www.elsevier.com/locate/agee

Agriculture, Ecosystems and Environment 118 (2007) 297–306

Effects of landscape complexity on farmland

birds in the Baltic States

Irina Herzon a,*, Robert Brian O’Hara b

a Department of Applied Biology, P.O. Box 27 (Latakartanonkaari 5), University of Helsinki, FIN-00014, Finlandb Department of Mathematics and Statistics, P.O. Box 4 (Yliopistonkatu 5), University of Helsinki, FIN-00014, Finland

Received 29 November 2005; received in revised form 26 April 2006; accepted 1 May 2006

Available online 13 July 2006

Abstract

Data on birds occurring in farmland in the Baltic States of Estonia, Latvia and Lithuania were related to the spatial organisation of farmed

habitats in three different agricultural landscape types. Species richness, abundance, and diversity of farmland bird communities, as well as

abundance of the most frequently observed species were positively related to the number of residual non-cropped elements within farmland,

the local mixture of annual crop and grass fields, and the variety of field types. The positive association of the species richness and abundance

of the farmland bird community with richness in residual habitats and crops was most prominent in open landscapes. The results suggest that,

by simplifying farmland structure and making it more homogenous, EU agricultural policies will have a detrimental effect on farmland bird

populations in Eastern Europe. Ways of better targeting of the agri-environment schemes are suggested.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Farmland birds; Habitat heterogeneity; Agricultural landscape; Agri-environment schemes

1. Introduction

During the last two decades birds inhabiting agricultural

landscapes in Europe have been intensively studied, mainly

because of the widespread and serious declines of many

species in countries where yields have increased through

farming (Donald et al., 2001; Newton, 2004; Vickery et al.,

2004b). The Baltic States, i.e. Estonia, Latvia and Lithuania,

are part of the Central and East European (CEE) region.

Following the collapse of the communist system in the early

1990s, their agricultural sector at first experienced a sharp

decline in production, more recently followed by a slow

recovery (FAOSTAT, 2005). There is evidence of population

increases in many organisms dependent on farmland in this

period (Gregory et al., 2005).

Accession to the European Union (EU) is regarded as a

potential threat to the CEE region’s farmland biota (Donald

et al., 2002; European Environment Agency, 2004) but it

* Corresponding author. Tel.: +358 9 7275013; fax: +358 9 75945940.

E-mail address: herzon@mappi.helsinki.fi (I. Herzon).

0167-8809/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2006.05.030

also brings new opportunities to support farmland wildlife

through agri-environment programmes. Correct identifica-

tion of on-farm measures within agri-environment pro-

grammes, their efficient targeting at both the national and

regional levels, and sound monitoring are all crucial (Kleijn

and Sutherland, 2003; Vickery et al., 2004a). Data on the

ecology, abundance and distribution of farmland organisms

are therefore urgently needed from various landscape and

farmland types across the whole accession region. Large-

scale studies on farmland birds from the Baltic region are

lacking except a special monitoring scheme in Latvia

(Priednieks et al., 1999).

A recent review by Benton et al. (2003) highlighted the

importance of heterogeneity of agricultural habitats in space

and time in maintaining farmland biodiversity in both

Europe and Northern America. In the current conditions of

the Baltic region, as in the whole of the CEE region, local

habitat richness and heterogeneity can be expected to play a

crucial role in supporting rich farmland communities. Small-

holding farms growing a variety of crops are still

commonplace (the proportion of farms smaller than 20 ha

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306298

ranges from 60% in Estonia to 80% in Latvia and Lithuania

(Salonen et al., 2001)). Crop production and cattle rearing

are not confined to distinct regions and mixed farming still

prevails.

The aim of this analysis was to look at the effects of

farmland structural complexity on farmland bird commu-

nities and specialist birds within different landscape types in

the Baltic States, and so provide information for agri-

environment schemes on the best options for supporting

farmland wildlife in the face of the predicted pending

intensification and homogenisation of farmland.

2. Material and methods

The Baltic region lies in Europe’s hemiboreal zone. It

occupies 175,116 km2, stretching for about 700 km in a

North-South direction and representing a biogeographical

continuum from forest-dominated Estonia (19.7% agricul-

tural land) to the more open agricultural Lithuania (53.4%

agricultural land) (Anon, 2003).

The proportion of farmland in the counties was used as

guidance for landscape type selection—ranging from

generally open to fragmented. Counties where farming

was marginal, i.e. largely comprised of small subsistence

plots embedded into the forest-dominated landscape were

avoided. The landscape type for each 100 km2 study area

was defined as open (containing over 80% of agricultural

land), semi-open (60–80%), and enclosed (40–60%). In each

county a study area of 100 km2 was chosen, and 1 km2

squares were selected at random from the grid. Four points

were placed in each square in a systematic way: at

approximately equal distances from the corners with a

minimum distance of 300 m between them. In Latvia, where

the counts were performed as part of an existing monitoring

scheme, two points per square were placed. However, the

same principle of area selection was followed as in this study

(Priednieks et al., 1999).

Fieldwork was conducted in spring–summer 2002. A

point count method with unlimited distance (Bibby et al.,

1992) with two 5-min visits to each point, at central dates

around mid May and mid June, was used. Counts were

started 1-h after sunrise to avoid the dawn peak in bird

activity and were carried out under good weather conditions.

The sequence with which points were visited was reversed

between the visits. Changing observers between squares was

logistically problematic and hence in Latvia and Lithuania

one person counted in one study area. However, a national

co-ordinator visited all areas and assisted with the habitat

description. All observers underwent training prior to

counting: the field methods and habitat descriptions were

tried out during a pilot year in 2001 with the same observers

(Herzon et al., 2002). For each point the maximum count of

individuals from the two visits was used. Feeding

individuals and foraging flocks were included in the analysis

but migrating birds and birds passing high overhead were

excluded. For the analysis data from the points within each

square were pooled.

Community metrics based on all species were the total

species richness (SR) and abundance (SUM). A sub-set of

species defined as ‘‘farmland specialists’’ was made based

on an independent assessment of such species for the whole

of Europe (Tucker and Evans, 1997) and adapted to the

region by local experts (Aunins and Strazds, pers. com.). The

respective metrics were species richness (SRF), abundance

(SUMF), and Shannon–Wiener diversity (FSDIV). Further a

subgroup of species shown to be in decline over most of their

European range as a result of agricultural intensification was

extracted (BirdLife International, 2004), and their species

richness (SRD) and abundance (SUMD) were calculated.

Numbers of individual species occurring on 20 and more

sites but excluding those which have large activity ranges

(mainly corvidae and hirundidae) were also modelled. In

order to assess a pattern of association of the farmland

specialist species with the spatial organisation of habitat the

species were grouped by the ecological guilds similar to

those by Pitkanen and Tiainen (2001). True field species are

such which breed and feed on fields and open margins; edge

species breed on field edges with high vegetation, reeds,

bushes or low trees, or on similar vegetation patches within

fields, and feed there or in open; tree and forest species breed

in trees, also in forest and feed on fields; and farmyard

species utilise habitats provided on farms such as trees,

bushes and buildings, their degree of association with

habitation may vary. Species with territories near to the

fields but both breeding and feeding elsewhere are regarded

as others.

Habitat variables were selected on the basis of their

importance for the studied bird group in similar studies in

the region (cf. Petersen, 1998; Priednieks et al., 1999). The

extent of each habitat was measured within a 100 m radius

around the counting points. The distance to the nearest

occurrence of major habitats other than farmland (forest,

extensive scrub or settlements) was estimated in the field and

validated from topographic maps for up to 200 m. The

habitat types were sketched onto the field maps, and the

percentage of their coverage was estimated from the field

maps in LUPA software (LUPA, 2002). The actual percent

of farmland in the areas of 100 km2 was estimated from

topographical maps in LUPA.

For each point four simple habitat structural indices

pertaining to the surrounding farmland were calculated:

distance to the nearest non-farmed habitat (DE), residual

habitat score (RH), variety of fields (VAR), and the mixture

score (MIX) (Table 1). Many of the indices commonly used

in landscape studies (cf. Turner, 1990) were not considered

appropriate for the scale and bird taxa measured here. The

residual habitat score was based on a count of the presence of

all non-cropped elements within the fields, such as ditches

(Table 1). The variety of fields reflected how many different

field types (crops, grassland types and abandoned fields)

were present per unit area. The mixture score referred to a

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306 299

Table 1

Median and range (in parentheses) of the explanatory variables and bird community metrics in three landscape types in the farmland of the Baltic States;

n = number of squares

Name (units) Description Open

landscape (n = 40)

Semi-enclosed

landscape (n = 51)

Enclosed

landscape (n = 25)

Habitat structure models

DE (m) Distance to the field edge

(settlements, forest, extensive

shrubbery), up to 200 m

210 (127.5–210) 182.5 (33.8–210) 130 (52.5–200)*

RH (number) Count of residual habitat elements 2 (0–5) 2 (0–6) 3 (1–6)*

VAR (num/ha) Count of all field types per area 5.03 (2–8.5) 4.64 (1–10.5) 5.76 (1–8.8)

MIX (yes/no) Combination of crop and grass fields 2 (0–4) 2 (0–4) 2 (0–4)

Residual habitat composition models

ARABLEa (%) All crop types 60.1 (1–100) 31.8 (0–100) 25 (0–52)*

GRASSa (%) All grassland types, incl.

abandoned fields

28.88 (0–97.5) 52.5 (0–96.3) 68.3 (31–96.3)*

SCRUBa (%) Area of any scrub 0 (0–5) 1.5 (0–17.5) 0.88 (20.3)

FORESTa (%) Any forest type 0 (0–12.5) 0 (0–20.0) 2 (0–16.0)

OTHERa (%) Ponds, bogs, and orchards 0 (0–6.3) 0 (0–10.3) 0.5 (0–10.0)

Db (m) Ditch with grassy banks 0 (0–700) 0 (0–1000) 0 (0–800)

FENCEb (m) Fence around pastures 0 (0–150) 0 (0–805) 0 (0–260)

ROADb (m) All road types 326 (0–985) 350 (0–1800) 190 (0–750)

ETLb (m) Electric and telephone line 170 (0–1049) 250 (0–1410) 260 (0–890)

TREEb (number) Trees in open

fields, isolated or small groups

0 (0–50) 1 (0–15) 1 (0–30)

THb (m) Hedges, with or without trees 0 (0–370) 0 (0–1550) 5 (0–480)

FBb (%) Farmsteads and farm buildings 0 (0–10) 0.25 (0–10) 0 (0–4)

DRIVb (m) Vegetated ditches and small rivers 125 (0–700) 0 (0–2100) 0 (0–430)

Community metrics

SR Total number of bird species 11 (2–26) 21 (2–35) 19 (11–34)

SRF Number of farmland specialist species 11 (2–25) 13 (2–19) 14 (9–25)

SRD Number of declining species 5 (2–10) 5 (2–9) 7 (4–10)

SUM Total abundance of all species 61 (26–108) 89 (28–223) 76 (52–107)

SUMF Abundance of farmland specialist species 58 (26–102) 70 (26–168) 55 (38–73)

SUMD Abundance of declining species 46 (26–85) 53 (22–103) 43 (26–62)

FSDIV Diversity of farmland specialist species 1.47 (0.2–2.8) 1.8 (0.4–2.3) 1.8 (1.2–2.6)

Significant ( p < 0.05) differences in habitat extent among the landscape types based on Kruskal–Wallis tests are denoted by an asterisk (*).a Field types used as control.b Habitats used in calculating residual habitat score RH.

local combination of crop fields and grassland. It varied from

0 to 4 depending on how many of the four points within a

square had a grass field neighbouring a crop field.

2.1. Statistical analysis

Three model sets were developed to assess a relative

importance of the spatial organisation of local habitat in

comparison with habitat composition, and the role of the

wider landscape type. A set of respective hybrid models

including all the variables was also created. In all models,

except for the landscape type, geographical co-ordinates

were fitted first to control for the geographical gradient in the

numbers of species and individuals across the region.

2.1.1. Set 1

Farmland structure. To assess the potentially varied

effect of the spatial organisation of local habitat depending

on the landscape type, all the indices, the landscape type and

its interactions with the indices were entered into a model.

2.1.2. Set 2

Non-cropped residual habitats. To assess an explanatory

power of the habitat composition models and to determine

which particular habitat elements within farmland were the

most influential, the extent of all types of the residual habitat

elements was fitted while controlling for the presence of the

main habitat types (crop and grass fields, scrub, and forest

within farmland, Table 1).

2.1.3. Set 3

Landscape type. The landscape type was entered as a

factor to assess the association of community metrics and

species distribution with any particular landscape.

Though the structural indices correlated among them-

selves (r > 0.5 for RH and VAR, r > 0.4 for MIX and VAR),

they all expressed different and ecologically meaningful

aspects of the spatial organisation of habitat. The strength of

correlation among the residual habitat variables did not

exceed 0.3. This level of correlation among explanatory

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306300

variables is not exceptional but the interpretation of the

estimates should be done with caution (Booth et al., 1994).

Generalised linear modelling in S-Plus 6.1 (Insightful,

2001) was used, where Poisson error structure was chosen in

all cases except the farmland bird diversity index (normal

error structure). Variables were selected in a stepwise

selection algorithm based on Akaike’s information criteria

corrected for a small sample size (AICc) (Burnham and

Anderson, 2002). Working with models containing interac-

tions, a rule of marginality was used, so that non-significant

main effect variables were only removed if their interactions

Table 2

Estimates for the final generalised linear models relating bird community attributes

Baltic States

Community

metrics

Intercept for the

landscapes

Retained structural indices

DE RH

Species richness 5.77 (0.9) 6.24

(1.117)

O 0.47

(0.076)

S �0.325

(0.085)

E �0.08

(0.103)

Farmland species

richness

O 6.24 (1.117) O 0.43

(0.085)

S 0.50 (0.126) S �0.31

(0.325)

E �0.03 (0.187) E �0.08

(0.124)

Declining species

richness

4.81 (1.406)

Total abundance O 6.66 (0.785) 0.03

(0.021)

O 0.32

(0.064)

S 0.45 (0.128) S 0.06

(0.073)

E 0.44 (0.181) E �0.22

(0.1)

Farmland species

abundance

6.93 (0.678) 0.08

(0.019)

O 0.23

(0.05)

S �0.08

(0.056)

E �0.24

(0.08)

Declining species

abundance

O 5.9 (0.611) 0.01

(0.017)

0.42

(0.044)

S 0.29 (0.079)

E 0.5 (0.126)

Farmland species

diversity

O 6.48 (1.403) O 0.44

(0.105)

S 0.67 (0.15) S 0.36

(0.124)

E 0.17 (0.23) E 0.099

(0.16)

Coefficients and standard errors (in parenthesis) standardised by S.D. of a respectiv

landscape type were found or estimates differed among landscape types, the differen

open (O) landscape type. Abbreviations: DE, distance to the edge; RH, residual haa Pseudo r2 (ps r2) provides a ratio of the explained deviance to the total devb AICc is the corrected Akaike’s information criterion.

with the landscape type were not in the model. In order to

evaluate which of the two, spatial organisation of local

habitat or habitat composition, would perform better for

community and species models, the resulting optimal

models for each set were also assessed with AICc.

The fit of each model was examined with residual plots to

detect heteroscedasticity. The explanatory variables were

not transformed but residuals were systematically plotted

against each of them to detect strong non-linear responses.

The strength of non-linear responses was checked through

adding respective 2nd order terms. In several cases these

with habitat spatial organisation in three agricultural landscape types in the

VAR MIX CO ps r2a AICcb

0.11

(0.023)

�0.11

(0.031)

0.52 298

0.074

(0.027)

�0.12

(0.03)

0.61 162

0.09

(0.037)

�0.09

(0.039)

0.21 75

O 0.16

(0.043)

O �0.07

(0.039)

�0.09

(0.023)

0.40 942

S �0.12

(0.05)

S 0.102

(0.045)

E �0.02

(0.074)

E �0.023

(0.06)

0.09

(0.018)

�0.1

(0.02)

0.45 587

O 0.06

(0.018)

O �0.08

(0.028)

�0.08

(0.015)

0.38 547

S �0.24

(0.05)

S 0.13

(0.034)

E �0.54

(0.069)

E 0.069

(0.044)

0.108

(0.037)

0.15

(0.03)

0.40 �271

e covariate are given. Whenever significant interactions of variable with the

ces for the semi-open (S) and enclosed (E) landscapes were compared to the

bitat; VAR, variety of fields; MIX, mixed crop and grass; CO, coordinates.

iance in Poisson models.

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Table 3

Results of: (i) habitat structure indices, (ii) habitat composition GLMs, and (iii) landscape only GLMs

Species Structural model Habitat model Landscape modela

ps r2b AICcc ps r2 AICc ps r2

True field species

Crex crex (55) +DE:S �RH:O +RH:S ++VAR 0.20 339 +++DRIV �TREE ��TH +ROAD 0.22 328 S O E 0.12

Vanellus vanellus (69) +DE:O �DE:E +RH:O +MIX:E

+++VAR ++CO

0.37 368 �TREE ���TH +++FENCE ���FB 0.33 383 S E O 0.13

Alauda arvensis (136) +++DE �RH:E +MIX:S +VAR:O 0.45 358 +D +++DRIV �TREE �TH +++FENCE

+ROAD +ETL �FB

0.41 357 O S E 0.24

Anthus pratensis (80) +MIX:O ���CO 0.19 294 +++DRIV +FENCE 0.23 290 ns

Motacilla flava (38) +++DE ���CO 0.37 256 �D +++DRIV ���TH ++FENCE +FB 0.44 241 O S E 0.06

Edge species

Saxicola rubetra (120) +RH:O +RH:S �RH:E +MIX:O

�MIX:S ���CO

0.28 414 ++DRIV ���TREE +FENCE 0.40 343 E S O 0.14

Locustella naevia (33) +++RH +VAR:O 0.34 248 +++DRIV ���TREE ���TH +++ROAD 0.49 203 S E O 0.08

Acrocephalus palustris (63) +RH:S +VAR:E 0.40 284 ++D +++DRIV +TH +++FENCE ��ROAD

+++ETL

0.34 311 O S E 0.05

A. schoenobaenus (24) +MIX:O +VAR:E 0.16 308 +++DRIV +++ROAD +FB 0.36 245 O S E 0.03

Sylvia communis (111) ���DE +++RH +VAR:O �CO 0.42 289 ++D +TREE +FENCE 0.38 306 E S O 0.08

Lanius collurio (22) +DE:O �DE:S +MIX:O 0.28 160 +DRIV ++FENCE �ROAD 0.23 164 S E O 0.08

Carpodacus erythrinus (37) ++RH +MIX:E ++CO 0.41 230 +D +FENCE ++FB 0.47 213 S E O 0.22

Emberiza schoeniclus (20) +++RH +MIX 0.27 105 +++D �FENCE 0.44 87 E S O 0.07

Forest species

Columba palumbus (43) +++DE +++RH ++VAF ���CO 0.39 268 +D +DRIV +++FENCE ++ETL 0.35 286 S E O 0.17

Turdus pilaris (43) The linear model did not converge +TH +FENCE +ETL 0.18 246 S E O 0.19

Carduelis cannabina (31) +++VAR ���CO 0.19 226 ��TREE +ETL 0.16 236 O S E 0.05

C. carduelis (22) ++VAR ���CO 0.33 115 ++DRIV +TH +ETL –FB 0.39 110 O S E 0.09

Emberiza citrinella (106) �DE +RH:O +MIX:S �MIX:E

++VAR

0.24 381 ++TREE ���FENCE 0.31 373 E S O 0.11

Farmyard species

Motacilla alba (34) ++DE +RH:O +++VAR 0.26 140 ��FENCE 0.23 144 ns

Sturnus vulgaris (89) +RH:O +RH:S �RH:E +MIX:S

�MIX:E �MIX:O +VAR:S

���CO

0.35 535 ��D –TH +++ROAD +++ETL 0.31 565 ns

Other species

Cuculus canorus (40) +DE:S +++RH ���CO 0.69 374 +D +DRIV ���TREE +++FENCE

+++ROAD +++ETL

0.68 390 S E O 0.21

Anthus trivialis (59) ���DE +RH1 +RH3 ���VAR 0.56 338 ++D +ROAD 0.46 336 E S O 0.21

Luscinia luscinia (68) +++RH +MIX:S +VAR:O +VAR:S

+++ROAD

0.45 531 +++D +++DRIV –TREE +++TH 0.49 494 S E O 0.1

Turdus merula (37) +RH:S +RH:O +MIX:S +VAR:O

+VAR:E

0.40 292 ��FENCE ���FB 0.39 287 E S O 0.17

T. philomelos (37) +RH:S +VAR:O �VAR:S 0.36 291 +++D ++ROAD +ETL ���FB 0.45 253 E S O 0.11

Hippolais icterina (20) +RH 0.12 148 +D +ROAD �ETL 0.20 148 ns

Sylvia borin (50) �DE +RH ++VAR +++CO 0.33 265 ++D 0.30 274 E S O 0.9

S. atricapilla (22) +++VAR 0.43 139 ��FENCE +++ROAD ���FB 0.51 199 E S O 0.18

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306302T

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were significant but only marginally so (0.01 < p < 0.05),

and thus were omitted from the further modelling for the

sake of simplicity. Models where the dispersion parameter

exceeded two were considered overdispersed. Where this

was true (for total bird and farmland specialist abundance)

the estimates and confidence limits were corrected by the

dispersion parameter (Crawley, 1993).

3. Results

All the community characteristics and abundance of many

species were negatively related to the latitude (Tables 2 and 3)

reflecting an increase in species richness and diversity of

farmland bird communities, as well as in the abundance of the

majority of the species, from north to south.

All the community metrics strongly related to one or

several aspects of the spatial organisation of local habitat

(Table 2). The number of declining species was positively

related only to the combination of crop and grass fields.

Significant interactions of structural characteristics with the

landscape type indicate that the effects of the local structure

vary among different landscapes. In all models the slope

difference indicated that the relationship of the community

characteristics with the residual habitat was strongest in

open landscape, except for effect of the combination of crop

and grass fields on total abundance (strongest in the semi-

open landscape).

The abundance of nearly all ‘‘true field species’’ was

positively related to the distance to the open field edge and

the variety of field types (Table 3). They were the only ones

for which residual habitat was only a weak positive predictor

and only in some landscapes. All ‘‘edge species’’, ‘‘farm-

yard species’’ and most ‘‘forest species’’ positively

responded to the increase in field variety, a combination

of crop and grass, and the number of residual habitats.

‘‘Other species’’ were more abundant closer to the field edge

and in fields with a more extensive non-cropped habitat

network. The effect of field variety and combination of crop

and grass was predominantly positive for many of these

species in at least one landscape type. Species of the ‘‘other’’

group were most clearly separated from the farmland

specialists by their positive association with the edge of open

farmed area.

When the effect of the main habitat types was controlled

for, the community metrics were positively correlated

mainly with the length of the ditches and rivers, fences,

roads, and electric lines; and negatively with the number of

trees, length of hedges and alleys, as well as the area of farms

and isolated buildings (Table 4).

For the ‘‘true field species’’ the most influential elements

with a positive effect were the extent of the vegetated

ditches and small rivers, fences and roads. The number of

trees, the length of hedges and tree alleys, and the area

of farmsteads had predominantly a negative influence

(Table 3). ‘‘Edge species’’ were more abundant with the

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306 303T

able

4

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imat

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increase in length of all ditch types, rivers, and fences, and

the area of farmsteads. There was no consistent significant

predictor for ‘‘forest species’’, although the abundance of

some of them was positively related to the length of electric

lines, fences, ditches, hedges or the number of trees. Most of

species from the ‘‘other’’ group were associated with

ditches, hedges and tree alleys, and roads, while many were

negatively affected by the area of farmsteads.

There were statistically significant differences between

the landscape types in all community attributes, except in the

numbers of farmland and declining species (Fig. 1). Despite

being statistically significant, landscape types on a scale of

1 km2 explained a very low percentage of variation in the

community metrics (7–13%). When the local spatial

organisation of habitat was controlled for, only a few

significant differences remained (Table 2).

Nearly all species associated predominantly with one or

two landscape types (Table 3). The ‘‘true field species’’ were

most abundant in the open and semi-open landscapes, and all

were least numerous in the enclosed landscape. The ‘‘edge

species’’ were more abundant in the enclosed or semi-

enclosed landscapes, except Acrocephalus schoebaenus and

A. palustris, which dominated in the open landscapes. The

‘‘forest species’’ were mostly observed in semi-open or

enclosed landscapes, except Carduelis cannabina and C.

carduelis, which dominated in the open landscape. All

‘‘other’’ species associated with the semi-open and enclosed

landscape types.

With regard to the AICc the model sets for the habitat

structure and habitat availability performed similarly for the

species richness and individual species abundance with the

differences within 5% (Tables 2–4). Spatial organisation of

farmed habitats had the best predictive power for species

Fig. 1. Estimates and S.E. standardised by S.D. of respective covariates for

the community metrics based on the final models for landscape types, where

(1) open landscape, (2) semi-enclosed, and (3) enclosed. SR: number of bird

species, SRF: number of farmland species, SRD: number of declining

species, SUM: abundance of all species, SUMF: abundance of farmland

species, SUMD: abundance of declining species and FSDIV: Shannon–

Wiener diversity of farmland species.

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306304

richness of farmland specialist birds and abundance of eight

farmland species.

4. Discussion

Our results corroborate a number of avian studies on the

positive relation between habitat heterogeneity and the

species richness at the scales of landscapes (cf. Bohning-

Gaese, 1997; Atauri and de Lucio, 2001) and fields

(Tryjanowski, 1999; Laiolo, 2005). It has been suggested,

however, that such correlation may be an artefact of habitat

heterogeneity considered without controlling for the

presence of particular species-rich habitats (Heikkinen

et al., 2004). The presence of species-rich habitats, e.g.

semi-natural grasslands or forest patches, especially if

embedded into a generally species-poor agricultural matrix,

may be crucial for bird species associated with them. The

relative importance of finer-scale heterogeneity in terms of

crop variety may be higher for birds dependant on the field

area itself. Unlike in Western Europe with its modern strict

division of fields into semi-natural grassland (species rich)

and cultivated fields (species poor), there is in the Baltics a

continuum of fields from intensively managed to largely

abandoned, which may lessen the significance of semi-

natural areas. In our set of hybrid models including both

habitat variables and structural indices (not shown here), the

same structural indices were retained as important predictors

additional to habitat variables.

The mosaic of different crops creates a varied structure of

vegetation and provides diverse resources in space and time,

as has been reviewed by Benton et al. (2003) for Europe and

N. America, and shown by Mangnall and Crowe (2003) for

South Africa. This study indicates that annual crop fields

neighbouring perennial grassland (including recently aban-

doned fields) is the most important combination of field

types for declining farmland species. Crop and grass contrast

most strongly in vegetation development, resource base, and

management (Evans, 1997). In predominantly arable

regions, grassland provides fledglings with a safe habitat

(Berg, 1991) while in grassland-dominated regions many

seed-eating farmland birds were shown to depend on cereal

fields for food (Robinson et al., 2001). Unimproved

grassland sites themselves may provide rich sources of

broad-leaved and grass seed.

The landscape type on a scale of 100 km2 explained little

of the variation in the number of species and individuals of

farmland birds. Farmland structural characteristics on a local

scale seem to be more important in shaping the farmland

bird community. While the presence of various habitats

adjoining fields enriches the total community, some of the

farmland ground-nesting birds avoid enclosed fields (Berg,

1991; Piha et al., 2003). The suitability of open field area can

be improved with sufficient variation of its inner structure

(Wilson et al., 2005). Models based on simple indices of the

field area heterogeneity had an explanatory power similar to

that of habitat composition models, especially so for the

‘‘true field species’’.

Out of all non-cropped elements the extent of ditches and

small rivers was the strongest positive predictor of the bird

community, and the only one with an exclusively positive

effect on individual species. A number of studies from

North-Eastern Europe (Priednieks et al., 1999; Piha et al.,

2003; Vepsalainen et al., 2005) showed their prominent role

in otherwise homogenous fields. Though it is still unclear

whether birds are attracted to ditches mainly because of

grassy margins and higher vegetation along them, or unique

resources such as water, damp soil and aquatic invertebrates,

ditches are likely to be a keystone structure (Tews et al.,

2004) for farmland birds in the region.

5. Conservation policy implications

Though the EU direct agricultural subsidies are no longer

connected to production level under the reformed CAP, they

will continue to above all serve a target of improving the

competitiveness of the EU agricultural sector and thus

further intensifying production (European Commission,

2003). This tends to simplify farmland structure (through

removal of non-cropped parcels and elements) as well as to

homogenise it (decrease of mixed farming and crop variety)

in areas with the best growing conditions. Alongside support

of especially valuable parcels of semi-natural habitats,

maintenance of diverse farmland overall should be given

priority when updating national agri-environment pro-

grammes in the Baltic region. Such provisions are currently

missing from the national programmes.

There are no easy practical countermeasures to the

spread of vast monoculture fields in place of the current high

field heterogeneity in the region, when the process of

specialisation is supported by the current economic climate.

Perhaps it is time to support farmers who retain a certain

level of ‘‘crop heterogeneity’’ and ‘‘non-cropped habitat

richness’’ per hectare of their farmed land. Indeed,

provisions for a minimum crop rotation were recently

included within the cross-compliance measures in Ger-

many, England, and Denmark (Farmer and Swales, 2004).

Continuation of mixed farming throughout the region is

another challenge. A higher support for retaining low-

intensity grazed pastures in otherwise cereal dominated

counties could be considered within the agri-environment

schemes (Vickery et al., 2004a).

Though the critical role of non-cropped habitats is

known, it remains unclear to what extent non-cropped

parcels can be sacrificed to land-use intensification and

still support viable bird populations (Fuller et al., 2004).

At any rate, the retention of both non-cropped elements

and crop variety in the Baltic region is likely to be a more

cost-effective option than their possible re-creation in

years to come. For example, in the UK creating mid-field

strips across large homogeneous fields (partly mimicking

I. Herzon, R. Brian O’Hara / Agriculture, Ecosystems and Environment 118 (2007) 297–306 305

ditches) is the most expensive option of the agri-

environment programme (DEFRA, 2006). The retention

of existing ditches is not compulsory for getting

agricultural subsidies as part of the cross-compliance

regime. Since their area is deducted from the farmed area

eligible for direct subsidies, the farmers have an incentive

to substitute ditches with subsurface drainage. Such

adverse development has been taking place in e.g. Finland

(Hietala-Koivu, 2002). Regional targeting in subsidising

maintenance and establishment of non-cropped elements,

as well as prioritising habitat requirements of species

dependant on farmland, is strongly advisable. For

example, positive effect of residual habitats is most

pronounced in open landscapes, and there they are least

detrimental to the open field birds.

The question remains as to the relative importance of

farmland structural heterogeneity versus low intensity of

field management. There are studies showing that a higher

level of insect diversity can be achieved within large fields

under low intensity compared to smaller but intensively

managed fields (in Buchs, 2003, p. 66). Reviews of the

factors affecting bird populations in the UK (Fuller, 2000;

Robinson and Sutherland, 2002) suggested that habitat loss

had a direct impact at the beginning of agricultural

intensification. The more subtle effects of increased

chemical inputs and growing mechanisation working

through population dynamics were the mechanisms further

degrading farmland bird communities. Currently in Britain

intensified management of crops affects the majority of bird

species as compared to e.g. hedge removal (Newton, 2004).

If similar factors operate in Eastern Europe, then both

retention of farmland heterogeneity and maintenance of a

network of extensively managed areas will be crucial aspects

of farmland biodiversity preservation in the long run.

Finally, our results support the importance of monitoring

the structure of farmland landscapes in the region as well as

species (Piorr, 2003), which is vital for E European farmland

biodiversity in these dynamic times of its agricultural sector

development.

Acknowledgements

This work could not been done without the thorough

work of the Baltic fieldworkers, and the organising help of

Ainars Aunins, Jaanus Elts, and Zydrunas Preiksa. This

research was financially supported by the Maj and Tor

Nessling Foundation, and the Kemira Foundation (Finland).

The paper improved greatly with the comments from Paul

Donald and Juha Helenius.

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