Agriculture, Ecosystems and Environment 77 (2000) 219–226
Factors affecting corn bunting Miliaria calandra abundance in a Portuguese agricultural landscape C. Stoate a,∗ , R. Borralho b , M. Araújob,1 a
The Game Conservancy Trust, Fordingbridge, Hampshire SP6 1EF, UK b ERENA, Av. Visconde Valmor 11-3◦ , 1000 Lisbon, Portugal
Received 14 December 1998; received in revised form 18 June 1999; accepted 1 July 1999
Abstract Breeding and wintering abundance of corn buntings in an agricultural landscape of Alentejo (southern Portugal) was assessed in relation to agricultural intensification and other environmental variables during 1994–1997, using distance sampling and multivariate regression. Bird abundance was lowest in intensively managed farmland in both seasons, and was related positively to fallow area in winter and to the presence of game management and oats in spring. Fallows and oats were associated with extensively managed farmland, but the implementation of a managed hunting regime was unrelated to agricultural intensification. The importance of extensive arable systems to corn bunting conservation is discussed. ©2000 Elsevier Science B.V. All rights reserved. Keywords: Agricultural intensification; Breeding abundance; Fallow; Montado; Winter abundance; Portugal
1. Introduction Corn buntings Miliaria calandra are associated, throughout their distribution range, with arable landscapes. Their numbers have declined in northern Europe since the 1960s as a result of agricultural intensification (Tucker and Heath, 1994). This decline is attributed to increased winter mortality, caused by reduced abundance of winter food (Donald and Evans, 1994), and to poor breeding performance caused by increased use of agro-chemicals (Aebischer and Ward, 1997; Brickle and Harper, in press). In∗ Corresponding author. E-mail address:
[email protected] (C. Stoate). 1 Present address: Biogeography and Conservation Laboratory, The Natural History Museum, Cromwell Road SW7 5BD London, UK. Tel.: +44-1425-652381; fax: +44-1425-651026.
creased use of herbicides throughout northern Europe has resulted in the loss of many arable weeds, and hence of phytophagous invertebrates, which provided an essential source of food for many farmland birds, as Potts (1986) demonstrated convincingly for grey partridge Perdix perdix. Corn bunting breeding densities are currently highest in Turkey, Spain and Portugal (Diaz and Telleria, 1997), where their numbers appear to be stable. Although cereal production has intensified in parts of these countries, there still are large areas of extensively managed farmland (Bignal and McCracken, 1996). In Portugal, Alentejo is the main cereal growing region, and extensive systems are still widely adopted there. Corn bunting has a widespread distribution in this region, and is described as abundant (Rufino, 1989; Elias et al., 1999).
0167-8809/00/$ – see front matter ©2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 8 0 9 ( 9 9 ) 0 0 1 0 1 - 2
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In these extensive arable systems, grazed fallow provides a source of food in the form of seeds in winter and invertebrates in summer. Cereal crops also support an invertebrate community with relatively large insects, such as caterpillars (Lepidoptera larvae) and grasshoppers (Orthoptera), taken by corn buntings during the breeding season in the north of their range (Brickle and Harper, in press). This study assesses the abundance of corn buntings in December and April in relation to three arable systems and other environmental variables in an agricultural landscape of Baixo Alentejo, southern Portugal, considering, in particular, various levels of agricultural intensification, and the potential effects of fallow area and private game interests.
2. Methods 2.1. Study area The study area included parts or all of five administrative regions in Baixo Alentejo (Ferreira do Alentejo, Aljustrel, Castro Verde, Ourique and Almodôvar), with a total area of 155,000 ha. Within this region, three land-use systems were recognised: intensive agriculture, extensive agriculture and ‘montado’ (Table 1). The intensive agriculture category is characterised by a greater frequency (>55%) of heavy soils, much of the area being irrigated. Wheat Triticum aestivum and barley Hordeum distichum are the main cereal crops, and silage grass Lolium sp., sunflower Helianthus annuus, sugar beet Beta vulgaris and oilseed rape Brassica napus are also grown. Wheat yields are 2.5–3.5 tonnes ha−1 without irrigation, but can be almost doubled with full irrigation (P. Eden, pers. comm., 1998). There are short rotations with little or no fallow (e.g., sunflower-1st cereal-2nd cereal). This system requires frequent use of fertiliser (130 units of N2 per hectare (P. Eden, pers. comm., 1998)) and herbicides relative to the other land-use categories. With the exception of some olive Olea europea groves, there is little tree cover. The extensive agriculture category is characterised by thin soils and the largest average farm size of the three categories (Table 1). There is no irriga-
Table 1 Agricultural statistics for the three land-use categories considered in Alentejo, Portugal (source: Cordovil, 1993). Intensive Extensive Montado Mean farm size (ha) (all farms) 48 Small farms 10 Medium farms 69 Large farms 310 Crop area (%) Total annual crops Winter cereals Sunflower Forage crops
161 24 151 519
66 17 154 438
81 45 20 6
42 40 0.3 2
28 21 0 7
Fallow
15
52
66
Perennial crops Olive Vines
11 10 1
Land area per tractor (ha)
58
125
194
Livestock (%) Sheep Cattle Pigs Goats
68 8 22 2
78 11 8 3
83 7 5 5
0.8 0.8 0
2 2 0
tion, and fallow area is relatively high. A typical rotation takes the form: plough fallow-1st cereal-2nd cereal-fallow-fallow, with fallow periods often lasting five years or more (Rio Carvalho et al., 1995). Wheat yields are 1.5–2.5 tonnes ha−1 , with yields at the lower end of this range being more common (P. Eden, pers. comm., 1998). Triticale Triticum aestivum × Secale cereale and oats Avena sativa are frequently grown in the extensive category, and grazed or cut for silage. The incorporation of a fallow period into the rotation, and the relatively low potential yields are associated with considerably lower annual inputs than in the intensive category. ‘Montado’ (equivalent to the Spanish ‘dehesa’) is characterised by thin soils and tree cover, dominated by holm oak Quercus rotundifolia and cork oak Q. suber. Like the extensive category, there is no irrigation and the fallow area is high. A typical rotation is similar to that of the extensive category, although the fallow stage is often longer and forage lupins Lupinus luteus may be included. Sheep Ovis aries, cattle Bos taurus and pigs Sus scrofa are kept in all three land-use categories. Zero grazing is adopted on some farms in the intensive cat-
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egory, but livestock normally graze fallows. Table 1 lists the proportion of crops and livestock in each category. Public hunting of wild game is open to all licensed hunters over much of the area, with no control on the game bags taken and without any management. However, some areas are managed as private or associative game estates, implying a control on game bags and some management measures. 2.2. Field methods A total of 115–250 m transects, starting in 1 km grid intersections and stratified by land-use categories, were walked along a random bearing. Transects were walked in the first three hours after dawn, or in the two hours before dusk, in December and April, from December 1994 to April 1997. Perpendicular distances from the transect line to each detected bird were estimated visually to the nearest 10 m. The number of birds seen together at an observation was recorded. Tree density estimates were based on counts within a belt 25 m each side of the transect line. After each spring count, the crop type and vegetation structure was sampled over the first 50 m of each transect, by plotting vegetation height on millimetric paper, and proportional cover was determined for each of seven height classes (Table 2). A Shannon diversity index of these cover classes was computed for each transect. Table 2 gives sources of other environmental variables. In April 1997, broad-leaved weed cover within cereal crops in extensive and intensive categories was estimated within a 4 m2 quadrat at 50 m intervals along each transect. Lepidoptera larvae and Orthoptera were sampled using one sample of ten sweeps (of approximately 3 m amplitude) along the start of each transect, using a 50 cm diameter net. 2.3. Analytical methods 2.3.1. Density estimates Density estimates were calculated for each land-use category using line transect sampling and the computer program DISTANCE (Buckland et al., 1993; Laake et al., 1993). For the data gathered in December, when birds were in flocks, flock size (mean 4.2, range 1–40) was accommodated within the analysis, and a hazard rate model with cosine adjustments was
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selected by the program. In April, flocks were not present and individuals or pairs seen together were recorded as single units. A half-normal model with cosine adjustments was selected by the program and applied to the April data. Analyses used (i) clusters (≥1 individual) as analytical units, (ii) ungrouped data, and (iii) untruncated perpendicular distance data. A variety of recommended robust estimators implemented by DISTANCE was used, the final model selected in each case being the one with the lowest Akaike’s Information Criterion value (Buckland et al., 1993). Differences in log-transformed density estimates (weighed by 1/variance) between years and land-use categories were tested using one-way ANOVA. 2.3.2. Environmental models In order to evaluate the main environmental factors affecting corn bunting abundance in the study area, two models (winter and spring) of corn bunting relative abundance were computed using multivariate regression and the number of buntings detected in each transect as the dependent variable. As this could have been affected by differential habitat visibility between land-use categories, it was checked before analysis whether there was a significant correlation between the visibility-corrected line transect density estimates and the mean number of buntings detected in each survey/farming system, using: (i) all cases (n = 18), and (ii) only significantly different line transect estimates (n = 6). These correlations were highly significant (rs = 0.92, p < 0.001, in the first case; rs = 1, p = 0, in the second), indicating that the number of buntings detected per transect and the line transect estimates had similar value as indices of relative abundance. Before analysis, the dependent variable was logarithmically transformed [A = log(x + 1)] to normalise the distribution of residuals and equalise the variances (Zar, 1984). Tables 2–4 list the independent variables considered and the sources of data. The data were incorporated and manipulated in a vector-based Geographic Information System (GIS-ArcCAD). A two-stage analysis was followed to generate the models, with a stepwise procedure in each stage. At the first stage, independent variables constant in each transect between years (see Table 2) and the logarithmically transformed between-year averages (A0 ) of the number of corn buntings detected per transect were
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Table 2 Independent variables considered in the environmental models of corn bunting relative abundance Variables
Sources of data sitea
Game management: managed or unmanaged Percentage of vegetation cover on the first 50 m of each transect, for the following height classes: 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, 100–400 cm, 400–800 cm Structural diversity of vegetation cover: Shannon diversity index of above Proportion of each land-use class along the transects; the land uses considered were plough, fallow, wheat, oats, rye, lupin, sunflower, and olive groves Tree density (trees/ha)a Farming system: montado, extensive agriculture, and intensive agriculturea Distance to the nearest water line (m)a Distance to the nearest dirt track (m)a Distance to the nearest road (m)a Average annual air humidity at 9 T.M.G. (%)a Average annual rainfall (mm)a Average annual temperature (◦ C)a Total length of field edges within a 500 m buffer around the starting point of the transect (m)a Diversity of land uses (Shannon index) within a 500 m buffer around the starting point of the transecta a Variables
Field work Field work
Field work Field work
Field work Field work, ERENA (1994) 1 : 25,000 topographic maps, GIS 1 : 25,000 topographic maps, GIS 1 : 25,000 topographic maps, GIS Serviço Meteorol´ogico Nacional (1975a) Serviço Meteorol´ogico Nacional (1975b) Serviço Meteorol´ogico Nacional (1975c) 1 : 30,000 aerial photographs of 1993, GIS 1 : 30,000 aerial photographs of 1993, GIS
with constant values within sites between years.
Table 3 Means ± standard errors of the independent variables constant in each 250 m transect between years Variables
Montado (n = 42)
Extensive agriculture (n = 42)
Intensive agriculture (n = 31)
Game managed sites (%) Tree density (trees/ha) Distance to water line (m) Distance to dirt track (m) Distance to road (m) Average air humidity (%) Average rainfall (mm) Average temperature (◦ C) Length of field edges within a 500 m buffer (m) Diversity of land uses within a 500 m buffer (Shannon index)
33.3 10.54 ± 0.70 88.48 ± 10.27 144.34 ± 17.19 701.62 ± 88.98 76.79 ± 0.37 60.95 ± 0.46 16.14 ± 0.08 3455.74 ± 136.54 0.39 ± 0.02
29.3 0.36 ± 0.32 81.63 ± 9.80 161.50 ± 20.42 870.02 ± 90.37 75.90 ± 0.31 59.74 ± 0.26 16.67 ± 0.15 1344.61 ± 155.48 0.20 ± 0.02
34.5 0.39 ± 0.15 102.21 ± 16.12 133.62 ± 22.24 612.67 ± 86.38 79.12 ± 0.26 63.55 ± 0.87 16.04 ± 0.05 1303.70 ± 185.70 0.18 ± 0.03
Table 4 Means ± standard errors of the independent variables not constant in each 250 m transect between years Variables
Cover diversity (Shannon index) Proportion plough Proportion fallow Proportion cereal Proportion lupin Proportion sunflower Proportion olive groves
Montado
Extensive agriculture
Intensive agriculture
Winter
Spring
Winter
Spring
Winter
Spring
– 0.04 ± 0.02 0.76 ± 0.04 0.22 ± 0.04 – – –
0.67 ± 0.04 0.03 ± 0.02 0.80 ± 0.03 0.19 ± 0.03 0.01 ± 0.01 – –
– 0.04 ± 0.02 0.70 ± 0.04 0.27 ± 0.04 – – –
0.40 ± 0.04 0.03 ± 0.01 0.71 ± 0.04 0.28 ± 0.04 – – –
– 0.49 ± 0.05 0.05 ± 0.02 0.39 ± 0.04 – – 0.04 ± 0.02
0.33 ± 0.05 0.25 ± 0.05 0.06 ± 0.02 0.68 ± 0.05 – 0.04 ± 0.02 0.04 ± 0.02
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considered exclusively. Variables entering the models (winter and spring) were selected through forward stepwise selection. In the second stage, 114 transects and two-year dummy variables were considered to account for between-site and between-year variation, and the remaining independent variables that changed between years within each transect. The dummy variables were entered at step 0, and the stepwise selection proceeded from there. Once the final models of this stage were determined, the dummy variables were replaced by the variables selected in stage 1, and ‘nested’ models with all the non-dummy variables selected in the two stages were fitted. Finally, it was checked whether the nested models successfully explained the between-transect and between-year variation by comparing the models with the dummy variables (model 1) with the nested models (model 2), using an F test, calculated as: (model 1 sum of squares − model 2 sum of squares)/(model 1 df − model 2 df)/(model 1 residual sum of squares)/(model 1 residual df), the degrees of freedom being equal to (model 1 df − model 2 df), (model 1 residual df). A correlation matrix of the selected variables was generated for the final winter and spring models. Additionally, significant differences between land-use categories for the variables selected were determined using one-way ANOVAs for the independent continuous variables and chi-square tests for the categorical ones.
3. Results 3.1. Density estimates In winter, corn bunting density was lowest in intensive, and highest in extensive categories in all three years (Table 5), but the differences were not significant among land-use categories (F2,6 = 2.89, ns), or among years (F2,6 = 1.83, ns). In the breeding season, lowest densities occurred again in intensively managed farmland. Montado supported higher breeding densities than extensive farmland in two years. Differences among land-use categories were significant (F2,6 = 11.89, p < 0.01), whereas those among years were not (F2,6 = 0.16, ns).
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3.2. Environmental models Table 6 presents the environmental models. Both nested models successfully explained the between-transect and between-year variation in corn bunting abundance, the comparisons with the two-stage models being non-significant (F104,165 = 1.33, p > 0.05 for the winter models; F114,203 = 1.10, p > 0.05 for the spring ones). Of the variables selected by the models, fallow area was significantly smaller in the intensive land-use category than in the others ANOVA (F2,270 = 98.71, p < 0.001), and temperature was higher in the extensive land-use category than the others (F2,109 = 10.76, p < 0.001). The area of oats in montado was greater than that in the other land-use categories (F2,318 = 4.31, p < 0.05). There was no significant difference in the occurrence of game management among land-use categories (χ22 = 0.25, ns). The variables selected for the winter models were all correlated (Fallow versus Intensive: r271 = −0.65; Fallow versus Temperature: r263 = 0.24; Intensive versus Temperature: r334 = −0.25; p < 0.001 for all), whereas only Oats and Intensive were significantly correlated in the spring data set (r319 = −0.12, p < 0.05). 3.3. Invertebrate abundance Mean numbers of Lepidoptera larvae per sweep net sample (± SE) did not differ between extensive (0.29 ± 0.14) and intensive cereal crops (0.23 ± 0.11, t36 = 0.023, ns), but, across both categories, their abundance was positively associated with weed cover (r36 = 0.671, p < 0.001). There was no such relationship for Orthoptera (r36 = 0.09, ns), but these were more abundant in extensive (1.06 ± 0.26) than intensive cereals (0.19 ± 0.09, t36 = 3.84, p < 0.001).
4. Discussion Both density estimates and environmental models reveal the negative association between corn bunting abundance and intensive agricultural systems. The relationship between corn bunting abundance and fallow land in winter is consistent with studies in northern
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Table 5 Relative density of corn buntings in three Alentejo farming systems in winter and spring (n = number of 250 m transects), 95% confidence limits (c.l.), total numbers of birds and the numbers of bird groups in winter (clusters) Montado
Winter 1994 1995 1996 Spring 1995 1996 1997
Extensive
Intensive
n
Density birds per km2
95% c.l.
Birds/ clusters
n
Density birds per km2
95% c.l.
Birds/ clusters
n
Density birds per km2
95% c.l.
Birds/ clusters
38 26 42
33.7 35.9 37.0
19.3–58.9 17.6–73.4 22.4–60.7
72/27 74/19 86/34
32 35 42
170.8 48.9 104.6
73.7–395.9 17.5–136.5 61.6–177.7
210/23 81/15 166/38
27 31 31
4.1 2.8 17.6
0.4–38.7 1.1–7.2 6.7–46.2
5/4 4/4 16/7
42 41 42
55.9 42.0 54.5
46.1–67.8 33.6–52.6 44.4–66.9
125 88 109
42 34 36
30.0 49.9 35.4
23.0–39.1 41.3–60.2 27.1–46.2
60 133 59
31 26 28
29.1 12.0 16.9
21.4–39.7 8.0–18.0 11.4–25.2
43 24 25
Europe (Donald and Evans, 1994), and with the findings of Diaz and Telleria (1997) in Spain. The association with fallow also explains the low winter density in intensively managed farmland. The relatively low winter densities associated with montado are also consistent with the findings of Diaz and Telleria (1997), who suggested that low densities in relatively enclosed habitats, such as montado and shrubland, might be the result of a greater risk of predation in these habitats. Whereas Diaz and Telleria (1997) report wider habitat use in winter than in the breeding season, the present data suggest the reverse. In Spain, highest breeding densities (120 birds per km2 ) were found in ‘treeless cereal cropland’ and grassland was preferred over cereals, whereas in this study, montado supports breeding densities at least as high as those of extensively managed open farmland. Tree density in Alentejo (10.54 trees/ha ± se = 0.70, range 2.40–18.40) could be lower now than in the past (Natividade, 1950; Palma et al., 1985), and than that in Spain,
because of government incentives to clear land for wheat growing in the first half of the century and later disease amongst oak trees (Vieira and Eden, 1995). The influence of private and associative game interests on breeding corn bunting abundance can be attributed to the maintenance of nesting and foraging habitats for the red-legged partridge Alectoris rufa, a species with similar habitat requirements to corn bunting. Game management is already suspected to contribute to the conservation of other non-game species, such as bustards Otis tarda and Tetrax tetrax, in Alentejo (Rio Carvalho et al., 1995). Aebischer and Ward (1997) found that British corn bunting breeding densities were positively correlated with caterpillar abundance in cereals. In Alentejo, ‘intensive’ farmland receives lower fertiliser and pesticide inputs than do cereal crops in northern Europe (Eurostat, 1997, 1998), and this may account for the fact that there was no difference in the abundance of caterpillars between ‘intensive’ and ‘extensive’ farm-
Table 6 Environmental models, TRANSECT and YEAR being dummy variables in the second-stage models Winter
Spring
First stage model (variables with constant values)
A0 = 4.64–0.78 Intensive –0.23 Temperature (r = 0.43, F2,95 = 10.61, p < 0.001)
A0 = 1.10–0.53 Intensive + 0.18 Game Management (r = 0.55, F2,95 = 20.90 p < 0.001)
Second stage model (variables with non-constant values)
A = 0.41 + TRANSECT + YEAR + 0.70 Fallow (r = 0 70 F107,165 = 1.50, p < 0.01)
A = 0.25 + TRANSECT + YEAR + 0.22 Oats (r = 0.69, F117,203 = 1.65, p < 0.001)
Nested model
A = 3.21 + 0.09 Fallow −0.44 Intensive −0.16 Temperature (r = 0.28, F3,261 = 7.33 p < 0001)
A = 0.41 + 0.25 Oats −0.21 Intensive + −0.09 Game Management (r = 0.42, F3,300 = 21.80, p < 0.001
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land. In the current study, corn buntings were observed carrying grasshoppers in the early morning (when they are probably easier to catch). Although abundance of Lepidoptera larvae appeared to be related to that of arable weeds, higher abundance of Orthoptera in extensively managed cereals could be the result of incorporation of fallows into the extensive system, which provide stable conditions over winter. As extensively managed farmland appears to offer suitable foraging habitats in both winter and spring, productivity and winter survival may be highest under this form of management. Although present in intensively managed areas in the breeding season, corn buntings in this habitat might represent sink populations (Pulliam, 1988), whose existence is dependent on extensive cereal management elsewhere in the region. In terms of corn bunting conservation, continuation of extensive management of open cereal cropland should be a priority. This habitat supports more bird species of current conservation concern than any other (Tucker and Heath, 1994; Santos, 1996; Araújo et al., 1996). Some of these species (e.g., lesser kestrel Falco naumanni, great bustard O. tarda, little bustard T. tetrax and roller Coracias garrulus) share the corn bunting’s requirement for relatively large invertebrates, such as Orthoptera. Extensive cereal production is increasingly difficult to justify in economic terms, and areas that are unsuitable for intensification are experiencing a longer fallow stage or complete abandonment, followed by scrub encroachment or afforestation (Baldock, 1991; Bignal and McCracken, 1996). Adoption of appropriately designed agri-environmental measures provides one opportunity for the conservation of corn buntings and other steppe species (Potts, 1997). In part of Alentejo, a zonal plan under EU regulation 2078/92 provides for economic measures to maintain arable management on environmental and social grounds, but implementation of such measures has been limited by a lack of state funding, and by poor ecological and economic understanding on the part of farmers. Encouragement of game management for private or associative hunting could provide one means of maintaining more ecologically beneficial practices. These issues should be addressed if the core of Europe’s corn bunting population and many of the continent’s most threatened birds are to be maintained.
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Acknowledgements This study was part of a wider project on biodiversity of European farming systems and was funded by ERENA and the European Commission Environment Programme (PL93-2239). Additional funding was provided by the Portuguese Institute for Agrarian Research (INIA) through the project PAMAF-8151. Peter Eden provided agricultural information. Nicholas Aebischer advised on statistical analysis. He, Francisco Moreira and an anonymous referee helped to improve the manuscript.
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