# Agricultural policies and biodiversity: impact of the CAP greening on bird abundance and diversity in France

Isabelle Chort, Aicha Hasni, Berk Öktem

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Isabelle Chort, Aicha Hasni, Berk Öktem. Agricultural policies and biodiversity: impact of the CAP greening on bird abundance and diversity in France. 2024. hal-04895155v2

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UMR 6031
TREE

Transitions
Energétiques et Environnementales

TREE WP No. 17
First version: December 2024
Revised: April 2025

AGRICULTURAL POLICIES AND
BIODIVERSITY: IMPACT OF
THE CAP GREENING ON BIRD
ABUNDANCE AND DIVERSITY
IN FRANCE

Isabelle CHORT
Aicha HASNI
Berk OKTEM

UMR TREE 6031
Avenue du Doyen Poplawski
64016 PAU Cedex
Mail : tree-wp@univ-pau.fr
Internet : http://tree.univ-pau.fr/

<figure><img src="image_2.png" /></figure>

CNRS

UNIVERSITÉ
DE PAU ET DES
PAYS DE L'ADOUR
# Agricultural policies and biodiversity: impact of the CAP greening on bird abundance and diversity in France*

Isabelle Chort†

Aicha Hasni‡

Berk Öktem§

April 25, 2025

## Abstract

In 2013, the European Union Common Agricultural Policy introduced a set of compulsory greening measures in its subsidies scheme in order to achieve environmental goals. We analyse the impact of this reform, and particularly the definition of Ecological Focus Areas (EFA), on bird diversity in France. We match bird observations from the French Breeding Bird Survey (BBS) to parcel level information on agricultural land use and type of crops. We exploit the heterogeneous exposure of bird observation sites to the reform depending on their ex-ante extent of arable area in a continuous difference-in-differences approach. We find evidence of an increase in overall bird abundance after 2015 in sites with a higher ex-ante proportion of arable land, driven by a small number of generalist species, and no impact on species richness and phylogenetic diversity. A decomposition by diet type suggests that vertebrate eaters were positively impacted by the reform. By contrast, cropland species experienced the most dramatic decline after 2015 in sites with a higher share of arable area. Estimations of the dynamic effects of the reform suggest that the observed positive effects of the reform on bird abundance were limited to the first two years after its implementation. Based on observational data, our findings confirm previous concerns regarding the effectiveness of the 2013 reform in preserving biodiversity.

**Keywords** : agriculture; biodiversity; agricultural policies; Common Agricultural Policy ; Ecological Focus Areas ; France

**JEL classification** : Q18; Q57 ; Q58

<footnote>*We are grateful to Jennifer Alix-Garcia, Tanguy Bernard, Raja Chakir, Philippe Delacote, François Libois, Karen Macours for helpful comments and suggestions. We also thank participants at the 99th annual AES conference, at workshops and seminars in Bordeaux (BSE), Clermont-Ferrand (CERDI), and Galatasaray University.</footnote>

<footnote>†(1) Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TREE, Bayonne ; (2) Institut Universitaire de France (IUF), Paris, France</footnote>

<footnote>‡Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TREE, Bayonne</footnote>

<footnote>§(1) Université Paris-Saclay, INRAE, AgroParisTech, Paris Saclay Applied Economics, France ; (2) Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TREE, Bayonne</footnote>
# 1 Introduction

The dramatic loss of biodiversity across the globe will lead to catastrophic consequences for billions of people if not addressed urgently (IPBES, 2019). The expansion of agricultural land and its intensification are major factors contributing to the reduction in biodiversity by increasing land simplification and agrochemicals use (Jaureguiberry et al., 2022; Raven and Wagner, 2021; Kehoe et al., 2017; Emmerson et al., 2016; Hendershot et al., 2020). Agricultural policies, by influencing farmers' choices through subsidies, shape agricultural landscapes and contribute to the degree and form of intensification (Helfenstein et al., 2020). They thus bear a huge responsibility as regards the preservation of biodiversity.

In Europe, the agricultural landscape has been largely shaped by the large amounts of subsidies delivered through the European Union (EU)'s Common Agricultural Policy (CAP). The CAP is one of the largest agricultural subsidy programs worldwide, and France is by far its largest beneficiary, with more than 9 billion euros a year (16% of the CAP budget). After decades of support to intensification, the EU passed in 2013 a reform of the CAP allegedly representing a turning point as to the inclusion of environmental objectives. With the purpose of promoting environment friendly agricultural practices through "greening" payments, one of the key measures concerned the definition of "Ecological Focus Area" (EFA), and the conditioning of CAP payments to the allocation of a minimum share of farmland to crop and land use types admitted as EFA. Namely, this component of the CAP targets farmers with more than 15 hectares of arable land who are required, from 2015 onwards, to dedicate a minimum of 5% of their arable area to EFA (Article 46 of EU-regulation 1307/2013). The 2013 reform has yet been largely criticized for its lack of ambition (Pe'er et al., 2014).

We investigate in this paper the impact of the 2013 CAP reform on bird diversity by linking data from the French Breeding Bird Survey (BBS) with parcel group level agricultural data from the Land Parcel Identification System (LPIS), available from 2007.

We choose to focus on birds for three main reasons. First, bird diversity matters per se as birds provide numerous ecosystem services, including in particular pest and weed control, seed dispersal, pollination, or soil fertilization (Whelan et al., 2015). Second, bird species have been largely studied and are among the better known animal species, as their monitoring is particularly well developed (Gregory et al., 2005). This implies that we can exploit high-quality data collected with a unified protocol over a large time period. Last, due to their position in the food chain, bird species are particularly relevant markers of biodiversity and ecosystems functioning (Koskimies, 1989; Gregory et al., 2005).

The French BBS provides bird observation data collected by volunteer skilled ornithologists following a common protocol in 2 km × 2 km squares throughout France. Over 2007-2021 period 2,770 squares were surveyed in total, each site being present 6.5 years on average in our sample. We use the geographic coordinates of BBS squares to match them with LPIS data, that contains geo-localized information on the type of crop grown on all parcels or parcel groups eligible to the CAP. We focus on three indicators of bird abundance, species richness and phylogenetic diversity, by bird specialization groups in terms of preferred habitat and diet.
Our main empirical strategy is based on the heterogeneous exposure to the reform depend-
ing on ex-ante square land use characteristics. We exploit the timing of the reform and the
variation in pre-reform average arable area across BBS squares by implementing a continuous
difference-in-differences (DiD) strategy. The panel structure of our data allows us to capture all
time-invariant characteristics of BBS squares that may be correlated with both environmental
indicators and land use choices. We additionally control for a wide set of time-varying poten-
tial confounding factors, including agricultural characteristics, temperatures and precipitations,
night-time luminosity, and amounts of agrochemicals.

Preliminary descriptive evidence shows a jump in the share of EFA in 2015, which continued to grow and reached nearly 14% of total arable area in 2021. While we find no correlation between EFA share and bird indicators, our results show that squares with a higher ex-ante proportion of arable land experienced a rise in bird abundance after 2015. However, this overall increase masks divergence across bird categories. Whereas the abundance of generalist birds, with no clear habitat specialization, increased, cropland birds, which were expected to benefit most from the reform, are found to decrease more after 2015 in squares with the largest share of arable land. Bird categorization in terms of diet reveals a positive and significant impact solely on the small group of vertebrate eaters, comprising four common diurnal raptors. Overall our results suggest that the 2013 CAP reform, and EFA in particular, had little impact on bird diversity, and has not succeeded in halting the decrease in cropland bird abundance.

Our analysis first contributes to the literature that investigates the impact of the CAP in
general, and the 2013 reform in particular. Several contributions have studied the consequences
of the CAP on dimensions such as compliance, agrochemicals use, none has directly explored
empirically the consequences of the 2013 reform on biodiversity. Chakir and Thomas (2022)
discuss the potential adverse environmental impact of a set-aside policy such as was in force
within the CAP from 1992 to 2008. Sauquet (2023) and Diop and Védrine (2025) both focus on
the crop diversification requirements introduced by the 2013 reform. Sauquet (2023) assesses
French farmers’ compliance with this dimension of the reform and states that a large majority of
farmers already complied with diversification requirements before the reform.<fnref n="1" /> Consistent with
this observation, Diop and Védrine (2025) find no impact of the crop diversification criterion on
farm level economic or input based measures of environmental efficiency. Varacca et al. (2023)
specifically study the effects of EFA requirements in the 2013 reform on Italian farms and find
no impact of the reform on agrochemicals use nor crop diversification. Based on similar model
simulations Gocht et al. (2017) find limited environmental impact of the 2013 CAP reform,
while Chiron et al. (2013) predict contrasting results of the reform depending on bird habitat
specialization and CAP scenarios. Hristov et al. (2020) use an agent-based model to simulate
land use changes following the 2013 CAP reform and estimate its potential impact of EFA on
several environmental indicators, including bird diversity. Their predictions about bird diversity
are strongly related with assumptions regarding fallow land. Closer to ours is the article by
Palmer et al. (2025) who empirically investigate the effect of set-aside on bird diversity in the

<footnote n="1">¹A similar assessment had been made by Josefsson et al. (2017) in the case a Sweden.</footnote>
England. They find a positive but small impact of set-aside on bird abundance, and no effect on diversity indicators. Our study relates to the broader economic literature on the evaluation of agricultural policies. In particular, Chabé-Ferret and Subervie (2013) investigate the impact of several Agri-Environment Schemes (AES) in the context of France on different environment-related practices. Schaub et al. (2025) shows the effectiveness of result-based environmental payments to farmers on the conservation of botanical diversity in the case of Switzerland.

Second, we contribute to the recently emerging literature in the economic field that links agricultural landscapes to biodiversity, with a particular focus on bird diversity. While the negative correlation between agricultural intensification and bird abundance and diversity has long been investigated by birds specialists (Donald et al., 2001), and largely confirmed in recent studies (Rigal et al., 2023), the interest of economists in the impact of agricultural practices on bird diversity is relatively recent. Strobl (2022) shows a positive relationship between crop and bird diversity in the United States, using a dataset similar to the French BBS. By contrast, Redlich et al. (2018) using landscape level data collected in Germany find no impact of crop diversity on bird richness and a positive effect of landscape heterogeneity. Noack et al. (2022) exploit the discontinuity in farm size at the former inner German border and find that farm size negatively affects bird diversity.

The remainder of the paper is organized as follows: Section 2 briefly reviews the literature on the agriculture-biodiversity nexus and provides details about the 2013 CAP reform. Section 3 presents the data used in the analysis. The empirical strategy is explained and discussed in Section 4. Estimation results are presented in Section 5. Last, Section 6 concludes.

# 2 Background

## 2.1 Biodiversity and agriculture

Biodiversity is commonly defined by the diversity of all life forms on the planet, including genes and ecosystems, as well as the evolutionary, ecological, and cultural patterns that are fundamental to life (Purvis and Hector, 2000). Consensus statements about the state of biodiversity show that biodiversity enhances the long-term sustainability of ecosystem functions and that ecological systems' role in capturing biologically important resources, generating biomass, and recycling and using biologically important nutrients is drastically reduced by the loss of biological diversity (Cardinale et al., 2012).

Relations between agricultural characteristics and ecosystem services are complex and go in both directions (Van Zanten et al., 2014). The implications of the decline of biodiversity are especially costly for sectors that directly rely on ecosystem services, and in particular agriculture. Pollination, for example, is valued around 15 billion euros per year in the EU (Gallai et al., 2009; Bauer and Wing, 2016). The decline of pollinator populations thus represents a serious threat to agriculture (Klein et al., 2007). Beyond the case of pollination, given as an example, biodiversity determines the sustainability of productive ecosystems and agricultural productivity (Norris, 2008; Chivian et al., 2002; Erisman et al., 2016). On the other hand, among all human
activities that are responsible for major changes and threats to ecosystems (Watson, 2005), one of the most important source of pressure is land use changes (Zebisch et al., 2004; Haines-Young, 2009; Jaureguiberry et al., 2022). Conversely, farming practices, including landscape management, crop choice, rotation and organic farming impact ecosystems and biodiversity (Tscharntke et al., 2005; Kremen and Miles, 2012; Pörtner et al., 2021).

## 2.2 The 2013 CAP reform

Seemingly breaking with decades of support to agricultural intensification, the 2013 reform of the CAP introduced several changes showing the integration of concerns regarding environment and biodiversity conservation.

Besides “cross-compliance” requirements and voluntary participation in AES, a key measure of the reform is the introduction of “greening payments”, amounting to 30% of direct payments under the first pillar, conditioned on a set of environment friendly farming practices. Three conditions were retained: crop diversification (Article 44 of EU-regulation 1307/2013), requirements regarding a minimum area of permanent grassland (Article 45 of EU-regulation 1307/2013) and “Ecological Focus Area” (EFA) (Article 46 of EU-regulation 1307/2013).

While grassland management was left up to member states, greening payments requirements associated with crop diversification and EFA depend on farm size, and in particular arable area (Article 43 of EU-regulation 1307/2013). Crop diversification requirements were little effective, at least in the French context, as Sauquet (2023) shows that 90% of eligible farmers were satisfying crop diversification conditions before the reform, this percentage raising to 93% after the reform.

The last category of greening measures is based on the definition of EFA. The final delineation that was retained includes a combination of semi-natural or natural habitats with high environmental conservation value, together with crops expected to have indirect positive impacts on environment by reducing the amount of inputs, such as nitrogen-fixing crops or cover crops. Last Member States had leeway to choose, within the EFA list, those that would be eligible. Specifically, member States should choose, by August 2014, at least one form of EFA from the following list: fallow land, terraces, landscape features, buffer strips, hectares of agro-forestry that receive or have received support under Article 44 of Regulation (EC) No 1698/2005 and/or Article 23 of Regulation (EU) No 1305/2013, strips of eligible hectares along forest edges, afforested areas, areas with nitrogen-fixing crops, with short rotation coppice with no use of mineral fertilizer nor plant protection products, with catch crops or green cover planted or germinated, each assorted with specific weighting factors. Farms where arable land covers more than 15 ha must devote at least 5% of their arable area to EFA.<fnref n="2" />. In 2016, in the EU, 68% of arable land was subject to EFA requirements. This percentage is probably much larger in France, as the average size of French farms was 59.59 ha in 2016, compared to 14.65 ha in the

<footnote n="2">²Exemptions were granted in few very specific cases, such as farms where at least 75% of the utilized agricultural area is under grass or other forage plants, is set-aside, covered with leguminous crops, and with remaining area is less than 30 hectares, or where at least 75% of the utilized area is permanent grassland, provided that the remaining area does not exceed 30 hectares.</footnote>
EU as a whole.<fnref n="3" /> Note that French agricultural statistics report farm size in terms of Utilized Agricultural Area (UAA), which is broader than arable area, as it includes permanent grassland and permanent crops. Farms with a UAA smaller than 20 ha accounted for 4.2% of total UAA in 2013, and 3.7% in 2016.<fnref n="4" /> In the EU, nitrogen-fixing and catch crops represented more than 70% of EFA in 2015, and fallow land 25% (Commission et al., 2017). Although monitoring data for France are not available, previous works suggest that EFA composition for France is in line with the EU average (Louhichi et al., 2018; Gocht et al., 2017).

The reform was adopted in December 2013, and entered into force in January 2014. However, in order to allow farmers sufficient time to adapt to the new conditions, most measures were fully effective starting from 2015. We thus consider in our analysis that the post-reform period starts in 2015.

# 3 Data

## 3.1 Bird diversity data

We use data on bird diversity from the French Breeding Bird Survey (BBS). The French BBS is a tool allowing breeding bird populations monitoring in time and space in France, using a standard protocol (Julliard and Jiguet, 2002; Fontaine et al., 2021). Since 2001, participants, who are volunteer skilled ornithologists, are randomly allocated to a 2 km × 2 km square within a 10 km radius of their municipality. The protocol consists in identifying and counting all birds by song and sight during 5 minutes at 10 listening points representative of the habitats present in the assigned square. Two monitoring sessions (three since 2011) are carried out during the spring period and the maximum abundance for each species at each collecting point is retained. A total of 2,893 different squares were created since 2001. Each square is followed by a single participants, which implies that if a volunteer stops participating, the square drops from the panel. Thanks to this feature of the protocol, we are able to control for heterogeneous skills of participants with the inclusion of square fixed-effects in our model. We use data from 2007 to 2021, that cover 2,770 squares in total (after dropping those for which latitude and longitude coordinates are missing). On average, over this period, 955 squares have been monitored every year. While 686 squares were surveyed only one year, the average lifespan in our sample is 6.5 years. 486 squares have been monitored for ten years or more, and 102 squares have been monitored continuously over 2007-2021. Observation squares are distributed across all regions (see Section 3.4 and Appendix Figure 1). 412 bird species were referenced in total, 162 of which represent more than 99% of the individuals observed (Lorrillière and Gonzalez, 2016). We follow Fontaine et al. (2021) and restrict our analysis to the 123 most common species, defined as “common birds”.

<footnote n="3">³Source: European Commission agri-food data portal.</footnote>

<footnote n="4">⁴INSEE, Tableaux de l'économie française.</footnote>
### 3.1.1 Classification of bird species

We use in this article different categorizations of bird species that are monitored in the French BBS data. The “all birds” category includes all 123 species listed in Fontaine et al. (2021) as “common birds in Metropolitan France”. For a subsample of 75 species, Lorrillière and Gonzalez (2016) construct a nomenclature based on species specialization in different habitat types. They single out 24 species specialized in agricultural environment (referred to as “farm birds” in our analysis), 24 species specialized in wooded environments (“wood birds”), and 13 species specialized in built environments (“built birds”). Non-specialized species fall into a fourth category called “generalist” birds.<fnref n="5" />

We additionally build on Noack et al. (2022) to single out, better than does the “farm birds” category, species that are specialized in cropland area, as those birds are expected to be the most affected by the CAP reform (Chiron et al., 2013). By applying a methodology similar to Noack et al. (2022), developed in Appendix B, we identify 10 bird species that are particularly abundant in cropland.

Last, we categorize bird species encountered in the BBS based on their dietary habits. We classify species included in the list of common birds depending on their main trophic niche, by comparing information obtained from Avibase<fnref n="6" />, Oiseaux.net, and Bowler et al. (2019) final database constructed based on EltonTraits 1.0 bird traits database (Wilman et al., 2014). We consider only birds for which feeding information is available and similar across all three sources, and exclude aquatic species. We obtain a classification of 79 common birds (out of 123) into invertivores (primarily invertebrates eaters, including in particular all insectivorous species), vertivores (primarily vertebrates eaters), granivores (primarily seed eaters)<fnref n="7" />, and omnivores.

### 3.1.2 Bird abundance and diversity indices

We use three indices to measure the evolution of bird abundance and diversity. Each of these indices is computed for the total number of species listed in the list of common birds, and for each of the sub-categories, depending on habitat or diet, defined above. We first use an index of **abundance**, constructed as the total number of observed birds in each observation square, for each year.

In order to measure the taxonomic diversity among observed birds, we use a **species richness** index defined as the number of different species observed in a given square and year.

Last, we resort to a **phylogenetic diversity** index, which accounts for species evolutionary history. More specifically, we use the Phylogenetic Diversity (PD) index of Faith (1992) according to which each species observed in a given square and year is connected to an evolutionary tree *T*, through a set of branches *b*. The PD index then writes:

<footnote n="5">⁵Lorrillière and Gonzalez (2016) construct their categories by comparing the distribution of the different species, as obtained in BBS data, in the three main habitat types, to the respective share of those habitats. A species is classified as specialized in a given habitat if it is more abundant in that habitat than would be predicted by a homogeneous distribution in the three habitats.</footnote>

<footnote n="6">⁶https://avibase.bsc-eoc.org/species.jsp?avibaseid=D3A260BCA65503C6</footnote>

<footnote n="7">⁷Exclusive herbivorous diet is a very rare case and *Carduelis Chloris* is the only herbivorous species in our sample. We include it in the “granivore” category as it is considered a seed eater by Bowler et al. (2019).</footnote>
$$PD = \sum_{K \in b_T} L_k \qquad (1)$$

where $b_T$ is the set of branches that connect each species to the tree root, and $L_k$ is the length of branch k.

Figure 3 and Figure 4 in Appendix A represent the evolution of abundance and the phylogenetic diversity index for all birds and cropland birds, respectively. Figure 3 shows that variations of the two indicators are strongly correlated when all birds are considered. The sudden drop in both indices observed in 2020 is a likely consequence of the first Covid-19 lockdown on data collection. We further discuss this issue in Section 5.4. While no clear time trend is perceptible for all birds, beyond annual fluctuations, Figure 4 seems to show a declining trend for cropland birds.

## 3.2 Agricultural data

We match bird indices at the square level with agricultural data from the French Land Parcel Identification System (LPIS) or Parcel Graphic Register (PGR). The LPIS geographic dataset provides yearly information on cultivated crops at parcel group (îlot) level since 2007, and parcel level since 2015.<fnref n="8" /> The database contains all parcels that were ever declared for Common Agricultural Policy (CAP) subsidies. The main limit of this dataset is that it does not include crop parcels that are not eligible to CAP aids. However, this is a secondary issue in our analysis, as we focus on temporary crops which are particularly well covered by the LPIS dataset. According to Levavasseur et al. (2016), the LPIS data covered in 2009 the huge majority of cropland and permanent grassland (99% and 95% respectively), but less than half of parcels with perennial crops, many of which being non eligible to European subsidies. LPIS data are spatial polygons, that we match with bird data using the geographic coordinates of the centroid of each BBS square. An example of such matching is provided in Figure 5.

We use in our analysis parcel-group level data from 2007 to 2021. A parcel-group or îlot is defined as a group of adjacent parcels cultivated by the same farmer. The median size of a parcel group was 1.9 hectares in 2014. Parcel-group level data contain information on the main crop or agricultural land use in this group of parcels, divided into 24 categories.<fnref n="9" /> We construct a measure of EFA area by aggregating the protein crops, other oilseeds, grain legumes, forage, fallow land, and miscellaneous crop groups. Our EFA variable is thus only an imperfect measure of actual EFA, as the LPIS data do not allow us to take into account all eligible landscape elements, and the use of LPIS aggregate categories may lead us to include in our definition non-eligible areas.

<footnote n="8">⁸Available at https://geoservices.ign.fr/rpg</footnote>

<footnote n="9">⁹Soft wheat, corn grain and silage, barley, other cereals, rapeseed, sunflower, other oilseeds, protein crops, fiber plants, fallow land, rice, grain legumes, forage, summer pastures and moorland, permanent grasslands, temporary grasslands, orchards, vineyards, nuts, olive groves, other industrial crops, vegetables or flowers, sugar cane, miscellaneous</footnote>
We additionally use LPIS data to calculate acreages of permanent pastures, permanent crops, and “Big Five” crops, that refer to the top five industrialized field crops (wheat, corn, barley, rapeseed and sunflower). Last, we create a variable for arable land, that groups together all annual crops, fallow land, and temporary grassland. Arable area is also equal to total agricultural area, as measured in LPIS data, minus area devoted to permanent pastures and permanent crops.

## 3.3 Additional data

To control for potential confounding factors, we use several complementary data sets from the following sources:

**Weather data:** Following Palmer et al. (2025) and Li et al. (2020), we control for precipitation and temperature patterns during the breeding season, which corresponds to the BBS observation period (April-May), as well as meteorological conditions during the previous winter (December-January), which may affect bird survival. We use the TerraClimate dataset, developed by Abatzoglou et al. (2018), which provides several climate variables with a monthly resolution and 4 km spatial resolution for the period 1958-2021. Their methodology combines climate models (CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55)) and historical estimates from WorldClim (Abatzoglou et al., 2018).<fnref n="10" /> We calculate minimum and maximum temperature and total precipitation for winter (December of year t-1 and January of year t) and spring (March, April, May, June of year t) for each French BBS square. The analysis is performed using Google Earth Engine (GEE).<fnref n="11" /> For temperature variables, we calculate the average over considered months and then take the average of all pixels within each BBS square for each year. As for precipitations, we sum precipitation amounts for each month and then sum the pixels corresponding to each BBS square.

**Nighttime lights:**

Nighttime lights indicators are used in a classic way as proxies for urbanization. Indeed, the extension of urban areas negatively impacts bird diversity (Sol et al., 2020). In addition, controlling for nighttime lights is crucial in our study, as light pollution is of direct concern for bird populations in that it alters their behaviour (Longcore and Rich, 2004; Hölker et al., 2010). We calculate for each STOC site the annual average nighttime light index based on extended nighttime light series elaborated by Chen et al. (2020).<fnref n="12" />

**Forest extent:** Forest cover is likely to affect bird diversity. We thus control for the annual tree cover extent in hectares for each square, based on Turubanova et al. (2023). The authors

<footnote n="10">¹⁰https://www.climatologylab.org/terraclimate.html</footnote>

<footnote n="11">¹¹https://developers.google.com/earth-engine/datasets/catalog/IDAHO EPSCOR TERRACLIMATE</footnote>

<footnote n="12">¹²Two major satellite based nighttime light sources are DMSP-OLS and NPP-VIIRS (Chen et al., 2020). DMSP-OLS covers 1992 to 2013 period while NPP-VIIRS covers since April 2012. The two series differ in particular in that they include different spatial resolution and measurement units. These products are not directly comparable therefore they have to somehow harmonised or unified. Chen et al. (2020) construct a VIIRS-like nighttime light series by developing an auto-encoder model enhanced with convolutional neural networks (CNN) and vegetation index (Chen et al., 2020). Basically, authors use input as vegetation index adjusted DMSP of 2013, called EANTLI (Zhuo et al., 2015), and NPP-VIIRS of same year as label to generate VIIRS like DMSP for pre-2013 period.</footnote>
provides spatial data with a resolution of 30 meters per pixel for tree canopy extent and height change in Europe.<fnref n="13" />

**Herbicides and pesticides:** Data on agrochemical orders by substance are available from 2013 at the postal code level (BNV-d, 2023). We create four variables based on the function and segment of each substance, first by separating herbicides and pesticides<fnref n="14" />, and by calculating separately amounts of herbicides and pesticides authorized for use in organic farming. First, the “total herbicide” variable is the sum of substance active for all herbicides.<fnref n="15" /> We match postal codes with municipality identifier codes in order to obtain variables defined at the municipality level and use latitude and longitude coordinates of BBS squares to map them with agrochemicals variables. Agrochemicals are crucial in our analysis, as the CAP reform, and EFA increase in particular, is expected to affect the use of herbicides and pesticides. Although anecdotal evidence suggests that catch crops may be associated to a larger quantity of herbicides, we find no such correlation in our data. On the contrary, we find, at the municipality level, a negative correlation between the share of EFA and agrochemicals amounts, herbicides in particular.<fnref n="16" /> Moreover, as discussed below in Section 4, amounts of agrochemicals also capture evolutions affecting the agricultural sector, and in particular the expansion of organic farming over the observation period.

**Land-use data:**

Last, we use CORINE Land Cover (CLC) data, to calculate the share of arable area in each BBS square and define the category of cropland birds (see Appendix Section B). CLC data are available at 6-year intervals and provide 44 land cover categories with a 100-meter spatial resolution at a European scale. We use the 2006 and 2012 editions to calculate the share of arable area in each BBS square. We construct the arable area category as the sum of “non-irrigated arable land” and “permanently irrigated land” categories.

All variables related to land use, weather, and agrochemicals are summarized in Appendix A Table 1, along with a brief description and data sources.

## 3.4 Descriptive statistics

Our main estimation sample is made of 2,084 BBS squares observed over 2007-2021, once dropping squares appearing only once over the period. Those 2 km × 2 km squares are located throughout metropolitan France, as illustrated in Appendix Figure 1, with some white areas, in particular in low population density parts of the Centre region.

Appendix Table 2 reports basic statistics of the different measures of bird diversity over 2007-2021 at the square level. 220 birds were observed annually on average, from 32 different species.

<footnote n="13">¹³It can be downloaded from https://glad.earthengine.app/view/europe-tree-dynamics. The authors employ Landsat and LiDAR systems, combined with the latest machine learning techniques, to create an accurate and consistent map of forest extent in Europe from 2001 to 2022. Trees are defined as vegetation with a height of more than 5 meters (Turubanova et al., 2023).</footnote>

<footnote n="14">¹⁴We calculate total pesticide by summing up quantities of substances that are classified either as fongicide, insecticide, and molluscicide.</footnote>

<footnote n="15">¹⁵We sum all substances for which the function variable is defined as “herbicide”.</footnote>

<footnote n="16">¹⁶Results not shown, available upon request.</footnote>
Generalist birds represent the largest number of individuals observed (92, on average), and built environment birds count the greater number of species (10 different species on average). Among bird categories, the phylogenetic diversity indicator is the highest for generalist birds.

The land use and agricultural characteristics of BBS squares are summarized in Table 3 in Appendix. Far ahead of forests (130 hectares on average), agriculture is predominant in BBS squares, as total agricultural area represents on average almost one half of square area (182 hectares over a theoretical area of 40,000 squared meters, that is 400 hectares).<fnref n="17" /> Arable land represents the largest part of agricultural area (118 ha), and average EFA amounts to 7 hectares or 8% of arable land. As reported in Appendix Table 4, we observe substantial heterogeneity across squares regarding arable land area. One fourth of BBS squares count less than 3 hectares of arable area, and 0.5 hectares of EFA. By contrast, arable area and EFA represent on average 289 ha and 11 ha respectively in BBS squares from the top quartile of the arable land distribution. We exploit this heterogeneity in our empirical strategy, as it implies a different degree of exposure to the CAP reform regarding EFA, that is indexed on arable area.

<figure><img src="image_3.png" /><figcaption>Figure 1: Evolution of Ecological Focus Area (EFA) - 2007-2021</figcaption></figure>

Source: LPIS data.

Figure 1 illustrates the evolution of EFA from 2007 to 2021. A jump is observed in 2015, corresponding to the CAP reform. EFA represented around 5% of arable area in 2014, almost doubled in 2015, and continued to grow until 2020, reaching 14% of arable area. In spite of a slight decline of arable area, the period is characterized by a net increase in EFA which increased by almost 1.5 million hectares from 2014 to 2021. However, as shown in Figure 1, the composition of EFA has markedly changed over 2007-2021. The most striking evolution is

<footnote n="17">¹⁷When accounting for terrain ruggedness, in particular in mountainous landscapes, the agricultural area can be more than 400 hectares.</footnote>
the dramatic decline of fallow land (from over 60% of EFA in 2007 to less than 20% in 2021), which was paralleled by an increase in forage crops from 2015 on. In 2021, over 40% of EFA is dedicated to forage crops.

<figure><img src="image_4.png" /><figcaption>Figure 2: Evolution of EFA composition</figcaption></figure>

Source: LPIS data.

Figure 2 in Appendix represents the geographical distribution of EFA (as a share of arable area) before the implementation of the CAP reform, and the variation in EFA between 2014 and 2021. In 2014, EFA part is largest in the South, especially in the Mediterranean area. After the reform, EFA increased in most regions with little exceptions. Among them, a stagnation or modest decline is observed in extended parts of the Northern regions of France, while mountainous areas in the Eastern Pyrenees and the Northern Alps experience the largest drops in EFA areas. Our empirical specifications include square fixed-effects and thus take into account the pre-reform heterogeneity in the development of environmental practices across locations.

# 4 Estimation strategy and identification

## 4.1 Correlation between EFA and bird indicators

We consider two alternative specifications. We first directly estimate the correlation between EFA and biodiversity with the following model:

$$Y_{it} = \alpha_0 + \alpha_1 EF A_{part_{it}} + X'_{it}\gamma + \sigma Region_i * Year_t + \phi_t + \delta_i + \varepsilon_{it} \quad (2)$$

where $Y_{it}$ represent alternatively our three measures of bird species abundance, richness, and
phylogenetic diversity in square *i* and year *t*. *EF Apart*<sub>it</sub> represents EFA area as a percentage of total arable area (taking values from 0 to 100). To control for potential confounding factors, we include in the *X*'<sub>it</sub> vector the following variables measured at the square level: areas (in hectares) of arable land, permanent pastures, and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), and the (log) number of years the squares has been observed. This latter variable captures participant experience, as each square is surveyed by a single participant over the whole 2007-2021 period.<sup>18</sup> The *Region*<sub>*i*</sub> *Year*<sub>*t*</sub> interaction between region dummies and a linear time trend captures time varying regional characteristics that may impact biodiversity. *φ*<sub>*t*</sub> and *δ*<sub>*i*</sub> are year and square fixed-effects. *ε*<sub>*it*</sub> is the error term, and standard errors are clustered at the square level. In additional regressions, we control for amounts (in tonnes) of pesticides and herbicides, that are available at the municipality (*commune*) level, as well as the share of agrochemicals (pesticides and herbicides) authorized for use in organic farming. These variables allow us to control in part for the growth of organic agriculture over our period of interest, that most probably correlates with both land use or agricultural practices and biodiversity. However, data on agrochemicals are available only from 2013, which is why we do not include those variables in our main specification.

With this first specification, we estimate the correlation between changes in the share of EFA within a specific square over time, and bird indicators. However, our estimates of $\beta_1$ may capture the impact of unobserved factors that vary over our the period under study and affect both our dependent and main explanatory variables. Potential confounding factors could include changes in the composition and preferences of the population, environmental awareness, local regulation or politics, or local consequences of climate change, among others. Although we try to limit such concern by controlling for a wide range of variables at the square, municipality, and region level, this strategy does not allow us to estimate a causal impact of the CAP reform on bird diversity.

## 4.2 Continuous DiD approach

We thus complement this first empirical analysis with a second approach that exploits the heterogeneous exposure to the CAP reform across locations. Indeed, the reform applies to agricultural zones, and more specifically, to arable area. According to 2013 CAP reform, EFA is expected to increase with arable area, as EFA requirements are expressed as a share of arable area (5% or arable area for farms of more than 15 hectares, see Section 2.1 above). We thus implement a continuous treatment analysis, where square level “treatment” corresponds to the exposure to the CAP reform and is a function of the pre-reform arable area. We estimate the following model :

<footnote n="18"><sup>18</sup>It seems important to include this variable in our model, as we may expect participants to the BBS to gain experience and skills in identifying an enumerating birds. We indeed find a positive correlation between the different bird indicators and the number of years a square has spent in our sample.</footnote>
$$Y_{it} = \beta_0 + \beta_1 \text{ArableArea}_i \times \text{Post2015}_t + X'_{it}\gamma + \sigma \text{Region}_i * \text{Year}_t + \phi_t + \delta_i + \epsilon_{it} \quad (3)$$

with $Y_{it}$ our bird indicators, $ArableArea_i$ the average share of arable area (in 100 ha) in square $i$ measured before the reform, that is over 2007-2014, $Post2015_t$ a binary variable equal to one from 2015 onwards. $X'_{it}$ is set of control variables that includes areas (in hectares) of “big five” crops, permanent pastures, and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), and the log number of years the square has been included in the panel to capture participant experience. $Region_i * Year_t$ capture region specific linear time trends that may impact biodiversity, $\phi_t$ and $\delta_i$ are year and square fixed-effects. $\epsilon_{it}$ is the error term, and standard errors are clustered at the square level. Clustering at the square level best captures heteroskedasticity induced by individual observers since, as explained above, each square being assigned to a single observer over the whole period. We nevertheless test the robustness of our results to different levels of clustering (department and department-year levels). We also allow for spatial correlation by using Conley (1999) standard errors with different distance cutoffs (see Section 5.5). As in our first model, we alternately estimate our model over the sub-period 2013-2021, and add to our list of controls variables for agrochemicals use that are available at the municipality level from 2013 onwards only.

In an alternative specification, we replace the continuous $ArableArea_i$ variable by dummies for the different quartiles of arable land shares. Last, we conduct an event-study to investigate the dynamic effects of the reform (in Section 5.3).

We additionally present in Appendix A.3 an alternative in which we instrument observed EFA area by the average share of arable area before the reform (over 2007-2014) interacted with the level of EFA requirements. The latter variable is equal to zero up to 2014, and is equal to five percent from 2015 onwards. Such a strategy has been used by Palmer et al. (2025) to assess the impact of set-aside on biodiversity in England. However, whereas they can exploit year-to-year variations in the level of set-aside requirements, one limitation in our case is that EFA requirements were introduced in 2015 and their level remained unchanged from then on. Results from the instrumental variable model are however very similar to our main DiD results. The identification assumptions of this IV model, which are closely related to those discussed in Section 4.3 below, are presented in Appendix A.3.

## 4.3 Threats to identification

This approach partially addresses the concerns raised by the potential endogeneity of EFA variations within squares but the identification of a causal effect of EFA increase on bird indicators is subject to several conditions.

First, and most importantly, although our variable of exposure to the reform, the share of arable area is measured on the period anterior to the reform, and thus not subject to manipu-
lation, it may be correlated with characteristics that also impact bird abundance and diversity. We control in our model for all of these characteristics that do not vary over time (such as soil characteristics, elevation, or hydrography) through square fixed-effects. However, other factors that vary over time may confound our effect. By controlling for a large set of square-level variables, we intend to capture most relevant time-varying factors, such as weather, forest extent, or nighttime lights, that are likely to be correlated with both the ex-ante area of arable land, and bird indicators. In addition, the inclusion of region specific time trends additionally controls for unobserved evolutions at a larger scale that may correlate with both land use and biodiversity. In spite of these precautions, we cannot exclude that our $β_1$ coefficient captures in part different evolutions of square level bird indicators that would be attributable to different underlying trends in the evolution of biodiversity in areas where arable land share is higher, compared to areas with a low share of arable land. Although not sufficient to rule out such an interpretation, descriptive evidence however suggests that the evolution of bird indicators was very similar in squares characterized by a share of arable land above the sample median and those below the median. Indeed, as is shown in Figure 3, although overall bird abundance is slightly higher in squares above the median, trends appear reasonably parallel before the implementation of the reform, except for a small divergence en 2014, then clearly diverge in 2015, and seem to remain parallel again after 2016.

<figure><img src="image_5.png" /><figcaption>Figure 3: Evolution of bird abundance (all birds) in squares with share of arable land below and above the sample median</figcaption></figure>

Source: LPIS and BBS data.

A second limitation of this model is that the $β_1$ coefficient may capture other changes affecting agriculture after 2015, besides EFA increase caused by the CAP reform. Indeed, Appendix Table 5 shows that, besides the increase in EFA, French agriculture underwent several evolu-
tions, including the above mentioned reduction in arable area due to the shift from temporary
to permanent pastures, and the decrease in area devoted to the top five field crops. However,
several arguments can be put forward in support of our interpretation that observed effects are
likely to be driven by the reform, and specifically its EFA component. First, as emphasized
above, by considering the pre-reform amount of arable area as our treatment variable, we focus
on changes related to specific crops and land use, namely temporary crops and pastures. These
changes surely go beyond increases in EFA, but $\beta_1$ does not capture all evolutions affecting agri-
culture as a whole after 2015. Second, the extensive list of variables and fixed-effects included
in our model allow us to control for a wide range of factors that are likely to vary within squares
before and after 2015. We control for changes in temperatures and precipitations at the square
level, and use of agrochemicals, and particularly amounts of pesticides and herbicides authorized
for use in organic farming, at the municipality level. We expect to capture with these latter
variables the increasing trend of organic farming, that may partly explain square-level difference
in biodiversity indicators at the beginning and the end of our observation period. Last, among
the different dimensions of the reform that may impact arable land, EFA requirements represent
the most effective one, as previous studies have shown that crop diversity requirements were
already met by a large majority of French farms before they were included in the CAP.

Third, the fact that our panel of bird observations is unbalanced may raise sample selection issues. Appendix Table 8 documents sample size, new entries and panel attrition by year. We observe a continuous decline in the number of surveyed squares from 2009 to 2016, followed by a recovery from 2017 to 2021, with the exception of the year 2020 in which data collection was affected by the Covid-19 lockdown. We specifically discuss potential changes in sample composition caused by the lockdown in Section 5.5 and show that our results are robust to dropping 2020. Appendix Table 8 does not suggest obvious change in panel entry and exit patterns after the reform, which would suggest sample recomposition effects that could bias our results. Nevertheless, in order to address potential sample selection issues due to the unbalanced structure of our panel, we re-estimate our model on the subsample of 102 squares that are observed throughout the entire period (see Section 5.5).

# 5 Results

## 5.1 EFA and biodiversity

As a preliminary analysis, we report in Table 1 the estimation results of Equation 2. As both year and square fixed-effect are included in the model, we control for any shock common to all squares - through year fixed effects - as well as for all time-invariant factors that may correlate with both EFA and biodiversity. However, this specification cannot account for square-specific time variables that may confound our effect. The main variable of interest is EFA as a share of arable area and observation units are BBS squares. In columns (1), (3), and (5) correspond to the entire period (2007-2021), whereas columns (2), (4) and (6) are limited to the 2013-2021 sub-period, for which we have data on agrochemicals sales at the municipality level. Our
results show no significant correlation between EFA and abundance nor diversity indices. By contrast, all three indices tend to be sensitive to temperature, especially over 2013-2021, and precipitations. Bird abundance is negatively correlated with the municipality level amount of pesticides.

We further refine the analysis by exploring the correlation between EFA part and bird indices by bird preferred habitat (in Appendix Table 6), and diet type (in Appendix Table 7). Although all coefficients are not reported in those two tables, regressions include the same set of controls and fixed effects as in Table 6. Results by bird category confirm findings from Table 1. We find no evidence of any significant correlation between EFA share and bird abundance, species richness, or phylogenetic diversity, on any bird category considered.

<table><caption>Table 1: Relation between EFA part and bird abundance and diversity for all birds</caption><thead><tr><th rowspan="2"></th><th colspan="2">Abundance</th><th colspan="2">Species richness</th><th colspan="2">Phylo. index</th></tr><tr><th>2007-2021</th><th>2013-2021</th><th>2007-2021</th><th>2013-2021</th><th>2007-2021</th><th>2013-2021</th></tr></thead><tbody><tr><td>EFA (% of arable)</td><td>0.017<br>(0.066)</td><td>-0.026<br>(0.054)</td><td>-0.002<br>(0.003)</td><td>-0.002<br>(0.004)</td><td>0.030<br>(0.116)</td><td>0.031<br>(0.144)</td></tr><tr><td>Arable area (ha)</td><td>-0.032<br>(0.103)</td><td>-0.108<br>(0.121)</td><td>0.001<br>(0.006)</td><td>-0.005<br>(0.007)</td><td>0.091<br>(0.210)</td><td>-0.188<br>(0.271)</td></tr><tr><td>Perm. pastures (ha)</td><td>0.006<br>(0.085)</td><td>-0.025<br>(0.081)</td><td>0.003<br>(0.004)</td><td>0.001<br>(0.005)</td><td>0.068<br>(0.177)</td><td>0.005<br>(0.217)</td></tr><tr><td>Perm. crops (ha)</td><td>0.151<br>(0.178)</td><td>-0.145<br>(0.324)</td><td>0.005<br>(0.012)</td><td>0.001<br>(0.019)</td><td>0.001<br>(0.463)</td><td>0.155<br>(0.845)</td></tr><tr><td>Forest extent (ha)</td><td>-0.104<br>(0.138)</td><td>-0.005<br>(0.195)</td><td>-0.008<br>(0.014)</td><td>0.007<br>(0.020)</td><td>-0.246<br>(0.415)</td><td>-0.035<br>(0.631)</td></tr><tr><td>Nighttime light</td><td>-0.175<br>(0.328)</td><td>0.343<br>(2.009)</td><td>-0.014<br>(0.021)</td><td>-0.018<br>(0.093)</td><td>-0.159<br>(0.876)</td><td>-2.926<br>(3.227)</td></tr><tr><td>Max. temp. (spring)</td><td>5.456<br>(5.483)</td><td>20.883*<br>(12.417)</td><td>0.034<br>(0.398)</td><td>1.035<br>(0.705)</td><td>9.053<br>(14.946)</td><td>55.266**<br>(26.892)</td></tr><tr><td>Min. temp. (spring)</td><td>-8.662<br>(11.698)</td><td>-28.527<br>(25.069)</td><td>-0.518<br>(0.561)</td><td>-2.263**<br>(0.974)</td><td>-11.737<br>(20.267)</td><td>-85.748**<br>(36.847)</td></tr><tr><td>Prec. (spring)</td><td>-0.108**<br>(0.048)</td><td>-0.111*<br>(0.066)</td><td>-0.002<br>(0.003)</td><td>-0.002<br>(0.004)</td><td>-0.081<br>(0.109)</td><td>-0.099<br>(0.154)</td></tr><tr><td>Max. temp. (winter)</td><td>-3.074<br>(3.342)</td><td>-0.289<br>(5.874)</td><td>-0.265<br>(0.212)</td><td>-0.398<br>(0.356)</td><td>-7.090<br>(8.036)</td><td>-17.935<br>(13.763)</td></tr><tr><td>Min. temp. (winter)</td><td>1.768<br>(3.539)</td><td>3.444<br>(4.952)</td><td>0.321<br>(0.247)</td><td>0.681*<br>(0.349)</td><td>10.553<br>(9.383)</td><td>29.819**<br>(13.635)</td></tr><tr><td>Prec. (winter)</td><td>0.100*<br>(0.054)</td><td>0.142*<br>(0.086)</td><td>0.004<br>(0.003)</td><td>0.002<br>(0.005)</td><td>0.231*<br>(0.132)</td><td>0.139<br>(0.176)</td></tr><tr><td>Ln(Experience)</td><td>4.376<br>(3.939)</td><td>1.133<br>(4.705)</td><td>0.243<br>(0.212)</td><td>0.323<br>(0.269)</td><td>5.501<br>(7.824)</td><td>6.116<br>(10.255)</td></tr><tr><td>Herbicides (tons)</td><td></td><td>0.249<br>(0.249)</td><td></td><td>-0.021<br>(0.017)</td><td></td><td>-0.813<br>(0.655)</td></tr><tr><td>Pesticides (tons)</td><td></td><td>-0.221**<br>(0.101)</td><td></td><td>-0.003<br>(0.008)</td><td></td><td>-0.191<br>(0.366)</td></tr><tr><td>Part bio. chem. (%)</td><td></td><td>-0.093<br>(0.101)</td><td></td><td>0.000<br>(0.005)</td><td></td><td>-0.018<br>(0.207)</td></tr><tr><td>Observations</td><td>13,635</td><td>7,301</td><td>13,635</td><td>7,301</td><td>13,635</td><td>7,301</td></tr><tr><td>Cluster nb.</td><td>2,084</td><td>1,401</td><td>2,084</td><td>1,401</td><td>2,084</td><td>1,401</td></tr><tr><td>Mean Y</td><td>220.825</td><td>220.525</td><td>32.424</td><td>32.599</td><td>1,306.027</td><td>1,308.099</td></tr><tr><td>R<sup>2</sup> (within)</td><td>0.010</td><td>0.010</td><td>0.007</td><td>0.009</td><td>0.008</td><td>0.013</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions include square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
## 5.2 Continuous DiD

In our second empirical approach, we take into account the endogeneity of EFA share, and estimate a continuous DiD model, which amounts to considering the initial extent of arable area as a predictor of post-reform EFA. By interacting arable area with a binary variable equal to one after the reform, we are able to exploit the variability in the local exposure to the reform and estimate its impact on bird abundance and diversity. Although this approach does not allow us to single out the impact of EFA specifically, the most important change affecting arable area in the 2013 CAP reform is EFA requirements, expressed as a share of farm-level arable area.

<table><caption>Table 2: Impact of EFA area on bird abundance and diversity (Continuous DiD)</caption><thead><tr><th rowspan="2"></th><th colspan="1">All birds</th><th colspan="4">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>3.5506**<br>(1.5930)</td><td>-0.7015<br>(0.4404)</td><td>0.4335<br>(0.2874)</td><td>1.0348<br>(0.6311)</td><td>0.7385<br>(0.6087)</td><td>-1.1106***<br>(0.3291)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>3.7775**<br>(1.7133)</td><td>-0.5661<br>(0.6175)</td><td>0.5873*<br>(0.3022)</td><td>0.9309<br>(0.6988)</td><td>1.0240<br>(0.6546)</td><td>-0.4668<br>(0.3564)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0653<br>(0.1140)</td><td>-0.0270<br>(0.0333)</td><td>0.0237<br>(0.0349)</td><td>-0.0119<br>(0.0300)</td><td>-0.0023<br>(0.0286)</td><td>-0.0373<br>(0.0229)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.2210<br>(0.1391)</td><td>0.0205<br>(0.0422)</td><td>0.0443<br>(0.0406)</td><td>0.0488<br>(0.0403)</td><td>0.0208<br>(0.0369)</td><td>0.0049<br>(0.0291)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>3.4841<br>(4.0899)</td><td>-1.0504<br>(2.0840)</td><td>2.1160<br>(1.4118)</td><td>-1.0380<br>(1.5548)</td><td>0.2524<br>(1.3853)</td><td>-1.4826<br>(1.5118)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>9.7002*<br>(5.3759)</td><td>2.3334<br>(2.7068)</td><td>1.9789<br>(1.7117)</td><td>1.6664<br>(2.1207)</td><td>1.3886<br>(1.9253)</td><td>1.8665<br>(1.9465)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

Estimation results of Equation 3 are reported in Table 2 (for bird categories by habitat type) and Table 3 (by diet). Coefficients shown in Table 2, Column (1), suggest that a greater exposure to the reform, characterized by a larger share of land devoted to temporary crops and pastures, significantly increases the abundance of birds (considering all “common birds” listed by Fontaine et al. (2021)). This effect is robust to controlling for agrochemicals use, for which data are available from 2013 only. Results suggest that a 100 hectare increase in arable area (measured before the reform) is associated with a 3.6 to 3.8 increase in the number of birds observed yearly after 2015. However, we find no significant impact of the reform on the number of different

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
species, and a positive effect on phylogenetic diversity that is only marginally significant over
the 2013-2021 period when controlling for agrochemicals. The increase in post-reform bird
abundance in squares that were more exposed to the increase in EFA is not homogenous across
bird categories. Columns (2) to (5), based on the classification established by Lorrillière and
Gonzalez (2016), suggest that the overall increase in abundance is driven by generalist and
wood birds, while the abundance of farm birds tends to decline (though not significantly). In
addition, separate estimations species by species (not shown) suggest that the overall increase is
driven by few species only, among which are, in descending order, the common starling (sturnus
vulgaris)<fnref n="19" />, the common wood pigeon (columba palumbus), and the common swift (apus apus).
The abundance of cropland birds, based on a category that we constructed by applying the
methodology developed by Noack et al. (2022) (see Appendix B), experiences by contrast a
significant decline after 2015 in squares where EFA is expected to have increased more. The
coefficient is no longer significant in Panel A2, when restricting the observation period to 2013-
2021.<fnref n="20" /> This finding suggests that, consistent with Figure 4, the decline in the number of
cropland birds in squares with a larger share of arable land started well before the reform.
Based on observations in Germany, Jerrentrup et al. (2017) indeed show that farmland birds
are negatively affected by the homogenisation of the agricultural landscape.

<footnote n="19">¹⁹Note that *sturnus vulgaris* belongs included in the list of common birds that are not classified by habitat type by Lorrillière and Gonzalez (2016)</footnote>

<footnote n="20">²⁰The loss of significance is indeed due to the restriction of the observation period rather than to the intro-
duction of agrochemicals controls, as even without those additional controls, the coefficient is similar in size and
not significant at conventional levels (results not shown).</footnote>
<table><caption>Table 3: Impact of EFA area on bird abundance and diversity by diet category (Continuous DiD)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.3477<br>(0.5330)</td><td>0.7388<br>(0.6245)</td><td>0.0437<br>(0.1912)</td><td>0.1070**<br>(0.0499)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.6654<br>(0.6502)</td><td>0.7975<br>(0.7631)</td><td>-0.0151<br>(0.2106)</td><td>0.1806***<br>(0.0623)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0295<br>(0.0563)</td><td>0.0275<br>(0.0344)</td><td>-0.0035<br>(0.0269)</td><td>0.0298*<br>(0.0163)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0208<br>(0.0701)</td><td>0.0735*<br>(0.0418)</td><td>0.0202<br>(0.0309)</td><td>0.0522**<br>(0.0213)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.2135<br>(2.1054)</td><td>0.9254<br>(1.3740)</td><td>-0.3049<br>(0.5226)</td><td>1.9480<br>(1.1990)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.1169<br>(2.8123)</td><td>1.4332<br>(1.7032)</td><td>0.0614<br>(0.6051)</td><td>3.6877**<br>(1.5895)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

Results by diet category, reported in Table 3, suggest that the reform had no impact on invertivores, omnivores and granivores, but significantly increased the abundance, richness and diversity of the vertivores category. This group contains four species of diurnal raptors.<fnref n="21" /> These findings are consistent with Butet et al. (2010), who focused on two of these species and connect their numerical decline to agricultural intensification and the reduction of natural and semi-natural sites such as hedgerows or grasslands areas, that are included in the CAP definition of EFA.

Appendix Section A.1 investigates potential non-linearities by substituting quartiles of arable area to our continuous measure. All our results, both positive - ie the increase in all birds and vertivores abundance - and negative - the decrease in cropland birds - are driven by squares in the fourth quartile, ie with the largest ex-ante part of arable land (289 hectares on average).

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>

<footnote n="21">²¹ *Falco tinnunculus* (common kestrel), *buteo buteo* (common buzzard), *circus aeruginosus* (western marsh harrier), and *accipiter nisus* or sparrowhawk</footnote>
## 5.3 Dynamic effects of the reform

In order to estimate the dynamic effects of the reform on bird abundance, we conduct an event-study analysis based on the estimation of the following model:

$$Y_{it} = \beta_0 + \sum_{k=2007, k \neq 2014}^{2021} \delta_k (ArableArea_i \times D_k) + \gamma X'_{it} + \sigma Region_i * Year_t + \phi_t + \delta_i + \varepsilon_{it} \quad (4)$$

$Y_{it}$ represents bird abundance in square $i$ and year $t$, $ArableArea_i$ is the pre-reform (2007-2014) average arable area (in 100 ha) in $i$, $D_k$ for $2007 \le k \le 2021$ and $k \ne 2014$ are binary variables capturing the number of periods (years) before and after the reform. The year dummy for 2014 (ie one year before the reform) is excluded. $\phi_t$ and $\delta_i$ are year and square fixed effects. $X'_{it}$ is the same set of control variables as in Equation 3. Standard errors $\varepsilon_{it}$ are clustered at the square level.

Results for all birds, farm birds, cropland birds and vertebrate eaters, with both the two-way fixed effects and the estimator developed by de Chaisemartin et al. (2022) (DcDh) are reported in Figure 4 to Figure 7. In spite of larger confidence intervals with the DcDh estimator, results are consistent across estimators. Plotted point estimates do not support evidence of obvious violations of the parallel trend assumption for the different categories, even though a jump is observed in the abundance of all common birds in 2008 and 2009 with the DcDh estimator. Positive effects of the reform (on all birds and vertebrate eaters) are observed in the first two years after the reform, and fade out rapidly as of 2017. By contrast, the negative impacts on farm and cropland birds appear two to three years after the implementation of the reform and seem to disappear in 2021. Event-study results for the other habitat and diet categories are shown in Appendix Figure 6 to Figure 11.
<figure><img src="image_6.png" /><figcaption>Figure 4: All birds</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 6: Cropland birds</figcaption></figure>

<caption>Figure 7: Vertebrate eaters</caption>

Note: we use user-written Stata command did_multiplegt_dyn (de Chaisemartin et al., 2022).
## 5.4 Mechanisms and discussion

While in theory EFA requirements were meant to promote environment friendly practices, the compromise that was reached between member States resulted in an enlarged definition of EFA so as to include forage crops or nitrogen fixing crops.

As a consequence, EFA is a broad and heterogeneous category, and the impact of an increase of EFA on bird diversity is expected to depend on its precise composition. Among EFA, besides landscape elements such as groves, or hedges that provide suitable nest sites but represent a small proportion of declared areas, the sub-category that is expected to have the largest potential positive impact on bird species, and among them on farmland birds specifically, is fallow land (Traba and Morales, 2019; Sanz-Pérez et al., 2019). In order to directly investigate the correlation between fallow area and bird indicators, we re-estimate Equation 2 replacing EFA area by fallow area. We restrict this analysis to the post-2015 period, for which we can exploit more precise parcel-level data that helps us more accurately identify fallow land. Since EFA area, and more crucially, the choice of crop or soil management type within EFA, is endogenously determined by farmers, this specification does not support causal interpretations. Consistent with the literature, estimation results reported in Table 9 indeed show that fallow area is positively correlated with farm and generalist birds abundance as well as wood bird species richness and phylogenetic diversity. By contrast, fallow area is not significantly correlated with any bird indicator when disaggregated by diet type.

Although France is the only EU country that did not communicate EFA monitoring data, anecdotal evidence however suggests that French farmers, as was also observed in Italy (Varacca et al., 2023), preferentially opted for more profitable forage and nitrogen-fixing crops than for fallows (Pe’Er et al., 2017), thus limiting the global impact of the introduction of EFA requirements on bird diversity. However, forage and cover crops, although they represent little benefit to a majority of bird species, are likely to have a positive impact on birds of prey. Indeed, as evidenced by regional administrations and agricultural organizations reports<fnref n="22" /> based on qualitative plot-level data, infestation of agricultural parcels by rodent species, and in particular by common voles, are more frequent with the use of cover crops and the reduction of tillage. These observations are consistent with the literature, that has long acknowledged the favourable conditions to rodent populations created by conservation agriculture and no-tillage practices (Giusti et al., 1996; Witmer et al., 2007). EFA requirements, and the choice of a majority of farmers to comply with those requirements by increasing forage and cover crops, may have thus contribute to increase the suitability of agricultural landscapes to diurnal raptor species thanks to a greater availability of preys. However, as shown in Figure 7 this increase appears to have been short-lived. A positive effect of the reform on vertebrate eaters is observed only in the first two years after its implementation. Increased pest control, and in particular the authorization in 2018 of new rodenticides based on zinc phosphides may explain the fading out of the positive effects of the reform on vertebrate eaters as of 2018. Note that an increase

<footnote n="22">²²Reports about vole control and monitoring are periodically issued by the different regional departments for food, agriculture, and forest (DRAAF) and Chambers of Agriculture.</footnote>
in the abundance of raptors may negatively affect populations of birds that are prey items for them<sup>23</sup>, however we find no such correlation in our data, and evidence in the literature is mixed (Amar et al., 2018; Chamberlain et al., 2009; Newton, 2010).

As birds are mobile species, studying bird diversity raises the question of the adequate level of analysis. In our main approach we choose to focus on the characteristics of the immediate surroundings of bird observations. We thus stick to the delimitation of BBS squares as we match the exact border of the theoretical observation square with LPIS data, providing us with information about the extent of agricultural land and the type of crops that are grown within each 2 km × 2 km square included in the BBS. However most bird populations are mobile and are likely to be influenced by environmental parameters beyond the unnatural boundaries of BBS squares in which they are observed. We thus create larger buffers by considering squares of 5 km around the centroid of each BBS square.<sup>24</sup> Results, reported in Appendix Table 10 and Table 11 confirm our main findings. The smaller size of estimated coefficients are explained by the larger area of buffers. A 100 ha increase in the pre-reform arable area in a 5 km buffer leads to 0.4 to 0.6 more birds observed annually after the reform. However, 100 ha represent only 4% of the theoretical area of a 5 km buffer, whereas it amounts to 25% of 2 km × 2 km BBS squares.

## 5.5 Robustness

### Sample selection

As discussed in Section 4.3, the fact that our panel is unbalanced, with squares entering and exiting the sample each year, may raise sample selection issues. Table 8 describes the evolution of the sample composition over the period under study. A first concern is related to the direct consequences of the Covid-19 pandemic, as the BBS data collection period in 2020 coincided with the first lockdown in France, that lasted from mid-March to mid-May. The drop in the number of squares observed in 2020 can be interpreted as a consequence of mobility constraints and/or difficulty to recruit and form new observers due to the lockdown. As reported in Appendix Table 8, only 739 BBS squares were surveyed in 2020, which represents a 19% drop compared to the previous year. By contrast, the number of squares surveyed in 2021 reached 915, the highest figure ever attained since 2013. In addition to reducing the number of observed squares, the lockdown seems to have impacted the geographic spread of the survey. This is what is suggested by Figure 8 that compares the geographical distribution of BBS squares surveyed in 2020 to those observed any other year.

<footnote n="23"><sup>23</sup>Accipiter Nisus, and Falco Tinnunculus, in particular, are predators of small bird species.</footnote>

<footnote n="24"><sup>24</sup>The choice of a 5 km threshold results from a trade-off, as larger buffers would amplify problems of overlapping squares.</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 8: Geographic distribution of BBS squares - Comparing 2020 to 2007-2019 and 2021</figcaption></figure>

Source: French BBS.
Last, a closer investigation reveals that squares in which no data were collected in 2020 are characterized by the attrition of more experienced observers than any other year. Squares absent from the sample in 2020 were present in our sample for 8.2 years on average, compared to 6.4 years for those that were absent either in 2019 or in 2021. Since each square is surveyed by a single participant all throughout the observation period, the number of years a square is surveyed captures the experience of the participant. As mentioned in Section 4.1 (footnote 17), we find a positive correlation between bird indicators and experience in our data. The attrition of experienced observers in 2020 is not a surprise in the context of the Covid-19 pandemic. Indeed, although we do not know the age of participants, those among them who have the larger BBS data collection experience are likely to be older, and age was a determinant of compliance with the first lockdown's regulations (Metzger et al., 2024).

Those three features - less bird observations, unevenly distributed, and collected by less experienced volunteers - may explain the abrupt drop in bird abundance and diversity in 2020 shown in Figure 3. We thus test the robustness of our main findings to the exclusion of observations collected in 2020. Estimation results of Equation 3, reported in Appendix Table 12 and Table 13, are very similar to our main results. This suggests that the observed decline in crop-land birds abundance, in particular, is not explained by the changes in the sample composition resulting from the 2020 lockdown.

Second, in order to account more generally for any potential bias arising from changes in the composition of our sample over time, we test the robustness of our main results to the restriction of the estimation sample to the balanced panel of observation, keeping only the 102 squares that were observed all throughout the 2007-2021 period. Although the number of observations is drastically reduced, causing estimated standard errors to increase, estimation results reported in Appendix Table 14 and Table 15 are consistent with our main findings, which suggests that sample selection issues are unlikely to drive our estimated effects.

## Spatial correlation

Given that we use data with a major geographic component, we expect our observations to be spatially correlated. We address this concern by estimating our model with Conley (1999) standard errors with different distance cutoffs. Table 16 and Table 17, in Appendix, report results replicating our main specification with Conley (1999) standard errors and a 50 km cutoff. The significance of our coefficients is only marginally affected and rather tends to increase. Very similar results are obtained with 100 km and 200 km distance cutoffs (tables not shown, available upon request).

# 6 Conclusion

We analyse in this paper the consequences of the 2013 reform of the Common Agricultural Policy on bird abundance and diversity in metropolitan France. We focus in particular on one feature of the 2013 reform, also known as the CAP “greening”, that imposed to most farmers to devote
a minimum of 5% of their arable area to natural elements, such as hedgerows, or cultivation practices that were expected to preserve biodiversity, thus called Ecological Focus Areas (EFA). We use bird observation data from the French Breeding Bird Survey (BBS) aimed at monitoring bird populations and construct a panel of geo-localized bird 2 km × 2 km observation sites from that we match with parcel-group level agricultural information. The reform has been largely effective as we observe a jump in EFA the year it was implemented (2015). Descriptive evidence however shows an absence of correlation between EFA increase and bird abundance or diversity indicators within BBS squares. As local farming decisions, and EFA in particular, may be endogenous, we exploit the pre-reform heterogeneity in agricultural area and crop or land use choice. Since EFA requirements are set as a proportion of arable area, we consider the local pre-reform share of arable land as an indicator of the exposure to the CAP reform, and especially to EFA increase. We control for a wide range of characteristics, from weather variable to nighttime lights and municipality level amounts of agrochemicals, to take into account potential confounding factors. We find that a larger pre-reform arable area is associated with a higher post-reform abundance of birds. By contrast, we find limited impact on species richness or phylogenetic diversity. Further distinguishing birds by preferred habitat and diet type, we find that the increase in overall bird abundance in most affected areas conceals a reduction in cropland birds and an increase in generalist and wood birds abundance. Results by diet type show that vertebrate eaters, namely diurnal raptor species, increased after 2015 in observation sites characterised by a larger ex-ante extent of arable land. Taken together, these findings suggest that the CAP reform was insufficient to limit the fall in bird diversity, in particular among birds specialized in cropland, whose decline, caused by agricultural intensification and reduction of fallow land (Traba and Morales, 2019; Sanz-Pérez et al., 2019; Gayer et al., 2019), has continued after 2015. Evidence from Germany, based on qualitative interviews, suggests that farmers first complied with EFA requirements by registering areas that already existed (Zinngrebe et al., 2017). The net increase in EFA is thus probably much smaller than appears from LPIS data. Furthermore, the broad and heterogeneous EFA category that was finally retained, beyond natural elements that host most plant and animal species (Cormont et al., 2016), illustrates the erosion of the 2013 reform project in the negotiation process prior to its adoption (Swinnen, 2015). As a result, in France, as is observed in other countries (Pe’Er et al., 2017; Varacca et al., 2023), farmers substituted catch crops and forage crops of little interest as regards biodiversity preservation to fallows. The 2013 reform thus failed to put a halt to the continuous reduction of fallow land and did not offset the dramatic loss of fallows caused by the abolition of fallow requirements in 2008. In spite of potential adverse environmental effects of set-aside policies (Chakir and Thomas, 2022), and rather mixed evidence of set-aside requirements on bird diversity in the English context (Palmer et al., 2025), the more flexible fallow management implied by the 2013 reform did not produce satisfactory outcomes as regards bird diversity. However biodiversity conservation in agricultural land goes beyond the question of fallows and requires combining different levels of approach to reintroduce complexity and functional diversity in agricultural landscapes (Estrada-Carmona et al., 2022). As argued by
Gregory et al. (2005), farmland birds indicators are well representative of overall biodiversity in those environments. The continued decline in cropland birds represents the most visible part of an overall loss of biodiversity in agricultural landscapes. Our results confirm that a more ambitious agricultural policy is needed to stop the decline of biodiversity in Europe, yet the 2023-2027 CAP, like its predecessor, does not fully address this challenge (Guyomard et al., 2023; Heyl et al., 2021; Pe’Er et al., 2019).
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A Additional tables and figures

<table><caption>Table 1: Description of variables on land-use, weather, and agrochemicals</caption><thead><tr><td>Variable Name</td><td>Description</td><td>Source</td></tr></thead><tbody><tr><td>Ecological focus area (EFA)</td><td>Miscellaneous, protein crops, other oilseeds, grain legumes, forage, fallow land</td><td>LPIS</td></tr><tr><td>Arable area</td><td>Soft wheat, corn grain and silage, barley, rape-seed, sunflower, forage, grain legumes, vegetables or flowers, sugar cane, miscellaneous, other industrial crops, rice, fiber plants, protein crops, other oilseeds, other cereals, fallow land, temporary grasslands</td><td>LPIS</td></tr><tr><td>Big 5</td><td>Soft wheat, corn grain and silage, barley, rape-seed, sunflower (in hectares)</td><td>LPIS</td></tr><tr><td>Permanent pastures</td><td>Permanent grasslands, summer pastures, moorland (in hectares)</td><td>LPIS</td></tr><tr><td>Permanent crops</td><td>Vineyards, orchards, olive groves, nuts (in hectares)</td><td>LPIS</td></tr><tr><td>Total agri. area</td><td>Arable area, permanent crops and permanent pastures (in hectares)</td><td>LPIS</td></tr><tr><td>Forest extent</td><td>Annual tree canopy extent, satellite-based with a 30m per pixel spatial resolution. Trees are defined as vegetation higher than 5 meters.</td><td>Turubanova et al. (2023)</td></tr><tr><td>Night-time light</td><td>Extended VIIRS-like night-time light series that harmonizes DMSP (1992-2013) and VIIRS (2012-onwards) datasets.</td><td>Chen et al. (2020)</td></tr><tr><td>Weather</td><td>We calculate the average maximum and minimum temperatures (°C) by BBS site for spring (March-June) and winter (December-January), and total precipitations in mm for the same periods</td><td>TerraClimate (Abatzoglou et al., 2018)</td></tr><tr><td>Agrochemicals</td><td>We calculate amounts of herbicides and pesticides based on sales of agrochemicals by chemical type at postal code level since 2013.</td><td>BNV-D (BNV-d, 2023)</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>Figure 1: Geographic distribution of BBS squares - 2007-2021</figcaption></figure>

Source: French BBS.
<table><caption>Table 2: Summary statistics of bird abundance and diversity indices for BBS squares - Period: 2007-2021</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>Min</th><th>P50</th><th>Max</th><th>N</th></tr></thead><tbody><tr><td><strong>Abundance</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>All birds</td><td>220.5</td><td>102.9</td><td>0.0</td><td>209.0</td><td>2,840.0</td><td>14321</td></tr><tr><td>Farmland birds</td><td>24.2</td><td>30.2</td><td>0.0</td><td>16.0</td><td>1,466.0</td><td>14321</td></tr><tr><td>Wood birds</td><td>35.8</td><td>27.8</td><td>0.0</td><td>30.0</td><td>885.0</td><td>14321</td></tr><tr><td>Generalist birds</td><td>92.1</td><td>42.4</td><td>0.0</td><td>89.0</td><td>569.0</td><td>14321</td></tr><tr><td>Built env. birds</td><td>42.0</td><td>45.0</td><td>0.0</td><td>32.0</td><td>1,075.0</td><td>14321</td></tr><tr><td>Cropland birds</td><td>25.3</td><td>21.7</td><td>0.0</td><td>20.0</td><td>245.0</td><td>14321</td></tr><tr><td>Invertivores</td><td>56.6</td><td>36.5</td><td>0.0</td><td>51.0</td><td>1,270.0</td><td>14321</td></tr><tr><td>Omnivores</td><td>78.0</td><td>41.0</td><td>0.0</td><td>74.0</td><td>1,504.0</td><td>14321</td></tr><tr><td>Granivores</td><td>10.8</td><td>10.5</td><td>0.0</td><td>8.0</td><td>500.0</td><td>14321</td></tr><tr><td>Vertivores</td><td>2.5</td><td>2.7</td><td>0.0</td><td>2.0</td><td>46.0</td><td>14321</td></tr><tr><td><strong>Species richness</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>All birds</td><td>32.4</td><td>8.2</td><td>0.0</td><td>33.0</td><td>63.0</td><td>14321</td></tr><tr><td>Farmland birds</td><td>4.5</td><td>2.6</td><td>0.0</td><td>5.0</td><td>13.0</td><td>14321</td></tr><tr><td>Wood birds</td><td>5.4</td><td>2.7</td><td>0.0</td><td>6.0</td><td>11.0</td><td>14321</td></tr><tr><td>Generalist birds</td><td>7.0</td><td>3.0</td><td>0.0</td><td>7.0</td><td>16.0</td><td>14321</td></tr><tr><td>Built env. birds</td><td>9.9</td><td>2.1</td><td>0.0</td><td>10.0</td><td>13.0</td><td>14321</td></tr><tr><td>Cropland birds</td><td>3.5</td><td>1.9</td><td>0.0</td><td>3.0</td><td>10.0</td><td>14321</td></tr><tr><td>Invertivores</td><td>11.7</td><td>3.9</td><td>0.0</td><td>12.0</td><td>28.0</td><td>14321</td></tr><tr><td>Omnivores</td><td>9.7</td><td>2.2</td><td>0.0</td><td>10.0</td><td>16.0</td><td>14321</td></tr><tr><td>Granivores</td><td>3.0</td><td>1.6</td><td>0.0</td><td>3.0</td><td>9.0</td><td>14321</td></tr><tr><td>Vertivores</td><td>1.2</td><td>0.9</td><td>0.0</td><td>1.0</td><td>4.0</td><td>14321</td></tr><tr><td><strong>Phylogenetic diversity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>All birds</td><td>1,304.6</td><td>291.1</td><td>0.0</td><td>1,322.2</td><td>2,407.3</td><td>14321</td></tr><tr><td>Farmland birds</td><td>300.8</td><td>151.9</td><td>0.0</td><td>314.0</td><td>784.5</td><td>14321</td></tr><tr><td>Wood birds</td><td>329.3</td><td>111.3</td><td>0.0</td><td>344.6</td><td>570.7</td><td>14321</td></tr><tr><td>Generalist birds</td><td>539.8</td><td>100.1</td><td>0.0</td><td>552.6</td><td>671.4</td><td>14321</td></tr><tr><td>Built env. birds</td><td>301.7</td><td>130.3</td><td>0.0</td><td>323.6</td><td>481.2</td><td>14321</td></tr><tr><td>Cropland birds</td><td>245.8</td><td>116.7</td><td>0.0</td><td>236.6</td><td>594.9</td><td>14321</td></tr><tr><td>Invertivores</td><td>530.1</td><td>143.0</td><td>0.0</td><td>539.2</td><td>970.0</td><td>14321</td></tr><tr><td>Omnivores</td><td>421.1</td><td>85.3</td><td>0.0</td><td>434.8</td><td>683.5</td><td>14321</td></tr><tr><td>Granivores</td><td>69.4</td><td>29.9</td><td>0.0</td><td>75.3</td><td>175.0</td><td>14321</td></tr><tr><td>Vertivores</td><td>92.9</td><td>65.4</td><td>0.0</td><td>85.0</td><td>226.3</td><td>14321</td></tr></tbody></table>

Source: French BBS.
<table><caption>Table 3: Summary statistics of land-use variables for BBS sites (all sample) - Period: 2007-2021</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>Min</th><th>P50</th><th>Max</th><th>N</th></tr></thead><tbody><tr><td><strong>Agricultural area</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total agri. area (ha)</td><td>182.0</td><td>116.2</td><td>0.0</td><td>191.6</td><td>524.8</td><td>14321</td></tr><tr><td>Arable area (ha)</td><td>118.0</td><td>112.3</td><td>0.0</td><td>86.5</td><td>524.8</td><td>14321</td></tr><tr><td>Perm. crops (ha)</td><td>3.8</td><td>16.8</td><td>0.0</td><td>0.0</td><td>226.1</td><td>14321</td></tr><tr><td>Perm. pastures (ha)</td><td>60.2</td><td>75.4</td><td>0.0</td><td>29.2</td><td>461.9</td><td>14321</td></tr><tr><td>Big 5 (ha)</td><td>77.4</td><td>90.4</td><td>0.0</td><td>35.1</td><td>409.7</td><td>14321</td></tr><tr><td>EFA area (ha)</td><td>7.2</td><td>14.7</td><td>0.0</td><td>0.5</td><td>185.5</td><td>14321</td></tr><tr><td>EFA (% of arable)</td><td>8.0</td><td>17.8</td><td>0.0</td><td>0.5</td><td>100.0</td><td>14321</td></tr><tr><td><strong>Forest extent</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Forest area (ha)</td><td>130.0</td><td>112.4</td><td>0.0</td><td>98.4</td><td>400.0</td><td>14321</td></tr><tr><td><strong>Nighttime lights</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nighttime light index</td><td>1.9</td><td>7.2</td><td>0.0</td><td>0.0</td><td>84.9</td><td>14321</td></tr><tr><td><strong>Weather variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Max. temp. (winter)</td><td>7.5</td><td>2.4</td><td>-4.5</td><td>7.5</td><td>15.4</td><td>14321</td></tr><tr><td>Min. temp. (winter)</td><td>1.3</td><td>2.3</td><td>-10.2</td><td>1.5</td><td>10.9</td><td>14321</td></tr><tr><td>Max. temp. (spring)</td><td>16.8</td><td>1.8</td><td>2.7</td><td>16.9</td><td>22.1</td><td>14321</td></tr><tr><td>Min. temp. (spring)</td><td>7.2</td><td>1.7</td><td>-3.9</td><td>7.2</td><td>14.6</td><td>14321</td></tr><tr><td>Prec. (winter)</td><td>57.0</td><td>23.8</td><td>9.5</td><td>53.6</td><td>207.1</td><td>14321</td></tr><tr><td>Prec. (spring)</td><td>96.1</td><td>29.2</td><td>31.4</td><td>94.1</td><td>232.7</td><td>14321</td></tr><tr><td><strong>Agrochemicals</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Herbicides (tons)</td><td>6.7</td><td>9.5</td><td>0.0</td><td>3.0</td><td>101.8</td><td>7928</td></tr><tr><td>Pesticides (tons)</td><td>6.9</td><td>15.6</td><td>0.0</td><td>1.6</td><td>248.5</td><td>7928</td></tr><tr><td>Herbicides bio. (tons)</td><td>0.0</td><td>0.1</td><td>0.0</td><td>0.0</td><td>9.6</td><td>7928</td></tr><tr><td>Pesticides bio. (tons)</td><td>3.0</td><td>9.7</td><td>0.0</td><td>0.1</td><td>165.1</td><td>7928</td></tr></tbody></table>

Source: LPIS (agricultural data), Turubanova et al. (2023) (forest extent)
Chen et al. (2020) (nighttime lights), and BNV-d (2023) (agrochemicals)
<figure><img src="image_10.png" /><figcaption>Figure 2: Geographic distribution of EFA part and evolution (municipality level)</figcaption></figure>

Source: LPIS data. Missing areas (in white) indicate municipalities with zero values for arable area.

<table><caption>Table 4: Summary statistics by quartiles of pre-reform arable area - Period: 2007-2014</caption><thead><tr><th></th><th>Q1<br>mean/sd</th><th>Q2<br>mean/sd</th><th>Q3<br>mean/sd</th><th>Q4<br>mean/sd</th></tr></thead><tbody><tr><td>Arable area (pre-reform) (ha)</td><td>3.24<br>(4.97)</td><td>54.49<br>(23.08)</td><td>156.30<br>(33.43)</td><td>288.93<br>(47.01)</td></tr><tr><td>EFA area (pre-reform) (ha)</td><td>0.52<br>(1.70)</td><td>2.91<br>(7.20)</td><td>4.98<br>(8.30)</td><td>10.97<br>(13.37)</td></tr></tbody></table>

Source: LPIS.
<table><caption>Table 5: Summary statistics - Pre-reform (2007-2014) and post-reform (2015-2021) agricultural characteristics of BBS squares</caption><thead><tr><th></th><th>Pre-reform<br>mean/sd</th><th>Post-reform<br>mean/sd</th><th>T-test<br>p</th></tr></thead><tbody><tr><td>EFA area (ha)</td><td>4.77<br>(9.87)</td><td>10.94<br>(16.93)</td><td>0.000</td></tr><tr><td>Total agricultural area (ha)</td><td>184.01<br>(118.68)</td><td>182.76<br>(113.89)</td><td>0.745</td></tr><tr><td>Arable area (ha)</td><td>128.19<br>(115.77)</td><td>113.02<br>(110.32)</td><td>0.000</td></tr><tr><td>Big 5 (ha)</td><td>83.75<br>(92.93)</td><td>75.93<br>(88.33)</td><td>0.009</td></tr><tr><td>Temporary pastures (ha)</td><td>23.15<br>(38.77)</td><td>11.33<br>(23.23)</td><td>0.000</td></tr><tr><td>Permanent pastures (ha)</td><td>52.75<br>(70.93)</td><td>64.91<br>(76.63)</td><td>0.000</td></tr><tr><td>Permanent crops (ha)</td><td>3.07<br>(13.14)</td><td>4.83<br>(20.94)</td><td>0.003</td></tr></tbody></table>

Source: LPIS.

<figure><img src="image_11.png" /><figcaption>Figure 3: Bird abundance and phylogenetic diversity (all birds) - 2007-2021</figcaption></figure>

Source: French BBS.
<figure><img src="image_12.png" /><figcaption>Figure 4: Bird abundance and phylogenetic diversity (cropland birds) - 2007-2021</figcaption></figure>

Source: French BBS.

<figure><img src="image_13.png" /><figcaption>Figure 5: Bird abundance and phylogenetic diversity (cropland birds) - 2007-2021</figcaption></figure>

Source: French BBS.
<table><caption>Table 6: Relation between EFA part and bird abundance/diversity</caption><thead><tr><th></th><th colspan="2">All birds</th><th colspan="3">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th></th><th>Farm birds</th><th>Wood birds</th><th>Generalist birds</th><th>Built birds</th><th></th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>0.0172<br>(0.0658)</td><td>-0.0004<br>(0.0086)</td><td>-0.0052<br>(0.0114)</td><td>-0.0174<br>(0.0182)</td><td>-0.0076<br>(0.0222)</td><td>-0.0016<br>(0.0058)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0257<br>(0.0538)</td><td>0.0051<br>(0.0113)</td><td>-0.0031<br>(0.0123)</td><td>-0.0174<br>(0.0202)</td><td>-0.0189<br>(0.0275)</td><td>-0.0022<br>(0.0067)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0016<br>(0.0033)</td><td>0.0004<br>(0.0010)</td><td>-0.0012<br>(0.0011)</td><td>-0.0010<br>(0.0009)</td><td>0.0007<br>(0.0010)</td><td>0.0004<br>(0.0006)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0021<br>(0.0041)</td><td>0.0004<br>(0.0012)</td><td>-0.0009<br>(0.0014)</td><td>-0.0011<br>(0.0010)</td><td>-0.0003<br>(0.0013)</td><td>-0.0003<br>(0.0007)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>0.0305<br>(0.1159)</td><td>0.0356<br>(0.0666)</td><td>-0.0276<br>(0.0423)</td><td>-0.0668<br>(0.0463)</td><td>0.0028<br>(0.0471)</td><td>0.0418<br>(0.0433)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>0.0308<br>(0.1443)</td><td>0.0682<br>(0.0826)</td><td>-0.0125<br>(0.0527)</td><td>-0.0677<br>(0.0537)</td><td>-0.0303<br>(0.0633)</td><td>0.0104<br>(0.0501)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions include the same set of control as in Table 1, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 7: Relation between EFA part and bird abundance/diversity by diet category</caption><thead><tr><th></th><th colspan="4">Bird categories by diet</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Invertivores</th><th>Omnivores</th><th>Granivores</th><th>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0244<br>(0.0210)</td><td>-0.0127<br>(0.0175)</td><td>0.0036<br>(0.0061)</td><td>0.0017<br>(0.0014)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0333<br>(0.0265)</td><td>-0.0149<br>(0.0208)</td><td>0.0016<br>(0.0059)</td><td>0.0025<br>(0.0020)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0025<br>(0.0019)</td><td>0.0003<br>(0.0010)</td><td>0.0006<br>(0.0008)</td><td>0.0003<br>(0.0005)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0017<br>(0.0025)</td><td>-0.0008<br>(0.0014)</td><td>0.0001<br>(0.0010)</td><td>0.0003<br>(0.0006)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0867<br>(0.0713)</td><td>0.0512<br>(0.0363)</td><td>0.0149<br>(0.0163)</td><td>0.0200<br>(0.0385)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA (% of arable)</td><td>-0.0573<br>(0.0930)</td><td>0.0109<br>(0.0491)</td><td>0.0132<br>(0.0215)</td><td>0.0249<br>(0.0487)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<table><caption>Table 8: Number of BBS squares surveyed and panel composition by year - 2007-2021</caption><thead><tr><th>Year</th><th>Number of BBS squares</th><th>First year observed</th><th>Last year observed</th></tr></thead><tbody><tr><td>2007</td><td>886</td><td>886</td><td>-</td></tr><tr><td>2008</td><td>1068</td><td>258</td><td>68</td></tr><tr><td>2009</td><td>1106</td><td>113</td><td>89</td></tr><tr><td>2010</td><td>1076</td><td>68</td><td>130</td></tr><tr><td>2011</td><td>973</td><td>75</td><td>94</td></tr><tr><td>2012</td><td>969</td><td>110</td><td>100</td></tr><tr><td>2013</td><td>950</td><td>83</td><td>149</td></tr><tr><td>2014</td><td>869</td><td>68</td><td>133</td></tr><tr><td>2015</td><td>754</td><td>50</td><td>132</td></tr><tr><td>2016</td><td>748</td><td>90</td><td>41</td></tr><tr><td>2017</td><td>839</td><td>82</td><td>56</td></tr><tr><td>2018</td><td>865</td><td>57</td><td>58</td></tr><tr><td>2019</td><td>891</td><td>81</td><td>78</td></tr><tr><td>2020</td><td>726</td><td>63</td><td>41</td></tr><tr><td>2021</td><td>915</td><td>-</td><td>915</td></tr></tbody></table>

Source: French BBS. Subsample of squares observed at least twice over 2007-2021

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions include the same set of control as in Table 1, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<figure><img src="image_14.png" /><figcaption>Figure 6: Generalist birds</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Figure 8: Built environment birds</figcaption></figure>

Note: we use user-written Stata command did_multiplegt Dyn (de Chaisemartin et al., 2022).
<figure><img src="image_16.png" /><figcaption>Figure 9: Invertivores</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Figure 11: Granivores</figcaption></figure>

Note: we use user-written Stata command did_multiplegt Dyn (de Chaisemartin et al., 2022).
<table><caption>Table 9: Relation between fallow part and bird abundance and diversity (parcel level LPIS data - 2015-2021)</caption><thead><tr><th rowspan="2"></th><th colspan="2">All birds</th><th colspan="3">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2015-2021)</strong></td></tr><tr><td>Fallows (% of arable)</td><td>0.1406<br>(0.2032)</td><td>0.0569*<br>(0.0295)</td><td>0.0366*<br>(0.0209)</td><td>0.1120*<br>(0.0679)</td><td>-0.1180<br>(0.1191)</td><td>-0.0104<br>(0.0211)</td></tr><tr><td>Observations</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2015-2021)</strong></td></tr><tr><td>Fallows (% of arable)</td><td>0.1577<br>(0.2070)</td><td>0.0594*<br>(0.0304)</td><td>0.0342<br>(0.0209)</td><td>0.1200*<br>(0.0685)</td><td>-0.1139<br>(0.1214)</td><td>-0.0104<br>(0.0211)</td></tr><tr><td>Observations</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2015-2021)</strong></td></tr><tr><td>Fallows (% of arable)</td><td>0.0132<br>(0.0116)</td><td>0.0019<br>(0.0044)</td><td>0.0073**<br>(0.0032)</td><td>0.0048<br>(0.0039)</td><td>-0.0064<br>(0.0041)</td><td>0.0027<br>(0.0025)</td></tr><tr><td>Observations</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2015-2021)</strong></td></tr><tr><td>Fallows (% of arable)</td><td>0.0147<br>(0.0114)</td><td>0.0024<br>(0.0042)</td><td>0.0072**<br>(0.0033)</td><td>0.0048<br>(0.0040)</td><td>-0.0057<br>(0.0040)</td><td>0.0027<br>(0.0025)</td></tr><tr><td>Observations</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2015-2021)</strong></td></tr><tr><td>Fallows (% of arable)</td><td>0.7283<br>(0.4966)</td><td>0.3519<br>(0.2886)</td><td>0.3477***<br>(0.1226)</td><td>0.2672<br>(0.1944)</td><td>-0.2857<br>(0.2097)</td><td>0.1735<br>(0.1823)</td></tr><tr><td>Observations</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td><td>4725</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2015-2021)</strong></td></tr><tr><td>Fallows (% of arable)</td><td>0.7254<br>(0.5012)</td><td>0.3578<br>(0.2847)</td><td>0.3289***<br>(0.1242)</td><td>0.2579<br>(0.2011)</td><td>-0.2620<br>(0.2079)</td><td>0.1540<br>(0.1831)</td></tr><tr><td>Observations</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td><td>4718</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions include the same set of control as in Table 1, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 10: Impact of EFA area on bird abundance and diversity (5 km buffers)</caption><thead><tr><th rowspan="2"></th><th colspan="1">All birds</th><th colspan="4">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.3813<br>(0.2877)</td><td>-0.1063<br>(0.0971)</td><td>0.0283<br>(0.0505)</td><td>0.1183<br>(0.1124)</td><td>0.0879<br>(0.1125)</td><td>-0.2174***<br>(0.0576)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.6649**<br>(0.3160)</td><td>-0.0008<br>(0.1211)</td><td>0.0525<br>(0.0561)</td><td>0.1399<br>(0.1300)</td><td>0.2144*<br>(0.1177)</td><td>-0.1133*<br>(0.0633)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0080<br>(0.0205)</td><td>-0.0067<br>(0.0062)</td><td>0.0028<br>(0.0061)</td><td>-0.0018<br>(0.0056)</td><td>0.0003<br>(0.0054)</td><td>-0.0062<br>(0.0042)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0451*<br>(0.0267)</td><td>0.0041<br>(0.0078)</td><td>0.0049<br>(0.0077)</td><td>0.0100<br>(0.0078)</td><td>0.0070<br>(0.0070)</td><td>0.0035<br>(0.0055)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.7145<br>(0.7432)</td><td>-0.1423<br>(0.3905)</td><td>0.3864<br>(0.2497)</td><td>-0.1122<br>(0.2923)</td><td>0.1563<br>(0.2605)</td><td>-0.1658<br>(0.2879)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>2.2430**<br>(1.0122)</td><td>0.5574<br>(0.5003)</td><td>0.3172<br>(0.3243)</td><td>0.3669<br>(0.4098)</td><td>0.5009<br>(0.3707)</td><td>0.5428<br>(0.3701)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 11: Impact of EFA area on bird abundance and diversity by diet category (5 km buffers)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0085<br>(0.0948)</td><td>0.1207<br>(0.1245)</td><td>-0.0146<br>(0.0355)</td><td>0.0158*<br>(0.0089)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.1578<br>(0.1172)</td><td>0.2360<br>(0.1475)</td><td>-0.0215<br>(0.0425)</td><td>0.0349***<br>(0.0114)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0060<br>(0.0101)</td><td>0.0037<br>(0.0065)</td><td>-0.0018<br>(0.0049)</td><td>0.0064**<br>(0.0031)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0097<br>(0.0134)</td><td>0.0111<br>(0.0085)</td><td>0.0026<br>(0.0059)</td><td>0.0120***<br>(0.0041)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.1371<br>(0.3833)</td><td>0.1217<br>(0.2555)</td><td>-0.0620<br>(0.0932)</td><td>0.4419**<br>(0.2246)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.3506<br>(0.5365)</td><td>0.2115<br>(0.3330)</td><td>0.0006<br>(0.1134)</td><td>0.8978***<br>(0.3051)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
## A.1 Robustness

<table><caption>Table 12: Impact of EFA area on bird abundance and diversity (dropping year 2020)</caption><thead><tr><th rowspan="2"></th><th colspan="2">All birds</th><th colspan="3">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>2.7488*<br>(1.5163)</td><td>-0.8921**<br>(0.4246)</td><td>0.1919<br>(0.2821)</td><td>0.8993<br>(0.6231)</td><td>0.4987<br>(0.5623)</td><td>-1.0911***<br>(0.3284)</td></tr><tr><td>Observations</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>2.9676*<br>(1.6557)</td><td>-0.8036<br>(0.6109)</td><td>0.3077<br>(0.2831)</td><td>0.6953<br>(0.6985)</td><td>0.8514<br>(0.6205)</td><td>-0.5129<br>(0.3581)</td></tr><tr><td>Observations</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0504<br>(0.1108)</td><td>-0.0262<br>(0.0329)</td><td>0.0229<br>(0.0347)</td><td>-0.0156<br>(0.0300)</td><td>-0.0045<br>(0.0289)</td><td>-0.0435*<br>(0.0228)</td></tr><tr><td>Observations</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.2008<br>(0.1376)</td><td>0.0206<br>(0.0418)</td><td>0.0416<br>(0.0409)</td><td>0.0418<br>(0.0406)</td><td>0.0233<br>(0.0369)</td><td>-0.0043<br>(0.0291)</td></tr><tr><td>Observations</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>2.8577<br>(3.9965)</td><td>-1.2359<br>(2.0699)</td><td>2.0510<br>(1.3825)</td><td>-1.2289<br>(1.5428)</td><td>0.0306<br>(1.3942)</td><td>-1.7227<br>(1.5004)</td></tr><tr><td>Observations</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>9.0035*<br>(5.3227)</td><td>2.1241<br>(2.6870)</td><td>1.7752<br>(1.7184)</td><td>1.3573<br>(2.1204)</td><td>1.3263<br>(1.9225)</td><td>1.4228<br>(1.9527)</td></tr><tr><td>Observations</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 13: Impact of EFA area on bird abundance and diversity by diet category (dropping year 2020)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0460<br>(0.5166)</td><td>0.3959<br>(0.6018)</td><td>0.0168<br>(0.1801)</td><td>0.0963*<br>(0.0501)</td></tr><tr><td>Observations</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.3359<br>(0.6087)</td><td>0.3613<br>(0.7549)</td><td>-0.0416<br>(0.1986)</td><td>0.1697***<br>(0.0626)</td></tr><tr><td>Observations</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0091<br>(0.0559)</td><td>0.0306<br>(0.0336)</td><td>-0.0039<br>(0.0267)</td><td>0.0236<br>(0.0167)</td></tr><tr><td>Observations</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-0.0045<br>(0.0698)</td><td>0.0767*<br>(0.0419)</td><td>0.0212<br>(0.0308)</td><td>0.0466**<br>(0.0215)</td></tr><tr><td>Observations</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-0.4642<br>(2.0820)</td><td>1.1540<br>(1.3461)</td><td>-0.3722<br>(0.5297)</td><td>1.5627<br>(1.2266)</td></tr><tr><td>Observations</td><td>12843</td><td>12843</td><td>12843</td><td>12843</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021 except 2020)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-0.5462<br>(2.7954)</td><td>1.5772<br>(1.7106)</td><td>0.0495<br>(0.6105)</td><td>3.3882**<br>(1.6059)</td></tr><tr><td>Observations</td><td>6512</td><td>6512</td><td>6512</td><td>6512</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of "big five" crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 14: Impact of EFA area on bird abundance and diversity (always observed squares)</caption><thead><tr><th rowspan="2"></th><th colspan="2">All birds</th><th colspan="3">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>1.9169<br>(3.2144)</td><td>-0.6549<br>(0.9422)</td><td>0.3393<br>(0.5357)</td><td>0.4140<br>(1.1980)</td><td>1.7327<br>(1.0576)</td><td>-1.8523***<br>(0.6381)</td></tr><tr><td>Observations</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-0.9870<br>(3.2261)</td><td>-1.8319<br>(1.4895)</td><td>0.1841<br>(0.5055)</td><td>-0.2328<br>(1.2987)</td><td>1.3576<br>(0.8892)</td><td>-1.8909**<br>(0.7863)</td></tr><tr><td>Observations</td><td>909</td><td>909</td><td>909</td><td>909</td><td>909</td><td>909</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.1065<br>(0.2558)</td><td>-0.0560<br>(0.0656)</td><td>0.1194*<br>(0.0645)</td><td>-0.0576<br>(0.0732)</td><td>0.0448<br>(0.0492)</td><td>-0.0629<br>(0.0525)</td></tr><tr><td>Observations</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0485<br>(0.2919)</td><td>-0.1141<br>(0.1056)</td><td>0.0547<br>(0.0812)</td><td>-0.0469<br>(0.0974)</td><td>0.1067<br>(0.0841)</td><td>-0.0419<br>(0.0685)</td></tr><tr><td>Observations</td><td>909</td><td>909</td><td>909</td><td>909</td><td>909</td><td>909</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>1.9140<br>(9.9653)</td><td>-2.3048<br>(4.3877)</td><td>6.5885**<br>(2.6537)</td><td>-4.3304<br>(3.9186)</td><td>2.5257<br>(2.6399)</td><td>-1.5481<br>(3.6348)</td></tr><tr><td>Observations</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>10.0603<br>(11.0741)</td><td>-6.5706<br>(6.9950)</td><td>1.2583<br>(3.5157)</td><td>-1.8138<br>(5.0292)</td><td>9.3518**<br>(4.3638)</td><td>-1.1492<br>(5.0703)</td></tr><tr><td>Observations</td><td>909</td><td>909</td><td>909</td><td>909</td><td>909</td><td>909</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of "big five" crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 15: Impact of EFA area on bird abundance and diversity by diet category (always observed squares)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.5470<br>(1.1718)</td><td>0.7239<br>(1.3576)</td><td>0.8572**<br>(0.3878)</td><td>0.2863**<br>(0.1115)</td></tr><tr><td>Observations</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.5116<br>(1.0934)</td><td>-0.5521<br>(1.7455)</td><td>0.3661<br>(0.3827)</td><td>0.2729**<br>(0.1360)</td></tr><tr><td>Observations</td><td>909</td><td>909</td><td>909</td><td>909</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0376<br>(0.1247)</td><td>0.0788<br>(0.0818)</td><td>0.0251<br>(0.0547)</td><td>0.0873**<br>(0.0362)</td></tr><tr><td>Observations</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-0.0419<br>(0.1503)</td><td>0.0608<br>(0.0918)</td><td>-0.0661<br>(0.0606)</td><td>0.1033*<br>(0.0532)</td></tr><tr><td>Observations</td><td>909</td><td>909</td><td>909</td><td>909</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-1.4340<br>(4.6503)</td><td>3.5728<br>(2.9907)</td><td>0.0869<br>(1.0674)</td><td>6.4428**<br>(2.4883)</td></tr><tr><td>Observations</td><td>1530</td><td>1530</td><td>1530</td><td>1530</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>-2.1198<br>(5.7888)</td><td>2.0133<br>(3.5134)</td><td>-2.0953*<br>(1.1740)</td><td>7.0993*<br>(3.7046)</td></tr><tr><td>Observations</td><td>909</td><td>909</td><td>909</td><td>909</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 16: Impact of EFA area on bird abundance and diversity (cluster Conley 50km)</caption><thead><tr><th rowspan="2"></th><th colspan="2">All birds</th><th colspan="3">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>3.5506***<br>(1.3047)</td><td>-0.7015*<br>(0.3721)</td><td>0.4335<br>(0.2699)</td><td>1.0348**<br>(0.5177)</td><td>0.7385<br>(0.5283)</td><td>-1.1106***<br>(0.2556)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>3.7775**<br>(1.5913)</td><td>-0.5661<br>(0.5982)</td><td>0.5873**<br>(0.2895)</td><td>0.9309<br>(0.6330)</td><td>1.0240<br>(0.6650)</td><td>-0.4668<br>(0.3287)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0653<br>(0.0947)</td><td>-0.0270<br>(0.0275)</td><td>0.0237<br>(0.0292)</td><td>-0.0119<br>(0.0272)</td><td>-0.0023<br>(0.0233)</td><td>-0.0373*<br>(0.0206)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.2210*<br>(0.1227)</td><td>0.0205<br>(0.0393)</td><td>0.0443<br>(0.0365)</td><td>0.0488<br>(0.0364)</td><td>0.0208<br>(0.0310)</td><td>0.0049<br>(0.0254)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>3.4841<br>(3.5026)</td><td>-1.0504<br>(1.8419)</td><td>2.1160*<br>(1.2086)</td><td>-1.0380<br>(1.3989)</td><td>0.2524<br>(1.1681)</td><td>-1.4826<br>(1.4158)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>9.7002**<br>(4.9444)</td><td>2.3334<br>(2.6297)</td><td>1.9789<br>(1.7167)</td><td>1.6664<br>(1.8935)</td><td>1.3886<br>(1.6086)</td><td>1.8665<br>(1.6850)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered using Conley (1999) error with spatial correlation kernel cutoff of 50 km. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 17: Impact of EFA area on bird abundance/diversity by diet category (Cont. DiD) (cluster Conley 50km)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.3477<br>(0.5172)</td><td>0.7388<br>(0.5280)</td><td>0.0437<br>(0.1476)</td><td>0.1070**<br>(0.0473)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.6654<br>(0.6605)</td><td>0.7975<br>(0.7316)</td><td>-0.0151<br>(0.1917)</td><td>0.1806***<br>(0.0587)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0295<br>(0.0466)</td><td>0.0275<br>(0.0280)</td><td>-0.0035<br>(0.0211)</td><td>0.0298**<br>(0.0151)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.0208<br>(0.0614)</td><td>0.0735*<br>(0.0390)</td><td>0.0202<br>(0.0291)</td><td>0.0522***<br>(0.0185)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.2135<br>(1.8398)</td><td>0.9254<br>(1.1055)</td><td>-0.3049<br>(0.4084)</td><td>1.9480*<br>(1.1054)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Arable (Pre-2015) X Post2015</td><td>0.1169<br>(2.6003)</td><td>1.4332<br>(1.5908)</td><td>0.0614<br>(0.5655)</td><td>3.6877***<br>(1.3516)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered using Conley (1999) error with spatial correlation kernel cutoff of 50 km. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
## A.2 IV estimation

### A.2.1 Construction of the instrument

We use an instrumental variable (IV) approach similar to that used in Palmer et al. (2025), in which the authors analyze the impact of the set-aside policy on bird diversity in England. The authors instrument potentially endogenous observed set-aside area by the total cereal area before the implementation of the set-aside policy interacted with the required share of set-aside land which varies over the years.

In our case, before 2015 there were no EFA requirements. Therefore, we construct an instrument that is equal to zero for years 2007 to 2014, and to 5% of the pre-policy average (2007–2014) arable area of a BSS grid from 2015 on. One difference with Palmer et al. (2025), and a limitation of this approach in our case is that our instrument does not vary over time after 2015.

Mathematically, our instrument, $Z_{it}$, equals to:

$$Z_{it} = \begin{cases} 0.05 \times \text{ArableArea}_i, & \text{if } t \ge 2015, \\ 0, & \text{if } t < 2015. \end{cases}$$

where $ArableArea_i$ equals to average arable area in a BSS square during 2007-2014.

This strategy relies on the assumption that our instrument is correlated with observed EFA area, and affects biodiversity only through its effect on EFA area. As already discussed in Section 4.3., the latter condition may be violated if unobserved time-varying factors are correlated with both the share of arable area and bird indicators. The parallel evolution of bird diversity in BBS squares below and above median in terms of arable land share before 2015 however tend support our exclusion restriction (see Figure 3).

### A.2.2 First and second stage equations

**First stage:**

$$X_{it} = \alpha_0 + \alpha_1 Z_{it} + \alpha'_2 W_{it} + \sigma \text{Region}_i \times \text{Year}_t + \gamma_t + \mu_i + \nu_{it},$$

where $X_{it}$ is EFA in hectare, $Z_{it}$ is our instrument defined above, $W_{it}$ is a vector of control variables, containing the same variables as in our main DiD specification, $\gamma_t$ represents year fixed effects, $\mu_i$ are BBS square fixed effects, $\sigma \text{Region}_i \times \text{Year}_t$ are region-year linear trends, and $\nu_{it}$ is the first-stage error term.

**Second stage:**

$$Y_{it} = \beta_0 + \beta_1 \hat{X}_{it} + \beta'_2 W_{it} + \sigma \text{Region}_i \times \text{Year}_t + \gamma_t + \mu_i + \epsilon_{it},$$
where $Y_{it}$ is alternately one of our three indicators of bird abundance or diversity, $\hat{X}_{it}$ is the predicted value of EFA derived from the estimation of the first-stage equation, $W_{it}$ is the same vector of control variables, $\gamma_t$ represents year fixed effects, $\mu_i$ are BBS square fixed effects, $\sigma\text{Region}_i \times \text{Year}_t$ are region-year linear trends, and $\epsilon_{it}$ is the second-stage error term.

## A.3 Results

In panel A1 of Table 18, we find that a one-hectare increase in EFA increases the abundance of all birds by 1.4 and decreases the abundance of birds specialized in croplands by 0.4, while having no impact on other birds.

In panel A2, when we control for agrochemicals, the negative effect on cropland birds is no longer statistically significant. We also observe an increase in woodland birds by 0.2, although significant at the 10% level only. The coefficient on the abundance of all birds remains positive and significant, although smaller in size. In this case, a one-hectare increase in EFA increases bird abundance by about one unit.

We do not find any significant impact on species richness, but the phylogenetic diversity of all birds increases by 2.7, significant only at the 10% level in Panel C2.

As appears from Table 19, a one-hectare increase in EFA increases the abundance of vertivore birds (i.e. diurnal raptors) by 0.04 for the period 2007–2021 and by 0.05 for the period 2013–2021 when agrochemicals are controlled for. We also observe an increase in species richness and phylogenetic diversity of vertivores, although less statistically significant. Although not strictly comparable to our main estimates, as in the IV model, we assess the impact of an increase in EFA rather than an increase in potential exposure to EFA requirements, results from the IV model are consistent with our main conclusions as regards the consequences of the reform : a limited positive impact on bird abundance, driven in particular by diurnal raptors, but a failure to address the decline in cropland birds.
<table><caption>Table 18: Impact of EFA area on bird abundance/diversity (IV estimation)</caption><thead><tr><th rowspan="2"></th><th colspan="1">All birds</th><th colspan="4">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>EFA</td><td>1.368**<br>(0.643)</td><td>-0.263<br>(0.169)</td><td>0.160<br>(0.113)</td><td>0.387<br>(0.248)</td><td>0.311<br>(0.233)</td><td>-0.417***<br>(0.142)</td></tr><tr><td>Observations</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA</td><td>1.052**<br>(0.503)</td><td>-0.158<br>(0.172)</td><td>0.164*<br>(0.088)</td><td>0.259<br>(0.199)</td><td>0.285<br>(0.187)</td><td>-0.130<br>(0.100)</td></tr><tr><td>Observations</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>EFA</td><td>0.025<br>(0.043)</td><td>-0.011<br>(0.012)</td><td>-0.011<br>(0.012)</td><td>-0.005<br>(0.011)</td><td>-0.000<br>(0.011)</td><td>-0.014<br>(0.009)</td></tr><tr><td>Observations</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA</td><td>0.062<br>(0.040)</td><td>0.006<br>(0.012)</td><td>0.012<br>(0.011)</td><td>0.014<br>(0.011)</td><td>0.006<br>(0.010)</td><td>0.001<br>(0.008)</td></tr><tr><td>Observations</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>EFA</td><td>1.3433<br>(1.5529)</td><td>-0.4072<br>(0.7776)</td><td>0.7845<br>(0.5580)</td><td>-0.3797<br>(0.5788)</td><td>0.1048<br>(0.5182)</td><td>-0.5753<br>(0.5731)</td></tr><tr><td>Observations</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA</td><td>2.702*<br>(1.539)</td><td>0.650<br>(0.764)</td><td>0.551<br>(0.483)</td><td>0.464<br>(0.594)</td><td>0.387<br>(0.540)</td><td>0.520<br>(0.549)</td></tr><tr><td>Observations</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures, and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January) (coefficients not shown), square and year fixed effects, as well as interactions between regions and year linear trend.</footnote>
<table><caption>Table 19: Impact of EFA area on bird abundance/diversity by diet category (IV estimation)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>EFA</td><td>0.1545<br>(0.2001)</td><td>0.2770<br>(0.2415)</td><td>0.0203<br>(0.0718)</td><td>0.0393**<br>(0.0198)</td></tr><tr><td>Observations</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA</td><td>0.1853<br>(0.1838)</td><td>0.2222<br>(0.2157)</td><td>-0.0042<br>(0.0586)</td><td>0.0503***<br>(0.0184)</td></tr><tr><td>Observations</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>EFA</td><td>0.0116<br>(0.0213)</td><td>0.0094<br>(0.0131)</td><td>-0.0013<br>(0.0100)</td><td>0.0108*<br>(0.0064)</td></tr><tr><td>Observations</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA</td><td>0.0058<br>(0.0196)</td><td>0.0205*<br>(0.0121)</td><td>0.0056<br>(0.0087)</td><td>0.0145**<br>(0.0062)</td></tr><tr><td>Observations</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>EFA</td><td>0.0984<br>(0.7871)</td><td>0.3090<br>(0.5221)</td><td>-0.1078<br>(0.1951)</td><td>0.7051<br>(0.4687)</td></tr><tr><td>Observations</td><td>13,635</td><td>13,635</td><td>13,635</td><td>13,635</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>38.436</td><td>38.436</td><td>38.436</td><td>38.436</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>EFA</td><td>0.0326<br>(0.7833)</td><td>0.3992<br>(0.4790)</td><td>0.0171<br>(0.1686)</td><td>1.0273**<br>(0.4590)</td></tr><tr><td>Observations</td><td>7,301</td><td>7,301</td><td>7,301</td><td>7,301</td></tr><tr><td>Kleibergen-Paap rk Wald F stat.</td><td>60.462</td><td>60.462</td><td>60.462</td><td>60.462</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of "big five" crops, permanent pastures, and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January) (coefficients not shown), square and year fixed effects, as well as interactions between regions and year linear trend.</footnote>
A.4 DiD with quartile interactions

In a complementary analysis, we replace the interaction term from Equation 3 between the post-reform dummy and the continuous variable representing square level arable area, by an interaction between the post-reform binary variable and dummies for quartiles of arable area. The first quartile, that encompasses squares little affected by the CAP reform, at least directly, as they count on average only 3.2 hectares of arable area (see Appendix Table 4), represents the reference category (excluded).

Results by habitat and diet category are reported in Table 20 and Table 21 respectively. We find an increase in bird abundance in the fourth quartile only. The coefficient is larger when restricting to the 2013-2021 period and controlling for agrochemicals. 10 to 13 more individuals are observed annually in post-reform years in BBS squares where pre-reform arable area is the largest (289 ha on average), compared to squares from the first quartile. This increase seems to be driven by birds specialized in built environments and woods, but also by bird species that are part of the “all birds” category but are not classified by preferred habitat by Lorrillière and Gonzalez (2016). Consistent with results shown in Table 2, post-reform abundance of cropland birds decreases all the more so as pre-reform arable area increases (column (6), Panel A1). Results reported in Panel B1 suggest that overall species richness decreases after the reform in squares belonging to the second quartiles, and that cropland bird species specifically declines in quartiles 2 to 4 compared to the first quartile. However, coefficients are not significantly different from zero when the observation period is limited to 2013-2021 in Panel B2. As for phylogenetic diversity, we find a reduction after the reform, especially among generalist and cropland birds, that is larger in the second and third quartiles or arable land.
<table><caption>Table 20: Impact of EFA area on bird abundance and diversity - Quartiles of arable area</caption><thead><tr><th rowspan="2"></th><th colspan="2">All birds</th><th colspan="3">Birds by Museum category</th><th>Cropland birds</th></tr><tr><th>(1)</th><th>(2)<br>Farm birds</th><th>(3)<br>Wood birds</th><th>(4)<br>Generalist birds</th><th>(5)<br>Built birds</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-2.0805<br>(6.3967)</td><td>0.7798<br>(0.9585)</td><td>0.3266<br>(1.5588)</td><td>-3.8903*<br>(2.2412)</td><td>1.3863<br>(2.6041)</td><td>-1.1274*<br>(0.6068)</td></tr><tr><td>Q3 X Post2015</td><td>-1.5329<br>(6.0889)</td><td>-3.6232**<br>(1.5351)</td><td>0.5216<br>(1.0968)</td><td>-4.5244**<br>(2.0600)</td><td>1.6705<br>(2.0801)</td><td>-3.2605***<br>(0.8624)</td></tr><tr><td>Q4 X Post2015</td><td>10.3570*<br>(5.4145)</td><td>-0.2831<br>(1.1933)</td><td>1.3388<br>(1.1021)</td><td>1.1298<br>(1.9666)</td><td>2.8943<br>(2.1207)</td><td>-3.6203***<br>(0.9489)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-0.0026<br>(5.9663)</td><td>1.3893<br>(1.0336)</td><td>0.9260<br>(1.5317)</td><td>-3.9932*<br>(2.1966)</td><td>0.9038<br>(2.4885)</td><td>-1.3166*<br>(0.6748)</td></tr><tr><td>Q3 X Post2015</td><td>1.5548<br>(5.9842)</td><td>-3.4687<br>(2.4209)</td><td>2.1275*<br>(1.1400)</td><td>-2.6219<br>(2.0468)</td><td>2.3856<br>(2.1945)</td><td>-2.2127***<br>(0.8472)</td></tr><tr><td>Q4 X Post2015</td><td>12.8312**<br>(5.4388)</td><td>0.3025<br>(1.4248)</td><td>2.0871*<br>(1.1313)</td><td>0.7804<br>(2.1726)</td><td>3.3436*<br>(2.0212)</td><td>-2.1213**<br>(1.0799)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-0.6252*<br>(0.3573)</td><td>-0.0809<br>(0.0883)</td><td>-0.1013<br>(0.1150)</td><td>-0.3255***<br>(0.0926)</td><td>-0.0006<br>(0.0895)</td><td>-0.1205**<br>(0.0598)</td></tr><tr><td>Q3 X Post2015</td><td>-0.3885<br>(0.3853)</td><td>-0.1471<br>(0.1026)</td><td>0.0396<br>(0.1173)</td><td>-0.2616***<br>(0.0986)</td><td>-0.0165<br>(0.0949)</td><td>-0.1676***<br>(0.0604)</td></tr><tr><td>Q4 X Post2015</td><td>0.0250<br>(0.3635)</td><td>-0.0753<br>(0.1036)</td><td>0.0495<br>(0.1144)</td><td>-0.1318<br>(0.0983)</td><td>0.0100<br>(0.0899)</td><td>-0.1390**<br>(0.0697)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-0.1061<br>(0.3951)</td><td>0.0202<br>(0.1045)</td><td>-0.0990<br>(0.1357)</td><td>-0.1279<br>(0.1034)</td><td>0.0523<br>(0.1104)</td><td>-0.0649<br>(0.0719)</td></tr><tr><td>Q3 X Post2015</td><td>0.0991<br>(0.4504)</td><td>-0.0289<br>(0.1237)</td><td>0.1529<br>(0.1380)</td><td>-0.0902<br>(0.1288)</td><td>0.0525<br>(0.1256)</td><td>-0.0948<br>(0.0790)</td></tr><tr><td>Q4 X Post2015</td><td>0.6019<br>(0.4529)</td><td>0.0826<br>(0.1304)</td><td>0.0723<br>(0.1327)</td><td>0.1352<br>(0.1343)</td><td>0.0558<br>(0.1162)</td><td>-0.0017<br>(0.0893)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="7"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-23.0259*<br>(12.4842)</td><td>-3.0806<br>(5.6579)</td><td>-4.9009<br>(4.0722)</td><td>-18.7009***<br>(5.0094)</td><td>0.3991<br>(4.4420)</td><td>-6.2705<br>(4.0169)</td></tr><tr><td>Q3 X Post2015</td><td>-14.8554<br>(13.4766)</td><td>-5.3444<br>(6.4660)</td><td>3.6252<br>(4.2559)</td><td>-13.9293***<br>(5.2193)</td><td>-0.2289<br>(4.6272)</td><td>-8.5344**<br>(4.0931)</td></tr><tr><td>Q4 X Post2015</td><td>5.0026<br>(13.2431)</td><td>-2.9894<br>(6.6710)</td><td>4.1153<br>(4.4757)</td><td>-9.0111*<br>(5.1666)</td><td>1.8274<br>(4.4433)</td><td>-6.6829<br>(4.7533)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="7"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-6.1476<br>(14.6524)</td><td>0.9749<br>(6.9722)</td><td>-6.1917<br>(5.0896)</td><td>-11.0127**<br>(5.5939)</td><td>3.5785<br>(5.5572)</td><td>-2.4119<br>(4.8296)</td></tr><tr><td>Q3 X Post2015</td><td>0.7843<br>(16.7306)</td><td>1.9239<br>(7.9287)</td><td>5.2404<br>(5.2391)</td><td>-2.9523<br>(6.7497)</td><td>2.5789<br>(6.1127)</td><td>-2.7110<br>(5.3231)</td></tr><tr><td>Q4 X Post2015</td><td>25.4538<br>(17.8350)</td><td>6.6177<br>(8.5878)</td><td>2.0888<br>(5.4497)</td><td>1.7370<br>(7.0894)</td><td>3.6643<br>(5.9932)</td><td>3.7678<br>(6.0579)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
<table><caption>Table 21: Impact of EFA area on bird abundance and diversity by diet category - Quartiles of arable area</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bird categories by diet</th></tr><tr><th>(1)<br>Invertivores</th><th>(2)<br>Omnivores</th><th>(3)<br>Granivores</th><th>(4)<br>Vertivores</th></tr></thead><tbody><tr><td colspan="5"><strong>Panel A1: Abundance (2007-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-1.7042<br>(2.5475)</td><td>-1.0086<br>(2.1498)</td><td>0.4975<br>(0.8726)</td><td>0.2017<br>(0.1306)</td></tr><tr><td>Q3 X Post2015</td><td>-1.1641<br>(1.9127)</td><td>-5.1600**<br>(2.2192)</td><td>-0.5793<br>(0.5934)</td><td>0.3085*<br>(0.1573)</td></tr><tr><td>Q4 X Post2015</td><td>0.2173<br>(1.8806)</td><td>2.4954<br>(1.9192)</td><td>0.5223<br>(0.6655)</td><td>0.2663*<br>(0.1543)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel A2: Abundance - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>0.1274<br>(2.5812)</td><td>-0.4540<br>(2.2041)</td><td>0.6367<br>(0.8258)</td><td>0.3296**<br>(0.1568)</td></tr><tr><td>Q3 X Post2015</td><td>1.2432<br>(2.2639)</td><td>-3.4446<br>(2.9970)</td><td>-0.0720<br>(0.6116)</td><td>0.4029**<br>(0.1810)</td></tr><tr><td>Q4 X Post2015</td><td>1.9310<br>(2.1101)</td><td>2.6557<br>(2.1113)</td><td>0.2804<br>(0.6919)</td><td>0.4957**<br>(0.1954)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel B1: Species richness (2007-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-0.0770<br>(0.1929)</td><td>-0.2838***<br>(0.0990)</td><td>-0.0524<br>(0.0822)</td><td>0.0272<br>(0.0437)</td></tr><tr><td>Q3 X Post2015</td><td>0.0664<br>(0.1962)</td><td>-0.1845*<br>(0.1092)</td><td>-0.0748<br>(0.0795)</td><td>0.0395<br>(0.0469)</td></tr><tr><td>Q4 X Post2015</td><td>0.0572<br>(0.1858)</td><td>0.0070<br>(0.1095)</td><td>0.0138<br>(0.0843)</td><td>0.0878*<br>(0.0513)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel B2: Species richness - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>0.1561<br>(0.2133)</td><td>-0.1559<br>(0.1219)</td><td>-0.0227<br>(0.0933)</td><td>0.0360<br>(0.0582)</td></tr><tr><td>Q3 X Post2015</td><td>0.1814<br>(0.2278)</td><td>-0.1003<br>(0.1434)</td><td>-0.0049<br>(0.0956)</td><td>0.0837<br>(0.0596)</td></tr><tr><td>Q4 X Post2015</td><td>0.1294<br>(0.2271)</td><td>0.1745<br>(0.1338)</td><td>0.0797<br>(0.0990)</td><td>0.1345**<br>(0.0671)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr><tr><td colspan="5"><strong>Panel C1: Phylogenetic diversity (2007-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>-4.4710<br>(7.1867)</td><td>-11.9641***<br>(3.6703)</td><td>-1.0592<br>(1.5410)</td><td>1.7212<br>(3.3464)</td></tr><tr><td>Q3 X Post2015</td><td>-0.1089<br>(7.3471)</td><td>-6.0198<br>(4.0987)</td><td>-0.7919<br>(1.5529)</td><td>2.3206<br>(3.5820)</td></tr><tr><td>Q4 X Post2015</td><td>0.4904<br>(6.9423)</td><td>-1.4236<br>(4.2826)</td><td>-0.4823<br>(1.6538)</td><td>5.5839<br>(3.8030)</td></tr><tr><td>Observations</td><td>13635</td><td>13635</td><td>13635</td><td>13635</td></tr><tr><td colspan="5"><strong>Panel C2: Phylogenetic diversity - Controlled for agrochemicals (2013-2021)</strong></td></tr><tr><td>Q2 X Post2015</td><td>5.3366<br>(8.1932)</td><td>-8.5123*<br>(4.5910)</td><td>-0.5654<br>(1.8593)</td><td>2.5192<br>(4.3897)</td></tr><tr><td>Q3 X Post2015</td><td>9.2189<br>(8.5658)</td><td>-3.6435<br>(5.5933)</td><td>0.3343<br>(1.8835)</td><td>5.0740<br>(4.5403)</td></tr><tr><td>Q4 X Post2015</td><td>2.1942<br>(9.1960)</td><td>1.2768<br>(5.4173)</td><td>0.4634<br>(1.9647)</td><td>9.1707*<br>(5.0074)</td></tr><tr><td>Observations</td><td>7301</td><td>7301</td><td>7301</td><td>7301</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. Standard errors are clustered at the BBS square level. All regressions control for areas (in hectares) of “big five” crops, permanent pastures and permanent crops, forest extent, nighttime lights (in raw format), maximum and minimum temperatures (in Celsius degrees) and total precipitations (in mm) during spring (March-June) and winter (December-January), the log number of years the square has been surveyed, square and year fixed effects, as well as interactions between region dummies and a linear time trend.</footnote>
Overall, we thus find that the increase in bird abundance is driven by squares with the larger ex-ante extent of arable area, while in those same squares the decline in cropland birds is the most drastic. Although our results on species richness and phylogenetic diversity suggest a negative impact of the reform in squares belonging to the second quartile of arable area they are not robust to restricting the period of analysis to 2013-2021.

## B Cropland birds

We use a methodology similar to that used by Noack et al. (2022) in order to construct the category of cropland birds. We derive land use information from the CORINE Land Cover (CLC) database for years 2006 and 2012, that is before the CAP reform. We match 2006 CLC data with 2007 BBS data, and 2012 CLC data with 2012 BBS data. Our estimates are based on an unbalanced two-period panel dataset, with 510 BBS squares observed in both periods.

We run the following regression for each bird species s included in the list of common birds provided by Fontaine et al. (2021):

$$y_{sitr} = \beta_0 + \beta_1 ArableAreaPart_{it} + \phi_t + \delta_r + \varepsilon_{sitr} \quad (5)$$

$y_{sitr}$ is the abundance of species s, observed in square i, region r, and year t. ArableAreaPart$_{it}$ represents the share of arable area in BBS square i. Arable area is constructed as the sum of non-irrigated arable land and permanently irrigated arable land CLC categories (in hectares). $\phi_t$ are year fixed-effects and $\delta_r$ region fixed-effects (based on the 2015 definition of regions, corresponding to the NUTS 1 level).

A bird species is considered as specialized in arable land habitat and categorised as “cropland bird” if $\beta_1 > 0$ with a p-value smaller than 0.01.

Tables 22 and 23 report the $\beta_1$ coefficients and its level of significance for each species.

According to this rule we define the following 12 species as birds specialised in cropland habitat : Acrocephalus schoenobaenus, Alauda arvensis, Circus cyaneus, Circus pygargus, Corvus corone, Coturnix coturnix, Emberiza calandra, Falco tinnunculus, Luscinia megarhynchos, Motacilla flava, Perdix perdix, and Sylvia communis
<table><caption>Table 22: Determination of cropland birds - species by species regressions results</caption><thead><tr><th>Specie's name</th><th>Coef.</th><th>Specie's name</th><th>Coef.</th><th>Specie's name</th><th>Coef.</th></tr></thead><tbody><tr><td>Accipiter nisus</td><td>0.001</td><td>Chroicocephalus ridibundus</td><td>0.179</td><td>Emberiza schoeniclus</td><td>-0.007</td></tr><tr><td>Acrocephalus arundinaceus</td><td>-0.020</td><td>Ciconia ciconia</td><td>-0.012</td><td>Erithacus rubecula</td><td>-0.059***</td></tr><tr><td>Acrocephalus palustris</td><td>-0.007</td><td>Cinclus cinclus</td><td>-0.012</td><td>Falco subbuteo</td><td>-0.002</td></tr><tr><td>Acrocephalus schoenobaenus</td><td>0.017</td><td>Circus aeruginosus</td><td>-0.000</td><td>Falco tinnunculus</td><td>0.003*</td></tr><tr><td><strong>Acrocephalus scirpaceus</strong></td><td><strong>0.019**</strong></td><td><strong>Circus cyaneus</strong></td><td><strong>0.009**</strong></td><td>Ficedula hypoleuca</td><td>-0.004</td></tr><tr><td>Actitis hypoleucos</td><td>0.004</td><td><strong>Circus pygargus</strong></td><td><strong>0.011***</strong></td><td>Fringilla coelebs</td><td>-0.077***</td></tr><tr><td>Aegithalos caudatus</td><td>0.003</td><td>Cisticola juncidis</td><td>0.022</td><td>Fulica atra</td><td>-0.074</td></tr><tr><td><strong>Alauda arvensis</strong></td><td><strong>0.141***</strong></td><td>Clamator glandarius</td><td>0.048</td><td>Galerida cristata</td><td>0.014</td></tr><tr><td>Alcedo atthis</td><td>-0.003</td><td>Coccothraustes coccothraustes</td><td>-0.027***</td><td>Gallinula chloropus</td><td>-0.009***</td></tr><tr><td>Alectoris rufa</td><td>0.008*</td><td>Columba livia</td><td>-0.116**</td><td>Garrulus glandarius</td><td>-0.025***</td></tr><tr><td>Anas platyrhynchos</td><td>0.001</td><td>Columba oenas</td><td>0.002</td><td>Himantopus himantopus</td><td>-0.033</td></tr><tr><td>Anthus campestris</td><td>0.000</td><td>Columba palumbus</td><td>-0.018**</td><td>Hippolais icterina</td><td><strong>0.028***</strong></td></tr><tr><td>Anthus pratensis</td><td>-0.028*</td><td>Coracias garrulus</td><td>-0.005</td><td>Hippolais polyglotta</td><td><strong>0.005**</strong></td></tr><tr><td>Anthus trivialis</td><td>-0.005</td><td><strong>Corvus corone</strong></td><td><strong>0.043***</strong></td><td>Hirundo rustica</td><td><strong>0.020**</strong></td></tr><tr><td>Apus apus</td><td>-0.149***</td><td>Corvus frugilegus</td><td>0.132</td><td>Jynx torquilla</td><td>-0.008**</td></tr><tr><td>Ardea alba</td><td>-0.003</td><td>Corvus monedula</td><td>-0.035**</td><td>Lanius collurio</td><td>-0.008***</td></tr><tr><td>Ardea cinerea</td><td>-0.006*</td><td><strong>Coturnix coturnix</strong></td><td><strong>0.014***</strong></td><td>Lanius senator</td><td>-0.002</td></tr><tr><td>Athene noctua</td><td>0.002</td><td>Cuculus canorus</td><td>-0.021***</td><td>Larus argentatus</td><td>0.073</td></tr><tr><td>Aythya fuligula</td><td>-0.089</td><td>Cyanistes caeruleus</td><td>-0.036***</td><td>Larus fuscus</td><td>-0.030</td></tr><tr><td>Branta canadensis</td><td>-0.061</td><td>Cygnus olor</td><td>-0.093</td><td>Larus michahellis</td><td>-0.803</td></tr><tr><td>Bubulcus ibis</td><td>-0.383</td><td>Delichon urbicum</td><td>-0.011</td><td>Locustella naevia</td><td>-0.005**</td></tr><tr><td>Burhinus oedicnemus</td><td>0.001</td><td>Dendrocopos major</td><td>-0.027***</td><td>Lophophanes cristatus</td><td>-0.017***</td></tr><tr><td>Buteo buteo</td><td>-0.002</td><td>Dendrocopos medius</td><td>-0.009**</td><td>Lullula arborea</td><td>-0.023***</td></tr><tr><td>Carduelis cannabina</td><td>0.013*</td><td>Dendrocopos minor</td><td>-0.000</td><td>Luscinia megarhynchos</td><td><strong>0.029***</strong></td></tr><tr><td>Carduelis carduelis</td><td>0.001</td><td>Dryocopus martius</td><td>-0.009***</td><td>Luscinia svecica</td><td>-0.015</td></tr><tr><td><strong>Carduelis chloris</strong></td><td><strong>0.007**</strong></td><td>Egretta garzetta</td><td>-0.038</td><td>Merops apiaster</td><td>0.033</td></tr><tr><td>Certhia brachydactyla</td><td>-0.017***</td><td><strong>Emberiza calandra</strong></td><td><strong>0.047***</strong></td><td>Milvus migrans</td><td>0.007</td></tr><tr><td>Certhia familiaris</td><td>-0.011***</td><td>Emberiza cia</td><td>0.015</td><td>Milvus milvus</td><td>0.012</td></tr><tr><td>Cettia cetti</td><td>0.011</td><td>Emberiza cirlus</td><td>0.003</td><td>Motacilla alba</td><td>0.001</td></tr><tr><td>Charadrius dubius</td><td>-0.008</td><td>Emberiza citrinella</td><td>0.005</td><td>Motacilla cinerea</td><td>-0.006***</td></tr><tr><td></td><td></td><td><strong>Emberiza hortulana</strong></td><td><strong>0.039**</strong></td><td></td><td></td></tr></tbody></table>

<table><caption>Table 23: Determination of cropland birds - species by species regressions results (continued)</caption><thead><tr><th>Specie's name</th><th>Coef.</th><th>Specie's name</th><th>Coef.</th></tr></thead><tbody><tr><td><strong>Motacilla flava</strong></td><td><strong>0.031***</strong></td><td>Pyrrhula pyrrhula</td><td>-0.007**</td></tr><tr><td>Muscicapca striata</td><td>-0.000</td><td>Rallus aquaticus</td><td>0.008</td></tr><tr><td>Numenius arquata</td><td>-0.043</td><td>Regulus ignicapilla</td><td>-0.019***</td></tr><tr><td>Oenanthe oenanthe</td><td>-0.018</td><td>Regulus regulus</td><td>-0.022***</td></tr><tr><td>Oriolus oriolus</td><td>-0.005</td><td>Riparia riparia</td><td>-0.125</td></tr><tr><td>Parus major</td><td>-0.048***</td><td>Saxicola rubetra</td><td>-0.026**</td></tr><tr><td>Parus montanus</td><td>-0.005</td><td>Saxicola rubicola</td><td>0.001</td></tr><tr><td>Passer domesticus</td><td>-0.025**</td><td>Serinus citrinella</td><td>0.000</td></tr><tr><td>Passer montanus</td><td>-0.001</td><td>Serinus serinus</td><td>-0.011***</td></tr><tr><td><strong>Perdix perdix</strong></td><td><strong>0.062***</strong></td><td>Sitta europaea</td><td>-0.034***</td></tr><tr><td>Periparus ater</td><td>-0.083***</td><td>Sterna hirundo</td><td>-0.094</td></tr><tr><td>Pernis apivorus</td><td>-0.020***</td><td>Streptopelia decaocto</td><td>-0.005</td></tr><tr><td>Petronia petronia</td><td>-0.080</td><td><strong>Streptopelia turtur</strong></td><td><strong>0.008**</strong></td></tr><tr><td>Phalacrocorax carbo</td><td>-0.139*</td><td>Strix aluco</td><td>0.000</td></tr><tr><td><strong>Phasianus colchicus</strong></td><td><strong>0.009**</strong></td><td>Sturnus vulgaris</td><td>0.023</td></tr><tr><td>Phoenicurus ochruros</td><td>-0.010***</td><td>Sylvia atricapilla</td><td>-0.033***</td></tr><tr><td>Phoenicurus phoenicurus</td><td>-0.015***</td><td>Sylvia borin</td><td>-0.006***</td></tr><tr><td>Phylloscopus bonelli</td><td>-0.032***</td><td>Sylvia cantillans</td><td>-0.034***</td></tr><tr><td>Phylloscopus collybita</td><td>-0.046***</td><td><strong>Sylvia communis</strong></td><td><strong>0.011***</strong></td></tr><tr><td>Phylloscopus sibilatrix</td><td>-0.043***</td><td>Sylvia curruca</td><td>-0.012***</td></tr><tr><td>Phylloscopus trochilus</td><td>-0.014***</td><td>Sylvia hortensis</td><td>0.054</td></tr><tr><td>Pica pica</td><td>-0.014***</td><td>Sylvia melanocephala</td><td>-0.058***</td></tr><tr><td>Picus canus</td><td>0.000</td><td>Sylvia undata</td><td>-0.059***</td></tr><tr><td>Picus viridis</td><td>-0.004**</td><td>Tachybaptus ruficollis</td><td>0.005</td></tr><tr><td>Podiceps cristatus</td><td>-0.038*</td><td>Tadorna tadorna</td><td>-0.030</td></tr><tr><td>Poecile palustris</td><td>-0.024***</td><td>Tetrax tetrax</td><td>0.078*</td></tr><tr><td>Prunella modularis</td><td>-0.001</td><td>Tringa totanus</td><td>0.012</td></tr><tr><td>Psittacula krameri</td><td>-0.003</td><td>Troglodytes troglodytes</td><td>-0.048***</td></tr><tr><td>Ptyonoprogne rupestris</td><td>0.031</td><td>Turdus merula</td><td>-0.037***</td></tr><tr><td></td><td></td><td>Turdus philomelos</td><td>-0.041***</td></tr><tr><td></td><td></td><td>Turdus pilaris</td><td>-0.022</td></tr><tr><td></td><td></td><td>Turdus torquatus</td><td>-0.007</td></tr><tr><td></td><td></td><td>Turdus viscivorus</td><td>-0.016***</td></tr><tr><td></td><td></td><td>Upupa epops</td><td>-0.007*</td></tr><tr><td></td><td></td><td>Vanellus vanellus</td><td>0.016</td></tr></tbody></table>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. INSEE (new) region and year fixed effects are included. Standard errors are clustered at the BBS square level.</footnote>

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01. INSEE (new) region and year fixed effects are included. Standard errors are clustered at the BBS square level.</footnote>