Agriculture, Ecosystems and Environment 118 (2007) 297–306 www.elsevier.com/locate/agee
Effects of landscape complexity on farmland birds in the Baltic States Irina Herzon a,*, Robert Brian O’Hara b b
a Department of Applied Biology, P.O. Box 27 (Latakartanonkaari 5), University of Helsinki, FIN-00014, Finland 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 identification of on-farm measures within agri-environment programmes, 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. Largescale 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. Smallholding farms growing a variety of crops are still commonplace (the proportion of farms smaller than 20 ha
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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 communities and specialist birds within different landscape types in the Baltic States, and so provide information for agrienvironment 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% agricultural 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 (Auninsˇ 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 Pitka¨nen 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
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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) Habitat structure models DE (m)
RH (number) VAR (num/ha) MIX (yes/no)
Description
Open landscape (n = 40)
Semi-enclosed landscape (n = 51)
Enclosed landscape (n = 25)
Distance to the field edge (settlements, forest, extensive shrubbery), up to 200 m Count of residual habitat elements Count of all field types per area Combination of crop and grass fields
210 (127.5–210)
182.5 (33.8–210)
130 (52.5–200)*
2 (0–5) 5.03 (2–8.5) 2 (0–4)
2 (0–6) 4.64 (1–10.5) 2 (0–4)
3 (1–6)* 5.76 (1–8.8) 2 (0–4)
Residual habitat composition models All crop types ARABLEa (%) GRASSa (%) All grassland types, incl. abandoned fields SCRUBa (%) Area of any scrub FORESTa (%) Any forest type OTHERa (%) Ponds, bogs, and orchards Ditch with grassy banks Db (m) FENCEb (m) Fence around pastures ROADb (m) All road types Electric and telephone line ETLb (m) TREEb (number) Trees in open fields, isolated or small groups THb (m) Hedges, with or without trees Farmsteads and farm buildings FBb (%) DRIVb (m) Vegetated ditches and small rivers Community metrics SR SRF SRD SUM SUMF SUMD FSDIV
Total number of bird species Number of farmland specialist species Number of declining species Total abundance of all species Abundance of farmland specialist species Abundance of declining species Diversity of farmland specialist species
60.1 (1–100) 28.88 (0–97.5) 0 0 0 0 0 326 170 0
(0–5) (0–12.5) (0–6.3) (0–700) (0–150) (0–985) (0–1049) (0–50)
0 (0–370) 0 (0–10) 125 (0–700) 11 (2–26) 11 (2–25) 5 (2–10) 61 (26–108) 58 (26–102) 46 (26–85) 1.47 (0.2–2.8)
31.8 (0–100) 52.5 (0–96.3) 1.5 (0–17.5) 0 (0–20.0) 0 (0–10.3) 0 (0–1000) 0 (0–805) 350 (0–1800) 250 (0–1410) 1 (0–15) 0 (0–1550) 0.25 (0–10) 0 (0–2100) 21 (2–35) 13 (2–19) 5 (2–9) 89 (28–223) 70 (26–168) 53 (22–103) 1.8 (0.4–2.3)
25 (0–52)* 68.3 (31–96.3)* 0.88 (20.3) 2 (0–16.0) 0.5 (0–10.0) 0 (0–800) 0 (0–260) 190 (0–750) 260 (0–890) 1 (0–30) 5 (0–480) 0 (0–4) 0 (0–430) 19 (11–34) 14 (9–25) 7 (4–10) 76 (52–107) 55 (38–73) 43 (26–62) 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 themselves (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
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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 interactions, a rule of marginality was used, so that non-significant main effect variables were only removed if their interactions
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
Table 2 Estimates for the final generalised linear models relating bird community attributes with habitat spatial organisation in three agricultural landscape types in the Baltic States Community metrics
Intercept for the landscapes
Species richness
5.77 (0.9)
Farmland species richness
O 6.24 (1.117)
Retained structural indices DE
RH
VAR
6.24 (1.117)
O 0.47 (0.076) S 0.325 (0.085) E 0.08 (0.103)
0.11 (0.023)
O 0.43 (0.085) S 0.31 (0.325) E 0.08 (0.124)
S 0.50 (0.126) E Declining species richness Total abundance
0.03 (0.187)
4.81 (1.406) O 6.66 (0.785)
0.03 (0.021)
O 0.32 (0.064) S 0.06 (0.073) E 0.22 (0.1)
O 0.16 (0.043) S 0.12 (0.05) E 0.02 (0.074)
S 0.45 (0.128) E 0.44 (0.181) 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)
0.09 (0.018)
Declining species abundance
O 5.9 (0.611)
0.01 (0.017)
0.42 (0.044)
O 0.06 (0.018) S 0.24 (0.05) E 0.54 (0.069)
S 0.29 (0.079) E 0.5 (0.126) Farmland species diversity
O 6.48 (1.403) S 0.67 (0.15) E 0.17 (0.23)
O 0.44 (0.105) S 0.36 (0.124) E 0.099 (0.16)
CO
ps r2a
AICcb
0.11 (0.031)
0.52
298
0.074 (0.027)
0.12 (0.03)
0.61
162
0.09 (0.037) O 0.07 (0.039) S 0.102 (0.045) E 0.023 (0.06)
0.09 (0.039) 0.09 (0.023)
0.21
75
0.40
942
0.1 (0.02)
0.45
587
O 0.08 (0.028) S 0.13 (0.034) E 0.069 (0.044)
0.08 (0.015)
0.38
547
0.108 (0.037)
0.15 (0.03)
0.40
271
MIX
Coefficients and standard errors (in parenthesis) standardised by S.D. of a respective covariate are given. Whenever significant interactions of variable with the landscape type were found or estimates differed among landscape types, the differences for the semi-open (S) and enclosed (E) landscapes were compared to the open (O) landscape type. Abbreviations: DE, distance to the edge; RH, residual habitat; VAR, variety of fields; MIX, mixed crop and grass; CO, coordinates. a Pseudo r2 (ps r2) provides a ratio of the explained deviance to the total deviance in Poisson models. b AICc is the corrected Akaike’s information criterion.
Table 3 Results of: (i) habitat structure indices, (ii) habitat composition GLMs, and (iii) landscape only GLMs Species
Structural model
True field species Crex crex (55) Vanellus vanellus (69)
Landscape model a
Habitat model 2b
ps r
AICc
c
2
AICc
0.22 0.33
328 383
SOE SEO
0.12 0.13
+++DRIV TREE
0.45
358
0.41
357
OSE
0.24
Anthus pratensis (80) Motacilla flava (38)
+MIX:O +++DE
CO CO
0.19 0.37
294 256
+D +++DRIV TREE TH +++FENCE +ROAD +ETL FB +++DRIV +FENCE D +++DRIV TH ++FENCE +FB
0.23 0.44
290 241
ns OSE
0.06
0.28
414
++DRIV
0.40
343
ESO
0.14
Locustella naevia (33) Acrocephalus palustris (63)
+RH:O +RH:S RH:E +MIX:O MIX:S CO +++RH +VAR:O +RH:S +VAR:E
0.34 0.40
248 284
0.49 0.34
203 311
SEO OSE
0.08 0.05
A. schoenobaenus (24) Sylvia communis (111) Lanius collurio (22) Carpodacus erythrinus (37) Emberiza schoeniclus (20)
+MIX:O +VAR:E DE +++RH +VAR:O +DE:O DE:S +MIX:O ++RH +MIX:E ++CO +++RH +MIX
0.16 0.42 0.28 0.41 0.27
308 289 160 230 105
+++DRIV TREE TH +++ROAD ++D +++DRIV +TH +++FENCE ROAD +++ETL +++DRIV +++ROAD +FB ++D +TREE +FENCE +DRIV ++FENCE ROAD +D +FENCE ++FB +++D FENCE
0.36 0.38 0.23 0.47 0.44
245 306 164 213 87
OSE ESO SEO SEO ESO
0.03 0.08 0.08 0.22 0.07
0.39
268
0.19 0.33 0.24
226 115 381
+D +DRIV +++FENCE ++ETL +TH +FENCE +ETL TREE +ETL ++DRIV +TH +ETL –FB ++TREE FENCE
0.35 0.18 0.16 0.39 0.31
286 246 236 110 373
SEO SEO OSE OSE ESO
0.17 0.19 0.05 0.09 0.11
++DE +RH:O +++VAR +RH:O +RH:S RH:E +MIX:S MIX:E MIX:O +VAR:S CO
0.26 0.35
140 535
FENCE D –TH +++ROAD +++ETL
0.23 0.31
144 565
ns ns
+DE:S +++RH
0.69
374
0.68
390
SEO
0.21
0.56 0.45
338 531
+D +DRIV TREE +++FENCE +++ROAD +++ETL ++D +ROAD +++D +++DRIV –TREE +++TH
0.46 0.49
336 494
ESO SEO
0.21 0.1
0.40
292
0.39
287
ESO
0.17
0.36 0.12 0.33 0.43
291 148 265 139
0.45 0.20 0.30 0.51
253 148 274 199
ESO ns ESO ESO
0.11
Forest species Columba palumbus (43) Turdus pilaris (43) Carduelis cannabina (31) C. carduelis (22) Emberiza citrinella (106) Farmyard species Motacilla alba (34) Sturnus vulgaris (89)
Other species Cuculus canorus (40) Anthus trivialis (59) Luscinia luscinia (68) Turdus merula (37) T. philomelos (37) Hippolais icterina (20) Sylvia borin (50) S. atricapilla (22)
CO
+++DE +++RH ++VAF CO The linear model did not converge +++VAR CO ++VAR CO DE +RH:O +MIX:S MIX:E ++VAR
CO
DE +RH1 +RH3 VAR +++RH +MIX:S +VAR:O +VAR:S +++ROAD +RH:S +RH:O +MIX:S +VAR:O +VAR:E +RH:S +VAR:O VAR:S +RH DE +RH ++VAR +++CO +++VAR
FENCE
TREE +FENCE
FB
+++D ++ROAD +ETL +D +ROAD ETL ++D FENCE +++ROAD
FB
FB
0.9 0.18
301
339 368
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0.20 0.37
Alauda arvensis (136)
+DE:S RH:O +RH:S ++VAR +DE:O DE:E +RH:O +MIX:E +++VAR ++CO +++DE RH:E +MIX:S +VAR:O
Edge species Saxicola rubetra (120)
TREE TH +ROAD TH +++FENCE FB
ps r2
ps r
SE ES SE SE SE Phylloscopus collybita (26) Ph. trochilus (36) Parus major (27) Oriolus oriolus (34) Fringilla coelebs (85)
DE ++RH DE +++RH +++RH MIX:E +++RH CO +RH:O +MIX:S +++VAR
0.26 0.56 0.29 0.51 0.34
208 228 164 202 391
FARMB +++TH –FB +TREE +++ROAD +++D +++ROAD ++D +++TH
FB
0.31 0.52 0.37 0.51 0.43
ps r2 ps r2b
AICc c
Habitat model Structural model Species
Table 3 (Continued )
Species are grouped into ecological guilds and their occurrence in squares (n = 136) is given in parenthesis; in bold are species declining in Europe. Variable abbreviations: DE, distance to the edge; RH, residual habitat; VAR, variety of fields; MIX, mixed crop and grass; CO, coordinates; DRIV, vegetated ditches and rivers; TREE, trees; TH, tree lines and hedges; ROAD, road; FENCE, fence; FB, farm buildings; ETL, electric lines. Landscape types: O, open landscape; S, semi-enclosed; E, enclosed. The + and signs indicate positive/negative relationship, where +/ stands for p < 0.05, ++/ for p < 0.01, and +++/ for p < 0.001. Significant relationships in a certain landscape only (O, S, and E) are indicated as e.g. DE: S. Farmland specialist species with low occurrence or large ranges: true field group: Circus pygargus, Perdix perdix, Coturnix coturnix, Charadrius dubius, Galinago galinago, Limosa limosa, Numenius arquata, Tringa totanus, Anthus campestris, Emberiza hortulana; edge group: Circus aeruginosus, Phasianus colchicus, Acrocephalus dumetorum, Oenanthe oenanthe; forest group: Buteo bureo, Aquila pomarina, Falco tinnunculus, Streptopelia turtur, Lalulla arborea, Pica pica, Corvus monedula, C. corone cornix, C. frugilegus, Carduelis chloris; Farmyard group: Ciconia ciconia, Columba livia, Apus apus, Hirundo rustica, Delichon urbica, Passer domesticus, P. montanus. a In decreasing order of species abundance. b Pseudo r2 provides a ratio of the explained deviance to the total deviance in Poisson models. c AICc is corrected Akaike’s information criterion, a smaller value of which indicates a more parsimonious model from the two sets.
0.11 0.29 0.7 0.17 0.14 196 209 150 203 339
O O O O O
ps r2 AICc
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302
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 semiopen 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’’, ‘‘farmyard 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
0.07 (0.034)
0.06 (0.03)
0.06 (0.03)
5.9 (0.611)
6.48 (1.403)
6.66 (0.785) 6.93 (0.678)
4.81 (1.406)
No residual habitats retained 0.04 (0.02)
0.08 (0.015) 0.09 (0.01)
0.05 (0.024) 5.77 (0.9) 6.24 (1.117)
Species richness Farmland species richness Declining species richness Total abundance Farmland species abundance Declining species abundance Farmland species diversity
Coefficients and standard errors (in parenthesis) standardised by S.D. of a respective covariate are given. Variable abbreviations: D, ditch; DRIV, vegetated ditches and rivers; TREE, trees; TH, tree lines and hedges; FENCE, fence; ROAD, road; ETL, electric lines; FB, farm buildings. a Pseudo r2 (ps r2) provides a ratio of the explained deviance to the total deviance in Poisson models. b AICc is corrected Akaike’s information criterion
285 0.47
489 0.34 0.05 (0.014) 0.04 (0.013) 0.07 (0.013) 0.04 (0.012)
0.12 (0.017) 0.07 (0.012) 0.04 (0.016) 0.05 (0.01) 0.03 (0.013) 0.04 (0.016) 0.04 (0.012)
0.03 (0.013)
871 550 0.07 (0.015) 0.03 (0.013)
0.06 (0.018) 0.04 (0.013)
0.40 0.43
69 0.23
297 170 0.46 0.30 0.09 (0.024) 0.12 (0.023) 0.06 (0.027) 0.04 (0.022)
TH TREE DRIV D Intercept
Retained structural indices Community metrics
Table 4 Estimates for the final GLMs relating bird community attributes with residual habitat composition.
FENCE
ROAD
ETL
FB
ps r2a
AICcb
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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 semienclosed 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.
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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. Bo¨hningGaese, 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 seminatural 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 abandoned 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; Vepsa¨la¨inen 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 programmes 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 Germany, England, and Denmark (Farmer and Swales, 2004). Continuation of mixed farming throughout the region is another challenge. A higher support for retaining lowintensity 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
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ditches) is the most expensive option of the agrienvironment 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 Bu¨chs, 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 Auninsˇ, Jaanus Elts, and Zydrunas Preiksˇa. 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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