Hostname: page-component-89b8bd64d-dvtzq Total loading time: 0 Render date: 2026-05-07T08:08:48.768Z Has data issue: false hasContentIssue false
  • English
  • Français

The importance of landscape heterogeneity and vegetation structure for the conservation of the Ortolan Bunting Emberiza hortulana

Published online by Cambridge University Press:  05 April 2023

Franz Löffler Open the ORCID record for Franz Löffler [Opens in a new window]  and
Thomas Fartmann Open the ORCID record for Thomas Fartmann [Opens in a new window]
Franz Löffler*
Affiliation:
Department of Biodiversity and Landscape Ecology, Osnabrück University, 49076 Osnabrück, Germany
Thomas Fartmann
Affiliation:
Department of Biodiversity and Landscape Ecology, Osnabrück University, 49076 Osnabrück, Germany Institute of Biodiversity and Landscape Ecology (IBL), 48157 Münster, Germany
*
*Corresponding author: Franz Löffler; Email: franz.loeffler@uos.de
Rights & Permissions [Opens in a new window]

Summary

Over the last decades, European farmland birds have strongly declined, mainly driven by agricultural intensification. The Ortolan Bunting Emberiza hortulana has suffered one of the most severe declines among farmland specialists. In order to maintain viable populations of the species in the long run, there is a vital need for evidence-based conservation measures. The main goal of this study was to detect the key drivers of breeding-territory selection and mating success of the species in an agricultural landscape in central Europe. We found that the landscape structure within the territories of Ortolan Bunting breeding pairs strongly varied from the overall habitat availability in the study area on both the territory and home-range scales. However, the environmental conditions also differed between the territories of breeding pairs and those of unpaired males. While landscape structure played an important role in breeding-territory selection, it had only weak effects on mating success. In contrast, crop type and vegetation height at potential nesting sites were important drivers of mating success. Overall, our study revealed that Ortolan Bunting has very complex breeding-habitat requirements. Only heterogeneous agricultural landscapes where (1) suitable song posts, (2) appropriate nesting sites, and (3) sufficient foraging habitats occur in close proximity are suitable for breeding. According to the findings of our study, agri-environmental schemes should primarily facilitate low-intensity farming practices that promote landscape heterogeneity, provide suitable nesting sites, and sustain a high abundance of invertebrate prey in farmlands.

Keywords

Agricultural intensificationBird conservationFarmland managementFunctional heterogeneityHabitat degradation

Information

Type
Research Article
Information
Bird Conservation International , Volume 33 , 2023 , e55
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of BirdLife International

Introduction

Over the last decades, terrestrial ecosystems all over the world have suffered an unprecedented decline of biodiversity (Foley et al. Reference Foley, DeFries, Asner, Barford, Bonan, Carpenter and Chapin2005, Stoate et al. Reference Stoate, Báldi, Beja, Boatman, Herzon, van Doorn and de Snoo2009, Cardoso et al. Reference Cardoso, Barton, Birkhofer, Chichorro, Deacon, Fartmann and Fukushima2020). Habitat loss due to land-use change is the most relevant driver behind this dramatic development. Since large parts of Europe’s terrestrial surface are used for agriculture (CLC 2012), agro-ecosystems play an important role in biodiversity conservation (e.g. Henle et al. Reference Henle, Alard, Clitherow, Corb, Firbank, Kull and McCracken2008, Sutcliffe et al. Reference Sutcliffe, Batáry, Kormann, Báldi, Dicks, Herzon and Kleijn2015). However, since the introduction of the European Common Agricultural Policies, land use in farmlands has been strongly intensified, especially in regions with productive soils (e.g. Donald et al. Reference Donald, Sanderson, Burfield and van Bommel2006, Reif and Vermouzek Reference Reif and Vermouzek2019). Agricultural intensification is particularly reflected by an increasing level of mechanisation and use of agro-chemicals, a simplification of crop rotation, and a large-scale expansion of monocultures such as maize (Benton et al. Reference Benton, Bryant, Cole and Crick2002, Robinson and Sutherland Reference Robinson and Sutherland2002, Sauerbrei et al. Reference Sauerbrei, Ekschmitt, Wolters and Gottschalk2014, Fartmann et al. Reference Fartmann, Jedicke, Streitberger and Stuhldreher2021a). Moreover, the aggregation of many small fields into a few larger ones has frequently caused a loss of landscape heterogeneity (Robinson and Sutherland Reference Robinson and Sutherland2002, Vickery and Arlettaz Reference Vickery, Arlettaz and Fuller2012, Šalek et al. Reference Šalek, Kalinová, Daňková, Grill and Żmihorski2021).

As a consequence, recent studies have revealed rapid declines of farmland specialists across various taxa (e.g. Newton Reference Newton2004, Donald et al. Reference Donald, Sanderson, Burfield and van Bommel2006, Meyer et al. Reference Meyer, Wesche, Krause and Leuschner2013, Bubová et al. Reference Bubová, Vrabec, Kulma and Nowicki2015). Birds are well-established indicators of overall farmland biodiversity and the ecological health of agro-ecosystems (Donald et al. Reference Donald, Green and Heath2001, Morelli et al. Reference Morelli, Jerzak and Tryjanowski2014). Among birds, farmland dwellers have experienced the steepest losses (Donald et al. Reference Donald, Sanderson, Burfield and van Bommel2006, Busch et al. Reference Busch, Katzenberger, Trautmann, Gerlach, Dröschmeister and Sudfeldt2020, Burns et al. Reference Burns, Eaton, Burfield, Klvaňová, Šilarová, Staneva and Gregory2021). The main reasons for their steep decline have been structural changes in breeding habitats and a reduction in food availability and accessibility, which is especially true for insectivorous species (Vickery et al. Reference Vickery, Tallowin, Feber, Asteraki, Atkinson, Fuller and Brown2001, Benton et al. Reference Benton, Bryant, Cole and Crick2002, Schaub et al. Reference Schaub, Martinez, Tagmann-Ioset, Weisshaupt, Maurer, Reichlin and Abadi2010). Moreover, long-distant migrants among farmland birds are particularly threatened as they are also subject to further threats outside their breeding ranges (e.g. Bairlein Reference Bairlein2016, Gremion et al. Reference Gremion, Marcacci, Mazenauer, Sori, Kebede, Ewnetu and Christe2022).

The Ortolan Bunting Emberiza hortulana is a long-distance migratory bird that breeds on the ground of arable fields throughout the vast majority of its European breeding range (Cramp and Perrins Reference Cramp and Perrins1994, Glutz von Blotzheim Reference Glutz von Blotzheim2011, Menz and Arlettaz Reference Menz and Arlettaz2011). In Europe, the species has undergone a significant population decline during the last decades, mainly driven by agricultural intensification in its breeding habitats together with illegal hunting during the autumn migration and environmental changes in their wintering grounds (Menz and Arlettaz Reference Menz and Arlettaz2011, Brambilla et al. Reference Brambilla, Gustin, Vitulano, Falco, Bergero, Negri and Bogliani2017, Jiguet et al. Reference Jiguet, Robert, Lorrillière, Hobson, Kardynal, Arlettaz and Bairlein2019, BirdLife International 2021, Gremion et al. Reference Gremion, Marcacci, Mazenauer, Sori, Kebede, Ewnetu and Christe2022). As a consequence, the Ortolan Bunting is listed as a species of European conservation concern (SPEC 2) and in Annex I of the European Birds Directive (BirdLife International 2017, 2021).

Previous studies aimed to identify habitat parameters which determine the occurrence of Ortolan Buntings in agricultural landscapes (e.g. Berg Reference Berg2008, Menz et al. Reference Menz, Mosimann-Kampe and Arlettaz2009, Vepsäläinen et al. Reference Vepsäläinen, Pakkala, Piha and Tiainen2005). Since habitat selection can vary strongly between different regions (Glutz von Blotzheim Reference Glutz von Blotzheim2011), the habitat requirements of the Ortolan Bunting are still not fully understood (Menz and Arlettaz Reference Menz and Arlettaz2011). Male Ortolan Buntings exhibit high territory fidelity (Conrads Reference Conrads1969) and, in contrast to females, often remain in formerly used territories, even if they have deteriorated (Dale Reference Dale2001). As a result, Europe’s Ortolan Bunting populations are often characterised by a strongly male-biased sex ratio, which leads to reduced reproduction in fragmented populations (Dale Reference Dale2001, Steifetten and Dale Reference Steifetten and Dale2006, Jiguet et al. Reference Jiguet, Arlettaz, Bauer, Belik, Copete, Couzi and Czajkowski2016). However, still little is known about the environmental factors affecting mating success.

The main goal of this study was to detect the key drivers of (1) breeding-territory selection and (2) mating success of the Ortolan Bunting in one of its last central European breeding strongholds (Prignitz region, north-eastern Germany). To address this issue, we compared the landscape structure between territories of breeding pairs (hereinafter referred to as BP), territories of unpaired territorial males (hereinafter referred to as UM), and randomly selected control samples (hereinafter referred to as CONTROL) on two different spatial scales: territory and home range. Furthermore, we assessed the impact of crop type and vegetation structure at potential nesting sites on the mating success of territorial males. Based on the findings of our study, we derived evidence-based recommendations for farmland management that will contribute to the long-term maintenance of the strongly declining bird species in agricultural landscapes.

Methods

Study species

The Ortolan Bunting is a passerine bird species with a western Palearctic breeding range (Menz and Arlettaz Reference Menz and Arlettaz2011, BirdLife International 2021). In Europe, breeding occurs in the southern part of the boreal zone, the temperate zone, and the Mediterranean Basin (Glutz von Blotzheim Reference Glutz von Blotzheim2011, Keller et al. Reference Keller, Herrando, Vorisek, Franch, Kipson, Milanesi and Márti2020, Jiguet et al. Reference Jiguet, Arlettaz, Bauer, Belik, Copete, Couzi and Czajkowski2016). There is usually only one brood per season. However, replacement broods were reported from some areas of Europe (Conrads Reference Conrads1969, Menz and Arlettaz et al. 2011). The wintering grounds of the long-distance migrant are located in sub-Saharan Africa (Glutz von Blotzheim Reference Glutz von Blotzheim2011, Jiguet et al. Reference Jiguet, Arlettaz, Bauer, Belik, Copete, Couzi and Czajkowski2016). The population trend across its breeding range is negative (BirdLife International 2021).

The Ortolan Bunting is a ground-nesting bird that typically breeds in open habitats. The European populations are largely restricted to dry regions with well-drained soils (Conrads Reference Conrads1969, Menz and Arlettaz et al. 2011). In northern and temperate Europe, the vast majority of Ortolan Buntings breed in arable fields (Glutz von Blotzheim Reference Glutz von Blotzheim2011, Menz and Arlettaz Reference Menz and Arlettaz2011).

On average, territories of Ortolan Buntings are ~3 ha in size (Conrads Reference Conrads1969, Berg Reference Berg2008, Glutz von Blotzheim Reference Glutz von Blotzheim2011). However, for foraging, the birds also often use larger home ranges up to 30 ha (Dale and Olsen Reference Dale and Olsen2002, Šalek et al. Reference Šalek, Zeman and Václav2019). While adult Ortolan Buntings are mainly granivorous, nestlings strongly depend on invertebrate prey (mainly Lepidoptera larvae, Coleoptera, and Orthoptera) (Conrads Reference Conrads1969, Glutz von Blotzheim Reference Glutz von Blotzheim2011, Menz and Arlettaz Reference Menz and Arlettaz2011).

Study area

The study area comprises the north-western part of the Prignitz region, which is located in north-eastern Germany along the border between the federal states of Mecklenburg-Western Pomerania and Brandenburg (53°09´N, 11°46´E and 53°20´N, 11°73´E) (Figure 1). Compared with the central European average, the climate in the study area is dry and warm, with a mean annual precipitation of 615 mm and a temperature of 9.3°C (1981–2010, Station Lenzen [Elbe], 53°06´N, 11°29´E) (German Meteorological Service 2021). The landscape is characterised by a flat undulating relief on sandy soils of the Weichsel ice age (Marcinek and Nitz Reference Marcinek and Nitz1973). Arable land is the dominating land-cover type in the study area (CLC 2012). At the time of the study maize (30%) and winter rye (25%) were the predominant crop types in the study area, together covering more than half of the arable land. The remaining area of arable land was covered in equal parts by winter wheat (12%), potato (11%), winter barley (10%), and winter rape (9%). The agricultural landscape is interrupted by pine forests. Fields are frequently bordered by woody boundaries such as avenues, copses, hedgerows, tree rows, and single trees with old oaks being the dominant tree species (Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009). The study area belongs to the EU Special Protection Area (SPA) Lower Elbe Valley. Along with the adjacent Wendland region, Prignitz represents the most important breeding stronghold of the Ortolan Bunting in central Europe (Gedeon et al. Reference Gedeon, Grüneberg, Mitschke and Sudfeldt2014) (Figure 1).

Figure 1.

Breeding distribution and number of territories of the Ortolan Bunting in Germany according to Gedeon et al. (Reference Gedeon, Grüneberg, Mitschke and Sudfeldt2014) and the location of the study area (Prignitz region).

Bird surveys

Birds were sampled May–June 2012 using standardised territory mapping in five distinct subareas with a mean size (±SE) of 408 ± 67 ha (range: 162–525 ha). Each subarea was visited five times between 05h00 and 10h00 with a time interval of at least seven days between each visit (Fischer et al. Reference Fischer, Flade, Schwarz, Südbeck, Andretzke, Fischer and Sudfeldt2005). In accordance with Bibby et al. (Reference Bibby, Burgess, Hill and Mustoe2000), all observations of territorial behaviour, such as singing males, were recorded on a map (scale: 1:1,500). Simultaneous observations of territorial behaviour were used to separate territories of different birds in spatial proximity. Following the guideline provided by Andretzke et al. (Reference Andretzke, Schikore, Schröder, Südbeck, Andretzke, Fischer and Schröder and2005), territory establishment was assumed if there were at least two observations of territorial birds at a minimum interval of seven days, one of which was in the period after 20 May.

Territories were classified into (1) those occupied by BP and (2) those of UM. Territories with birds giving prolonged alarm calls and territories with other evidence for breeding (i.e. adults carrying nesting material or feeding adults) were considered BP. Territorial males that were continuously singing throughout the breeding season but did not show any signs of mating were classified as UM (Andretzke et al. Reference Andretzke, Schikore, Schröder, Südbeck, Andretzke, Fischer and Schröder and2005, Steifetten and Dale Reference Steifetten and Dale2006). To assess habitat preferences of the Ortolan Bunting, we compared BP with UM and CONTROL. The control samples were randomly selected using the tool “create random points” in ArcGIS 10.3. Altogether 55 control samples were analysed, which corresponded to the mean of the numbers of BP and UM. To make sure that the environmental conditions in the study area were adequately represented, we used stratified random sampling for the selection of the control samples (i.e. the number of control samples in the subareas was proportional to their area size).

Environmental parameters

Landscape structure

In June 2012, we recorded habitat types based on the German habitat classification scheme (Riecken et al. Reference Riecken, Finck, Raths, Schröder and Ssymank2003) (see Appendix). According to the area requirements of the Ortolan Bunting (see above), the cover of habitat types was mapped on two spatial scales, i.e. territory (100 m radius, 3.1 ha) and home range (300 m radius, 28.3 ha), around the centres of all territories and control samples (Berg Reference Berg2008, Šalek et al. Reference Šalek, Zeman and Václav2019). Since previous studies detected an effect of crop type on the territory selection of the Ortolan Bunting (e.g. Conrads Reference Conrads1969, Berg Reference Berg2008), arable fields were additionally classified according to their crop. Furthermore, the variable “woody boundary” was classified into “avenue”, “copse”, “hedgerow”, “tree row”, and “single tree”. Moreover, the habitat data were used to calculate landscape heterogeneity (H’) within territories (BP and UM) and CONTROL samples using the Shannon Index (Morelli et al. Reference Morelli, Pruscini, Santolini, Perna, Benedetti and Sisti2013, Fartmann et al. Reference Fartmann, Kämpfer, Brüggeshemke, Juchem, Klauer, Weking and Löffler2018, Schwarz et al. Reference Schwarz, Trautner and Fartmann2018, Brüggeshemke et al. Reference Brüggeshemke, Drung, Löffler and Fartmann2022):

$$ {H}^{\prime}\hskip0.35em =\hskip0.35em \sum \limits_i{p}_i\;\mathrm{\ln}\;{p}_i\;\mathrm{with}\;{p}_i\hskip0.35em =\hskip0.35em \frac{n_i}{N} $$

where N is the overall area of the territory or home range and ni is the area of each habitat type within the two spatial levels. To avoid overfitting, habitat types covering an area of less than 1% either within territories or home ranges were excluded from all following analyses.

Vegetation structure

We investigated the vegetation structure of potential nesting sites during the time of territory establishment (i.e. first observation date) for both BP and UM (see Appendix). In particular, we recorded crop type, vegetation cover (%), and height (cm) in the fields bordering the main song post of territorial males. Within these fields, vegetation cover and height were determined as the average value of three randomly chosen plots (2 m × 2 m) per field. Since nesting sites of Ortolan Buntings are usually situated close to their song posts (Conrads Reference Conrads1968), these plots were situated within a distance of 5–20 m from the field edge (hereinafter referred to as potential nesting sites). The data of crop type at potential nesting sites were additionally used to calculate the Jacobs Index (Jacobs Reference Jacobs1974): D = (r – p) /(r + p – 2rp), where r is the proportion of a crop type used by BP and UM, respectively, and p the overall proportion of the crop type in the study area.

Statistical analysis

All statistical analyses were performed using R 3.4.1. (R Development Core Team 2021). In order to account for possible spatial autocorrelation, “subarea” was set as a random factor in all models. We additionally calculated Moran’s I statistics to test for spatial autocorrelation in model residuals using the R package lctools (Kalogirou Reference Kalogirou2020). To avoid multicollinearity, strongly intercorrelated variables were excluded prior to all multivariable analyses (Spearman rank correlations |rs| >0.6) (see Appendix) (Dormann et al. Reference Dormann, Elith, Bacher, Buchmann, Carl, Carré and García Marquéz2013).

In all multivariable analyses, we applied model averaging based on an information–theoretic approach (Grueber et al. Reference Grueber, Nakagawa, Laws and Jamieson2011). To identify the most important predictors of territory selection and mating success, all possible combinations of environmental parameters were tested resulting in a set of different candidate models. These candidate models were ranked based on Akaike’s information criterion corrected for small sample sizes (AICC) using the “dredge” function (R package MuMIn) (Bartón Reference Bartón2020). Only top-ranked models within ΔAICC <3 were used for model averaging (Grueber et al. Reference Grueber, Nakagawa, Laws and Jamieson2011). Uninformative parameters were excluded from the model-averaging procedure (Arnold Reference Arnold2010). Model validation was performed using diagnostic plots (normality of residuals [Normal Q-Q] and non-linear patterns of residuals [residuals vs. fitted values]).

Differences in the area of habitat types and landscape heterogeneity between BP, UM, and CONTROL were assessed using univariate Linear Mixed-effects Models (LMMs) for each of the two spatial scales (territory and home range) (R package: lme4) (Bates et al. Reference Bates, Maechler, Bolker and Walker2021). The respective environmental parameter was used as response variable, and territory type (i.e. BP, UM, and CONTROL, respectively) was included as a categorical predictor. The overall statistical significance of each model was assessed by likelihood ratio tests (type III test). Pairwise comparisons of territory types were carried out using Tukey contrasts as a post-hoc test (R package multcomp) (Hothorn et al. Reference Hothorn, Bretz, Westfall, Heiberger, Schützenmeister and Scheibe2021).

To analyse the effects of landscape structure on breeding-territory selection (BP vs. CONTROL) and mating success (BP vs. UM), we applied multivariable Generalised Linear Mixed-effects Models (GLMMs) with a binomial error distribution (R package: lme4) (Bates et al. Reference Bates, Maechler, Bolker and Walker2021) (results based on top-ranked models within ΔAICC <3). In these analyses, we ran three different model sets for each of the two spatial scales (territory and home range): (1) a habitat-type model including the area of each of the 10 major habitat types (e.g. arable land, woody boundary, improved grassland, etc.); (2) a song-post model containing the area of potential song posts (i.e. area of each of the five subtypes of woody boundary); (3) a crop-type model including the area of each of the seven crop types (Table 1).

Table 1.

Differences in the area of habitat types (mean ± SE, ha) in territories of breeding pairs (BP), unpaired males (UM), and control samples (CONTROL) on the territory and home-range scales. Differences between the groups were tested using univariable Linear Mixed-effects Models (LMM) with the habitat type as a response variable, territory type as a predictor, and subarea as a random factor. Significant differences are highlighted in bold type (***P <0.001, **P <0.01, *P <0.05, n.s. = not significant). Results of pairwise comparisons are indicated by different letters (P <0.05).

To assess the impact of vegetation structure (i.e. crop type and vegetation height, which was strongly correlated with vegetation cover, see Table 1) at potential nesting sites on mating success (BP vs. UM), we applied a multivariable GLMM with a binomial error distribution (R package: lme4) (Bates et al. Reference Bates, Maechler, Bolker and Walker2021); results based on top-ranked models within ΔAICC <3. Winter rye served as the baseline in the categorical predictor variable “crop type”. As explorative data analysis suggested a hump-shaped relationship between mating success and vegetation height, the predictor variable “vegetation height” was centred and squared prior to the GLMM analyses. Furthermore, differences in absolute frequencies of crop types at potential nesting sites of BP and UM were compared using Fisher’s exact test.

Results

Abundance of the Ortolan Bunting

In total, we recorded 111 Ortolan Bunting territories in the five subareas, which corresponded to an abundance of 0.54 territories/10 ha. However, BP (N = 60) accounted for only 55% of the territories detected. The remaining 45% were occupied by UM (N = 51).

Differences in environmental conditions between territories and control

Environmental conditions strongly differed between BP, UM, and CONTROL, both on the territory and home-range scales (Table 1, Figure 2). Overall, territories of both BP and UM were characterised by a higher landscape heterogeneity on the territory but not the home-range scale in comparison with CONTROL (Figure 2). Arable land, with maize, winter rye, and winter wheat as the prevailing crops, was the dominant habitat type in BP, UM, and CONTROL on both spatial scales (Table 1). While the overall area of arable land did not differ on the territory scale, the home ranges of both BP and UM were characterised by larger areas of arable land compared with CONTROL. Our study also revealed distinct differences in the cover of several crop types, especially on the home-range scale (for more details see Table 1). Apart from that, we generally found similar patterns concerning the other parameters of landscape structure on the two spatial scales. In particular, the overall area of woody boundaries was larger in BP and UM than in CONTROL, both on the territory and home-range scales. These differences were also reflected in the consideration of landscape elements representing suitable song posts (e.g. avenues, copses, and solitary trees, for more details see Table 1). While the area of field margins clearly increased from CONTROL to UM to BP, the area of semi-natural grasslands was smaller in BP and UM compared with CONTROL. In addition, we detected a larger area of paths/roads on the territory scale in BP and UM compared with CONTROL.

Figure 2.

Differences in landscape heterogeneity (Shannon Index H’) on the (a) territory and (b) home-range scales. Significant differences between the groups (P <0.05) are indicated by different letters.

Effects of landscape structure on breeding-territory selection (BP vs. CONTROL)

There were some similarities but also some differences in the environmental parameters determining breeding-territory selection on the territory and home-range scales (Table 2, Figure 3). On both scales the likelihood of territory establishment increased with area of woody boundaries and field margins (habitat-type model), the area of avenues and copses (song-post model), and the area of winter barley and winter wheat (crop-type model). On the territory scale, the likelihood of territory establishment additionally increased with the area of tree rows, single trees, and hedgerows, which represent other potential song posts (song-post model). On the home-range scale, we also found a positive relationship between breeding-territory selection and the area of arable land (habitat model) and the area of winter rye and potatoes (crop-type model). Even though model accuracy (i.e. marginal and conditional R²) was generally high (Nakagawa et al. Reference Nakagawa, Johnson and Schielzeth2017), models performed better on the territory scale.

Table 2.

Relationship between breeding-territory selection (N BP = 60 vs. N CONTROL = 55, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) and landscape structure on the territory and home-range scales. “Subarea” was set as a random factor in all models. Model-averaged coefficients were derived from the top-ranked models (ΔAICC <3). R² m = variance explained by fixed effects, R²c = variance explained by both fixed and random effects (Nakagawa et al. Reference Nakagawa, Johnson and Schielzeth2017). Moran’s I statistics were based on global models (Kalogirou Reference Kalogirou2020). Significance levels are indicated as ***P <0.001, **P <0.01, *P <0.05.

Figure 3.

Probability of breeding-territory selection (N BP = 60 vs. N CONTROL = 55, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) in relation to significant habitat types on the territory and home-range scales (see Table 2 for detailed GLMM statistics).

Effects of landscape and vegetation structure on mating success (BP vs. UM)

In contrast to the territory selection of BP (see above), we observed only weak effects of landscape structure on mating success (Table 3). On the territory scale, the likelihood of mating increased with the area of field margins (habitat-type model) and avenues (song-post model). We found no effects of landscape structure on the home-range scale.

Table 3.

Relationship between mating success (N BP = 60 vs. N UM = 51, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) and landscape structure on the territory and home-range scales. “Subarea” was set as a random factor in all models. Model-averaged coefficients were derived from the top-ranked models (ΔAICC <3). R² m = variance explained by fixed effects, R²c = variance explained by both fixed and random effects (Nakagawa et al. Reference Nakagawa, Johnson and Schielzeth2017). Moran’s I statistics were based on global models (Kalogirou Reference Kalogirou2020). Significance levels are indicated as ***P <0.001, **P <0.01, *P <0.05, n.s. = not significant.

Crop type and vegetation height at potential nesting sites (field bordering the main song post of a territorial male, see above) had clear effects on mating success (Figure 4, Table 4). Almost one-third of BP was found in winter rye and one-third in winter wheat (Figure 4a). The remaining third of BP mainly occurred in winter-barley and potato fields. While the proportion of UM was similar in winter rye and potato, they were found at a significantly higher frequency in maize and winter rape and a lower frequency in winter wheat. Compared with the overall availability in the study area, BP showed a preference for winter wheat while maize and rape were clearly underrepresented in territories of BP (Figure 4b). The GLMM analysis revealed that occurrence of winter wheat and maize as well as crop height were the main predictors of mating success (Table 4). The occurrence of winter wheat and an intermediate vegetation height at potential nesting sites, which was strongly intercorrelated with vegetation cover (P <0.05, r s = 0.76), fostered the likelihood of mating. In contrast, the occurrence of maize had a negative effect on mating success. Overall, the model had a very high explanatory power.

Figure 4.

(a) Proportion of territories and (b) Jacobs Index indicating preferences for crop types at potential nesting sites within territories of breeding pairs (BP) (N = 60,) and unpaired males (UM) (N = 51,) in relation to the overall availability within the study area (zero line). Differences in the frequencies between BP and UM were tested using Fisher’s exact test. Significance levels are indicated as **P <0.01, *P <0.05.

Table 4.

Relationship between mating success (N BP = 60 vs. N UM = 51, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) and vegetation structure and crop type at potential nesting sites (field bordering the main song post). “Subarea” was set as a random factor. Model-averaged coefficients were derived from the top-ranked models (ΔAICC <3). R² m = variance explained by fixed effects, R²c = variance explained by both fixed and random effects (Nakagawa et al. Reference Nakagawa, Johnson and Schielzeth2017). Moran’s I statistics were based on global models (Kalogirou Reference Kalogirou2020). ^2 = variable was centred and squared. Significance levels are indicated as *P <0.05, n.s. = not significant.

Discussion

Our study revealed that the landscape structure within territories of Ortolan Bunting BP strongly varied from the overall habitat availability in the study area on both the territory and home-range scales. However, the environmental conditions also differed between BP and UM. All territories were located in the agricultural landscape. Arable land was the prevailing habitat type in territories and positively affected the habitat selection of BP on the home-range scale. Moreover, the likelihood of BP occurrence increased with a larger area of woody boundaries and field margins on both spatial scales. While landscape structure strongly affected the selection of breeding territories, it had only weak effects on mating success. However, mating success strongly depended on crop type and vegetation height at potential nesting sites.

The Ortolan Bunting has very complex breeding-habitat requirements, in particular on the territory scale. Only heterogeneous agricultural landscapes where suitable song posts, appropriate nesting sites, and sufficient foraging habitats are available in close proximity are suitable for breeding (e.g. Berg Reference Berg2008, Glutz von Blotzheim Reference Glutz von Blotzheim2011, Menz and Arlettaz Reference Menz and Arlettaz2011, Brambilla et al. Reference Brambilla, Gustin, Vitulano, Negri and Celada2016). In line with these studies, we found that territories of BP and UM had a higher landscape heterogeneity on the territory scale. Since we found no differences in landscape heterogeneity and much lower explanatory power of the habitat-type and song-post models on the home-range scale, it seems that the species’ habitat requirements are generally less complex at this spatial level.

In our study, the likelihood of breeding-territory establishment increased with the availability of woody boundaries on both the territory and home-range scales. In the song-post model, the area of all five subtypes of woody boundaries had a positive effect on territory establishment on the territory scale. On the home-range scale, however, only the area of avenues and copses fostered territory establishment. Prominent trees providing an all-round view are the preferred song posts of Ortolan Bunting males (Conrads Reference Conrads1969, Berg Reference Berg2008, Menz and Arlettaz Reference Menz and Arlettaz2011, Vepsäläinen et al. Reference Vepsäläinen, Pakkala, Piha and Tiainen2005). Consequently, the strong relationship between breeding-territory selection and the area of several types of woody boundaries on the territory scale can mainly be explained by the role woody boundaries play as song posts. In the study area, oaks are among the most dominant trees within the woody boundaries and are also most frequently used as song posts (Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009). However, oak-rich woody boundaries may also serve as important foraging habitats for the Ortolan Bunting on both spatial scales. In central Europe, oaks are among the trees with the highest diversity of phytophagous insects and provide a high abundance of prey during the breeding season (Conrads Reference Conrads1969, Brändle and Brandl Reference Brändle and Brandl2001). The diet of young nestlings of Ortolan Buntings mainly consists of Lepidoptera caterpillars (Conrads Reference Conrads1969, Glutz von Blotzheim Reference Glutz von Blotzheim2011). These are known to be an abundant and high-quality food source during the hatching period of nestlings, especially the larvae of the Green Oak Tortrix (Tortrix viridana) (Conrads Reference Conrads1969).

In the habitat-type models, the area of field margins additionally fostered breeding-territory establishment on both spatial scales. This is likely due to their importance as foraging habitats. For older chicks of the Ortolan Bunting, Coleoptera and Orthoptera are of more importance as food sources (Glutz von Blotzheim Reference Glutz von Blotzheim2011). Within agricultural landscapes, field margins foster the abundance of both insect groups and generally increase the availability of arthropod prey for insectivorous farmland birds (e.g. Kuiper et al. Reference Kuiper, Ottens, Cenin, Schaffers, van Ruijven, Koks and Berendse2013). This is especially true for nutrient-poor field margins with a high phytodiversity, which are still widespread in the study area (Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009).

Moreover, the likelihood of breeding-territory establishment increased with the overall area of arable land on the home-range scale. Since the home range is mainly used for foraging (Dale and Olsen Reference Dale and Olsen2002), we assumed that the predictors on this spatial scale mainly reflect preferences for potential foraging habitats. When foraging on the ground, Ortolan Buntings prefer habitats rich in bare ground such as arable fields with sparse vegetation cover (Menz et al. Reference Menz, Mosimann-Kampe and Arlettaz2009). However, their suitability as foraging habitat differs depending on the crop type. Accordingly, we also found effects of the area of various crops in the crop-type models. The likelihood of territory establishment increased with the area of winter barley and winter wheat on both spatial scales. Larger areas of winter rye and potatoes additionally fostered territory establishment on the home-range scale. Due to their open vegetation structure at the beginning of the breeding season, these crops appear generally suitable for foraging (Menz et al. Reference Menz, Mosimann-Kampe and Arlettaz2009). However, the preferences for larger areas of winter barley and winter wheat fields on the territory scale also reflect the breeding requirements of the species (see below, Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009).

It should be noted that the analyses considering landscape structure may be subject to some statistical issues. Since territories of farmland birds are often clustered along linear landscape elements such as woody boundaries, spatial autocorrelations between territories should be carefully checked to ensure statistical independence of bird territories in studies such as ours (Dormann et al. Reference Dormann, McPherson, Araújo, Bivand, Bolliger, Carl and Davies2007). Given the lack of spatial autocorrelation in our study, we think that it is appropriate to tolerate certain spatial overlaps between territories in favour of a larger sample size (Zuckerberg et al. Reference Zuckerberg, Cohen, Nunes, Bernath-Plasted, Clare, Gilbert and Kozidis2020). Although the analyses considering the two spatial scales revealed different results in our study, it is obvious that the factors on the territory and home range are not independently affecting Ortolan Bunting occurrence. Nevertheless, they were considered separately to avoid multicollinearity among predictors and to reflect the different habitat requirements of the Ortolan Bunting on the territory and home-range scales, respectively.

While mating success was weakly affected by landscape structure, it was strongly driven by crop type and vegetation height at potential nesting sites. Almost two-thirds of BP occurred in winter rye or winter wheat. In contrast, maize and winter rape at potential nesting sites were almost completely avoided. While UM were less frequently found in winter wheat, their proportion was clearly higher in maize and winter rape. In the GLMM analysis, an intermediate crop height and winter wheat at potential nesting sites increased the likelihood of mating, while maize reduced it. Our findings corroborate results of previous studies showing that an arable field’s suitability for breeding of the Ortolan Bunting depends on vegetation structure, which is closely related to crop type (Berg Reference Berg2008, Morelli et al. Reference Morelli, Santolini and Sisti2012, Šalek et al. Reference Šalek, Zeman and Václav2019). On the one hand, a warm microclimate is of crucial importance for the successful development of Ortolan Bunting chicks under the cool climatic conditions of central Europe (Vepsäläinen et al. Reference Vepsäläinen, Pakkala, Piha and Tiainen2005, Glutz von Blotzheim Reference Glutz von Blotzheim2011). On the other hand, the vegetation has to provide sufficient shelter against predators and adverse weather conditions (Conrads Reference Conrads1969). Conrads (Reference Conrads1968) pointed out that the vegetation height within fields should not exceed 30 cm during the period of territory establishment. Moreover, Šalek et al. (Reference Šalek, Zeman and Václav2019) found that an intermediate cover of bare ground was preferred by Ortolan Buntings. Accordingly, the negative effect of maize is due to its very sparse vegetation during the time of territory establishment in the study area (Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009, Menz and Arlettaz Reference Menz and Arlettaz2011). However, winter cereals in general and winter wheat with its lower vegetation height in particular generally provide suitable nesting sites (Bernardy Reference Bernardy, Dziewiaty, Spalik and Südbeck2008, Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009). In addition to winter cereals, potato fields were colonised by BP, although to a lesser extent. Although they hardly provide vegetation cover at the beginning of the breeding season, their structure appears suitable for breeding from the end of May onwards (Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009). Therefore, it can be assumed that they are especially suitable for late broods (e.g. replacement broods, second broods) (Glutz von Blotzheim Reference Glutz von Blotzheim2011).

The main goal of this study was to detect key drivers of territory selection and mating success of the Ortolan Bunting in agricultural landscapes. In conclusion, our study has shown that only heterogeneous agricultural landscapes where suitable song posts, appropriate nesting sites, and sufficient foraging habitats occur in close proximity are suitable for breeding. In particular, the presence of oak-rich woody boundaries, field margins, and winter-cereal fields with an intermediate vegetation height promoted breeding territory establishment in the study area. Woody boundaries are important song posts, and cereal fields provide potential nesting habitats for the Ortolan Bunting (Bellenhaus and Fartmann Reference Bellenhaus and Fartmann2009, Glutz von Blotzheim Reference Glutz von Blotzheim2011). In addition, all three habitat types are likely used for foraging during the breeding period (Menz and Arlettaz Reference Menz and Arlettaz2011).

Implications for conservation

Ortolan Bunting males generally arrive earlier on the breeding grounds than females (Conrads Reference Conrads1969, Yosef and Tryanowski Reference Yosef and Tryjanowski2002). Additionally, they have much higher territory fidelity and their territory selection is primarily driven by the availability of song posts (Conrads Reference Conrads1969, Keusch Reference Keusch1991, Yosef and Tryanowski Reference Yosef and Tryjanowski2002, see also above). On the contrary, nesting-site selection is carried out by the females and mainly depends on crop type and its vegetation structure (Keusch Reference Keusch1991, Menz and Arlettaz Reference Menz and Arlettaz2011). The high territory fidelity of the males makes the Ortolan Bunting very sensitive to environmental alterations, especially structural changes at potential nesting sites (e.g. large-scale changes in cultivated crops). As in previous studies (Dale Reference Dale2001, Steifetten and Dale et al. Reference Dale, Steifetten, Osiejuk, Losak and Cygan2006), the population in the study area was characterised by a strongly male-biased sex ratio. UM accounted for 45% of the territories, which indicates that reproduction is reduced and the population could decline further in the future (Jiguet et al. Reference Jiguet, Arlettaz, Bauer, Belik, Copete, Couzi and Czajkowski2016).

During recent years, the large-scale expansion of energy crops such as maize and rape have contributed to rapid agricultural intensification in central European agricultural landscapes including the study area (Sauerbrei et al. Reference Sauerbrei, Ekschmitt, Wolters and Gottschalk2014, LfU Brandenburg Reference Brandenburg2018, Fartmann et al. Reference Fartmann, Jedicke, Streitberger and Stuhldreher2021a,Reference Fartmann, Kämpfer, Brüggeshemke, Juchem, Klauer, Weking and Löfflerb). This results in landscape homogenisation, a loss of nesting sites, and a reduction of food supply for the Ortolan Bunting (Menz and Arlettaz Reference Menz and Arlettaz2011). Since, the bird surveys underlying our study were conducted in 2012, it can be expected that the situation has further worsened further during the last 10 years. In most parts of central Europe, the species still exhibits a strong decline. Accordingly, the threat status of the Ortolan Bunting in Germany changed from “Vulnerable” to “Endangered” in the most recent Red List assessment (Ryslavy et al. Reference Ryslavy, Bauer, Gerlach, Hüppop, Stahmer, Südebeck and Sudfeldt2020). Therefore, measures are urgently needed to promote suitable breeding conditions and increase food availability in agricultural landscapes. Agri-environmental schemes should primarily facilitate low-intensity farming practices that promote landscape heterogeneity and sustain a high abundance of invertebrate prey in farmland habitats (e.g. Birrer et al. Reference Birrer, Spiess, Herzo, Markus, Kohli and Lugrin2007, Brambilla et al. Reference Brambilla, Gustin, Vitulano, Falco, Bergero, Negri and Bogliani2017, Ponce et al. Reference Ponce, Bravo and Alonso2014). Increasing crop diversity towards larger areas of summer cereals, the reduction of fertiliser application, and wider spaces between sowing rows in winter cereals may increase the availability of suitable nesting and foraging sites within arable fields (Conrads Reference Conrads1969, Vepsäläinen et al. Reference Vepsäläinen, Pakkala, Piha and Tiainen2005, Menz and Arlettaz Reference Menz and Arlettaz2011). Additionally, we recommend reducing the use of pesticides to increase invertebrate abundance, which very likely would have broad benefits for the Ortolan Bunting and overall farmland biodiversity. Moreover, field margins cultivated with legume–cereal mixes provide well-suited refuges for breeding birds (Bernardy et al. Reference Bernardy, Dziewiaty, Spalik and Südbeck2008). Since they can also provide suitable breeding conditions for the species in otherwise unsuitable crops (e.g. maize and rape), such measures should be increasingly part of agri-environmental schemes. It has been shown that the implementation of conservation measures locally contributed to a stabilisation of the Ortolan Bunting populations (Jiguet et al. Reference Jiguet, Arlettaz, Bauer, Belik, Copete, Couzi and Czajkowski2016). Since the current Common Agricultural Policies of the EU largely fail to bring benefits for farmland biodiversity, large-scale conservation actions which may help to maintain viable populations of the Ortolan Bunting in the long run are still lacking.

Acknowledgements

We would like to thank Dr Mattia Brambilla and an anonymous reviewer for helpful comments on an earlier version of the manuscript.

Supplementary Materials

To view supplementary material for this article, please visit http://doi.org/10.1017/S0959270923000023.

References

Andretzke, H., Schikore, T. and Schröder, K. (2005) Artsteckbriefe: Ortolan (Emberiza hortulana). Pp. 135–695 in P. Südbeck, H. Andretzke, S. Fischer, K. Schröder and, C. Sudfeld eds. Methodenstandards zur Erfassung der Brutvögel Deutschlands. Dachverband Deutscher Avifaunisten, Radolfzell, Germany.Google Scholar
Arnold, T. (2010) Uninformative parameters and model selection using Akaike’s Information Criterion. J, Wildl. Manage. 74 : 1175–1178.CrossRefGoogle Scholar
Bairlein, F. (2016) Migratory birds under threat. Science 354 : 547–548.CrossRefGoogle ScholarPubMed
Bartón, K. (2020) Multi-Model Inference (Package MuMIn: version 1.43.17). https://cran.r-project.org/web/packages/MuMIn.Google Scholar
Bates, D., Maechler, M., Bolker, B. and Walker, S. (2021) Linear Mixed-Effects Models Using Eigen and S4 (Package lme4: version 1.1-19). http://CRAN.R-project.org/web/packages/lme4.Google Scholar
Bellenhaus, V. and Fartmann, T. (2009) Die habitatbindung des Ortolans (Emberiza hortulana) in der Prignitz (NW-Brandenburg). Naturschutz Landschaftspflege Niedersachsen 45 : 59–63.Google Scholar
Benton, T. G., Bryant, D. M., Cole, L. and Crick, H. Q. P. (2002) Linking agricultural practice to insect and bird populations: a historical study over three decades. J. Appl. Ecol. 39 : 673–687.CrossRefGoogle Scholar
Berg, Å. (2008) Habitat selection and reproductive success of ortolan buntings Emberiza hortulana on farmland in central Sweden – the importance of habitat heterogeneity. Ibis 150 : 565–573.CrossRefGoogle Scholar
Bernardy, P., Dziewiaty, K., Spalik, S. and Südbeck, P. (2008) Was charakterisiert ein gutes Ortolan Emberiza hortulana-Revier? Eine analyse als grundlage für schutzbemühungen. Vogelkundliche Bericht Niedersachsen 40 : 127–138.Google Scholar
Bibby, J., Burgess, N., Hill, D. and Mustoe, S. (2000) Bird census techniques. Second edition. London, UK: Academic Press.Google Scholar
BirdLife International (2017) European birds of conservation concern: population trends and national responsibilities. Cambridge, UK: BirdLife International,Google Scholar
BirdLife International (2021) European Red List of birds. Luxembourg: Publication Office of the European Union.Google Scholar
Birrer, S., Spiess, M., Herzo, F., Markus, J., Kohli, L. and Lugrin, B. (2007) The Swiss agri-environment scheme promotes farmland birds: but only moderately. J. Ornithol. 148 : 295–303.CrossRefGoogle Scholar
Brambilla, M., Gustin, M., Vitulano, S., Falco, S., Bergero, V., Negri, I., Bogliani, G., et al. (2017) Sixty years of habitat decline: impact of land-cover changes in northern Italy on the decreasing ortolan bunting Emberiza hortulana. Reg. Environ. Change 17 : 323–333.CrossRefGoogle Scholar
Brambilla, M., Gustin, M., Vitulano, S., Negri, I. and Celada, C. (2016) A territory scale analysis of habitat preferences of the declining Ortolan Bunting Emberiza hortulana. Bird Study 63 : 52–57.CrossRefGoogle Scholar
Brändle, M. and Brandl, R. (2001) Species richness of insects and mites on trees: expanding Southwood. J. Anim. Ecol. 70 : 491–504.CrossRefGoogle Scholar
Brüggeshemke, J., Drung, M., Löffler, F. and Fartmann, T. (2022) Effects of local climate and habitat heterogeneity on breeding-bird assemblages of semi-natural grasslands. J. Ornithol. 163 : 695–707.CrossRefGoogle Scholar
Bubová, T., Vrabec, V., Kulma, M. and Nowicki, P. (2015) Land management impacts on European butterflies of conservation concern: a review. J. Insect Conserv. 19 : 805–821.CrossRefGoogle Scholar
Burns, F., Eaton, M. A., Burfield, I. J., Klvaňová, A., Šilarová, E., Staneva, A. and Gregory, R. D. (2021) Abundance decline in the avifauna of the European Union reveals cross-continental similarities in biodiversity change. Ecol. Evol. 11 : 16647–16660.CrossRefGoogle ScholarPubMed
Busch, M., Katzenberger, J., Trautmann, S., Gerlach, B., Dröschmeister, R. and Sudfeldt, C. (2020) Drivers of population change in common farmland birds in Germany. Bird Conserv. Internatn. 30 : 335–354.CrossRefGoogle Scholar
Cardoso, P., Barton, P. S., Birkhofer, K., Chichorro, F., Deacon, C., Fartmann, T., Fukushima, C. S., et al. (2020) Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242 : 108426.CrossRefGoogle Scholar
CLC. (2012) Land Cover Data. Corine Land Cover. https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012.Google Scholar
Conrads, K. (1968) Zur ökologie des ortolans am rande der Westfälischen Bucht. Vogelwelt Beiheft 2 : 7–21.Google Scholar
Conrads, K. (1969) Beobachtungen am ortolan (Emberiza hortulana L.) in der Brutzeit. J. Ornithol. 110 : 379–420.CrossRefGoogle Scholar
Cramp, S. and Perrins, C. M. eds. (1994) The birds of the western Palearctic. Vol. IX. Oxford, UK: Oxford University Press.Google Scholar
Dale, S. (2001) Female-biased dispersal, low female recruitment, unpaired males, and the extinction of small and isolated bird populations. Oikos 92 : 344–356.CrossRefGoogle Scholar
Dale, S. and Olsen, B. F. (2002) Use of farmland by Ortolan Buntings (Emberiza hortulana) nesting on a burned forest area. J. Ornithol. 143 : 133–144.CrossRefGoogle Scholar
Dale, S., Steifetten, O., Osiejuk, T. S., Losak, K. and Cygan, J. P. (2006) How do birds search for breeding areas at the landscape level? Interpatch movements of male ortolan buntings. Ecography 29 : 886–898.CrossRefGoogle Scholar
Donald, P. F., Green, R. E. and Heath, M. F. (2001) Agricultural intensification and the collapse of Europe’s farmland bird populations. Proc. R. Soc. Lond. B. Biol. Sci. 268 : 25–29.CrossRefGoogle Scholar
Donald, P. F., Sanderson, F. J., Burfield, I. J. and van Bommel, F. P. J. (2006) Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agric. Ecosyst. Environ. 116 : 189–196.CrossRefGoogle Scholar
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., García Marquéz, J. R., et al. (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36 : 27–46.CrossRefGoogle Scholar
Dormann, C. F., McPherson, J. M., Araújo, M. B., Bivand, R., Bolliger, J., Carl, G., Davies, R. G., et al. (2007) Methods to account for spatial autocorrelation in the analyses of species distributional data: a review. Ecography 30 : 609–628.CrossRefGoogle Scholar
Fartmann, T., Jedicke, E., Streitberger, M. and Stuhldreher, G. (2021a) Insktensterben in Mitteleuropa. Ursachen und gegenmaßnahmen. Ulmer, Stuttgart, Germany.Google Scholar
Fartmann, T., Kämpfer, S., Brüggeshemke, J., Juchem, M., Klauer, F., Weking, S. and Löffler, F. (2018) Landscape-scale effects of Christmas-tree plantations in an intensively used low-mountain landscape – applying breeding bird assemblages as indicators. Ecol. Indic. 94 : 409–419.CrossRefGoogle Scholar
Fartmann, T., Poniatowski, D. and Holtmann, L. (2021b) Habitat availability and climate warming drive changes in the distribution of grassland grasshoppers. Agric. Ecosyst. Environ. 320 : 107565.CrossRefGoogle Scholar
Fischer, S., Flade, M. and Schwarz, J. (2005) Revierkartierung. Pp. 47–58 in P. Südbeck, H. Andretzke, S. Fischer, K. Schröder and Sudfeldt, C. eds. Methodenstandards zur Erfassung der Brutvögel Deutschlands. Dachverband Deutscher Avifaunisten, Radolfzell, Germany.Google Scholar
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., et al. (2005) Global consequences of land use. Science 309 : 570–574.CrossRefGoogle ScholarPubMed
Gedeon, K. C., Grüneberg, C., Mitschke, A. and Sudfeldt, C. (2014) Atlas Deutscher brutvogelarten. Münster, Germany: Stiftung Vogelmonitoring Deutschland and Dachverband Deutscher Avifaunisten.Google Scholar
German Meteorological Service, (2021) DWD Climate Data Center. https://opendata.dwd.de/climate_environment/CDC/.Google Scholar
Glutz von Blotzheim, U. N. ed. (2011) Handbuch der Vögel Mitteleuropas. Wiebelsheim, Germany: Aula Verlag.Google Scholar
Gremion, J., Marcacci, G., Mazenauer, J., Sori, T., Kebede, F., Ewnetu, M., Christe, P., et al. (2022) Habitat preferences of the Ortolan Bunting (Emberiza hortulana) in its prime wintering grounds, the cereal-dominated Ethiopian Highlands. Ibis 164 : 74–87.CrossRefGoogle Scholar
Grueber, C. E., Nakagawa, S., Laws, R. J and Jamieson, I. G. (2011) Multi-model inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24 : 699–711.CrossRefGoogle Scholar
Henle, K., Alard, D., Clitherow, J., Corb, P., Firbank, L., Kull, T., McCracken, D., et al. (2008) Identifying and managing the conflicts between agriculture and biodiversity conservation in Europe – a review. Agric. Ecosyst. Environ. 124 : 60–71.CrossRefGoogle Scholar
Hothorn, T., Bretz, F., Westfall, P., Heiberger, R. M., Schützenmeister, A. and Scheibe, S. (2021) Simultaneous Inference in General Parametric Models (R Package multcomp). https://cran.r-project.org/web/packages/multcomp/index.html.Google Scholar
Jacobs, J. (1974) Quantitative measurement of food selection: a modification of the forage ratio and Ivlev’s Electivity Index. Oecologia 14 : 413–417.CrossRefGoogle ScholarPubMed
Jiguet, F., Arlettaz, R., Bauer, H.-G., Belik, V., Copete, J. L., Couzi, L., Czajkowski, M. A., et al. (2016) An update of the European breeding population sizes and trends of the Ortolan Bunting (Emberiza hortulana). Ornis Fenn. 93 : 186–196.Google Scholar
Jiguet, F., Robert, A., Lorrillière, R., Hobson, K. A., Kardynal, K. J., Arlettaz, R., Bairlein, F., et al. (2019) Unravelling migration connectivity reveals unsustainable hunting of the declining ortolan bunting. Sci. Adv. 5 : eaau2642.CrossRefGoogle ScholarPubMed
Kalogirou, S. (2020) Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools. (Package lctools: version 0.2-8). https://cran.r-project.org/web/packages/lctools.Google Scholar
Keller, V., Herrando, S., Vorisek, P., Franch, M., Kipson, M., Milanesi, P., Márti, D., et al. (2020) European breeding bird atlas 2: distribution, abundance and change. Barcelona, Spain: European Bird Census Council and Lynx Edicions.Google Scholar
Keusch, P. (1991) Vergleichende Studie zu Brutbiologie, Jungenentwicklung, Bruterfolg und Populationsökologie von Ortolan Emberiza hortulana und Zippammer E. cia im Alpenraum, mit besonderer Berücksichtigung des unterschiedlichen Zugverhaltens. Dissertation, University of Bern.Google Scholar
Kuiper, M. W., Ottens, H. J., Cenin, L., Schaffers, A. P., van Ruijven, J., Koks, B. J., Berendse, F., et al. (2013) Field margins as foraging habitat for skylarks (Alauda arvensis) in the breeding season. Agric. Ecosyst, Environ. 17 : 10–15.CrossRefGoogle Scholar
Brandenburg, LfU. (2018) Managementplan für das FFH-Gebiet 354 Mittlere und Obere Löcknitz. Land Brandenburg, Germany.Google Scholar
Marcinek, J. and Nitz, B. (1973) Das tiefland der Deutschen Demokratischen Republik – leitlinien seiner oberflächen. Leipzig, Germany: Hermann Haack Gotha.Google Scholar
Menz, M. H. M. and Arlettaz, R. (2011) The precipitous decline of the ortolan bunting Emberiza hortulana: time to build on scientific evidence to inform conservation management. Oryx 46 : 122–129.CrossRefGoogle Scholar
Menz, M. H. M., Mosimann-Kampe, P. and Arlettaz, R. (2009) Foraging habitat selection in the last Ortolan Bunting Emberiza hortulana population in Switzerland: final lessons before extinction. Ardea 97 : 323–333.CrossRefGoogle Scholar
Meyer, S., Wesche, K., Krause, B. and Leuschner, C. (2013) Dramatic losses of specialist arable plants since the 1950s/60s – a cross regional analysis. Divers. Distrib. 19 : 1175–1187.CrossRefGoogle Scholar
Morelli, F., Jerzak, L. and Tryjanowski, P. (2014) Birds as useful indicators of high nature value (HNV) farmland in Central Italy. Ecol. Indic. 38 : 236–242.CrossRefGoogle Scholar
Morelli, F., Pruscini, F., Santolini, R., Perna, P., Benedetti, Y. and Sisti, D. (2013) Landscape heterogeneity metrics as indicators of bird diversity: determining the optimal spatial scales in different landscapes. Ecol. Indic. 24 : 372–379.CrossRefGoogle Scholar
Morelli, F., Santolini, R. and Sisti, D. (2012) Breeding habitat of red-backed shrike Lanius collurio on farmland hilly areas of Central Italy: is functional heterogeneity one important key? Ethol. Ecol. Evol. 24 : 127–139.CrossRefGoogle Scholar
Nakagawa, S., Johnson, P. C. D. and Schielzeth, H. (2017) The coefficient of determination R² and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14 : 20170213.CrossRefGoogle ScholarPubMed
Newton, I. (2004) The recent declines of farmland bird populations in Britain: an appraisal of causal factors and conservation actions. Ibis 146 : 579–600.CrossRefGoogle Scholar
Ponce, C., Bravo, C. and Alonso, J. C. (2014) Effects of agri-environmental schemes on farmland birds: do food availability measures improve patterns obtained from simple habitat models? Ecol. Evol. 14 : 2834–2847.CrossRefGoogle Scholar
R Development Core Team (2021) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.r-project.org.Google Scholar
Reif, J. and Vermouzek, Z. (2019) Collapse of farmland bird populations in an Eastern European country following its EU accession. Conserv. Lett. 12 : e12585.CrossRefGoogle Scholar
Riecken, U., Finck, P., Raths, U., Schröder, E. and Ssymank, A. (2003) Standard-biotoptypenliste für Deutschland. Second edition. Schriftenreihe für Landschaftspflege und Naturschutz 75. Bonn, Germany: Federal Agency for Nature Conservation.Google Scholar
Robinson, R. A. and Sutherland, W. J. (2002) Post-war changes in arable farming and biodiversity in Great Britain. J. Appl. Ecol. 39 : 157–176.CrossRefGoogle Scholar
Ryslavy, T., Bauer, H.-G., Gerlach, O., Hüppop, O., Stahmer, J., Südebeck, P. and Sudfeldt, C. (2020) Rote Liste der brutvögel Deutschland. Sixth edition. Berichte zum Vogelschutz 57 : 13–112.Google Scholar
Šalek, M., Kalinová, K., Daňková, R., Grill, S. and Żmihorski, M. (2021) Reduced diversity of farmland birds in homogenized agricultural landscape: a cross-border comparison over the former Iron Curtain. Agric. Ecosyst. Environ. 321 : 107628.CrossRefGoogle Scholar
Šalek, M., Zeman, V. and Václav, R. (2019) Habitat selection of an endangered European farmland bird, the Ortolan Bunting Emberiza hortulana, in two contrasting landscapes: implications for management. Bird Conserv. Internatn. 29 : 144–158.CrossRefGoogle Scholar
Sauerbrei, R., Ekschmitt, K., Wolters, V. and Gottschalk, T. K. (2014) Increased energy maize production reduces farmland bird diversity. Glob. Change Biol. Bioenergy 6 : 265–274.CrossRefGoogle Scholar
Schaub, M., Martinez, N., Tagmann-Ioset, A., Weisshaupt, N., Maurer, M. L., Reichlin, T. S., Abadi, F., et al. (2010) Patches of bare ground as a staple commodity of declining ground-foraging insectivorous farmland birds. PLoS One 5 : e13115.CrossRefGoogle ScholarPubMed
Schwarz, C., Trautner, J. and Fartmann, T. (2018) Common pastures are important refuges for a declining passerine bird in a pre-alpine agricultural landscape. J. Ornithol. 159 : 945–954.CrossRefGoogle Scholar
Steifetten, O. and Dale, S. (2006) Viability of an endangered population of ortolan buntings: the effect of a skewed operational sex ratio. Biol. Conserv. 132 : 88–97.CrossRefGoogle Scholar
Stoate, C., Báldi, A., Beja, P., Boatman, N. D., Herzon, I., van Doorn, A., de Snoo, G., et al. (2009) Ecological impacts of early 21st century agricultural change in Europe – a review. J. Environ. Manage. 91 : 22–46.CrossRefGoogle ScholarPubMed
Sutcliffe, L. M. E., Batáry, P., Kormann, U., Báldi, A., Dicks, L. V., Herzon, I., Kleijn, D., et al. (2015) Harnessing the biodiversity value of Central and Eastern European farmland. Divers. Distrib. 21 : 722–730.CrossRefGoogle Scholar
Vepsäläinen, V., Pakkala, T., Piha, M. and Tiainen, J. (2005) Population crash of the ortolan bunting Emberiza hortulana in agricultural landscapes of southern Finland. Ann. Zool. Fennici 42 : 91–107.Google Scholar
Vickery, J. and Arlettaz, R. (2012) The importance of habitat heterogeneity at multiple scales for bird in European agricultural landscapes. Pp. 177–204 in Fuller, R. ed. Birds and habitat: relationships in changing landscapes. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Vickery, J. A., Tallowin, J. R., Feber, R. E., Asteraki, E. J., Atkinson, P. W., Fuller, R. J. and Brown, V. K. (2001) The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. J. Appl. Ecol. 38 : 647–664.CrossRefGoogle Scholar
Yosef, R. and Tryjanowski, P. (2002) Differential spring migration of Ortolan Bunting Emberiza hortulana by sex and age at Eilat, Israel. Ornis Fenn. 79 : 173–180.Google Scholar
Zuckerberg, B., Cohen, J. M., Nunes, L. A., Bernath-Plasted, J., Clare, J. D. J., Gilbert, N. A., Kozidis, S. S., et al. (2020) A review of overlapping landscapes: pseudoreplication or a red herring in landscape ecology? Curr. Landsc. Ecol. Rep. 5 : 140–148.CrossRefGoogle Scholar
Figure 0

Figure 1. Breeding distribution and number of territories of the Ortolan Bunting in Germany according to Gedeon et al. (2014) and the location of the study area (Prignitz region).

Figure 1

Table 1. Differences in the area of habitat types (mean ± SE, ha) in territories of breeding pairs (BP), unpaired males (UM), and control samples (CONTROL) on the territory and home-range scales. Differences between the groups were tested using univariable Linear Mixed-effects Models (LMM) with the habitat type as a response variable, territory type as a predictor, and subarea as a random factor. Significant differences are highlighted in bold type (***P <0.001, **P <0.01, *P <0.05, n.s. = not significant). Results of pairwise comparisons are indicated by different letters (P <0.05).

Figure 2

Figure 2. Differences in landscape heterogeneity (Shannon Index H’) on the (a) territory and (b) home-range scales. Significant differences between the groups (P <0.05) are indicated by different letters.

Figure 3

Table 2. Relationship between breeding-territory selection (NBP = 60 vs. NCONTROL = 55, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) and landscape structure on the territory and home-range scales. “Subarea” was set as a random factor in all models. Model-averaged coefficients were derived from the top-ranked models (ΔAICC <3). R²m = variance explained by fixed effects, R²c = variance explained by both fixed and random effects (Nakagawa et al.2017). Moran’s I statistics were based on global models (Kalogirou 2020). Significance levels are indicated as ***P <0.001, **P <0.01, *P <0.05.

Figure 4

Figure 3. Probability of breeding-territory selection (NBP = 60 vs. NCONTROL = 55, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) in relation to significant habitat types on the territory and home-range scales (see Table 2 for detailed GLMM statistics).

Figure 5

Table 3. Relationship between mating success (NBP = 60 vs. NUM = 51, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) and landscape structure on the territory and home-range scales. “Subarea” was set as a random factor in all models. Model-averaged coefficients were derived from the top-ranked models (ΔAICC <3). R²m = variance explained by fixed effects, R²c = variance explained by both fixed and random effects (Nakagawa et al.2017). Moran’s I statistics were based on global models (Kalogirou 2020). Significance levels are indicated as ***P <0.001, **P <0.01, *P <0.05, n.s. = not significant.

Figure 6

Figure 4. (a) Proportion of territories and (b) Jacobs Index indicating preferences for crop types at potential nesting sites within territories of breeding pairs (BP) (N = 60,) and unpaired males (UM) (N = 51,) in relation to the overall availability within the study area (zero line). Differences in the frequencies between BP and UM were tested using Fisher’s exact test. Significance levels are indicated as **P <0.01, *P <0.05.

Figure 7

Table 4. Relationship between mating success (NBP = 60 vs. NUM = 51, multivariable Generalised Linear Mixed-effects Models [GLMMs] with binomial error structure) and vegetation structure and crop type at potential nesting sites (field bordering the main song post). “Subarea” was set as a random factor. Model-averaged coefficients were derived from the top-ranked models (ΔAICC <3). R²m = variance explained by fixed effects, R²c = variance explained by both fixed and random effects (Nakagawa et al.2017). Moran’s I statistics were based on global models (Kalogirou 2020). ^2 = variable was centred and squared. Significance levels are indicated as *P <0.05, n.s. = not significant.

Supplementary material: File

Löffler and Fartmann Supplementary Appendix

Löffler and Fartmann Supplementary Appendix

Download Löffler and Fartmann Supplementary Appendix(File)
File 15.5 KB