Toward the next Common Agricultural Policy reform: Determinants of avian communities in hay meadows reveal current policy's inadequacy for biodiversity conservation in grassland ecosystems

Abstract

Semi‐natural grasslands are among the richest European ecosystems in terms of biodiversity. However, they have been severely affected by farming intensification and land abandonment, which have been both exacerbated by the European Union's Common Agricultural Policy (CAP). The most recent CAP included a “greening” measure dedicated to grassland conservation, presumed to be beneficial to biodiversity; however, scientific evidence about its effectiveness is still scarce.
In the Alps, hay meadows have undergone dramatic management changes in recent decades. We used a comprehensive community ecology approach to highlight how the multi‐scale and interacting effects of such changes impact birds, with the aim of providing knowledge to support improvements to the CAP.
Birds were surveyed at 63 landscape units in northeast Italy, equally subdivided into areas dominated by (a) extensive hay meadows, (b) intensive hay meadows, and (c) areas formerly dominated by meadows but partially converted into other agricultural land use. This environmental gradient mirrors in space the temporal gradient of the agricultural changes that have recently occurred in the Alps.
Community composition, species richness, and the number of meadow‐specialist species were analysed according to environmental predictors (i.e. landscape, meadow management, and topography), and to spatial factors. We aimed to disentangle the exclusive and joint fraction of variation explained by each of them.
Meadow conversion, allowed by the CAP in force, created a shift in community composition towards assemblages dominated by generalist species at the expense of meadow specialists. The cover of intensive meadows was negatively correlated with species richness, whereas the number of meadow specialists was negatively correlated with the cover of early‐mown (i.e. within the third week of June) meadows. Mowing date was, in turn, related to elevation, with meadows at higher elevations mown later in the season, and to meadow intensification (the use of external inputs, in particular liquid manure, leads to earlier and more frequent cuts per year).
Policy implications. Our study confirms the concerns about effectiveness of the European Union's Common Agricultural Policy greening grassland measure in conserving biodiversity in those ecosystems. We suggest rethinking the Common Agricultural Policy environmental prescriptions to account for the importance of meadow management in determining bird diversity patterns in Alpine hay meadows. Finally, we highlight market‐based conservation strategies as complementary approaches for preserving grassland biodiversity.

Full text

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wileyonlinelibrary.com/journal/jpe J Appl Ecol. 2019;56:604–617.
© 2019 The Authors. Journal of Applied Ecology
© 2019 British Ecological Society
Received: 3 Septem ber 2018
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Accepted: 20 N ovember 2018
DOI : 10.1111/136 5-2664.13332
RESEARCH ARTICLE
Toward the next Common Agricultural Policy reform:
Determinants of avian communities in hay meadows reveal
current policy's inadequacy for biodiversity conservation in
grassland ecosystems
Giacomo Assandri1,2 | GiuseppeBogliani2 | PaoloPedrini1 | MattiaBrambilla1,3
1MUSE. Sezione Zoologia dei Verteb rati,
Trento, Italy
2Department of Earth and Environmental
Science s, Universit y of Pavia, Pavia, It aly
3Fondazione Lombardia per
l'Ambiente, Settore biodiversità e aree
protette, Seves o (MB), Italy
Correspondence
Giacomo Assan dri
Email: giacomo.assandri@gmail.com
Handling Editor: Gavin Siriwardena
Abstract
1. Semi-natural grasslands are among the richest European ecosystems in terms of
biodiversity. However, they have been severely affected by farming intensifica-
tion and land abandonment, which have been both exacerbated by the European
Union's Common Agricultural Policy (CAP). The most recent CAP included a
“greening” measure dedicated to grassland conservation, presumed to be benefi-
cial to biodiversity; however, scientific evidence about its effectiveness is still
scarce.
2. In the Alps, hay meadows have undergone dramatic management changes in re-
cent decades. We used a comprehensive community ecology approach to high-
light how the multi-scale and interacting effects of such changes impact birds,
with the aim of providing knowledge to suppor t improvements to the CAP.
3. Birds were surveyed at 63 landscape units in northeast Italy, equally subdivided
into areas dominated by (a) extensive hay meadows, (b) intensive hay meadows,
and (c) areas formerly dominated by meadows but partially converted into other
agricultural land use. This environmental gradient mirrors in space the temporal
gradient of the agricultural changes that have recently occurred in the Alps.
4. Community composition, species richness, and the number of meadow-specialist
species were analysed according to environmental predictors (i.e. landscape,
meadow management, and topography), and to spatial factors. We aimed to dis-
entangle the exclusive and joint fraction of variation explained by each of them.
5. Meadow conversion, allowed by the CAP in force, created a shift in community
composition towards assemblages dominated by generalist species at the expense
of meadow specialists. The cover of intensive meadows was negatively correlated
with species richness, whereas the number of meadow specialists was negatively
correlated with the cover of early-mown (i.e. within the third week of June) mead-
ows. Mowing date was, in turn, related to elevation, with meadows at higher ele-
vations mown later in the season, and to meadow intensification (the use of
external inputs, in particular liquid manure, leads to earlier and more frequent cuts
per year).

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1 | INTRODUCTION
The support towards agriculture provided by the Common
Agricultural Policy (CAP) is the most expensive part of the European
Union's budg et (40% of the total E U expenses; Euro pean Commissi on,
2013). CAP has been widely acknowledged as the major driver of
agricultural intensification and abandonment in Europe (at least
from the 70s onward), with strong negative impacts on biodiversity
(Bignal, 1998; Renwick et al., 2013).
These effects led to a 2013 CAP reform (in force until 2020),
that was announced to be “greener ” with 30% of direct payments
to farmers dependent on “greening measures” aimed at halting bio-
diversit y loss. One such measure was targeted at counteracting per-
manent grassland reduction (Pe'er et al., 2014).
Most European grasslands are semi- natural habitats charac-
terized by native plants, but they were created to sustain livestock
through maintenance by mowing and/or grazing. Semi- natural grass-
lands are among the most iconic and biodiversity- rich European
landscapes (Pykälä, 2000; Veen, Jef ferson, De Schmidt, & Van Der
Straaten, 2009), covering c. 8% of the continent and 35% of the uti-
lized agricultural areas (Smit, Metzger, & Ewert, 2008).
Permanent grassland has decreased in the EU by 6.4% between
1993 and 2011, and by 11.8% in countries that joined the EU by
2004 (Pe'er et al., 2014), due to the conversion into other land- use or
abandonment (Laiolo, Dondero, & Ciliento, 2004; MacDonald et al.,
2000). In several regions, remaining grasslands have been strongly
intensified (Humber t, Dwyer, Andrey, & Arlettaz, 2016). These
two opposite processes have been worsened by the CAP (Donald,
Pisano, Rayment, & Pain, 2002; Souchère et al., 2003) and have had
major negative impacts on grassland biodiversity (Monteiro, Fava,
Hiltbrunner, Della Marianna, & Bocchi, 2011; Vickery et al., 2001).
The last CAP greening measure dedicated to permanent grass-
land (enacted in 2013) is unlikely to have widespread positive ef-
fects on grassland biodiversity because: (a) it allows a further 5%
reduction in grassland ex tent at a national/regional scale by 2020,
an amount higher than the current loss rate in several regions; (b) it
has an obligation to maintain the overall grassland area, but not the
individual parcels, allowing farmers to plough, reseed, and relocate
them, (c) it does not distinguish among grassland t ypologies, so that
grasslands of any natural value contribute the same to the overall
grassland quota; (d) further degradation by management intensifica-
tion is allowed due to the lack of targeted environmental prescrip-
tions (Dicks et al., 2014; Pe'er et al., 2014, 2017).
Scientific evidence of the measure's effectiveness is still very
scarce, given the short period of implementation, and its im-
pacts on biodiversity can only be estimated (Pe'er et al., 2017).
Notwithstanding this, discussion on the next reform of the CAP has
advanced (European Commission, 2017), and appropriate evalua-
tions on grassland measures are urgently required to halt biodiver-
sity loss and to address management strategies and polic y for this
ecosystem, which is pivotal for biodiversity conservation in Europe.
Hay meadows represent the most emblematic and biodiversity-
rich traditional agroecosystem in the Alpine region (Agnoletti, 2013;
Kampmann et al., 2008) and are an urgent conser vation issue due to
the ongoing deep changes in traditional farming (Fischer, Rudmann-
Maurer, Weyand, & Stöcklin, 2008; Henle et al., 2008). They are
permanent semi- natural grasslands, maintained by mowing and
only occasionally grazed. Their widespread occurrence in the Alps
is related to an extensive form of livestock farming, in which cattle
(mainly cows) spend the summer in mountain pastures and the other
seasons in stables, fed with the fodder obtained from meadows
(Marini, Klimek, & Battisti, 2011; Monteiro et al., 2011).
From the second half of the 20th century, and more markedly
in the last c. 40 years, the dairy sector in the Alps has undergone
deep changes, concentrating livestock into a few, much larger farms.
These farms are highly specialized and breed larger and more pro-
ductive cows (e.g. Holstein Friesian), largely fed with concentrated
cereal feedstuff. This results in a reduction in summer grazing in up-
land pastures and in a higher production of organic fertilizer, often
deposited on meadows as liquid manure (Graf, Muller, Jenny, & Jeny,
2014; Marini et al., 2011). Simultaneously, marginal, less produc-
tive grasslands have been abandoned or converted into profitable
cropland (Monteiro et al., 2011; Zimmermann, Tasser, Leitinger, &
Tappeiner, 2010).
Several factors have influenced the transformation of the
dairy sector in the Alps, including social and cultural changes,
6. Policy implications. Our study confirms the concerns about effectiveness of the
European Union's Common Agricultural Policy greening grassland measure in con-
serving biodiversity in those ecosystems. We suggest rethinking the Common
Agricultural Policy environmental prescriptions to account for the importance of
meadow management in determining bird diversity patterns in Alpine hay mead-
ows. Finally, we highlight market-based conservation strategies as complementary
approaches for preserving grassland biodiversity.
KEYWORDS
Alps, avian assemblages, EU, greening, meadow specialist

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local investments in mountain development , technological inno-
vation and policy, although the CAP played a crucial role in sus-
taining this process (Marini et al., 2011; Tappeiner, Tappeiner,
Hilbert, & Mattanovich, 2003). These agricultural changes con-
tinue to impact plant communities (Marini, Scotton, Klimek, &
Pecile, 200 8; Niedrist, Tasser, Lüth, Dalla Via, & Tappeiner, 2009;
Pierik, Gusmeroli, Marianna, Tamburini, & Bocchi, 2017) and in-
vertebrate assemblages (Andrey, Humbert, Pernollet, & Arlettaz,
2014; Marini, Fontana, Scotton, & Klimek, 2008). However, it is
uncertain how they are affecting the upper level of food webs.
Although several bird species are sensitive to meadow manage-
ment (Britschgi, Spaar, & Arlettaz, 2006; Sergio, Marchesi, &
Pedrini, 2009), and especially to mowing (Broyer, 2009; Pedrini,
Rizzolli, Rossi, & Brambilla, 2012; Strebel, Jacot, Horch, & Spaar,
2015), to the best of our knowledge, studies on the multi- faceted
effects of modern hay- meadow management on avian communi-
ties had never been per formed in the Alps.
Here we investigate the effects of recent grassland transfor-
mation on hay- meadow bird communities, considering broad- (e.g.
landscape) and fine- sc ale (e.g. management) drivers in the Alps. We
aimed at disentangling the role in structuring avian assemblages of:
(a) meadow conversion into more productive crops; (b) landscape-
scale intensification, and (c) in- field meadow intensification (increase
in external input s and changes in mowing regimes). Our ultimate goal
is to identif y best options for biodiversity conservation in this iconic
agroecosystem and to provide recommendations for improving the
forthcoming CAP reform.
2 | MATERIALSANDMETHODS
2.1 | Studyarea,design,andbirdsurveys
The study was conducted in the Trento province (NE Italy; 45.67–
46.51°N; 10.51–11.96°E; Figure 1a). Here, anthropogenic grasslands
are scattered above 250 m and are more widespread between 80 0
and 2,000 m a.s.l.
Permanent hay meadows cover roughly 200 km2, representing
3.3% of the province sur face and 14.8% of the Utilized Agricultural
Area (ISTAT, 2010).
In 2010, 54,927 Livestock Units occurred in Trentino, distrib-
uted over c. 1,400 farms (La Notte et al., 2015). From 1990 to 2010,
the overall surface of hay meadows in this region almost halved
(Provincia Autonoma di Trento 2017), leading to a considerable in-
crease in stocking rate (Scotton, Pecile, & Franchi, 2012). Rural aban-
donment was lower than in other Alpine areas, partly due to specific
incentives in the framework of the Rural Development Programs,
and meadow reduction was primarily due to conversion into other
crops (Marini et al., 2011; Streifeneder, Tappeiner, Ruffini, Tappeiner,
& Hoffmann, 2007).
Birds were surveyed along 63 20 0 m- long linear transects, scat-
tered over nine areas along an altitudinal gradient (310–1,565 m
a.s.l.) encompassing the entire belt in which meadows are found in
the study area (Figure 1b).
Bird surveys and environmental variable collections were per-
formed within a 100 m- buffer around the transec t; these 7.15-
ha Landscape Units (LU) became the sampling units of the study
and were selected according to a stratified design: 22 unit s were
dominated by extensive hay meadows, 20 unit s by intensive hay
meadows, and 21 by hay meadows partly conver ted into other
crops.
These three grassland typologies (extensive, intensive, con-
verted) approximate well the modifications that occurred in the last
decades in hay meadow- dominated landscapes in our study area,
and more in general in several areas in the Alps. In other words, we
studied the avian communities found at a specific time in multiple
sites chosen along a gradient of hay meadow intensification, as-
suming this 1- year “snapshot” as a proxy of the community changes
occurred in the last c. 40 years in relation to the widespread
changes from extensive to intensive and then converted grassland.
This approach, known as space- for- time substitution, is commonly
adopted in ecological studies when long- term data are unavailable
and assumes that changes in space reflect those in time, that is,
that there are no important interactions with other factors (e.g.
climate). This is a simplification of the patterns occurring in real
systems but can still prove useful to understand the impacts of en-
vironmental changes on biodiversity (Bennett, Radford, & Haslem,
2006; Pickett, 1989).
We censused birds during three visits in the 2017 breeding sea-
son (12–24.05; 13–23.06; 2–12.07), surveying six/seven transects
per morning (see Assandri, Bogliani, Pedrini, & Brambilla, 2017a,
2017b and Supporting Information Appendix S1 for fur ther details).
2.2 | Avianandenvironmentalvariables
From bird sur veys, we derived three community variables for each
LU: communit y composition, breeding species richness (number of
breeding species), and the number of meadow specialist species.
Before computing community variables, we removed records re-
lated to juveniles, over flying birds, species not breeding in the study
area, and species observed only once. Composition assessment
was based on the maximum abundance of each species recorded
across the three surveys, which were entered into a site (i.e. LU) by
species matrix. L ate season high counts (i.e. those exceeding of 10
units the mean of the previous session counts in the same LU) of
early- breeding species, which display gregarious habits at the end
of the breeding seasons (e.g. flocks of finches, sparrows, and cor-
vids) were excluded to avoid abundance over- estimation (Jakobsson
& Lindborg, 2017).
We considered meadow specialists to be the species which:
(a) are known to depend on grassland habitats in the study region
(Pedrini, Caldonazzi, & Zanghellini, 2005), and (b) with >50% of their
precise locations falling within meadow. Those include Crex crex,
Coturnix coturnix, Saxicola rubetra, Anthus trivialis, Alauda arvensis,
Sylvia nisoria, Lanius collurio, Turdus viscivorus, Emberiza citrinella. Pica
pica, and Corvus corone, which are habitat generalists in the region,
satisf y only the second condition and thus were discarded.

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FIGURE1 Study area. (a) Location of the study area in the Alpine region (Trentino in orange). (b) The 63 landscape units investigated
are shown in black and hay- meadow cover is in green. (c) Det ail on one landscape unit showing the field mapping of the landscape and
management variables. Base maps: Natural Ear th; Or tofoto 2011 ©AGEA – A genzia per le Erogazioni in Agricoltura, Roma

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At each LU, we measured three types of environmental vari-
ables: landscape, management, topography, and built a set of spatial
variables based on LU coordinates. Landscape variables were mea-
sured on aerial photographs that had been validated and updated
in the field. We calculated the relative cover of meadows, urban
areas, woodlands, traditional high- stem orchards, shrublands/fal-
lows (including small wetlands), and converted meadows (former
meadows recently converted into other arable crops, intensive
orchards, vineyards, or greenhouses). On the basis of these, we
calculated a land- cover H’ Shannon diversit y index (Laiolo, 2005).
The length of hedgerows (with and without trees) and tree rows,
and the number of isolated trees and shrubs were also assessed.
Management variables were evaluated at the parcel scale (i.e. a
meadow with a defined plant community, spatial arrangement, and
management characteristics; 882 parcels identified); the percent-
age surface of highly intensive and that of early mown meadows
occurring in each LU were used as management predictors. Highly
intensive meadows were defined as species- poor meadows, highly
fertilized (85–420 kg N ha−1 year−1), and mown 2–3 times per year
in contrast with low intensive meadows (species- rich meadows, not
fertilized or poorly fertilized (0–150 kg N ha−1 year−1), and subject
to only 1 or, rarely, two cuts per year) (Scotton et al., 2012). Early
mown meadows were defined (in contrast with late mown) as mead-
ows mown before the end of the third week of June (see Supporting
Information Appendix S1).
Topographic variables (mean elevation and slope) were derived
from a 1- m resolution digital elevation model. Spatial variables
were built by means of Moran's eigenvector maps (MEMs) (Dray,
Le ge ndr e, & Pe res- Ne to, 2006 ), a meth od th at pr odu ces flex ibl e spa -
tial predictors starting from sample plot coordinates, and capturing
spatial ef fect s at multiple spatial scales that can be used in regres-
sion and ordination to account for spatial autocorrelation (Borcard,
Gillet, & Legendre, 2011). Further details are available in Supporting
Information Appendix S1.
2.3 | Analyses
All the analyses were performed with r version 3.4.1 (R Core Team,
2017). We scrutinized the three groups of environmental predic-
tors to avoid common statistic al problems, following Zuur, Ieno,
and Elphick (2010). We left out meadow cover and landcover di-
versity from subsequent analyses due to high collinearity with
converted meadow area, both pairwise (Spearman's Rho: meadow
cover = −0.75; p < 0.001; landcover diversit y = −0.90; p < 0.0 01)
and multivariate (GVIF: converted meadow cover = 29.91; meadow
cover = 40.39; landcover diversity = 11.83). Elevation and slope
showed a low collinearity (Rho < 0.5), and both were ret ained. We
applied a log+1 transformation to hedgerows, tree rows, and slope
to reduce the weight of outliers. All the explanatory variables were
standardized before analysis (Cade, 2015; Schielzeth, 2010).
We separately tested the effect of each group of environmen-
tal and spatial (MEM) predictors on the three response variables.
Community composition was analysed by means of redundancy
analysis (RDA) per formed with the package vegan (Oksanen et al.,
2017). The abundance site- by- species matrix was first Hellinger-
transformed to make it appropriate for linear analyses (Legendre
& Gallagher, 2001). Global significance (p < 0.05) was assessed by
means of ANOVA- like permutation (N = 999) tests. Overall, signifi-
cant RDAs were obt ained by using forward selection (per formed in
adespatial package; Dray et al., 2017), which retained significant vari-
ables by applying a double- stopping criterion (Blanchet, Legendre, &
Borcard, 2008), which reduces type I errors and the overestimation
of explained variance.
We built Poisson GLMs to evaluate predictor effect s on spe-
cies richness and the number of meadow specialists by adopting
an information- theoretic approach (Burnham & Anderson, 2002)
and ranking all possible models for each set of explanatory vari-
ables separately according to the relative value of Akaike's infor-
mation criterion corrected for small sample size ( AICc). The most
parsimonious models (ΔAICc ≤ 2) were selected and averaged
within each group of predictors weighing by model weights, and
obtaining model- averaged coefficients, their relative SEs, and the
relative variable importance (Johnson & Omland, 2004) for each
explanatory variable applying the “zero- method” (sensu Grueber,
Nakagawa, Laws, & Jamieson, 2011). “Uninformative parameters”
(Arnold, 2010), that is the variables which, when included, de-
termined an increase in the model's AICc value, were discarded
(Richards, 2008; Richards, Whittingham, & Stephens, 2011). The
inclusion of more models (ΔAICc ≤ 6) led to substantially similar
results. For subsequent variation partitioning (VP) analyses, we
retained only the variables with confidence intervals of parameter
estimates not encompassing zero.
Variation partitioning was applied to disentangle the unique and
joint fraction of variation among the response variables explained
by the four sets of predictors. We performed VP on parsimonious
models (simplified models resulting from model selection; Peres-
Neto & Legendre, 2010) (see Supporting Information Appendix S1
for details).
3 | RESULTS
We obtained 3,037 bird records referring to 88 species; we re-
moved records related to 13 species observed only overflying
LUs, 10 exclusively migrant species and 6 species obser ved only
once (Supporting Information Table S2). The final dataset included
59 species and 2,04 0 individuals. Ten species alone accounted for
c. 59% of the data (see Supporting Information Appendix S2).
3.1 | Communitycomposition
The community composition was significantly structured by
eight environmental variables (Supporting Information Table S3).
Saxicola rubetra, A. trivialis, E. citrinella, C. coturnix, and P. pic a were
grouped together and were negatively correlated with the cover
of converted meadows (and with orchard and hedgerows, which

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were in turn positively correlated with the agricultural landscapes
that were dif ferent from meadows). This group of species also in-
cluded at its margins, T. viscivorus (positively related to shrubs and
woodland) and L. collurio (Figure 2a). All these species but the lat-
ter were also positively associated with elevation and secondarily,
slope and negatively with early mown grassland. Variables indicat-
ing low- elevation agroecosystems different from meadows were
positively associated with Chloris chloris, Serinus serinus, Passe r
montanus, Turdus merula, Passer italiae, and less strongly with
Linaria cannabina, Jynx torquilla, Picus viridis (these three favoured
by hedgerows) and Muscicapa striata, Turdus philomelos, Cya nistes
caeruleus, Sylvia atricapilla, Parus major, Columba palumbus, which
were related to more forested landscapes. The woodland special-
ists found within sampled LUs (which are meadow- dominated)
were all clearly aligned according to woodland cover, with several
also favoured by slope (Figure 2a–c).
Ten MEMs were retained in the parsimonious spatial RDA
(Suppor ting Information Table S3). RDAs had an overall low explan-
atory power (Figure 4), as is commonly found in ecological studies
(Borcard et al., 2011).
3.2 | Speciesrichnessandmeadowspecialist
species
The most supported models on the ef fect of environmental and
spatial predictors on species richness and the number of meadow
specialists are summarized in Supporting Information Tables S4 and
S6. Species richness was positively affected by woodland cover,
the length of woody hedgerows and tree rows, and slope, and
negatively by the cover of highly intensive meadows and elevation
(Figure 3a–f). The positive effect s of urban cover and hedgerow
length were weaker, since their estimates had confidence intervals
encompassing zero. Two MEMs were retained after model selection,
but only MEM12 had confidence inter vals not encompassing zero
(Supporting Information Table S5).
The number of meadow specialists was negatively affec ted
by the cover of converted meadows and of early mown mead-
ows, plus the length of tree rows and orchard cover (although the
latter had confidence intervals encompassing zero), positively by
elevation plus the number of shrubs (although its confidence in-
tervals encompassed zero) (Figure 3g–i). Nine MEMs were retained
after model selection, but only MEM3 and MEM9 had confidence
FIGURE2 Distance biplots of the effects of environmental
predictors (a. landscape; b. management; c. topography) on avian
community composition according to parsimonious redundancy
analyses (RDA). Selected variables are represented by arrows.
Species are abbreviated with six letters (initials of genus and
species; see Supporting Information Table S2). Only species with
goodness- of- fit >0.10 are shown. Angles between species and
predictors reflect their correlation (angle <90°: positive correlation;
angle >90° negative correlation; angle = 90 °: no correlation). Values
in parentheses give the percentage of total variance explained by
each canonical axis. N = 63

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intervals for the estimate not encompassing zero (Suppor ting
Information Table S7).
3.3 | Variationpartitioning
Variation partitioning highlighted a complex contribution of dif-
ferent sets of predictors in explaining variation in the response
variables; shared fractions were often conspicuous, indicating that
multiple factors co- explain the observed patterns (Figure 4). For
communit y composition and the number of meadow specialists,
the intersection of all four components was particularly relevant.
For community composition, the spatial component had the main
role in explaining the obser ved variation, and 41% of this compo-
nent was unique (i.e. not joint), followed by landscape and topog-
raphy components, which were for three quarters interrelated to
other components. Management (cover of early mown meadows)
explained a reduced quota of variation and was always interrelated
with other components. For species richness, variation was mainly
explained by landscape (35% uniquely) and topography compo-
nents (mostly jointly with landscape), whereas management (cover
FIGURE3 Graphical representation of the effect of environmental predictor on species richness (a–f) and number of meadow specialists
(g–i) as predicted by averaged models. Only variables for which confidence intervals of estimates did not include zero are shown. Other
predictors included in the models are kept constant at their mean value. Variables in subfigures c and e were log+1 transformed for the
analyses (untransformed values are displayed to facilitate understanding). 95% CI of the mean are shown in grey. N = 63

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of highly intensive meadows), and spatial components had a limited
explanatory power. Vice- versa, the number of meadow specialists
was mainly jointly explained by management (cover of early mown
meadows) and topography (elevation); about half of the variation ex-
plained jointly by management and topography was also explained
by the landscape component (cover of converted meadows). The
contribution of the spatial component was mostly interrelated with
that of other components.
4 | DISCUSSION
We used a broad and comprehensive community ecolog y approach
to show the impacts of the multi- scale and interacting trajectories
of Alpine grassland changes on birds, which here occupy the high-
est levels of the trophic web. Mountains have been reported to act
as refuges for lowland farmland species, which strongly declined at
lower elevations in response to agricultural mechanisation and in-
tensification (Schmid, Luder, Naef-Daenzer, Graf, & Zbinden, 1998).
A study in the French Alps showed that this is still true for farmland
generalists, but that farmland specialists (which match quite well
with our meadow specialists) also declined at elevations higher than
1,00 0 m (Archaux, 2007). Coherent patterns had been repor ted
from the Swiss Alps (Korner, Graf, & Jenni, 2017) and suggest that
recent agricultural transformations are also impacting these species
in mountain “refuges”, which are rapidly losing their conservation
potential for farmland birds.
These negative transformations are at least partly linked with
the European CAP. The greening measure specifically included in
the current CAP for permanent grassland, aimed at halting grass-
land biodiversit y loss, is unlikely to invert this negative trend (Pe'er
et al., 2014). One of the main shortcomings of this measure is that
it allows a further 5% grassland reduction at national and regional
scales. Our study highlighted that one of the major causes of grass-
land reduction in the Alps, the conversion of hay meadows into other
crops, does not drive an increase in overall avian biodiversity (spe-
cies richness) in a meadow dominated landscape. Such an increase in
species richness could have been expected, because different crops
co- occurring at a landscape scale could increase overall landscape
heterogeneity, which is widely recognized to be positively correlated
with the diversity of many taxa (Benton, Vicker y, & Wilson, 2003;
Fahrig et al., 2015). Results showed that meadow conversion caused
a shift in the community composition towards assemblages domi-
nated by generalist species, especially Turdidae and Fringilladae,
which are known to adapt to intensive permanent monocultures
(i.e. apple orchards and vineyards; Assandri et al., 2017a; Brambilla,
Assandri, Martino, Bogliani, & Pedrini, 2015), which usually replace
semi- natural grassland in the Alps. These changes occurred at the
expense of the meadow specialists, which disappear with the in-
crease in these crops. The conversion of hay meadows into other
crops thus has a clear negative ef fect on meadow specialists, which
are mostly decreasing species at the European level.
The cover of highly intensive meadows was negatively correlated
with overall species richness. This pattern could be due to both the
fact that intensive meadows tend to occur in more agriculturally in-
tensive landscapes (which in most cases host a lower biodiversity
than extensive ones; e.g. Verhulst, Báldi, & Kleijn, 2004), and to a
negative e ffect of meadow i ntensification p er se on bird assembl ages,
via an effect on generalist species too. These latter species, which
are not obligate meadow- dwellers (e.g. doves, woodpeckers, raptors,
wagtails, cor vids, thrushes, finches, starling, and sparrows), often use
meadows for feeding. This negative effect did not emerge for the
communit y composition and meadow specialists, mainly affected by
the mowing regime. Early mown meadows (i.e. those mown within
the third week of June) have considerably fewer meadow specialists
than meadows mown later. The impact of (intensive) hay making on
FIGURE4 Venn diagrams for variation partitioning showing the percentage contribution of landscape, management, topography, and
spatial component s in explaining (a) community composition, (b) species richness, and (c) the number of meadow specialists in the 63
landscape units. Circle areas are roughly proportional to the percentage of variation explained. Areas within overlapping circles indicate
approximate percent variation shared by different components. Numbers in bracket s inside component labels refer to the percentage of
overall variation explained by a component (including shared quotas). Values <0 not shown

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birds determines high rates of nest destruction and nestling mort ality
(Buckingham, Giovannini, & Peach, 2015; Schekkerman, Teunissen, &
Oosterveld, 2009), finally resulting in lowering reproductive success
(Broyer, 2009; Müller, Spaar, Schifferli, & Jenni, 2005), modifying be-
haviour (Grüebler, Schuler, Spaar, & Naef- Daenzer, 2015) and even
mating systems, sexual selection, and consequently, evolutionary
processes (Perlut et al., 2008), turning once favourable habitats into
eco lo gica l tr ap s (Broyer, Cur tet, & Boissenin, 2012). Als o in our stu dy
area, modern hay- making is resulting in a drastic decline (sometimes
eventually leading to local extinction) of once very common meadow
birds breeding on the ground (e.g. C. coturnix, C. crex, A. arvensis,
A. trivialis, S. rubetra, and E. citrinella; Brambilla & Pedrini, 2013;
Pedrini et al., 2005; this study).
The date of mowing, as shown by variation partitioning
(Figure 4c), is strictly related to elevation, with meadows at higher
elevations mown later in the season (see also Suppor ting Information
Figure S3). This is mirrored by the positive ef fect of elevation on
meadow specialists, opposite to the effect on species richness in our
study area and, in general, in mountain ranges world- wide (McCain
& Grytnes, 2010; Nogués- Bravo, Araújo, Romdal, & Rahbek, 2008).
This confirms the key importance of meadow management and it s
interaction with seasonal progression (which is delayed at higher
elevation) for grassland birds in areas with elevation gradients
(Brambilla & Pedrini, 2011; Brambilla & Rubolini, 2009).
Recent intensification in meadow management also determines
an earlier mowing, since the two are intimately connected in a cyclic
positive feedback: the use of external inputs (water/fertilizer supple-
mentation) allows advanced mowing (and then increased number of
cuts/year) and to sustain a larger number of (more productive) cattle,
which produce more manure that is usually deposited on meadows,
further increasing their productivity (Scotton, Sicher, & Kasal, 2014).
In- field meadow intensification thus also negatively affects meadow
specialists. It also promotes new vegetation types dominated by a
few species to the point that, after some years, only a few nitrophi-
lous species of low forage value occur, and the semi- natural mead-
ows have to be ploughed and re- sown with industrial seed mixtures
(Andrey et al., 2014; Humber t et al., 2016; Marini, Scotton, et al.,
2008). This is also a widespread practice in our study areas, where
9% of meadows are reseeded, and 10% are dominated by weeds
(e.g. Apiaceae), that are those near to being ploughed and reseeded
(see Suppor ting Information Appendix S1). This causes a shift from
a long- established ecosystem dominated by autochthonous plants
and harbouring structured animal communities (Humbert, Ghazoul,
& Walter, 2009), into a new, artificial, and temporary one, with few
(and often a llochthonous) species. However, according to th e current
grassland CAP measure, these degraded meadows, as well as the
intensively managed ones, contribute the same as the biodiversity-
rich, permanent, and unimproved grassland in reaching the grassland
quota needed to access the greening requirement under the CAP
(Pe'er et al., 2014).
Previous studies highlighted that steepness better predicts
the occurrence of meadows with high diversity of plants (Marini,
Scotton, et al., 2008) and invertebrates (Marini et al., 2011) than
elevation. This occurs because steeper slopes prevent meadow in-
tensification (e.g. irrigation and fertilization are difficult to achieve);
additionally, the more extreme microclimates of these grasslands
are disadvantageous to more competitive plant species while fa-
vouring less competitive ones, allowing them to co- exist and to in-
crease diversity (Marini, Scotton, et al., 2008). Comparably, in our
study areas, less intensified meadows and richer plant communities
are found on steeper slopes (Figure 5). However, slope has a pos-
itive effect on overall avian species richness, but not on meadow
specialists. The community composition analysis indeed suggests
that the positive effect of slope on species richness is most likely
related to the fact that the steepest slopes are associated with aban-
doned areas invaded by shrubs and first- stage successional woods
and this, in a landscape dominated by meadows, allows some species
(e.g. Prunella modularis, Troglodytes troglodytes, Phylloscopus collybita,
Erithacus rubecula) to occur, increasing the overall species number
(Laiolo et al., 2004). This is also confirmed by the observed positive
effect of woodland cover (and also of hedgerow and tree row length)
on overall species richness.
Landscape linear elements, such as hedgerows and tree rows,
often favours birds in agroecosystems (Baudr y, Bunce, & Burel,
2000; Hinsley & Bellamy, 200 0); however, hedgerow net works (or
other forms of tree and shrub restoration) may negatively affect
grassland specialists, via habit at fragmentation for open- habitat
species (Assandri, Bogliani, Pedrini, & Brambilla, 2017c; Besnard &
Secondi, 2014). In our study, tree rows do not have any effect on
meadow specialists, whereas woody hedgerows have a negative
(non- significant) ef fect. Conversely, isolated shrubs are positively re-
lated with most of these species, whereas (shrubby) hedgerows are
positively related with species found in mixed farmland (e.g. P. v ir-
idis, J. torquilla, Sturnus vulgaris, P. italiae) and locally with two spe-
cies commonly found in grassland, L. collurio and S. nisoria. In areas
in which meadows were converted to other crops, the maintenance
or creation of hedgerows with shrubs could sustain richer avian
communities and declining farmland species, whereas in grassland-
dominated areas, where hedgerows are not part of the traditional
landscape, their creation is not recommended.
5 | CONCLUSIONS
With the next CAP reform expected soon, recommendations for
major amendments to current measures are particularly timely and
are required to reverse the negative trend of farmland species and,
in particular, of avian grassland specialists, which still do not show
any recovery (Gamero et al., 2017; Inger et al., 2014). If the conser-
vation of grassland biodiversity is a priority for the European Union,
the CAP greening measure referring to permanent grassland must be
rethought. Our study confirms the concerns about its low expected
effectiveness for biodiversity conser vation (Pe'er et al., 2014)
also for birds. The further 5% grassland reduction at the national
and regional scale means - in most cases- fur ther conversion into
other crops, with potentially severe consequences for biodiversity.

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ASSAN DRI et Al .
Additionally, the lack of distinction between intensive, low- natural
value grasslands and traditional, high- natural value grasslands, could
lead to fur ther biodiversity losses, since vast extents of permanent
grassland could be transformed into non- permanent grassland regu-
lated by ploughing and reseeding, which are allowed by the measure.
If the 5% threshold had to be maintained, it would be essential to
avoid the conversion of unimproved/low- intensive meadows. This
implies that further efforts and resources should be allocated to
map unimproved meadows and to design high tier agri- environment
schemes to compensate farmers for the income loss due to, for ex-
ample, mowing being delayed after the third week of June.
This considered, agri- environmental schemes specifically
thought to halt the decline of grassland birds and to maintain
grassland surface resulted in mixed effects (Broyer, Curtet, &
Chazal, 2014); thus complementary strategies are desirable to
address this conservation issue from a different perspective. In
the U.S.A., sustainable market- based conservation models were
suggested as the best opportunity to conserve grassland bird
populations. These models assume that consumers will pay more
for a product if its sustainabilit y, healthiness, and quality are evi-
dent (Perlut, 2014). In an Alpine perspective, these models could
take the form of self- sustaining dairy micro- economies, based on
the promotion of the local specific characteristics (“buy local”),
which can enhance product quality, while promoting the main-
tenance of traditional landscapes, which in turn favour tourism
and other recreational activities (Assandri, Bogliani, Pedrini, &
Brambilla, 2018a; Lindemann- Matthies, Briegel, Schüpbach, &
Junge, 2010), and, hopefully, grassland biodiversity. These ini-
tiatives should be recognized, sustained (by e.g. dedicated mea-
sures in the framework of Rural Development Programmes),
and controlled by public authorities and guaranteed by means
of dedicated qualit y brand and certification for the producers,
highlighting the support to biodiversity and mountain traditional
agriculture given by a product, which in turn justifies its higher
cost.
Meadow management, although mediated by the topographic,
landscape, and spatial context, played a fundamental role in al-
lowing meadow specialists to persist in grassland landscapes, thus
conservation strategies for grassland should necessarily include
well- focused management prescriptions and should be shared and
discussed with farmers and other stakeholders in order to develop
sustainable and effective solutions.
FIGURE5 Joint effect of slope and elevation on meadow typology and intensification level. Each point refers to a meadow parcel
(N = 882). The 16 meadow typologies found in the study area are grouped (by colours) in five categories: yellow: Arrhenateretum meadows;
blue: meadow rich of species typical of non- fertilized soils (dominated by Bromus, Festuca, Avenula, Agrostis); orange: highly fertilized,
disturbed, and reseeded meadows; violet: wet meadows; green: Trisetetum meadows. The meadow intensification level is shown by symbols:
triangle - low intensive; square - high intensive (see Supporting Information Appendix S1 for typolog y legend and for fur ther details)

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ACKNOWLEDGEMENTS
M. Anderle and A . Iemma helped with fieldwork and cartography.
The meadow typolog y map was provided by Provincia Autonoma
di Trento (Servizio Sviluppo Sostenibile e Aree Protette); G. Tomasi
and A. Bertolli (Fondazione Museo Civico di Rovereto) updated it
for several localities. K. Horwat revised the English. The associated
editor Gavin Siriwardena, Mar tin Grüebler, and another anonymous
reviewer provided ver y helpful comments on a first version of the
paper. GA is supported by a Post Doc grant funded by MUSE and co-
funded by Ser vizio Sviluppo Sostenibile e Aree protette and Ser vizio
Politiche Sviluppo Rurale - PAT.
AUTHORS’CONTRIBUTIONS
G.A ., M.B., and P.P. conceived the idea; G.A. carried out fieldwork
and led the analyses, helped by M.B.; P.P. acquired funding; G.A. and
M.B. wrote a first draft of the paper; G.B. super vised the research
development; all authors contributed critically to the drafts and gave
final approval for publication.
DATAACCESSIBILITY
Data available from the Figshare repository at https://doi.
org/10.6084/m9.figshare.7296905.v3 (Assandri, Bogliani, Pedrini, &
Brambilla, 2018b).
ORCID
Giacomo Assandri https://orcid.org/0000-0001-5161-5353
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