Further evidence of continent-wide impacts of agricultural

Agriculture, Ecosystems and Environment 116 (2006) 189–196 www.elsevier.com/locate/agee

Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000 Paul F. Donald a,*, Fiona J. Sanderson a, Ian J. Burfield b, Frans P.J. van Bommel b b

a RSPB, The Lodge, Sandy, Bedfordshire SG19 2DL, UK BirdLife International European Division, Droevendaalsesteeg 3, P.O. Box 127, 6700 AC Wageningen, The Netherlands

Received 8 September 2005; received in revised form 27 January 2006; accepted 14 February 2006 Available online 30 March 2006

Abstract Between 1990 and 2000, farmland birds showed a significant decline across Europe, a trend not shared by bird assemblages of other habitats over the same period. Mean trends for each farmland species in the period 1990–2000 were positively correlated with trends over the period 1970–1990, and there was little change in population trajectory for most species over the 30-year period. Of the 58 species classed by an independent assessment as being primarily birds of farmland, 41 showed negative overall mean trends across Europe in 1990–2000, 19 of them significant. There was a significant negative correlation between mean national trends of all farmland species and indices of national agricultural intensity. This relationship strengthened when the 19 declining species were considered alone, and was not apparent when only non-declining species were considered. Population trends of terrestrial non-farmland bird species over the same period were unrelated to agricultural intensity. Trends in farmland bird populations were independent of the proportion of farmland under agri-environment prescriptions. The results support earlier evidence that population trends of farmland birds across Europe can be predicted from gross national agricultural statistics. Substantial changes in agricultural policy, particularly the removal of economic incentives that lead to agricultural intensification, are required if 2010 targets for halting loss of biodiversity are to be met in an enlarged European Union. # 2006 Elsevier B.V. All rights reserved.

Keywords: Population decline; European Union; Agri-environment schemes; Cereal yield; Conservation

1. Introduction During the last three decades, there have been widespread and severe declines in populations of farmland birds and other wildlife across Europe that have been attributed to a general process of agricultural intensification (e.g. Tucker and Heath, 1994; Donald et al., 2001; Gregory et al., 2005). This may reflect the strong negative correlation between the human appropriation of net primary production and bird species diversity (Haberl et al., 2005). The process of intensification encompasses a wide range of components, including increased levels of mechanisation and chemical use, changes in the areas of different crop types, changes in the times of sowing and harvesting, the spread of monocultures, increased * Corresponding author. Tel.: +44 1767 680551; fax: +44 1767 692365.

E-mail address: paul.donald@rspb.org.uk (P.F. Donald).

0167-8809/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2006.02.007

stocking densities, changes in soil moisture and the loss of small, non-farmed habitats such as ponds and hedgerows (e.g.

Aebischer et al., 2000a; Stoate et al., 2001; Vickery et al., 2001; Robinson and Sutherland, 2002; Newton, 2004). Not only have spatial and temporal correlations between agricultural intensification and biodiversity decline been identified (e.g. Chamberlain et al., 2000; Donald et al., 2001; Gregory et al., 2005), but in a number of cases, the demographic mechanisms underlying such declines have also been elucidated (Newton, 2004; Sanderson et al., 2006).

Perhaps, the most convincing evidence of the causal relationship between agricultural intensification and bird population declines comes from experimental or semiexperimental demonstrations that reversing intensification can lead to a rapid recovery in bird populations that had declined following earlier intensification (e.g. Aebischer et al., 2000b; Peach et al., 2001). Evidence for links between

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agricultural intensification and biodiversity loss is not confined to Europe (Clay, 2004), and bird population declines have been reported on agricultural land in North America (e.g.

Brennan and Kuvlesky, 2005), Africa (e.g. So¨derstro¨m et al., 2003) and Asia (e.g. Semwal et al., 2004).

Understanding the link between agricultural intensity and farmland biodiversity is of considerable importance, because after millennia of landscape modification, farmland now constitutes the single largest habitat in Europe, comprising around 45% of the total European land area (Food and Agriculture Organisation; http://apps.fao.org/).

The Common Agricultural Policy (CAP), the main instrument of agricultural policy in the European Union, has had severe adverse effects on farmland biodiversity in the EU15 (the first 15 Member States of the EU), and could potentially have even more severe impacts in the 10 Member States that joined in May 2004 (Donald et al., 2002; BirdLife International, 2004). Newly joining Member States tend to support higher bird population densities than countries that have been subject to the CAP for longer (Sanderson et al., 2006). Although agri-environment schemes have existed across the EU since 1992, and became compulsory under the 2003 CAP reform (http://www.europa.eu.int/scadplus/leg/ en/lvb/l11089.htm), at present less than 5% of CAP spending goes towards agri-environment schemes (European Commission, 2004). Furthermore, many of these schemes do not have the restoration of farmland wildlife populations as a key objective, most have been so poorly monitored that any positive effects might be undetected, and many have been implemented on land that is already rich in biodiversity (Kleijn and Sutherland, 2003). Consequently, there has been a generally poor correlation between the distribution of agri-environment programmes and trends in wildlife populations, though the general trend has been towards an increase in biodiversity where such schemes are implemented (Kleijn et al., 2001, 2004). However, where they have been designed around evidence-based prescriptions, and properly targeted and monitored, they have been shown to deliver benefits to biodiversity (Peach et al., 2001; Evans et al., 2002). Agri-environment schemes represent the only available mechanism to reduce declines in farmland biodiversity over large areas (Vickery et al., 2004). They are therefore of importance if the 2010 targets to reduce or halt biodiversity loss, agreed in the European Union at the 2001 Gothenburg Summit, across Europe at the 2003 Kiev Summit and globally at the 2002 World Summit on Sustainable Development, are to be met. Identifying links between agricultural policies and practices and their biodiversity impacts over large geographical regions will facilitate the design of more effective agrienvironment schemes and support calls to change the CAP from an instrument that damages the environment to one that enhances it (Sanderson et al., 2006).

Because European agricultural policy, which provides the economic and policy drivers of both agricultural intensification and ecological amelioration, operates at national and supra-national scales, it is necessary to assess whether the

results of research undertaken at small spatial scales can be generalised across far wider areas. Such an assessment is particularly timely, because recent and forthcoming changes to the composition of the European Union provide an opportunity to further overhaul the CAP. A previous analysis of population trend data covering the period 1970–1990 pointed to evidence of continental-scale effects of agricultural intensification on farmland bird populations (Donald et al., 2001). In this paper, data covering the period 1990–2000 were analysed to determine whether such patterns are consistent in space and time. Declining and vulnerable species were identified and trends in farmland bird populations compared with those of groups of species occupying other habitats.

2. Methods The Birds in Europe database (BirdLife International, 2004) contains a single estimate of the population size and trend for each breeding species in each country in Europe for the period 1990–2000. Data were collected via a continentwide network of national coordinators, who sought information on each species from relevant experts, monitoring organisations and regional contributors. Estimates were derived from a combination of published literature and unpublished survey data, recording all reference sources used.

Of the 9987 possible species/country combinations, population trend data were available for 8838 (88.5%). In the great majority of cases, each species/country trend was expressed as a trend class, which allowed the many different national monitoring schemes (Vorı´sˇek and Marchant, 2003) to be compared directly across countries. Trend classes ranged from 5 to +5 in the following steps: (1) indicated a change of up to 20%; (2) a change of 20–29%; (3) a change of 30–49%; (4) a change of 50–79%; and (5) a change of over 80%. These ranges were chosen to follow the IUCN Red List Criteria thresholds (IUCN, 2001). The sign of the trend indicated the direction of the change. Population stability or fluctuations around an unchanging mean were represented by a value of zero. There were thus 11 possible population trend categories.

For some country/species combinations, precise estimates of population change were included in the database, but these were converted to the appropriate trend class for analysis.

Derivation of figures sometimes involved interpretation and extrapolation by national coordinators, so each country/ species trend assessment was allocated a three-level quality code: (1) indicating that the trend was based on qualitative rather than quantitative data; (2) that the trend was based on a mixture of qualitative and quantitative data; and (3) that the trend was based entirely on quantitative data.

Species were classified to habitat using the habitat categories of Tucker and Evans (1997), who divided the percentage of each European species’ population occurring in each habitat into three classes; >75% (for all species), 10– 75% and <10% (for Species of European Conservation Concern (SPECs) only). All species with more than 75% of

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their population occurring in the following eight habitats were classified as specialists of that habitat: marine; coastal; inland wetland; tundra, mires and moorland; boreal and temperate forests; Mediterranean forest, shrubland and rocky habitats; agricultural and grassland; and montane grassland. In addition, species with 10–75% of their population using only one of the above habitat types was classed as a specialist of that habitat, either according to Tucker and Evans (1997) for SPECs or according to the description of Snow and Perrins (1998) for non-SPECs. Tucker and Evans (1997) further subcategorise farmland and woodland habitats; species with 10– 75% of their population in three or more woodland or farmland sub-categories in Tucker and Evans (1997) and 10– 75% of their population in only one other habitat category were classified as woodland or farmland species, respectively.

Any species not falling into one of the above categories was classified as a habitat generalist. This resulted in 74 species being identified as primarily farmland species, although excluding from analyses species occurring in fewer than five countries (following the criteria of Donald et al., 2001) reduced this number to 58. Farmland species were further classified (from Snow and Perrins, 1998) into four dietary categories (granivore/grazer, insectivore, predator and omnivore), to assess whether different functional groups exhibited different population trends. Birds in Europe contains data from 52 territories, some of them (e.g. Svalbard, Azores) not politically independent. Territories with negligible areas of agricultural land and territories for which agricultural data were not available from the FAOSTAT database were excluded. This excluded Andorra, Armenia, the Azores, the Canary Islands, the Faeroe Islands, Gibraltar, Greenland, Iceland, Liechtenstein, Madeira, Malta and Svalbard, leaving 40 countries in the analyses.

Indices of agricultural intensity for each country were extracted from the FAOSTAT database of the UN Food and Agriculture Organisation (http://apps.fao.org/). These included cereal yield (t ha 1), fertiliser use (t ha 1 of all types), number of tractors per unit area of agricultural land, and livestock density (head of cattle ha 1 of grassland). In a previous analysis of similar data (Donald et al., 2001), the estimate of cereal yield was extracted from FAOSTAT as the average yield across all cereal types. In the current analysis, yields of the four main cereal types (wheat, barley, oats and rye) were extracted separately, since yield differs significantly between cereal type and the proportion of the cereal crop contributed by each type varies between countries. The yield of wheat Triticum was selected as an index of intensity of cereal management, because it is the most widely grown cereal (comprising 43% of the total European cereal area), and is very strongly positively correlated with the yields of other cereal types (r38 > 0.85, P < 0.001). Data were extracted for the years 1993 and 2000, but they were so strongly correlated that only data for 1993 were used, being closer to the mid-point of the period covered, and the first year for which data were available for all countries. The proportion of the total farmed area of each EU15 Member

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State under agri-environment prescriptions was calculated from data in Kleijn and Sutherland (2003).

2.1. Data analysis Mixed effects models were used to model the 11-level trend variable. Graphical examination and residual analyses suggested that the trends followed an approximately normal distribution, so normal errors were assumed in the modelling. Since not all species occurred in all countries, and since trends of different species within countries and within species across countries were unlikely to be independent, both species and country were entered as factorial fixed or random effects in all models. All models were weighted by the data quality code, and all models excluded species occurring in fewer than five countries. To derive mean trends for each species, species was fitted as a fixed factor and country as a random factor, this order being reversed to derive mean country trends. When sub-groupings of species (e.g. comparing trends of species in different habitats) were included as predictor variables, conditional hierarchical mixed models were used, fitting the subgrouping of interest as a fixed factor and the main class (species) nested within the sub-grouping as a random factor (Littell et al., 1996). Direct comparisons could not be made between the trends in 1970–1990 and 1990–2000 because the periods differed in duration and because trends in 1970– 1990 were measured on a scale of 2 to +2, rather than 5 to +5. However, trends across species in each period were assessed using correlation to assess whether the general trajectory of population change was similar in both periods.

The trends estimated by the models described above do not necessarily reflect changes in the overall population of birds, as they are weighted by data quality rather than population size. A significantly non-zero negative estimated species trend, for example, would indicate that the mean trend of that species across all countries was negative, but not necessarily that the overall population declined, since small increases in very large populations in a small number of countries could outweigh large declines in small populations in many countries. Rather, the estimated trends indicate the mean trajectory of populations across a large number of countries, a metric of equal importance to changes in absolute population size in guiding policy to protect biodiversity across many countries. However, the possibility was also assessed that apparently negative species trends, or trends of groups of species, could arise in situations where overall numbers in the species or species group were actually stable or increasing.

Each species/country trend was weighted in these models by the proportion of the total European population of that species occurring in that country. Each species therefore received the same overall weight in the models, but estimated mean trends for each species accounted for their population distribution.

Species/country populations that declined to extinction during the period (n = 48), and so had zero weight, were arbitrarily assigned a weight equal to the mean of the 48

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lowest weights included in the model. Because trends could not also be weighted by data quality, trends based solely on qualitative data were excluded from these analyses, reducing sample sizes. The necessity to weight by population size, and therefore accept reduced sample sizes, was assessed by correlating mean species scores from models weighted by data quality with those derived from models weighted by population size, based on analysis of the same reduced data set. A strong positive correlation between the trends estimated by each of these two methods would indicate that the type of bias described above does not exist, and therefore that using the larger data set and weighting by data quality is likely to be preferable.

Trends estimated from these models cannot be converted to a percentage population decline, because the original ordinal trends cover a range of possible values, but they do fall along a ratio scale, allowing further parametric analysis.

Mean country trends for all farmland species (estimated from models weighted by data quality; see Section 3), and for the significantly declining and increasing farmland species separately, were correlated against each explanatory agricultural variable, and the easting and northing of the capital city, in turn. Trend was then entered as the dependent variable into backwards selection least squares regression models in which the northing and easting of the capital city and the agricultural variables were all entered as explanatory variables. Backwards deletion at a = 0.05 was used to derive the minimum adequate model. Because agricultural variables such as cereal yield might simply be correlates of factors affecting all species within a country, further analyses were carried out in which the mean country population trend of all non-farmland species (excluding marine species) was modelled in terms of the same agricultural variables as the farmland species.

Fig. 1. Comparison of mean estimated species population trends when estimated using population size as a weight and when using data quality as a weight (r355 = 0.68, P < 0.0001).

mean overall trend of farmland birds across Europe was 0.50 (S.E. = 0.16), a significant deviation from zero (t = 3.24, P < 0.005). This was significantly more negative than the mean trend across all other species (+0.177, S.E. = 0.10; test of difference t = 4.44, P < 0.0001) and across all other terrestrial species (+0.09, S.E. = 0.10; test of difference t = 3.99, P < 0.0001). Farmland birds had significantly more negative mean trends than species assemblages of most other habitats, and were the only group to show a significant overall mean negative trend (Fig. 2). Trends within the first 15 Member States of the European Union were more negative than trends in other European countries, though not significantly so.

Mean population trends of individual farmland species in 1990â&#x20AC;&#x201C;2000 were positively correlated with trends in

3. Results Trends estimated by models weighted by data quality were extremely similar to those estimated by models weighted by population size (Fig. 1), indicating that the average trend across countries is a very strong predictor of overall population change. Because of the larger sample size available using the former method, and because population sizes themselves had associated quality codes that could not simultaneously be included in the models, only results from models weighted by data quality are presented.

Of the 58 farmland species included in the analyses, 41 (71%) had negative estimated mean trends and 17 (29%) had positive trends. Trends of 27 species differed significantly from zero at a = 0.05, 19 showing decreases and eight increases. Of the 40 countries included in the analyses, 32 had negative mean trends, 13 of them significantly so at a = 0.05.

Only two countries, Austria and Cyprus, showed significantly positive mean trends across all farmland species. Estimated trends for each farmland species are given in Appendix A. The

Fig. 2. Mean trends across all European countries of bird species in a number of habitats (shown 1 S.E.): 1, marine (25 species); 2, coastal (22 species); 3, inland wetlands (59 species); 4, tundra, mires and moorland (29 species); 5, boreal and temperate forests (67 species); 6, Mediterranean habitats (24 species); 7, farmland and grassland (58 species); 8, montane grassland (eight species); 9, habitat generalists (93 species); see text for methods of classification. Habitat numbering follows the order of Tucker and Evans (1997). Trends for habitats 1 (P = 0.0001), 2 (P < 0.05) and 7 (P = 0.001) differed significantly from zero. Mean trends for all habitats except four and eight differed significantly (at P < 0.005) from the mean trend for farmland (habitat 7).

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193

Fig. 3. Comparison of mean trends in 1970–1990 with those in 1990–2000 for each of 58 farmland species (r = 0.60, P < 0.0001).

1970–1990, with the majority of species falling into the lower left quadrant, indicating decline in both periods (Fig. 3).

Univariate analyses demonstrated significant relationships between mean country trends of farmland species and certain indices of agricultural intensity, particularly cereal yield (Table 1). Backwards selection multiple regression models of the mean country trend across all 58 farmland species retained only wheat yield, which was negatively correlated with trend (Fig. 4a). When the 19 species showing significantly negative trends were considered separately, the model again selected only wheat yield, which alone explained 45% of the variance in mean trends (Fig. 4b). In both cases, polynomial terms were rejected by the model, suggesting a linear relationship. Mean country trends for the subset of eight increasing species were poorly explained by agricultural variables, although in univariate analyses they were positively correlated with indices of agricultural intensity (Table 1). There was no significant correlation between cereal yield and the population trends of non-declining farmland species (r38 = 0.02, P > 0.5), or between cereal yield and the trends of terrestrial non-farmland species (r38 = 0.14, P > 0.1). There was no relationship between the proportion of agricultural land under agri-environment schemes and the mean population trends of Table 1 Pearson correlation coefficients between mean country scores for three groups of farmland species and indices of agricultural intensity (d.f. = 39 in each case)

Northing Easting Wheat yield Tractors per hectare Livestock density Fertiliser use * ** ***

P < 0.05.

P < 0.005.

P < 0.0005.

All species (n = 58)

Declining species (n = 19)

Increasing species (n = 8)

ns ns 0.42* ns ns ns

0.48** 0.34* 0.66*** ns ns 0.42*

0.41* ns 0.40* ns 0.35* 0.42*

Fig. 4. Relationship between mean country trend and yield of wheat for: (a) all 58 farmland species (r38 = 0.42, P < 0.01); (b) the 19 significantly declining species (r38 = 0.66, P < 0.0001). Filled circles represent EU15 Member States.

all farmland species (r13 = 0.14, P > 0.5) or declining farmland species (r13 = 0.18, P > 0.5). When analysed by feeding guild, trends for each guild were negative but only significantly so for insectivores, although the trend of insectivores did not differ significantly from those of other guilds. Amongst the set of 19 significantly declining species, there was no significant difference in mean trends between the four dietary classes.

4. Discussion The results provide evidence that, across Europe, previously documented declines in farmland bird populations across Europe for the period 1970–1990 continued between 1990 and 2000, so a pattern of decline is detectable in farmland bird populations for at least 30 years. This pattern of long-term decline was not apparent in bird assemblages of other habitats, suggesting that declines in farmland bird populations were driven by factors specific to that habitat, rather than being part of a general decline in bird

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recent review of the effectiveness of agri-environment schemes (Kleijn and Sutherland, 2003) concluded that few schemes were documented or monitored sufficiently well to demonstrate unqualified benefits. Where the assessment of biodiversity impacts has been made a priority, however, correctly targeted agri-environment schemes based on ecological research have brought about rapid improvements for bird populations (e.g. Peach et al., 2001; Evans et al., 2002; Vickery et al., 2004). Wider adoption of correctly targeted agri-environment options, whether directed across all species or targeted at the 19 declining species shown in Appendix A, is likely to be rewarded with rapid biodiversity benefits. The linearity of the relationship in Fig. 3b suggests that even modest reductions in overall agricultural intensity could result in increases in bird populations that are detectable at very broad spatial scales. The provision of agrienvironment schemes became compulsory within all EU countries under the 2003 CAP reform, along with a number of other reforms such as the partial decoupling of production and subsidy, which could encourage extensification and hence benefit farmland birds. However, accession to the EU is still predicted to lead to intensification in new Member States (EEA, 2004; Sanderson et al., 2006). Furthermore, proposed cuts to the EU Rural Development budget that supports agrienvironment schemes suggest that the financial future of effective agri-environment schemes is by no means assured (S. Armstrong-Brown, personal communication).

populations across the continent. The strong correlation between population trends of declining farmland species and certain indices of agricultural intensity, and the lack of such a correlation with non-declining farmland species or nonfarmland species, suggested agricultural intensification as a plausible and likely causal factor. However, not all farmland species exhibited patterns of population decline. Of the 58 species included in the analyses, 17 exhibited positive overall mean trends, eight of them significant. Mean country trends of the eight increasing species were positively correlated with a number of indices of agricultural intensity, suggesting that agricultural intensification is not universally deleterious and that a small number of species might benefit, as has been suggested in the UK for corvids (Gregory and Marchant, 1996; Barnett et al., 2004).

While using simple indices of agricultural intensification, such as cereal yield, hides a number of more subtle and potentially more important changes (Fox, 2004), the relationship shown in Fig. 4b between population trends of declining farmland species and one such index is striking and persuasive. While such correlations do not indicate causality, it is revealing that the population trends of nondeclining farmland birds, and of terrestrial non-farmland birds, were uncorrelated with indices of agricultural intensity. Furthermore, the fact that countries where agriculture is heavily subsidised and countries with little or no state support for agriculture all fell along the same regression line suggests that the relationship is robust across a wide spectrum of political and economic systems.

The results suggest that there is no evidence of recoveries in farmland bird populations in countries with more land under agri-environment prescriptions, possibly because a high proportion of such prescriptions are not targeted at bird populations and may not actually result in a decrease in agricultural intensity at a field scale. Schemes aimed at improving soil structure or providing small areas of nonfarmland habitat might not be expected to reduce indicators of overall agricultural intensity such as cereal yield. That cereal yield is such a strong correlate of population trends which suggests that crop management, as well as (or rather than) the management of smaller areas of non-cropped land, is important in determining populations of farmland birds. A

Acknowledgements We are grateful to many people who collected the information for Birds in Europe, to the national coordinators who collated and supplied it, and to the sponsors who funded the project, all of whom are credited in the final publication (BirdLife International, 2004). Des Callaghan and Umberto Gallo-Orsi managed the early stages of data compilation.

For comments on the paper and other helpful suggestions, we are grateful to David Kleijn, Richard Gregory, Richard Bradbury, Zoltan Waliczky, Andy Evans, David Gibbons, Giovanna Pisano, Debbie Pain, the editor and two anonymous referees.

Appendix A List of farmland species included in the analyses, with mean trend (1990–2000, derived as species parameter estimates from a mixed model with species fitted as a fixed factor and country as a random factor) across all countries. Species arranged in order of increasing trend, most severely declining species first. t-values indicate significance of differences from a trend of zero. * P < 0.05, **P < 0.005, ***P < 0.001. Dietary classifications adopted in the analyses are also given: Ins, insectivorous; Pre, predator of vertebrates; Omn, omnivore; Gra, granivore. European threat status is from BirdLife International (2004), as is the estimate of the percentage of each species’ global population occurring in Europe. Assessments of European threat status in parentheses are provisional. Global threat status is from IUCN (www.iucnredlist.org), NT, near-threatened, DD, data deficient.

Species Glareola nordmanni Circus macrourus

Mean trend 3.83 3.23

t 4.39*** 3.89***

Diet

European threat status

Global threat status

Global population in Europe (%)

Ins Pre

Endangered (Endangered)

DD NT

25–49 25–49

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195

Appendix A (Continued ) Species Coracias garrulous Emberiza hortulana Galerida cristata Pterocles orientalis Lanius senator Lanius minor Burhinus oedicnemus Perdix perdix Falco naumanni Streptopelia turtur Saxicola rubetra Melanocorypha calandra Calandrella rufescens Otis tarda Athene noctua Alectoris rufa Vanellus vanellus Alauda arvensis Motacilla flava Limosa limosa Calandrella brachydactyla Sylvia nisoria Hirundo rustica Sturnus vulgaris Anthus pratensis Asio flammeus Acrocephalus paludicola Tetrax tetrax Falco vespertinus Falco cherrug Miliaria calandra Passer montanus Emberiza citrinella Falco tinnunculus Lanius collurio Sturnus roseus Tyto alba Locustella naevia Otus scops Nycticorax nycticorax Turdus pilaris Bucanetes githagineus Oenanthe isabellina Columba palumbus Falco biarmicus Oenanthe pleschanka Circus pygargus Corvus frugilegus Sylvia communis Bubulcus ibis Coturnix coturnix Merops apiaster Crex crex Ciconia ciconia Carduelis carduelis Buteo rufinus

Mean trend 2.74 2.21 2.12 1.96 1.83 1.79 1.68 1.54 1.39 1.34 1.24 1.18 1.12 1.1 1.04 1.02 0.95 0.83 0.82 0.8 0.74 0.69 0.67 0.67 0.66 0.64 0.64 0.56 0.49 0.43 0.42 0.36 0.31 0.24 0.17 0.12 0.11 0.04 0.01 0.07 0.14 0.26 0.29 0.39 0.46 0.56 0.64 0.7 0.82 0.95 0.99 1 1.07 1.17 1.18 2.11

t 6.85*** 5.81*** 5.10*** 1.75 4.11*** 4.16*** 3.95*** 4.50*** 2.79* 3.65** 3.65** 2.10* 1.22 2.24* 2.73* 0.98 2.79* 2.34* 2.36* 2.17* 1.29 1.58 1.89 1.85 1.67 1.67 1.03 0.83 1.07 0.94 1.15 0.97 0.88 0.69 0.48 0.16 0.3 0.1 0.03 0.18 0.37 0.14 0.36 1.12 0.68 0.7 1.86 2.07* 2.32* 1.25 2.78* 2.58* 3.27** 3.62*** 3.38** 3.81***

Diet

European threat status

Global threat status

Global population in Europe (%)

Ins Omn Omn Gra Pre Ins Ins Gra Pre Gra Ins Omn Gra Omn Pre Gra Ins Omn Ins Ins Omn Ins Ins Omn Omn Pre Ins Omn Pre Pre Gra Gra Gra Pre Pre Omn Pre Ins Pre Pre Omn Gra Ins Gra Pre Ins Pre Omn Ins Ins Omn Ins Omn Pre Gra Pre

Vulnerable (Depleted) (Depleted) (Declining) (Declining) (Declining) (Vulnerable) Vulnerable Depleted Declining (Secure) (Declining) Declining Vulnerable (Declining) (Declining) Vulnerable (Depleted) (Secure) Vulnerable Declining Secure Depleted Declining (Secure) (Depleted) (Vulnerable) Vulnerable (Vulnerable) Endangered (Declining) (Declining) (Secure) Declining (Depleted) Secure (Declining) (Secure) (Depleted) Depleted (Secure) (Secure) (Secure) Secure Vulnerable (Secure) Secure (Secure) Secure Secure (Depleted) (Depleted) Depleted Depleted Secure (Vulnerable)

NT

50–74 50–74 25–49 5–24 50–74 50–74 25–49 25–49 25–49 25–49 75–94 25–49 25–49 50–74 25–49 100 50–74 25–49 25–49 50–74 5–24 50–74 5–24 25–49 75–94 5–24 >95 75–94 25–49 <5 50–74 25–49 50–74 5–24 25–49 25–49 5–24 50–74 50–74 5–24 75–94W <5 5–24 75–94 <5 5–24 50–74 25–49 50–74 5–24 5–24 25–49 50–74 75–94 25–49 5–24

Vulnerable

Vulnerable

Vulnerable NT NT Endangered

NT

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