinity (<1 km radius), whereas for woodland seasonal migrants the

best predictors of richness after patch area were two regional environmental gradients. The initial cue to settlement for migrants may

be at a coarse regional scale, with selection for suitable landscapes that have a greater extent of woodland cover. Edge species

showed different responses: they were influenced by the diversity of structural features in woods, and were a more-dominant

component of the avifauna in isolated woods in open fenland environments of the region. Significant relationships between coarse

regional gradients (25 km2 units) and bird assemblages in small woods (0.5–30 ha) suggest that population and community processes

in the avifauna operate across a broader scale than local patch neighbourhoods. They also highlight the importance of adopting a

landscape or regional perspective on potential changes to land-use in rural environments, and on the conservation management of

small reserves.

� 2003 Elsevier Ltd. All rights reserved.

Keywords: Landscape context; Habitat fragmentation; Breeding birds; Small woods

1. Introduction

Throughout the world, small areas of natural or semi-

natural habitat have an important role in maintaining

biodiversity in human-dominated environments (Saun-

ders et al., 1987; Forman, 1995; Laurance and Bierre-

gard, 1997). Consequently, much attention has been

given to identifying factors that influence the conserva-

* Corresponding author.

E-mail address: [email protected] (A.F. Bennett).

0006-3207/$ - see front matter � 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.biocon.2003.11.003

tion value of such areas. Many studies have described

relationships between the number of animal species, or

occurrence of particular species, and attributes of hab-

itat patches such as their size, shape, habitat diversity

and management history (Freemark and Merriam, 1986;

Bennett, 1987; Loyn, 1987; Bolger et al., 1997; Deacon

and Mac Nally, 1998). The location of patches in rela-

tion to other suitable habitat has also been identified asa significant influence. Thus, features such as the dis-

tance to potential source populations (Opdam et al.,

1985; Newmark, 1991; Thomas and Jones, 1993), the

mail to: [email protected]

192 A.F. Bennett et al. / Biological Conservation 119 (2004) 191–206

amount of nearby similar habitat (Askins et al., 1987;

Jansson and Angelstam, 1999; Graham and Blake,

2001), and the presence of linking habitats (Bright et al.,

1994; Haas, 1995) have been shown to complement

patch-level characteristics as influences on the distribu-tion of animal species.

The growing recognition of the importance of land-

scape context (e.g. Wiens, 2002; Wiens et al., 2002) has

led to closer scrutiny of the structure of landscapes

within which habitat patches are located. It is not only

similar areas of habitat that may be important influ-

ences, but also the composition and spatial arrangement

of other land-uses of differing quality for the biota (e.g.urban areas, roads, wetlands, arable land). Increasing

attention is being given to the extent to which the overall

structure of the landscape may influence the status of

populations and communities (Pearson, 1993; Bennett

and Ford, 1997; Fuller et al., 1997; Baillie et al., 2000;

Pope et al., 2000; Soderstrom and Part, 2000; Bellamy

et al., 2003). Indeed, it has been suggested (Diamond

et al., 1987; Saunders et al., 1991; Hobbs, 1993) thatprocesses operating in the wider landscape or regional

land mosaic may ultimately have as great, or greater,

influence on the composition and status of the biota in

small patches than within-patch processes. First, sur-

rounding land-uses can modify the dynamics of meta-

populations by influencing the capacity of species to

disperse through the landscape. Different forms and

intensities of land-use offer different levels of �resistance�to the movements of species (Laurance, 1991; Gascon

et al., 1999; Lindenmayer et al., 2002). Second, partic-

ular land-uses may offer additional food, shelter or other

resources that complement those available to species

within habitat patches (Pope et al., 2000). Third, other

plant and animal species or disturbance processes as-

sociated with surrounding land-uses can have a marked

impact on habitat quality in isolated patches (Saunderset al., 1991). There may be invasion or degradation of

natural habitats (Hobbs, 1993; Scougall et al., 1993;

Abensperg-Traun et al., 1996), or the imposition of new

predators and competitors that result in population

declines or change in the structure of assemblages

(Andr�en, 1992; Grey et al., 1998).

Therefore, one expects that habitat patches of similar

size or vegetation type will show differences in their bi-otic communities depending on the context of the patch

in relation to patterns of land-use and the physical en-

vironment. Of course, different species will respond to

landscape context in different ways and at different

scales. A number of studies have detected context effects

by testing the influence of habitat availability within a

defined radius of a patch, on the occurrence or richness

of species within that resource patch (Brennan et al.,2002). The scale for such studies has often been 6 1 km

(e.g. Pearson, 1993; Jansson and Angelstam, 1999;

Soderstrom and Part, 2000; Graham and Blake, 2001), a

relatively small distance when studying a mobile taxon

such as birds. Context effects have seldom been tested

over a larger landscape scale (e.g. 1–10 km) or in rela-

tion to environmental variation and land use across the

entire region in which study sites are located.In this study we examine the potential effect of regional

context on assemblages of breeding birds in small woods

in south-eastern England. Previous work in this study

area showed that the richness of breeding bird species

(Bellamy et al., 1996a) and the distribution of individual

species (Hinsley et al., 1995a) are influenced both by the

characteristics of woods in which birds nest and habitat

attributes in the surrounding landscape. Distances of upto 1 km radius around woods were used to assess features

such as woodland cover and length of hedgerows. The

various types of responses to landscapes in the region

suggested that species are making different sets of choices

between large-scale habitat factors and fine-scale selec-

tion of sites for breeding (Hinsley et al., 1995b). Choices

at larger scales appear to be important for some species.

For example, analysis of the distribution of the nuthatchSitta europaea (Bellamy et al., 1998) indicated the im-

portance of variation in woodland cover between regions

in southern England. Consequently, in this paper we use

data pertaining to land-use and the physical environment

to identify regional environmental gradients across a

2100 km2 region, independently of the locations of woods

where birds were censused. We then examine three as-

pects of breeding assemblages in woods – species rich-ness, relative species turnover and composition – to

explore the extent to which the context of a wood in re-

lation to these regional gradients influences the status of

its bird assemblage. The implications of regional context

effects for the conservation of biota are then discussed.

2. Methods

2.1. Study area

The study was carried out in south-east England in aregion of 2100 km2 (70 km� 30 km) in the counties of

Cambridgeshire and Lincolnshire (Fig. 1). Two main

landform types occur in the region: flat expanses of

drained fenland in the east and more-undulating coun-

tryside of heavy clays in the south-west. Land-use

throughout the region is dominated by intensive arable

farming, with the main crops being autumn-sown cereals

(barley, wheat), oilseed rape, sugar beet and potatoes.Towns and numerous villages are scattered through-

out, the largest urban areas being Peterborough and

Huntingdon.

Presently, there is <5% cover of woodland in the

study region, mainly woods of <10 ha, with the largest

being Monks Wood (158 ha). Most woodlands are

Peterborough

Whittlesey

Huntingdon

St. Ives

A1(M)

(A14)

52N 10’ 56s0W, 23’,33s

52N 48’ 16s

0E 04’ 31s

Spalding

10 km

N

Fig. 1. The study area in eastern England, showing 5� 5 km landscape units used to define regional gradients in land-use.

A.F. Bennett et al. / Biological Conservation 119 (2004) 191–206 193

dominated by deciduous broad-leaved trees (mainly

common ash Fraxinus excelsior, English oak Quercus

robur and field maple Acer campestre), although someconifers and mixed woodlands are present. The present

structure of all woodlands is a consequence of human

management and many of those in the fenland were

planted in the last century. Hedges, principally of haw-

thorn Crataegus monogyna, separate many fields and

form linear networks of varying density in farmland.

To identify regional gradients in environment and

land-use, the study area was divided into a grid of�landscape units�, each 5 km� 5 km in size (n ¼ 84

units).

2.2. Bird censuses

Bird censuses were carried out in three successive

years (1990, 1991, 1992) by using a territory mapping

technique (see Hinsley et al., 1995a,b). Each wood wassearched systematically on four occasions during the

breeding season, with alternate visits by two independent

observers. The same procedure was used in each year.

Additional visits to a subset of sites confirmed that the

census procedure reliably detected breeding bird species

in woods (Hinsley et al., 1995b). A total of 151 woodswas censused in each of the three years, but in this study

we use only those woods P 0.5 ha. Species that were

poorly surveyed (e.g. owls, woodcock Scolopax rusticola)

as well as gamebirds (e.g. pheasant Phasianus colchicus,

red-legged partridge Alectoris rufa) and water birds

(e.g. mallard Anas platyrhynchos, moorhen Gallinula

chloropus) were excluded from analyses.

We examined three characteristics of assemblages inwoods; species richness, annual turnover of species, and

the composition of the avifauna. Of particular interest

was how birds with different ecological requirements

respond to the regional context. Consequently, for

analyses of species richness and species turnover, three

(non-exclusive) subgroups of bird species were selected

for closer attention (Table 1). �Woodland migrants�(n ¼ 6 species) are seasonal migratory species whosemain habitat is woodland. They leave England each year

and select woodland habitats on their return in spring.

�Woodland dependents� (n ¼ 15) are species considered

Table 1

Bird species recorded breeding in woods in the study area, the number of woods occupied in one or more years, and species listed as woodland

migrants, woodland dependents, and edge species

Species Scientific name No. of woods (n ¼ 88) Ecological groups

Woodland migrant Woodland dependent Edge species

Sparrowhawk Accipiter nisus 22 *

Kestrel Falco tinnunculus 9 *

Stock dove Columba oenas 26 *

Woodpigeon Columba palumbas 88 *

Collared dove Streptopelia decaocto 7 *

Turtle dove Streptopelia turtur 41

Green woodpecker Picus viridus 4

Great-spotted

woodpecker

Dendrocopus major 25 *

Pied wagtail Motacilla alba 1 *

Wren Troglodytes troglodytes 88

Dunnock Prunella modularis 80

Robin Erithacus rubecula 78

Nightingale Luscinia megarhynchos 8 * *

Blackbird Turdus merula 87

Song thrush Turdus philomelos 57 *

Sedge warbler Acrocephalus schoenobaenus 4

Reed warbler Acrocephalus scirpaceus 9

Lesser whitethroat Sylvia curruca 12

Whitethroat Sylvia communis 49

Garden warbler Sylvia borin 34 * *

Blackcap Sylvia atricapilla 64 * *

Chiffchaff Phylloscopus collybita 22 * *

Willow warbler Phylloscopus trochilus 56 *

Goldcrest Regulus regulus 8 *

Spotted flycatcher Muscicapa striata 30 *

Long-tailed tit Aegithalos caudatus 30 *

Marsh tit Parus palustris 23 *

Willow tit Parus montanus 1 *

Coal tit Parus ater 8 *

Blue tit Parus caeruleus 78

Great tit Parus major 68 *

Nuthatch Sitta europaea 1 *

Treecreeper Certhia familiaris 28 *

Jay Garrulus glandarius 13 *

Magpie Pica pica 23 *

Jackdaw Corvus monedula 6 *

Rook Corvus frugilegus 3 *

Carrion crow Corvus corone corone 35 *

Starling Sturnus vulgaris 27 *

House sparrow Passer domesticus 19 *

Tree sparrow Passer montanus 8 *

Chaffinch Fringilla coelebs 84

Greenfinch Carduelis chloris 29 *

Goldfinch Carduelis carduelis 11 *

Linnet Carduelis cannabina 5 *

Bullfinch Pyrrhula pyrrhula 26

Yellowhammer Emberiza citrinella 30 *

Reed bunting Emberiza schoeniclus 8 *

Corn bunting Miliaria calandra 2 *

Nocturnal species, water birds and game birds are not included.


to be strongly dependent on woodland for their persis-

tence in the study area. A number of species that regu-

larly use woodland but which also occur in gardens or

shrubland were excluded from this category (e.g. wren,robin, blue tit, willow warbler – see Table 1 for scientific

names). �Edge species� (n ¼ 19) are those resident birds

(i.e. non-migratory) that may breed in woodland but

spend much of their time in surrounding environments.

Species richness was calculated as the number of

species of each subgroup breeding in a wood in eachbreeding season. The mean value from three years of

censuses was used for analyses. Species �turnover� was


calculated as the change in the number of species be-

tween successive breeding seasons, based on the formula

T ¼ ðC þ EÞ=ðS1 þ S2Þ, where C is the number of species

colonizing from year 1 to year 2, E the number of local

extinctions between year 1 and year 2, and S1 and S2 arethe number of species present in year 1 and year 2, re-

spectively (Hinsley et al., 1995b). This is a measure of

relative turnover, rather than absolute turnover, because

change is expressed as a proportion of the number of

species present. For each subgroup of birds, turn-

over was calculated for each wood for the 1990–91 and

1991–92 breeding seasons and the mean value used for

analyses.To investigate variation in the composition of the

total bird assemblage, a data set of the frequency of

occurrence of each species in each wood was prepared,

with values of 0, 1, 2 or 3 to represent the number of

years in which each species was recorded breeding in the

wood. Ordination of these data was undertaken by

multi-dimensional scaling (MDS) based on the Bray-

Curtis similarity measure (Primer 5 software, Clarke andGorley, 2001). The dimensions represent the relative

position of each wood in relation to other woods based

on the composition of breeding bird species. Thus,

woods with a similar avifaunal composition will have a

similar score (i.e. be close together in multidimensional

Table 2

Response and predictor variables used in analyses, transformations employ

for variables

Response variables Mean (range) Predicto

Species richness Woods

Woodland migrants1 1.7 (0–6) Area (h

Woodland-dependents1 3.1 (0–11.7) Perimet

Edge species1 3.4 (1.0–11.3) Shape

Structur

Species turnover

Woodland migrants1 0.46 (0–1.0) Local co

Woodland dependents1 0.41 (0–1.0) Woodla

Edge species 0.30 (0–0.56) Hedge l

Distanc

Composition Distanc

MDS1 (woodland birds)

MDS2 (edge/generalist birds) Regiona

MDS3 (scrub species) Mean a

Range i

Cover o

Cover o

Number

Length

Length

Hedges

Hedges

Cover o

Cover o

Cover o

Cover o

Cover o

Note that composition variables are ordination dimensions derived from m1square root, 2arcsine

ffiffiffi

xp

, 3 log10, and4 log10ðxþ 1Þ.

space), whereas woods with contrasting species compo-

sition will be widely separated in multidimensional

space. To interpret the ordination in terms of bird spe-

cies, the MDS dimension scores for each wood were

correlated (Spearman rank correlation) with species�frequencies for that wood. However, because the same

data are used both to derive and interpret the ordination

dimensions, it is not appropriate to assign statistical

significance to these correlations, but their magnitude

assists in identifying trends in species composition.

2.3. Woodland, local context and regional gradient

variables

Variables were collated in relation to (i) patch-level

attributes of each wood, (ii) the local context of each

wood and (iii) the regional gradient in the physical

environment and land-use (Table 2).

Variables for the first two categories have been de-

scribed previously and used to identify correlates of

species richness for the entire avifauna (Bellamy et al.,1996a) and the occurrence of individual species (Hinsley

et al., 1995b) – but not for the ecological groups de-

scribed here. To avoid an excessive number of variables,

we selected a subset representing those of most impor-

tance in prior analyses. For woods, these were area

ed, and the mean and range (in parentheses) of untransformed values

r variables Mean (range)

a)4 2.8 (0.5–29.9)

er length (km)3 0.8 (0.3–3.8)

1.5 (1.1–5.1)

al diversity 4.3 (1–9)

ntext of woods

nd (ha) within 1 km4 14.0 (0–54.5)

ength (km) within 1 km 6.3 (0.1–17.3)

e to nearest wood (km)4 0.33 (0.01–2.30)

e nearest wood P 10 ha (km)4 2.8 (0–10.6)

l environment

nnual rainfall (mm) 916 (795–1043)

n elevation (m)3 24.8 (2–60)

f pelosol soils2 33.0 (0–100)

f gley soils2 45.6 (0–100)

of soil groups 2.1 (1–4)

of roads (km)3 24.8 (7.6–51.5)

of drains/waterways (km) 36.8 (12.3–85.7)

> 2 m (index)1 5.4 (0–21)

< 2 m (index)1 5.2 (0–23)

f woodland (%)2 1.2 (0–11.3)

f arable land (%)2 73.7 (14.2–93.6)

f urban land (%)2 9.4 (2.0–48.0)

f managed grassland (%)2 11.9 (1.3–35.2)

f rough grassland (%)2 1.5 (0.1–11.9)

ultidimensional scaling MDS (see text and Table 3). Transformations:


(AREA), perimeter (PERIM), shape (SHAPE) and the

number of structural features (STRUCTDIV), such as

presence of broad-leaved woodland, coniferous wood-

land, dense shrubs, clearings and ponds (Bellamy et al.,

1996a). For local context, the selected variables weredistance to nearest wood (DISNEAR), distance to

nearest wood P 10 ha (DISNR10), area of woodland

within 1 km (WOOD1K) and length of hedgerows

within 1 km (HEDGE1K) (Table 2).

Patterns of variation at the regional level (study area

of 2100 km2) were determined by measuring attributes

for each landscape unit (5� 5 km area) independently of

any particular wood that occurred therein. Two maintypes of attribute were included: measures of the phys-

ical environment (soils, elevation, rainfall) and land-use

(land cover types, roads, waterways, hedges).

Mean annual rainfall was obtained for the midpoint

of each unit, based on a 30-year (1961–1990) baseline

climatology of Great Britain (Barrow et al., 1993). The

range in elevation (m) was the difference between the

highest and lowest points in the landscape unit, derivedfrom a digital elevation model. The length of roads (km)

and length of drains and waterways (km) in each unit

were summed from the Bartholomews digital map of

Great Britain, 1993 (1:250,000). Soil characteristics for

each landscape unit were derived from the Soil Map of

England and Wales (1:250,000 scale) (Soil Survey of

Great Britain, 1983). The percent cover of two �major

groups� of soil were estimated for each unit to the closest5%. �Pelosol� soils (major group 4) are slowly permeable

clayey soils that crack deeply in dry seasons. �Ground-

water gley� soils (major group 8) have mottled or grey

subsoils resulting from periodic waterlogging by a fluc-

tuating groundwater table. In addition, the number of

soil �groups� (n ¼ 12 for the study area) in each unit was

counted directly from the map.

The percent cover of different land-uses in each unitwas calculated from the Institute for Terrestrial Ecology

Land Cover Map of Great Britain 1990 (Fuller et al.,

1994). Five main types of land-use were recognized here,

based on combinations of similar land-cover categories:

woodland (including deciduous and conifer woodland),

managed grassland (pasture, meadow/unimproved grass),

rough grassland (marsh/rough grass, ruderal/rough

grass), arable (arable/tilled) and urban (urban/industrial,suburban/rural development).

Hedges form networks of shrubby vegetation in

farmland that are ecologically important for birds (Os-

borne, 1984; Green et al., 1994; Hinsley and Bellamy,

2000). They are poorly discriminated by the land-cover

map and so an index of the abundance of hedges was

obtained by field observation. A stratified random pro-

cedure was used to locate four points in each landscapeunit, and at the closest roadside location to each point

the number of hedges along field margins within a radius

of 200 m was counted. Tall (>2 m) and short (<2 m)

hedges were noted separately and tree rows included

with tall hedges. Data for the four points were summed

as an index for each landscape unit.

2.4. Data analysis

We examined relationships between a set of �response�variables representing richness, turnover and composi-

tion of breeding birds in woods, and a set of �predictor�variables representing the woodland, the local context of

the wood, and the position of the wood in regional en-

vironmental gradients.

Variables were inspected for normality and skewness,and transformed where appropriate to improve nor-

mality. Percentage and proportion variables were trans-

formed by using the arcsine square-root transformation,

and others by logarithmic or square-root transformation

(Table 2).

There were many significant correlations among the

set of 14 attributes measured for landscape units, and so

principal components analysis was used to reduce thedata to a small number of components or composite

variables that described major gradients across the re-

gion. Thus, each 5 km� 5 km landscape unit had a value

for each of the derived principal components, which

represents that landscape�s position in relation to the

particular regional gradient. Woods were then assigned

the value for the landscape unit in which they were lo-

cated; all woods in a particular landscape unit had thesame value for position in the regional gradient. Woods

that overlapped unit boundaries were excluded from

analysis. Landscape units were directly adjacent to each

other (Fig. 1), and so there is potential for spatial au-

tocorrelation in determining their position in regional

gradients. However, this is likely to have limited influ-

ence on the analyses presented here because the depen-

dent variables in the analyses are bird assemblages inindividual woods, not overall landscape units.

To explore the relative importance of local context

variables and regional environmental gradients on re-

sponse variables, partial correlation was undertaken

controlling for the influence of significant wood-level

variables.

The main part of the analysis involved the use of

multiple regression to develop models of the relation-ships between response and predictor variables. A major

difficulty in multiple regression is the collinearity often

found between predictor variables (Mac Nally, 2000;

Quinn and Keough, 2002), which hinders efforts to

identify causal relationships in ecological data. This was

particularly relevant in this study because the predictor

variables included two sets of related variables, �localcontext� and �regional gradients�. To address this prob-lem, the regression modelling involved several stages.

First, for each response variable, a series of possible

models were constructed by using forward and back-


ward stepwise selection, and best subsets procedures.

This exploratory process allowed an appreciation of

variables that consistently appeared to be important.

Second, a �best� model was selected for each response

variable by using the Bayesian Information Criterion(BIC) to identify the most appropriate set of predictor

variables, as outlined by Mac Nally (2000). The BIC is

used as a means to find a compromise between model fit

(i.e. variance explained) and model complexity (number

of terms). An automated procedure was used that

computed all possible subsets (i.e. all 1-variable models,

2-variable models and 3-variable models, etc) and cal-

culated the BIC for the model with greatest R2 at eachsubset level. The �best� model was then selected as that

for which the combination of model fit and model

complexity gave the lowest BIC value (Mac Nally,

2000). Regression models were checked for points with

high influence (by using Cook�s distance), and plots of

standardized residuals vs fitted values were inspected. If

a selected model was not robust to the removal of any

data points with very high influence (e.g. a variable be-comes non-significant), then it was rejected.

The final stage in model development was the use of

hierarchical partitioning (see Mac Nally, 2000; Quinn

and Keough, 2002) to examine the extent to which the

relationship of each predictor variable with the response

variable involved �shared� variance (i.e. shared with other

predictor variables) or �independent� variance (i.e. inde-

pendent of other predictor variables). For example, apredictor variable may have a high correlation with the

response variable but much of this may be �shared� withother variables that are correlated in a similar way –

suggesting that this is not an important causal relation-

ship. In contrast, if much of the variance is �independent�,then it suggests that an important causal relationship

may be evident. If the variables identified as having sig-

nificant independent explanatory power are also thoseincluded in the �best� predictive model based on model

complexity and fit, then it suggests that a meaningful set

of predictors have been obtained (Mac Nally, 2000).

Hierarchical partitioning was carried out by using an R-package program (Walsh andMac Nally, 2003) based on

Chevan and Sutherland (1991). Variables that made a

significant independent contribution were identified by

using a randomization procedure (Mac Nally, 2002).

3. Results

3.1. Variation in breeding bird assemblages among woods

Data on species richness, species turnover and the

composition of breeding bird assemblages were calcu-lated for 88 woods ranging from 0.5 to 29.9 ha in area.

Most woods were small; the mean size was 2.8 ha and

only 16% (14/88) were larger than 5 ha. A total of 49

species was recorded breeding in these woods during the

study (Table 1). The 3-year means for the number of

species per wood ranged from 5 to 24, with an overall

mean of 12.6 species.

Six species of migrants primarily depend on wood-land for breeding (Table 1). Eighteen percent of woods

did not have any of these migrants in any year, but 63%

of woods had at least one species each year. The most

widespread species was the blackcap (65 woods in at

least one year). In only one wood (8.0 ha in size) did all

six species breed each year. The mean turnover of mi-

grants for all woods was 0.46 (range 0–1.0). In only 17%

of woods where migrants occurred (12/72) was there noturnover in the identity of species each breeding season.

Species strongly dependent on woodland (n ¼ 15

species) were recorded breeding in 92% of woods during

the study, with 78% of woods having at least one species

present in each year. The number of species per wood

ranged from 0 to 11.7 (mean 3.1); the maximum of 11.7

species (i.e. 3-year mean) was recorded in the largest

wood censused (29.9 ha). The mean turnover of wood-land dependents was 0.41 (range 0–1.0) with only two

woods (2.5%) having no change in identity of these

species during the study.

One or more of the 19 resident edge species were

breeding in every wood in each year, with a range from 1

to 11.3 species per wood (Table 2). The woodpigeon was

the most widespread species, occurring in all 88 woods

during the study. The turnover of edge species rangedfrom 0 (in 7% of woods) to 0.56.

Variation in avifaunal composition was quantified by

ordination of the total bird assemblage in woods. This

resulted in a solution with three dimensions (MDS,

stress¼ 0.13). These were interpreted by identifying

species strongly correlated with each dimension (Table

3). The first dimension (MDS1) clearly represents the

extent to which woodland species dominate the assem-blage, as shown by strong positive correlations with

species such as long-tailed tit, marsh tit, great-spotted

woodpecker and treecreeper. The second dimension is

interpreted as a measure of the extent to which the as-

semblage comprises edge species that can forage outside

the wood (e.g. turtledove, greenfinch) and generalist

species widespread in shrubby or wooded vegetation

(e.g. wren, blackbird, dunnock) (Table 3). The thirddimension, MDS3, appears to represent a gradient from

assemblages that include species which occur in scrub

(positive correlation – whitethroat, bullfinch, willow

warbler) to those typical of open farmland or dwellings

(negative correlation – house sparrow, starling).

3.2. Regional gradients in physical environment and land-

use

Principal components analysis of variables repre-

senting the physical environment and land-use in 84

Table 3

Correlation coefficients (Spearman rank) for the relationship between

frequency of occurrence of bird species in woods over three breeding

seasons and the three dimensions from an ordination (multidimen-

sional scaling, MDS) of bird species occurrence in woods

Species MDS1 MDS2 MDS3

Whitethroat 0.62

Lesser whitethroat 0.43

Bullfinch 0.65 0.47

Willow warbler 0.71 0.38

Garden warbler 0.62 0.35

Blackcap 0.83

Long-tailed tit 0.80

Treecreeper 0.77

Great tit 0.73

Blue tit 0.72

Chiffchaff 0.68

Marsh tit 0.67

Robin 0.66

Great-spotted woodpecker 0.56

Jay 0.52

Chaffinch 0.50

Spotted flycatcher 0.44

Nightingale 0.42

Song thrush 0.47 0.48

Dunnock 0.71

Turtle dove 0.67

Blackbird 0.55

Wren 0.45

Stock dove 0.36

Reed bunting 0.38

Greenfinch 0.38

House sparrow 0.51 )0.43Starling )0.49

Values are shown only for strong correlations (Rs P 0:35, n ¼ 88

woods).

Table 4

Factor loadings from a principal components analysis (with varimax

rotation) of variables for each landscape unit (5 km� 5 km)

Variable RG1 RG2 RG3

Pelosol soils (%)1 0.946 )0.101 )0.064Elevation range (m)2 0.907 0.032 0.314

Gley soils (%)1 )0.881 )0.091 )0.336Mean annual rainfall (mm) )0.808 0.171 )0.268Drains and waterways (km) )0.743 0.120 )0.228Hedges (<2 m)3 0.739 0.257 0.112

Hedges (>2 m)3 0.758 0.376 )0.019Managed grassland (%)1 0.621 0.586 0.296

Rough grassland (%)1 0.631 0.158 0.573

Woodland (%)1 0.461 )0.108 0.587

Arable (%)1 )0.343 )0.782 )0.446Urban (%)1 )0.168 0.870 0.222

Roads (km)2 )0.066 0.769 )0.436Soil groups 0.066 0.246 0.868

Variance (%) 46.9 18.2 9.1

Cumulative variance (%) 46.9 65.1 74.2

Values in bold indicate variables highly significantly correlated

(P < 0:001) with the principal components (RG1, RG2 and RG3).

Transformations: 1arcsine, 2 log10 and 3square root.


landscape units resulted in a solution with three factors

that had eigenvectors >1.0, together representing 74.2%

of variance in the data (Table 4). The first component,

RG1 (46.9% of variance), represents a primary gradientin physical environment and land-use across the region

(Fig. 2). At one end (negative values) are landscape

units on the flat drained fenland in the north-east that

have �gley� soils, higher rainfall, many drains and are

dominated by arable farmland. At the other end are

those on more-undulating land that have pelosol soils,

and where there is greater abundance of hedges, man-

aged and rough grassland, and woodland (Table 4).The second component, RG2 (18.2%), appears to rep-

resent an urban-rural gradient. It contrasts landscape

units with urban land-use, many roads, tall hedges and

managed grassland (Table 4), with those dominated by

arable land and open country. The former include

landscape units that encompass the main concentra-

tions of towns in the region (e.g. Peterborough, Hun-

tingdon, St. Neots). The third principal component hashigh values for landscape units with diverse soil groups

and greater cover of rough grassland and woodland,

and low values for units dominated by arable land and

having high road density (Table 4). It appears to dis-tinguish landscape units in the centre of the region, at

the interface between flat fenland and undulating

countryside.

Woods in which birds were censused were located in

26 of the 84 landscape units, with a mean of 3.4 woods

(range 1–10) per unit. These landscape units spanned the

range of variation in regional gradients RG1 and RG3,

but were less complete for RG2. There was low repre-sentation of woods in landscape units at the strongly

�urban� end of this gradient (Fig. 3).

3.3. Variables influencing richness, composition and

turnover of breeding assemblages

There were significant intercorrelations (P < 0:001)between the local context of woods and their position inthe regional gradients (Table 5): RG1 was correlated

with AREA (r ¼ 0:367), WOOD1K (r ¼ 0:748), HED

GE1K (r ¼ 0:711) and DISNR10 (r ¼ �0:583), and

RG2 was correlated with HEDGE1K (r ¼ 0:504).Woods in landscape units in the low-lying fenland gen-

erally have relatively little surrounding woodland or

hedges, whereas those at the other end of the environ-

mental gradient were more likely to have nearbywoodland and hedges.

After controlling for wood-level attributes, variables

representing local context and regional environmental

gradients explained significant additional variance in the

richness and composition of assemblages (Table 6).

However, this was not the case for relative turnover of

breeding birds in woods, which was primarily influenced

by properties of the woodland.

Fig. 2. Spatial variation in one of the regional gradients, RG1, for

landscape units across the study area. Continuous values for this

variable have been grouped into four levels for illustration. Squares

with darkest shading represent landscape units on the low-lying fen-

land, dominated by arable farming (large negative values for RG1),

while those with lightest shading represent landscape units on undu-

lating land with pelosol soils and greater abundance of hedges and

woodland (large positive values for RG1).

3210-1-2

2

1

0

-1

RG2

RG

1

Fig. 3. The position of all 84 landscape units in the region in relation to

regional gradients RG1 and RG2. Data points shown by a solid square

are those landscape units in which woods censused for birds were lo-

cated. Note that landscape units at the strongly �urban� end of the

gradient RG2 (positive values) were not well represented in the study.

Table 5

Correlation coefficients for the relationship between regional envi-

ronmental gradients and variables describing woods and their local

context

Variables Regional gradients

RG1 RG2 RG3

Area 0.367 0.112 0.065

Perimeter 0.226 0.129 )0.012Shape )0.170 0.023 )0.088Structural diversity 0.046 0.082 )0.168

Woodland within 1 km 0.748 0.286 )0.015Hedge length within 1 km 0.711 0.504 )0.037Distance to nearest wood )0.340 )0.177 0.041

Distance to nearest

wood¼ 10 ha

)0.583 )0.257 )0.209


3.4. Models of species richness, species turnover and

avifaunal composition

3.4.1. Species richness

The �best� model for richness of woodland migrants

included three variables, woodland area and the position

of the wood within regional gradients RG1 and RG3

(Table 7), together accounting for 60.1% of the variance.

Thus, there were more species of migrants in larger

woods and in woods in undulating countryside with

hedges, woodland and grassland rather than in flat

fenland (RG1). The inclusion of the gradient RG3 in the

model indicates that woods in landscape units where

diverse soil types and greater woodland cover occur at

the interface between fenland and undulating country-

side, also have relatively greater richness of migrants.Woodland area had the most significant influence on

richness of woodland migrants and it also had the

greatest level of �independent� variance as revealed by

hierarchical partitioning (Fig. 4(a)). RG1 also made a

significant independent contribution to richness of

woodland migrants (Fig. 4(a)). Thus, the congruence of

the best model (based on model fit and complexity) with

variables that make significant independent contribu-tions (AREA, RG1) suggests that this model has a

sound basis. However, given the intercorrelations (Table

5), it is not surprising that there was considerable

�shared� variance between variables representing local

Table 7

Significant variables in �best� multiple regression models for species richness, species turnover and composition of birds in woods in eastern England

Dependent variable Significant predictor variables Variance explained (R2adjust)

Species richness

Woodland migrants AREA (+) RG1 (+) RG3 (+) 60.1%

Woodland dependent AREA (+) HEDGE1K (+) WOOD1K (+) 70.4%

Edge species STRUCTDIV (+) RG1 ()) DISNR (+) 30.0%

Species turnover

Woodland migrants AREA ()) PERIM (+) 39.3%

Woodland dependents AREA ()) 22.1%

Edge species RG2 (+) 6.3%

Composition

MDS1 AREA (+) HEDGE1K (+) WOOD1K (+) 66.9%

MDS2 STRUCTDIV (+) DISNR (+) RG1 ()) 35.9%

MDS3 AREA (+) 3.5%

The direction of the coefficient for each variable is shown in parentheses. Transformations used for variables are given in Table 2.

Table 6

Partial correlations for the relationship between species richness, species turnover and composition of birds in woods and variables representing local

context and regional environmental gradients, after removing the effects of significant wood-level attributes

Dependent variables Significant variables

at wood level

Local context of woods Regional gradients

WOOD1K HEDGE1K DISNEAR DISNR10 RG1 RG2 RG3

Species richness

Woodland migrants AREA, PERIM 0.418 0.353 )0.165 )0.270 0.403 0.258 0.072

Woodland dependent AREA, SHAPE 0.414 0.471 )0.184 )0.266 0.400 0.340 )0.053Edge species STRUCTDIV )0.365 )0.184 0.324 0.317 )0.364 )0.136 0.164

Species turnover

Woodland migrants AREA, PERIM )0.089 )0.256 )0.027 )0.036 )0.078 )0.116 )0.079Woodland dependent AREA )0.072 )0.100 0.132 0.056 0.067 )0.075 )0.054Edge species – )0.117 0.084 0.080 0.077 0.039 0.271 0.046

Composition

MDS1 AREA 0.489 0.494 )0.191 )0.405 0.489 0.348 0.067

MDS2 STRUCTDIV )0.364 )0.195 0.368 0.371 )0.305 )0.168 0.043

MDS3 AREA )0.008 0.002 )0.154 0.054 0.015 0.012 0.063

Correlations significant at P < 0:001 are shown in bold. Transformations used for variables are given in Table 2.


context and the regional gradient (RG1) (Fig. 4(a)).

Three of the local context variables (WOOD1K,

HEDGES1K, DISNR10) also made substantial inde-

pendent contributions to explaining woodland migrantrichness in univariate comparisons (Fig. 4(a)). Conse-

quently, although the best model appears sound, vari-

ables representing local context also have some

influence.

The number of woodland-dependent species was best

explained by woodland area and local context of the

wood, with the best regression model incorporating

three attributes (R2adj ¼ 70:4%); area, length of hedge-

rows and amount of woodland within 1 km (Table 7).

Each of these variables made significant independent

contributions to richness of woodland-dependent spe-

cies (Fig. 4(b)), again suggesting that this best model has

a sound ecological basis. However, although not in-

cluded in the model, the regional gradient RG1 also

made a significant independent contribution.

Richness of edge species showed a different pattern,

with greater richness occurring in woods with a high

structural diversity of habitats (Table 7). The best model

initially identified included structural diversity, wood-land perimeter and area of woodland within 1 km.

However, after removal of a single data point with very

high influence, perimeter was no longer a significant

variable. Consequently, a robust alternative was ac-

cepted as best model, comprising structural diversity,

regional gradient RG1 and distance to nearest wood

(Table 7). Each of these latter variables made significant

independent contributions to explaining variation inedge species richness (Fig. 4(c)).

3.4.2. Species turnover

For woodland migrants and woodland-dependent

species, the only significant variables in models of spe-

cies turnover between breeding seasons were attributes

of the wood itself, especially area (Table 7). Turnover of

0

0.2

0.4

0.6

0.0

0.2

0.4

0.6

0.8

0.0

0.2

0

0.2

0.4

0.6

0.0

0.2

0.4

0.0

0.1

A P SD WD HG DN D10 RG1 RG2 RG3

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 4. Total (clear) and independent (shaded) variance in response

variables that are attributed to predictor variables relating to woods,

local context and regional gradients. Response variables are for species

richness of (a) woodland migrants, (b) woodland dependents and

(c) edge species; and species composition gradients (d) MDS1 (wood-

land species dominated, (e) MDS2 (edge species dominated) and (f)

MDS3 (scrub/garden species). Predictor variables are shown as: A

area, P perimeter, SD structural diversity, WD woodland within 1 km,

HG hedge length within 1 km, DN distance to nearest wood, D10

distance to nearest wood¼ 10 ha, and regional gradients RG1, RG2

and RG3. Contributions to variance for species turnover are not

shown because only wood-level attributes were significant predictors in

regression models.


woodland migrants increased as the size of the wood

decreased and as perimeter length increased. Turnoverof woodland-dependent species also increased as wood

size decreased. There was no evidence for significant

influence of either local context or regional environ-

mental gradients.

For edge species, the best model for turnover in-

cluded one variable, RG2, which accounted for 6.3% of

the variance (Table 7). This was also the only variable

that made a significant independent contribution to

turnover of edge species. There was a trend for greater

turnover of edge species in woods situated in landscape

units characterized by dense road networks and man-

aged grassland.

3.4.3. Composition of the avifauna

The best model for the first dimension in species

composition (MDS1 – dominated by woodland species)

showed that assemblages dominated by woodland birds

are significantly associated with large woods with many

hedges and much woodland nearby (Table 7). An al-

ternative model comprising woodland area and regional

gradients RG1 and RG3 had similar explanatory power(R2

adj ¼ 65:6% vs 66.9%). The two models may refer to

the same causal factor because RG1 is strongly corre-

lated with HEDGE1K and WOOD1K and all three

have much shared variance (Fig. 4(d)), but each also has

independent variance.

Breeding assemblages in which edge and generalist

species are prominent (MDS2) were strongly associated

with three variables, diversity of structural features,distance to nearest wood and RG1 (Table 7, Fig. 4(e)).

Size of the wood was not a significant predictor; rather,

these assemblages were more likely to occur in woods

with high structural diversity, isolated from other

woods, in open farm landscapes across the region.

The only significant predictor for MDS3 was wood-

land area, but this accounted for just 3.5% of the vari-

ation (Table 7, Fig. 4(f)).

4. Discussion

A fundamental principle in landscape ecology is that

�landscape context matters�. This is not only relevant to

conceptual development in the discipline (e.g. Forman,

1995; Wiens, 1995) but is of great practical importancefor the conservation of biodiversity in human-domi-

nated environments. Many species now occupy patches

of habitat within heavily modified land mosaics in which

the composition and configuration of habitats differ

markedly from the natural state. Likewise, many small

conservation reserves are surrounded by a diversity of

land-uses. It is imperative that the focus of management

and conservation activities extends beyond the patch orreserve (Saunders et al., 1991; Wiens, 1995; Bennett,

1999), but in order to �manage the mosaic� effectivelymuch more needs to be known about contextual effects.

Knowledge is required concerning: (i) the elements or

processes in the surrounding environment that influence

patch-level processes; (ii) the spatial scale over which

these elements or processes influence taxa of concern;

and (iii) the ecological mechanisms by which landscapecontext effects operate in a particular situation (e.g. by

reducing dispersal in metapopulations, changing pred-

ator-prey relationships, modifying hydrologic regimes).


Our aim here was to consider the extent to which

land-use and variation in the physical environment at a

relatively coarse regional scale (cf. local scale of <1 km)

influenced avifaunal dynamics in small woods: specifi-

cally, the richness, relative turnover and composition ofbreeding bird assemblages.

4.1. Types of context effects

Patch-level properties, area and structural diversity,

were the most important predictors in all but one model

(Table 7) and accounted for more variance than did

context variables. However, the consistent inclusion ofcontext variables in the models and the independent

variance explained by such variables (Fig. 5), clearly

demonstrated that assemblages of breeding birds in

woods were influenced by factors that extend beyond the

wood itself. Two aspects of these results were of par-

ticular interest.

First, context variables were prominent in the �best�models for species richness (for all three groups) and foravifaunal composition (two MDS dimensions), but less

so for the relative turnover of species (Table 7). Turn-

over of woodland migrants and woodland dependents

was best explained by patch-level attributes only (area,

perimeter), while for edge species there was a weak re-

lationship with RG2 (6.3% of variance explained). These

results are consistent with previous analyses of coloni-

zation and extinction dynamics of bird species in theregion (Hinsley et al., 1995b; Bellamy et al., 1996b).

Colonisations and extinctions were common in woods

Fig. 5. Comparison of the total independent variance attributed to

wood-level variables (shaded), local context variables (hatched) and

regional gradient variables (clear) for each response variable. Response

variables are: (1) richness of woodland migrants, (2) richness of

woodland dependents, (3) richness of edge species, (4) turnover of

woodland migrants, (5) turnover of woodland dependents, (6) turnover

of edge species, (7) composition MDS1 (woodland species dominated),

(8) composition MDS2 (edge species dominated) and (9) composition

MDS3 (scrub/garden species). The variable for �shape� of woods was

not included in hierarchical partitioning analyses because correlations

with response variables were biased by several extreme values for patch

shape.

over the three breeding seasons. For most individual

species the probability of colonisation was not corre-

lated with patch or other variables, but the probability

of extinction was inversely related to population size,

or its correlate, woodland area (Bellamy et al., 1996b).Most local extinctions occurred in woods containing

from 1 to 3 breeding pairs of a species, and extinction

appeared to be a stochastic process to which small

populations are particularly vulnerable. For species

largely dependent on woodland habitats, the number

of breeding pairs is primarily determined by area of

woodland and the quality of habitat available – prop-

erties of a particular wood (Bellamy et al., 1996b). Thus,as found here, the relative turnover for woodland de-

pendents and woodland migrants appears to be driven

primarily by patch-level characteristics due to their in-

fluence on probability of extinction.

Edge species are not restricted to woodland habitat

but may nest there while living mainly in surrounding

farmland. Annual turnover for these species is not a

consequence of local extinction of a resident populationbut rather depends on where in their range individuals

may choose to nest in a given year. It is not surprising

that relative turnover for this group of 19 species was not

related to woodland area but to the wider landscape –

albeit weakly. The positive relationship with regional

gradient RG2 suggests that turnover is greater for woods

located in landscapes heavily influenced by urban de-

velopment and high road density. The edge species forwhich most colonisations and extinctions occurred were

the magpie and carrion crow, followed by greenfinch,

stock dove and house sparrow. The regional context may

affect turnover as a result of anthropogenic disturbance

to breeding sites from people or road traffic, or by there

being a greater range of alternative (non-woodland) nest

sites associated with land-uses in these environments.

Second, the type of context variables differed mark-edly among different groups of birds, reinforcing the

importance of examining ecological groups of birds

separately, rather than the entire avifauna. For wood-

land-dependent species, context effects related to the

amount and configuration of similar wooded habitat or

hedges in the vicinity of the study wood. After control-

ling for area, woods were more likely to have a greater

richness of woodland species, and assemblages domi-nated by such species, if there were numerous hedges and

other woodlands in the surrounding environment. Sim-

ilar results have been found in other studies (e.g. Askins

et al., 1987) and are generally interpreted in terms of

metapopulation function (e.g. Opdam, 1991). The larger

the set of local populations, the greater the number of

dispersing individuals that can supplement small popu-

lations or recolonise when local extinctions occur.Hedgerows serve as an additional habitat resource for

some species (Hinsley and Bellamy, 2000) and can fa-

cilitate movement of individuals among woods.


The richness of edge species, in contrast, was not

directly related to either area or increased local prox-

imity of forest. Edge species were more likely to be

present in woods with diverse structural features, that

are isolated from other woodlands in more-open envi-ronments across the region (Table 7). A diversity of

habitat structures provides a greater range of nesting

sites and nesting opportunities. The trend for a greater

number of edge species in isolated woods (see also

McCollin, 1993; Bellamy et al., 1996a) can be attributed

to a �concentration� effect in woods surrounded by other

land-uses. That is, for species that forage in farmland

but nest in woodland edges or woods, there are feweralternative choices for nesting sites in localities where

woods are sparse. Thus, an isolated wood may be used

by birds from a wide area of surrounding farmland,

whereas a similar wood in a more-wooded landscape is

one among a number of nesting alternatives.

4.2. Are regional-scale influences important?

An important objective was to assess the influence of

�regional context� effects at a coarse resolution (units of 5

km� 5 km) on the avifauna in woods. A major difficulty

was the intercorrelations among variables representing

regional environmental gradients and local context. The

correlation between the main regional gradient (RG1)

and extent of woodland and hedgerows at a local level

suggests that, to some extent, landscape structure at alocal level reflects regional environmental variation –

particularly the underlying differences between the two

main landforms. However, based on our rigorous ap-

proach, we conclude that regional gradients across the

2100 km2 study area are of ecological significance. The

inclusion of regional variables in the �best�model for four

response variables means that they were the most im-

portant predictors (i.e. greatest variance explained) forthose response variables. In addition, hierarchical parti-

tioning revealed that regional gradient variables had in-

dependent explanatory power (Fig. 5), as well as having

substantial shared influence with other variables. We find

it striking that environmental variation at the coarse

resolution of 5� 5 km units has such explanatory power

for bird communities in small woodsmostly<3 ha in size.

Regional context effects may be evident for severalreasons. They may represent similar causal relationships

to those operating at a local level, such as the configu-

ration of wooded habitat, but simply operating at a

broader scale. Particular species or groups of species may

respond to a coarser scale because of their habitat area

requirements and scale of movements (e.g. Rolstad,

1991; Bellamy et al., 1998). Alternatively, there may

be other aspects of the regional environment to whichassemblages respond, such as human land-uses that

modify the availability of resources (Green et al., 1994;

Enoksson et al., 1995) or which impose new types of

disturbance that extend over large distances (e.g. road

effects – Lyon, 1983; Forman, 2000). Also, thresholds

may occur in the amount of habitat available in the

landscape, below which effectiveness of dispersal through

the landscape, or other population processes, may bedisrupted (With and Crist, 1995; With and King, 1999).

There is evidence for dispersal linking populations in the

study area for several species (Bellamy et al., 2003), in

which case differences in regional habitat availability

may affect species occurrence and dynamics at the local

scale. All these reasons for regional context effects may

be operating in this study.

The importance of regional gradients RG1 and RG3for woodland migrants suggests they may be responding

to the environment at a relatively coarse scale when they

return to England and select breeding territories each

year. Greater numbers of woodland migrants occurred

in larger woods and woods in landscape units in undu-

lating countryside, and in those with diverse soil groups

at the interface with fenland (RG3). Paradis et al. (1998)

found that migrants disperse further than resident spe-cies for both breeding dispersal and natal dispersal.

Thus, on return to the study region in south-east En-

gland, there may be a multi-stage process of habitat

selection, with an initial cue to settlement involving as-

sessment at a relatively coarse scale of landscape units

where a greater extent of woodland is present. This is

followed by further stages in which woodland size and

habitat quality influence selection of a breeding terri-tory. We are not able to determine particular compo-

nents of landscape units to which migrants are

responding but it is likely to involve the extent or con-

figuration of suitable wooded habitat.

Other evidence that avifaunal assemblages respond to

the environment at a regional scale comes from annual

variation in the distribution of species among woods of

different sizes (Hinsley et al., 1996; Bellamy et al., 2000,2003). Some species (e.g. great tit, blue tit, robin) show a

trend toward lower density in small woods during years

with low regional population levels while others (e.g.

dunnock, blackbird) occur in lower density in larger

woods in such years. There appears to be a redistribu-

tion of territories at a regional level such that woodland

habitats of better quality for that species are occupied

preferentially.Changes in the availability of resources in farmland

and variation in disturbance regimes may contribute to

the influence of regional gradients on edge species, but

specific relationships could not be identified. Richness of

edge species and their dominance in avifaunal assem-

blages were greater for woods in open landscapes of the

fenland, surrounded by arable farming (RG1). This can

be attributed to a �concentration� effect of a smallnumber of nesting habitats in a large area (discussed

above). It is also possible that in these parts of the re-

gional gradient there is more effective �supplementation�


of resources for edge species in the farmland. Relative

turnover of edge species, as discussed above, may also

be influenced by changes in resources or disturbance

regimes in particular landscape units (RG2).

4.3. Implications for conservation

The significance of relationships between regional

environmental gradients and bird assemblages in small

woods highlights several important points for conser-

vation. First, different species or groups of species re-

spond to landscape context in different ways. Given

different ecological requirements and different levels ofmobility, species are likely to perceive the landscape or

region in a multitude of ways. The use here of three

ecological groups was effective in identifying some

general differences in responses, especially the marked

differences between edge species and woodland depen-

dents, but also the stronger response of migrants than

residents to regional gradients. Wiens et al. (2002) sug-

gested that a useful approach for conservation man-agement would be to identify functional groups of

species based on similar responses to landscape struc-

ture. These may give further insight into the ecological

characteristics that influence the distribution of species

in land mosaics.

Second, there is a need for greater understanding of

the scale over which context effects operate for different

components of the biota. The relationship betweencoarse regional gradients based on 5 km� 5 km units

and bird assemblages in woods of mean size 2.8 ha,

suggests that population and community processes in

the avifauna may operate at broader scales than ex-

pected. This issue has practical relevance for the man-

agement of particular habitats or nature reserves. It

would be of great value to know the distance over which

land-use changes surrounding a site are likely to influ-ence within-site population processes (e.g. through im-

migration or dispersal).

Last, the presence of regional context effects is a further

reminder of the importance of a landscape or regional

perspective on potential changes in land-uses in human-

dominated environments. Urbanization, road building,

agricultural developments, land clearing and other re-

gional disturbances, as well as restoration measures, mayall have subtle effects on the biota over distances much

greater than their proponents anticipate. Such broader

effects should be considered when evaluating the envi-

ronmental impacts of proposed changes in land-use.

Acknowledgements

This research was initiated while AFB was on study

leave at Centre for Ecology and Hydrology, Monks

Wood. He thanks Dr. Barry Wyatt for the use of facil-

ities at Monks Wood, the School of Ecology and En-

vironment, Deakin University for study leave, and Land

and Water Australia for a Travelling Fellowship that

assisted with travel costs. We thank Gary Luck and Jim

Radford for their comments on the manuscript. Thanksalso to all the landowners, managers and keepers who

allowed access to their land.

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