# Do agri-environmental schemes target effectively species, habitats and pressures in French Natura 2000 network?

Paul Rouveyrol, Marie-Caroline Prima

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Paul Rouveyrol, Marie-Caroline Prima. Do agri-environmental schemes target effectively species, habitats and pressures in French Natura 2000 network?. Agriculture, Ecosystems & Environment, 2024, 373, pp.109114. 10.1016/j.agee.2024.109114. hal-04943784

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# Do agri-environmental schemes target effectively species, habitats and pressures in French Natura 2000 network?

Paul Rouveyrol<sup>a, *</sup>, Marie-Caroline Prima<sup>a, b</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Agri-environment schemes

Natura 2000

Common agricultural policy

Pressure-state-response

Agricultural intensification

Targeting

## ABSTRACT

Agriculture intensification is one of the most impacting drivers of European biodiversity decline. The Natura 2000 (N2000) network constitutes a key tool for conservation of species and natural habitats in Europe. Its action to counteract negative impacts of agriculture is mainly based on implementation of Agri-environment schemes (AES) in the N2000 sites. As economical resources are usually limited, spatial targeting is essential for such conservation policy. Here we evaluated whether AES distribution was in spatial coordination with species and habitat conservation states, and agriculture-driven pressures in the French N2000 network. Using the Pressure-State-Response framework, we tested the hypothesis that AES were mainly implemented in sites mostly exposed to pressures, having higher representativeness of species and habitats and higher degradation levels. We combined different sources of information (i.e., standard data forms of the N2000 policy, data collected by the observatory of rural development and national geographical information) to produce pressure, state and response spatial indicators. We declined the analysis per category of pressures related to agricultural activities and considered species and habitat vulnerability to each pressure category. We found limited spatial coordination between AES, pressure and state variables: only 37 % of the fitted models were significant. Significant relationships between AES implementation and agricultural intensification pressure were mostly negative. Considering all indicators, our hypotheses were disproven for more than half of the significant relationships. The results revealed a spatial mismatch between AES distribution and the ecological needs within the N2000 network, as more resources were devoted to sites less exposed to pressure. Our results raise the need of a more strategic approach for the N2000 policy. Indeed, the state of biodiversity conservation and the level of human pressures should be automatically considered a-priori of any implementation of management measures to effectively focus the efforts on the causes of current biodiversity crisis.

## 1. Introduction

Current biodiversity crisis in Europe needs urgent answers. Dramatic losses of species and ecosystems at continental scale are now well documented and drivers of this crisis have also been clearly identified (Hallmann et al., 2017; Allan et al., 2017, IPBES, 2018, Wagner et al., 2021). Agricultural activities are among the most impacting drivers of biodiversity change due to their major spatial coverage and historical importance (Stoate et al., 2009, Batáry et al., 2015). Agriculture had historically favored large part of European biodiversity (Díaz and Concepción, 2016; Díaz et al., 2021), and extensive practices are still promoting many species (Halada et al., 2011, Lomba et al., 2015). However, the recent agricultural intensification, including pesticide and fertilizer

use and habitat destruction, had worrying impacts on ecosystems (Donald et al., 2001, Geiger et al., 2010, Rigal et al., 2023). The need to face this biodiversity decline has triggered the development of conservation policies from local to European scales. Following the Bern Convention, European Union (EU) implemented the Natura 2000 (N2000) network, which constitutes its main tool to reach the objective of halting biodiversity loss (Evans, 2012, Hermoso et al., 2019). It is therefore critical to assess whether the N2000 policy is correctly conducted to reduce agriculture-driven pressures on biodiversity.

N2000 policy focuses on a selection of species and habitats of Community interest, whose lists are provided in the annexes of the 'Birds Directive' (1992) and the Directive on the Conservation of Natural Habitats and of Wild Fauna and Flora (1979), also known as 'Habitats

<footnote><sup>a</sup> Patrinat (OFB - CNRS - MNHN), 36 rue Geoffroy-Saint-Hilaire, Paris 75005, France</footnote>

<footnote><sup>b</sup> Office Français de la Biodiversité, Service Anthropisation et Fonctionnement des Ecosystèmes Terrestres, 5 allée de Béthléem, Gières 38610, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: paul.rouveyrol@mnhn.fr (P. Rouveyrol).</footnote>
Directive' (Council of the European Communities, 1992). Under these 'Nature Directives', member states have to designate a functional network of protected sites and define appropriate management measures to ensure the restoration or maintenance of these habitats and species at a favorable conservation status (Council of the European Communities, 1992).

Effectiveness of N2000 network has been studied by many authors who generally compared evolution of state indicators inside and outside N2000 sites (Rodríguez-Rodríguez et al., 2019, Anderson et Mammides, 2020, Concepción, 2020). For example, Kajzer-Bonk and Nowicki (2022) showed more positive biodiversity trends inside than outside sites unlike Rada et al. (2019) who found no difference. Similarly, reduction of steppe-species population or habitats were proven to be larger outside sites (Silva et al., 2018, Gameiro et al., 2020). Most other studies used breeding bird surveys, with converging conclusions to a positive effect of N2000 network on bird responses (Pellissier et al., 2019, Princé et al. 2021, Floogl et al., 2022). However, not all studies emphasized a positive impact of N2000 policy on bird trends: in Northern Italy, farmland and woodland bird species had steeper declining trends inside N2000 sites compared to outside (Portaccio et al., 2021), and in Spain, N2000 network did not prevent bird decline in perennial crops (Díaz et al., 2022). In some cases, impact of N2000 network was shown to be negative (Kindvall et al., 2022) or at least null (Lisón and Sánchez-Fernández, 2017, Loos et al., 2021). Even though such contradictory effectiveness outputs could derive from different management strategies within N2000 sites, such factor has rarely been accounted for in these studies.

Birds and Habitats directives aim to combine social and economic facets, consequently the N2000 network has been built in a logic of sustainable development (McCaulley, 2008). Within N2000 sites, stakeholders can engage in contracts, including Agri-Environmental Schemes (AES), co-financed by the EU and the member state, that provide financial support in exchange of produced actions. AES are considered as one of the main responses to environmental problems associated with agriculture activities (Díaz and Concepción, 2016, Dupraz and Guyomard, 2019, Falco et al., 2021). The common agricultural policy (CAP) reform in 2014 introduced others tools to counteract negative impacts of intensive agriculture (Díaz and Concepción, 2016, Pe'er et al., 2022), and notably the greening (i.e. requirements for crop diversification, grassland conservation and the establishment of ecological focus areas became compulsory for farmer to receive 30 % of CAP direct payments). However, beneficial effects of these innovations have proven to be very low (Alliance Environnement, 2019, European Court of Auditors 2017, Pardo et al., 2020). In any case, AES are the main CAP monetary expenses which can partly be restricted to N2000 sites (Díaz et al., 2022), and indeed N2000 sites are generally targeted for AES implementation (European Court of Auditors, 2011, Zmihorski et al., 2016, Brodzinska et al., 2020). Therefore, AES are likely to play an important role in promoting biodiversity in farmland in N2000 sites. Within the N2000 network, the general purpose of AES is to maintain or restore the good conservation status of habitats and species of Community interest which have justified the site designation (Batáry et al., 2015). Total payments for AES in European Union accounted for 6,3 % of Common Agricultural Policy Budget which represents €58.4 bn./year (Pe'er et al., 2020). AES funding constitutes a major part of the N2000 budget (EC 2018, Kindvall et al., 2022), consequently the ability of AES to deliver significant effect on biodiversity is crucial for the N2000 policy. Many studies provided an evaluation of the effect of AES on biodiversity at different scales, including various taxonomic groups. The review provided by Batáry et al. (2011) showed that species richness and abundance were significantly increased across all studies from AES. This was confirmed by Haaland et al. (2011) for pollinators on wildflowers strip AES. However, Uthes and Matzdorf (2013) considered these results to be often case-specific and not generalizable. More recent studies conducted at local scale mainly showed positive impact of AES on biodiversity (Budka et al., 2019, Lewis-Phillips et al., 2019, Walker et al., 2019, but see also

Berg et al., 2019). For example, Franks et al. (2018) showed that out of ten evaluated AES, six resulted in a higher probability than would be expected by chance of a significant positive impact on count, trend or productivity of farmland birds. At landscape-scale, and using long-term data, Redhead et al. (2022) showed that bird trends were stable or increasing in presence of AES whereas the same species were declining in equivalent landscapes without AES. However, most studies conducted at broader scale, i.e., at regional, national or European level, comparing biodiversity trends between farmlands having or not AES (Gamero et al., 2017, Hülber et al., 2017, Calvi et al., 2018, Walker et al., 2018, Angell et al., 2019), were not conclusive on AES effectiveness. For examples, Walker et al., 2018 found that farmland bird index showed more positive changes in abundance on farms with AES in at least one of the three evaluated regions but, meanwhile, Princé et al. (2012) detected a positive effect of AES on bird trend for only one species out of 19 evaluated and Calvi et al. (2018) found that this effect was very limited and often not positive. AES effectiveness is therefore well-established at local level but shows more heterogeneous results at larger scale. Yet, few studies provided a mechanistic explanation of these scale-dependent contrasting successes (Batáry et al., 2015, Díaz and Concepción, 2016, Pe'er et al., 2022). Understanding these variations in effectiveness is particularly relevant knowing that AES are at the moment not able to reach the N2000 global objective of restoring the good conservation status of species and habitats: indeed only 15 % of habitats and 27 % of species are in a favorable condition (EEA 2020).

AES uptake is dependent on voluntary behaviour by farmers within N2000 sites: consequently, there are neither evidence-based implementation, nor control that implementation is performed on relevant sites. Yet, many studies have argued for the importance of setting AES in sites where they are the most needed at broad scale to provide both local and global positive biodiversity outcomes (Kleijn and Sutherland, 2003, Bayliss et al., 2005, Uthes and Matzdorf, 2013, Meyer et al., 2015, Díaz and Concepción, 2016). Although others factors play an important role (Díaz and Concepción, 2016, Pe'er et al., 2022), spatial targeting is an important prerequisite for AES effectiveness. First AES implementation must consider the level of pressure exposure (Brooks et al., 2006, Allan et al., 2019). Indeed, farmers could only apply AES in areas where they already use agricultural practices that are favorable to biodiversity, i.e., with low pressure level. In such case, farmers participation in AES would be disconnected of environmental needs (Chabé-Ferret and Subervie, 2013, Raggi et al., 2015). This risk was already identified by the European Court of Auditors in 2011, deploring 39 % of reviewed contracts implemented in sector without pressures. Some evidences of such mismatch have since been well-established for AES policy: Früh-Müller et al. (2019) evaluated the spatial correlation between the AES distribution in Germany and environmental pressure indicators and revealed a partial mismatch between the two. At European level, Biffi et al. (2021) found a positive correlation between AES implementation and nitrate balancing but negative correlation with proportion of agricultural land use. More globally, AES were found to be more implemented in extensive farming systems than in intensive ones (Hynes and Garvey, 2009, Zimmermann and Britz, 2016). Concerning the N2000 policy, Hermoso et al. (2022) carried out a gap analysis to identify pressures with no reported management action in N2000 network and found that two thirds of the pressures were not tackled by management actions. But this study used data with very low spatial resolution and did not consider specifically the implementation of AES. A potential spatial mismatch between AES implementation and the actual need of pressure reduction at specific locations within N2000 network could be critical for conservation objectives as agriculture-driven pressures are known to have strong negative effects on conservation status of species and habitats (Donald et al., 2001), but yet remains poorly evaluated, as few studies considered specifically the application of AES within N2000 (Santana et al., 2014).

Other potential mismatches can reduce AES effectiveness. Indeed, limited positive effects can be expected if AES are implemented where
species that should take advantage of it (i.e., target species) are not
abundant. More specifically, AES should have a higher valuable impact
if implemented in sites with higher representativeness, i.e., that host an
important share of the targeted species populations. This question has
been assessed with mixed results at country level: AES seem to be
implemented at suitable locations concerning farmland bird in France
(Princé et al. 2012), but not in Poland (Żmihorski et al., 2016). At Eu-
ropean level, no relation was found between AES and biodiversity
intactness index (Biffi et al., 2021). Within N2000 network, this spatial
adequacy between AES implementation and the actual biodiversity
distribution has however rarely been evaluated (but see Princé et al.
2012, Kaligarič et al., 2019). It would therefore be useful to assess
whether this relationship is proven for all species and habitats of Com-
munity interest to better understand the mechanisms of AES
effectiveness.

At last, conservation status, i.e. extinction risk or level of degradation of species and habitat of Community interest should also be integrated. Indeed, management of degraded sites through restoration measures should also have a major function for biodiversity preservation (Aronson and Alexander, 2013, Suding et al., 2015). The ambition of the Habitats Directive is to maintain or restore, at favourable conservation status, every natural habitat and species of Community interest (Council of the European Communities, 1992). The implementation of restoration measures where the conservation status of such habitats and species is low is thus essential. As AES can be a tool for restoring farmland ecosystem (Wade et al., 2008), it is also highly relevant to implement them on sites with poor conservation status. This condition is not fulfilled for some European conservation budget: for example, funds of Life program, the main financial tool of the European Union's Biodiversity Strategy, have been mostly directed towards species under lower risk of extinction, while those most at risk have been underfunded (Hermoso et al., 2018, Giakoumi et al., 2019). Looking for similar mismatches concerning AES in N2000 sites to assess the ability for AES to restore degraded ecosystems is also urgent.

Various criteria must be considered to evaluate whether AES are
targeting the most appropriate N2000 sites: the presence of habitats and
species, their conservation status and the causes of degradation. The
widespread Pression-State-Response (PSR) framework (OECD, 1993)
seems therefore appropriate to define indicators to be used for such
evaluation. In the PSR model, human activities exert Pressures (P)
producing changes in the State (S) of the environment. These changes
may trigger political Responses (R). PSR models have been used in
methodologies for management of AES, mainly at local level (Zalidis
et al., 2004, Lütz and Felici, 2009). Here, the PSR model offers a con-
ceptual framework adapted to evaluate the relationship between
response variables (i.e., AES spatial distribution), state variables (i.e.,
related to distribution and conservation status of species and habitats of
Community interest which may suffer from agriculture-driven pres-
sures) and pressure variables (i.e., agriculture-driven pressures) within
N2000 sites. While most studies concerning N2000 policy assessment
focused on the effect of response variables on state variables, here we
propose to evaluate the effect of state and pressure variables on response
variables to provide a better mechanistic understanding of the effec-
tiveness of AES implementation in N2000 sites. In other words, we
suggest that both biodiversity conservation status and pressure level
within N2000 sites are not enough evaluated prior AES implementation,
which could explain a low effectiveness of AES, due to inappropriate
spatial allocation of resources.

French N2000 network is among the largest one in Europe, with very
diverse ecosystems due to the presence of four terrestrial biogeograph-
ical regions. Moreover, France receives the highest CAP expenditures
(European Commission, 2021) and, from 2014 to 2019, 80 % of pay-
ments made for French N2000 contracts were only dedicated to AES,
thus providing a relevant study case (Rouveyrol and Leroy, 2021). In this
study, we used the PSR framework to evaluate whether the imple-
mentation of AES within French N2000 sites during the 2015–2018

period (response variables) was in spatial coordination with three var-
iables before the implementation, notably: 1) agriculture-driven pres-
sure distribution (pressure variables), 2) the valuable role of N2000 sites
for conservation (VRC), in that they are representative of species and
habitats of Community interest vulnerable to agriculture-driven pres-
sure (state variable 1) and 3) the conservation status (CS) of species and
habitats of Community interest vulnerable to agriculture-driven pres-
sure (state variable 2). We assessed the spatial coordination of these
variables in order to test different hypothesis of “good management”,
that is AES are set where they are mostly needed in terms of biodiversity
conservation and human-related pressure reduction. Notably, we tested
three hypotheses (Fig. 1):

- H1: AES were mostly implemented in sites most exposed to pres-
sures, i.e., with higher levels of agriculture-related pressures.

- H2: AES were mostly implemented in sites with higher represen-
tativeness of species and habitats of community interest, i.e., with higher
levels of VRC.

- H3: AES were mostly implemented in sites with higher degradation
level, i.e., with lower levels of CS.

These hypotheses may be mutually exclusive alternatives. For
example, if AES target areas of highest agricultural intensity (H1), they
are unlikely to target areas with most species of Community interest
(H2) as agricultural intensification is a factor of decline of these species.
Meanwhile, sites with higher degradation are probably different from
sites with higher representativeness of species and habitats. Conse-
quently, our goal was to test whether one of the different strategies
emerged within the N2000 policy in France, or if no strategy was
identified. Finally, we assessed whether the conservation status of spe-
cies and habitats of Community interest vulnerable to agriculture-driven
pressure were correlated to the level of agriculture-driven pressures in
French N2000 sites before AES implementation, to evaluate whether the
link pressure-state was confirmed in our studied system.

Pressure level represents factors of detrimental effect generated by agricultural activities on species and habitats. It is calculated from various proxy. VRC measures the total area of vulnerable habitats and of vulnerable species per N2000 site for each pressure category. CS is the average conservation status of these habitats and species.

## 2. Methods

2.1. N2000 network in France

In 2021, French N2000 network covers 12,9 % of the mainland area, with 1756 protected sites designed either as special protection areas (SPAs, 403 sites designated under the Birds directive) or as special areas of conservation (SACs, 1353 sites, designated under the Habitats directive, « Building the Natura 2000 Network » s. d.). Sites cover both terrestrial and marine areas. N2000 sites regularly evolve to improve coverage of habitats and species, for example to integrate newly discovered species or habitat distributions, leading to site fusion, division, shrinkage or extension. We restricted our analysis to sites that were neither divided nor merged with others to be able to match the data of different time periods with the same sites throughout the study timeframe. We also removed marine sites and only kept terrestrial area of mixed sites. In total, we used the data of 1017 N2000 sites including 218 special protection areas and 799 special areas of conservation. In the following analysis, we worked with the May 2014 shapefile of N2000 sites to identify site delineation (INPN, 2022).

Member states are required to report the conservation status of wild
species and natural habitats designated in the directives through Nature
Directives reporting database and Standard Data Forms (EC 2011, EEA
2020). In Standard Data Forms reports, an evaluation is provided for
each designated species or habitat of each N2000 site while in reporting
database, information is collected at national or biogeographical scale.
For reporting, these assessments are provided by national experts (more
than 400 experts mobilized for the Habitat Report in 2019) and are
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Hypothesis to be tested with evaluation of spatial coordination between AES implementation in N2000 sites (response variable) and pressure and state variable (VRC and CS).</figcaption></figure>

based on up-to-date scientific knowledge (Bensettiti and Gazay, 2019). Standard Data Forms provide information on both population distribution and status that derived from biological field inventories at each site, under local administration responsibility. Reporting database provides a list of major pressures identified for each designated species and habitat according to the Natura 2000 pressure and threat classification. In the following analysis, we used the 2019 directives reporting database and the Standard Data Forms from December 2021. We assumed that these databases provide information on species and habitat conservation status before the period of AES implementation (i.e., before 2015) as very few of the corresponding field have been updated since 2015 (Rouveyrol et al., 2018).

## 2.2. Pressure data

The N2000 pressure classification describes 219 types of pressures organized in 15 large categories (EEA 2022). Based on this classification, we selected 33 pressures which have a clear link with agriculture (30 from agricultural activities category, 2 from human-related changes in hydrologic cycles and 1 from pollutions from multiple sources). We then reorganized them in 5 new categories related to 1) the use of pesticides (PEST) 2) habitat loss due to agricultural intensification (HL), 3) agricultural abandonment (AA), 4) nitrate pollution (NP). We also included a larger category (5) overlapping some of the previously mentioned pressures related to agricultural intensification in general (AI) (Appendix S1).

We then calculated a spatial proxy for each pressure category and each N2000 site corresponding to the period before the implementation of AES using available datasets from multiple sources. Specifically, for the PEST category, we used the 2015 French Pesticide Purchase Reporting Database consisting in the amount (in kilograms) of pesticide substances bought together with the postcode of the buyer (Eau France, 2022). We combined pesticide substances with their level of toxicity using the median lethal dose (LD₅₀) of each substance obtained from the Pesticide Properties Database (University of Hertfordshire, 2022). The median lethal dose corresponds to the quantity of substance given all at once, that leads to the death of half of a group of test animals, expressed in amount of substance per amount of bodymass. We compared oral LD₅₀ in birds and mammals for each substance and kept the higher value (Emmanuelle Porcher, personal communication). We then divided the amount of each purchased substance summed over each municipality by its associated LD₅₀. Note that among the 431 substances bought in 2015, we didn't have LD₅₀ for 92 of them but it represented only 2.4 % of the total amount of substances purchased. Finally, we extracted the proportion of overlap of each municipality over each N2000 site and calculated a weighted sum of the corrected quantity of substance for each site.

For the HL category, we used the European CORINE Land Cover (CLC) inventory produced in 2000 and 2012 (Copernicus Land

Monitoring Service, 2022). CLC is a land monitoring program consisting in an inventory of land cover in 44 classes from high resolution satellite imagery. We selected polygons of 7 classes among the 44 available including arable lands, permanent crops and annual crops associated with permanent crops (CLC nomenclature codes: 211, 212, 213, 221, 222, 223, 241) for both the 2000 and 2012 CLC shapefile maps. Then, we extracted the surface of overlap of the selected polygons over each N2000 site and divided it by site area to obtain the proportion of each N2000 site occupied by agricultural lands in 2000 and 2012. We calculated the difference in proportion between 2012 and 2000 to have an estimation of habitat loss due to agricultural intensification in each N2000 site. Finally, we truncated to 0 all negative values as they could not systematically reflect a pressure decrease, i.e., agricultural lands could have been converted to artificial lands (Tóth, 2012).

For the AA category, we also used CLC inventory produced in 2000 and 2012. Specifically, we selected polygons of pastures, shrub and/or herbaceous vegetation associations and sparsely vegetated areas (CLC nomenclature codes: 231, 321 and 333) in 2000 CLC shapefile map and polygons of forest, moors and heathland, sclerophyllous vegetation and transitional woodland/shrub (CLC nomenclature codes: 311, 312, 313, 322, 323, 324) in 2012 CLC shapefile map. We extracted the surface of overlap of the selected polygons over each N2000 site and divided it by site area to obtain the proportion of each N2000 site occupied by pastures and herbaceous vegetation in 2000 and by forest and pre-forest stage of development in 2012. Finally, we calculated the difference in proportion between 2012 and 2000 to have an estimation of agricultural land abandonment in each N2000 site.

For the nitrate pollution (NP), we used physicochemical survey of surface streams and bodies of water from the French Naiade database (MTE - OFB - BRGM, 2022). We used the analysis of 2013 collection of water samples providing concentrations (in mg/L) of nitrates over stations dispatched throughout the whole territory. We removed samples for which analysis was qualified as uncertain resulting in measurement of 29765 nitrate concentrations. We then predicted each concentration over a 5 km² pixel raster by applying an ordinary kriging to the data (Wackernagel, 2003). Finally, we extracted the proportion of overlap of each pixel over each N2000 site and calculated a weighted average of nitrate concentrations for each site.

For the AI category, we calculated a standardized cumulative index of pressure including the 2015 corrected quantity of pesticide substances, the 2013 nitrate concentration and the evolution of agricultural land between 2000 and 2012 for each N2000 site. More precisely, we first centered and scaled each index of pressure as they didn't have the same units. Then, we summed the four standardized indexes. Finally, we centered again the cumulative index for it to only be null or positive by adding its absolute maximal negative value.
## 2.3. State data

We used Standard Data Forms reports to acquire information on the valuable role of each site to the conservation of species and habitats of Community interest within each N2000 (VRC - state variable 1) and on the conservation status of the same species and habitats (CS - state variable 2). We defined VRC as the representativeness of species and habitats within the sites. We selected the variables CONSERVE and POPULATION of the SPECIES table and the variables CONSERVE, REPRESENT and AREA of the HABITAT table, both tables being provided in the 2019 Standard Data Forms database. The CONSERVE variable is reported for each population or each habitat of each site and corresponds to a qualitative assessment of conservation status varying between A (i.e., excellent conservation) to C (i.e., average or reduced conservation). The POPULATION variable indicates for each species the percentage of the national population that reside within each site and varies between A (i.e., 15–100 %), B (i.e., 2–15 %), C (i.e., 0–2 %) and D (non-significant). Similarly, the REPRESENT variable assesses site representativity for the designated habitat types and consequently, identifies sites where habitat presence is non-significant (i.e., REPRESENT = D). Finally, the AREA variable provides the surface (in hectares) of each habitat within each N2000 site.

Vulnerability of species and habitats to pressures. We used the lists of relation species-pressure and habitat-pressure provided by the reporting database at the national scale to identify the vulnerability of each species and habitat to our pressure category. We only retained pressures and not threats among the lists as threats refer to potential future pressure. We kept all types of pressure ranking (low, medium or high importance) to be conservative. For birds, we also filtered on pressures that were listed as inside the member state (i.e., France). Finally, species-pressure or habitat-pressure relationship are reported at the biogeographical scale by the experts meaning that we had to merge these lists with the biogeographical domain of N2000 sites. Specifically, we calculated the proportion of overlap of each site over each French biogeographical region and assigned to each site the domain for which the proportion was the highest.

Calculation of Valuable Role for Conservation (VRC). We excluded populations or habitats that had the lowest value of POPULATION or REPRESENT (i.e., equal to D). Then, on one hand, we calculated the total amount of vulnerable habitat (in hectares) per N2000 site for each pressure category. On the other hand, we calculated the total number of vulnerable species per N2000 site for each pressure category, classified by POPULATION value (i.e., A, B or C). When several populations of the same species were reported in a site, we kept the higher POPULATION value (A > B > C). Then, we calculated a weighted sum for each site as follows,

$$VRC_{s,p}^{\text{Species}} = 50 * N_{p}^{\text{Pop=A}} + 10 * N_{p}^{\text{Pop=B}} + N_{p}^{\text{Pop=C}} \quad (1)$$

Where, VRC<sub>s,p</sub><sup>Species</sup> is the valuable role of site s to the conservation of species vulnerable to pressure category p, N<sub>p</sub><sup>Pop=A</sup>, N<sub>p</sub><sup>Pop=B</sup> and N<sub>p</sub><sup>Pop=C</sup> are the number of species vulnerable to pressure category p with POPULATION values equal to A, B and C, respectively.

Calculation of Conservation Status (CS). We combined information from CONSERVE, POPULATION and REPRESENT variables notably by assigning to each species and habitat a conservation status equals to 0 if POPULATION value equals D (or for habitats, if REPRESENT = D), 1 if CONSERVE equals C, 2 if CONSERVE equals B and 3 if CONSERVE equals A. Several populations of the same species could be reported if populations have different site-use behavior (breeder, migratory or wintering). In that case, we calculated an average conservation status of all populations of each species within a N2000 site. Then, we linked species and habitats to their vulnerability to the 6 pressure categories using the relationship species-pressure and habitat-pressure presented above. A species or a habitat was considered as vulnerable to a pressure

category as long as it was vulnerable to at least one of the pressures mentioned in the pressure category (see Appendix S1). Finally, we calculated a species or habitat conservation status for each pressure category and each site by first, selecting only species or habitats that were vulnerable to the pressure category of interest, then by averaging conservation status of the selected species or habitats.

## 2.4. Response data

Information about AES was provided by the Observatory of rural development, which is managed by the French National Research Institute for Agriculture, Food and Environment. AES correspond to environment-friendly farming techniques that farmers commit to adopt for a five-year minimum period. In France, farmers could choose between 69 different AES, concerning all agricultural areas. Several AES can be combined on a same plot. To be in temporal adequacy with pressure and state variables (i.e., before 2015), we used information on AES implemented between 2015 and 2018. From this database, we selected the amount (in euros) of AES implemented by N2000 site over the 2015–2018 period, all types of measures taken together. We also calculated both the proportion of site area and the proportion of useful agricultural area covered by AES, all types of measures taken together. We computed useful agricultural area using the CLC inventory produced in 2018. Notably, we selected polygons of arable lands, permanent crops, pastures, heterogeneous agricultural areas, herbaceous vegetation and wetlands (CLC nomenclature codes: 211, 213, 221, 222, 223, 231, 241, 242, 243, 244, 321, 322, 411, 412, 421, 422) and calculated the area of each N2000 site occupied by these polygons in 2018.

We also calculated the surface (in hectares) of AES targeting each pressure category in each site. To this end, we identified for each detailed measure which pressure it is supposed to restrain (Appendix S1). For instance, we associated the AES “absence of mineral or organic fertilization in grassland” with the pressure “Nitrate pollution” and “Agricultural intensification”. We obtained in this way a set of detailed measures for each pressure category and we summed their area over each N2000 site.

## 2.5. Statistical analysis

We used linear models and generalized linear models to assess the spatial coordination of AES implemented between 2015 and 2018 with spatial distribution of pressure, CS and VRC variables within N2000 sites before the implementation. Specifically, we fitted a generalized linear model with quasi-Poisson distribution to the amount (rounded to the nearest euro) spent for AES implementation including AI pressure and CS of species and habitats vulnerable to AI as explanatory variables. We used generalized linear models with a binomial distribution to predict both the proportion of site area and the proportion of Useful Agricultural Area covered by AES from AI pressure and CS of species and habitats vulnerable to AI. We also run generalized linear models with gamma distribution to fit the surface of AES targeting each pressure category (PEST, HL, AA, AI) as a function of the corresponding pressure category and CS of species and habitats vulnerable to the same pressure category. We also evaluated the link between CS of species and habitat vulnerable to each pressure and the level of pressure for each category. To do so, we fitted linear models with Gaussian distribution to CS of species and habitats vulnerable to each pressure category with the corresponding pressure category as an explanatory variable. In all models, we included region (NUTS level 2) ID and a local Moran index to account for spatial autocorrelation in the response variable. Local Moran's I measures the similarity between neighborhood observations of a variable (Anselin, 1995). In our analysis, we defined N2000 sites as neighbors as long as they were within the median distance of all sites (i.e. 440 km) and computed a distance-based matrix of neighborhood weights with exponential decay (Månsson et al., 2017). We then calculated a local Moran's I for each observation of each response variable using this
matrix of neighborhood weights. All the models were run with: 1) all the data, 2) data of Special Protection Areas only, 3) data of Special Areas for Conservation only, 4) species data only and 5) habitat data only. For species and habitat separate models only, we included VRC variables as covariates as these variables could not be used in the same model for habitats and species due to different units. All statistical analyses were performed using *stats* and *spdep* packages of R software (R Core Team, 2020).

# 3. Results

## 3.1. AES distribution

Summary of AES implementation in French N2000 network is provided in Table 1. Detailed information are given in Appendix S3. Overall, extensive farming practices (mainly in grasslands) and absence of meadow fertilization covered most of AES surface and monetary expenses. Besides, environmental conservation measures corresponded most of the time to the maintenance of floristic richness of permanent grassland, highlighting that most AES efforts focused on grassland ecosystem, either for reducing intensive practice, like fertilization or overgrazing, or for preventing land abandonment.

## 3.2. Spatial concordance between pressure and response variables

Over 26 fitted models to test for the spatial coordination between agriculture-driven pressure distribution and AES implementation within N2000 sites, only seven showed that pressure distribution was significantly correlated with AES distribution (Table 2). Therefore, most of the fitted models (73 %) did not show any significant relationship. The global financial amount of AES targeting agricultural intensification, i.e., including all AES excepted the one targeting agricultural abandonment, was not correlated to the distribution of agricultural intensification except when solely habitat data were considered (Table 2). In the latter case, however, less money was spent for AES targeting agricultural intensification in sites under high pressure than low pressure. Agricultural intensification level was also negatively correlated to the proportion of useful agricultural area covered by these AES when all the data, data of special area of conservation or habitat data were selected (Table 2). Consequently, regarding useful agricultural area covered by AES, sites under high pressure received less AES than sites under low pressure. In addition, the spatial distribution of AES implementation did not match (i.e., no significant effect) the spatial distribution of agricultural intensification when site area was accounted for (Table 2).

Considering AES targeting each pressure separately, no relationship was found between the surface they covered and the spatial distribution of the use of pesticides or habitat loss due to agricultural intensification. Negative correlation was found between agricultural abandonment and AES targeting this pressure only when species data were considered. Nitrate pollution was the only pressure variable positively correlated with AES targeting this pressure when all data or special area of conservation data were selected. Accordingly, AES implementation within French N2000 sites proved to generally mismatch the distribution of

pressures that they are supposed to lessen, or even be in contradictory distribution.

## 3.3. Spatial coordination between state and response variables

**Relationship between VRC and AES** - Six models showed a significant correlation between AES and VRC spatial distributions, over nine fitted models (Table 2). Notably, the global financial amount of AES targeting agricultural intensification was positively correlated to VRC for both species and habitats vulnerable to agricultural intensification, indicating that more money was spent for AES on valuable N2000 sites (Table 2). The proportion of site area and the proportion of useful agricultural area covered by these AES were also positively correlated to VRC in sites occupied by vulnerable habitats to agricultural intensification but no significant relationship was detected when species were considered. Very few relationships arose when the correlation between AES targeting each pressure separately and VRC of vulnerable habitats or species were evaluated, and if so it revealed that more AES were implemented where VRC were the lowest (i.e., negative correlation, Table 2).

**Relationship between CS and AES** - The spatial distribution of AES implementation generally did not match the spatial distribution of the conservation status of vulnerable species and habitats to agriculture-related pressures (6 models with significant relationship out of 26 fitted, Table 2). However, when the relationship was significant, it always indicated that more AES were implemented or more money was spent for AES in sites protecting species and habitats with low conservation status (Table 2).

## 3.4. Spatial coordination between state and pressure variables

Among the 24 fitted models to test the relationship between pressure levels and conservation status of habitats and species vulnerable to these pressures within N2000 sites, only three showed significant effects. However, all these models revealed that CS of vulnerable species and habitats were lower when the level of pressure was higher in N2000 sites (Table 3). More precisely, higher use of pesticides or agricultural abandonment matched more degraded conservation status of species and habitats of Community interest within the French Natura 2000 protected network.

## 3.5. Conclusions concerning validation of the hypotheses to be tested

Fig. 2 summarizes the proportion of fitted models which validated or invalidated the three distinct “good management” hypotheses we presented in the introduction. Most relationships were not significant. Overall, the hypotheses we formulated were invalidated in 57 % of the significant relationships (Fig. 2).

<caption>**Table 1**</caption>

Distribution of agri-environmental schemes (AES) implementation in French N2000 network in 2018.

<table><thead><tr><th rowspan="2"></th><th colspan="4">Special Areas of Conservation</th><th colspan="4">Special Protection Areas</th></tr><tr><th>area (ha)</th><th>% area</th><th>amount</th><th>% amount</th><th>area (ha)</th><th>% area</th><th>amount</th><th>% amount</th></tr></thead><tbody><tr><td>more extensive farming - measures at farm level</td><td>124 847</td><td>30,9 %</td><td>8 342 710 €</td><td>16,4 %</td><td>181 656</td><td>36,7 %</td><td>12 227 433 €</td><td>22,3 %</td></tr><tr><td>absence of meadow fertilization</td><td>112 411</td><td>27,8 %</td><td>21 935 988 €</td><td>43,1 %</td><td>117 877</td><td>23,8 %</td><td>22 737 565 €</td><td>41,4 %</td></tr><tr><td>agro-pastoral management</td><td>79 647</td><td>19,7 %</td><td>6 261 122 €</td><td>12,3 %</td><td>107 040</td><td>21,6 %</td><td>8 206 588 €</td><td>14,9 %</td></tr><tr><td>environmental conservation measures</td><td>23 428</td><td>5,8 %</td><td>2 516 326 €</td><td>4,9 %</td><td>30 152</td><td>6,1 %</td><td>3 130 541 €</td><td>5,7 %</td></tr><tr><td>more extensive farming - measures at parcel level</td><td>21 963</td><td>5,4 %</td><td>6 306 247 €</td><td>12,4 %</td><td>12 251</td><td>2,5 %</td><td>2 492 736 €</td><td>4,5 %</td></tr><tr><td>control of meadow mowing</td><td>21 550</td><td>5,3 %</td><td>3 221 108 €</td><td>6,3 %</td><td>22 847</td><td>4,6 %</td><td>3 417 764 €</td><td>6,2 %</td></tr><tr><td>adjustment of meadow grazing pressure</td><td>18 875</td><td>4,7 %</td><td>2 056 082 €</td><td>4,0 %</td><td>21 875</td><td>4,4 %</td><td>2 329 870 €</td><td>4,2 %</td></tr><tr><td>combination of measures</td><td>1 425</td><td>0,4 %</td><td>281 625 €</td><td>0,6 %</td><td>1 746</td><td>0,4 %</td><td>352 136 €</td><td>0,6 %</td></tr></tbody></table>
<caption>**Table 2**</caption>

Coefficient estimates with their 95 % confidence intervals (in brackets) of models testing for the spatial coordination of agri-environmental measures (AEM) implemented in French N2000 sites between 2015 and 2018 with agriculture-driven pressure distribution, conservation status of species and/or habitats vulnerable to agriculture-driven pressures and the valuable role of N2000 sites to the conservation (VRC) of species or habitats vulnerable to agriculture-driven pressures before 2015. UAA = useful agricultural area, N2000 = terrestrial N2000 sites, SPAs = terrestrial special protection areas, SACs = terrestrial special areas of conservation, N = number of observations. Significant estimates are bolded.

<table><thead><tr><th></th><th></th><th>All data</th><th>SPAs</th><th>SACs</th><th>Species</th><th>Habitats</th></tr></thead><tbody><tr><td>Amount spent for AEM (in €)</td><td></td><td>N = 1671</td><td>N = 214</td><td>N = 1457</td><td>N = 877</td><td>N = 718</td></tr><tr><td>All agricultural intensification</td><td>Pressure</td><td>0.010 [-0.059; 0.080]</td><td>0.023 [-0.102; 0.148]</td><td>-0.070 [-0.164; 0.025]</td><td>-0.032 [-0.138; 0.074]</td><td>-0.141 [-0.195; -0.087] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td></tr><tr><td></td><td>Conservation status</td><td>-0.387 [-0.605; -0.170] <sup>***</sup></td><td>0.165 [-0.253; 0.583]</td><td>-0.176 [-0.366; 0.013]</td><td>-0.679 [-1.01; -0.352] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td><td>-0.312 [-0.523; -0.101] <sup>**</sup></td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>0.491 [0.331; 0.651] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td><td>0.854 [0.787; 0.921] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td></tr><tr><td>Proportion of site area covered by AEM</td><td></td><td>N = 1671</td><td>N = 214</td><td>N = 1457</td><td>N = 877</td><td>N = 718</td></tr><tr><td>All agricultural intensification</td><td>Pressure</td><td>-0.078 [-0.264; 0.062]</td><td>-0.034 [-0.487; 0.338]</td><td>-0.109 [-0.320; 0.054]</td><td>-0.069 [-0.322; 0.087]</td><td>-0.111 [-0.402; 0.128]</td></tr><tr><td></td><td>Conservation status</td><td>0.049 [-0.285; 0.389]</td><td>0.108 [-1.14; 1.28]</td><td>0.078 [-0.292; 0.458]</td><td>-0.110 [-0.654; 0.424]</td><td>0.112 [-0.560; 0.787]</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>0.159 [-0.148; 0.455]</td><td>0.237 [0.071; 0.413] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td></tr><tr><td>Proportion of UAA covered by AEM</td><td></td><td>N = 1633</td><td>N = 212</td><td>N = 1421</td><td>N = 860</td><td>N = 699</td></tr><tr><td>All agricultural intensification</td><td>Pressure</td><td>-0.208 [-0.356; -0.071] <sup>*</sup>,<sup>*</sup></td><td>-0.051 [-0.419; 0.254]</td><td>-0.235 [-0.399; -0.083] <sup>*</sup>,<sup>*</sup></td><td>-0.166 [-0.368; 0.007]</td><td>-0.263 [-0.492; -0.058] <sup>*</sup></td></tr><tr><td></td><td>Conservation status</td><td>0.009 [-0.238; 0.258]</td><td>0.187 [-0.665; 1.03]</td><td>0.009 [-0.269; 0.290]</td><td>-0.084 [-0.486; 0.315]</td><td>-0.132 [-0.641; 0.375]</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>0.144 [-0.086; 0.370]</td><td>0.242 [0.116; 0.373] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td></tr><tr><td>Surface of AEM (in hectares)</td><td></td><td>N = 1671</td><td>N = 214</td><td>N = 1457</td><td>N = 877</td><td>N = 718</td></tr><tr><td>All agricultural intensification</td><td>Pressure</td><td>0.001 [0.000; 0.002]</td><td>0.000 [0.000; 0.000]</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>CS</td><td>0.002 [0.000; 0.004] <sup>*</sup></td><td>0.000 [-0.002; 0.001]</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Surface of AEM (in hectares)</td><td></td><td>N = 1217</td><td>N = 210</td><td>N = 1007</td><td>N = 743</td><td>N = 324</td></tr><tr><td>Use of pesticides</td><td>Pressure</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>Conservation status</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Surface of AEM (in hectares)</td><td></td><td>N = 1540</td><td>N = 209</td><td>N = 1331</td><td>N = 778</td><td>N = 640</td></tr><tr><td>Habitat loss due to agricultural intensification</td><td>Pressure</td><td>0.000 [-0.001; 0.001]</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>Conservation status</td><td>0.000 [-0.001; 0.001]</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Surface of AEM (in hectares)</td><td></td><td>N = 1444</td><td>N = 209</td><td>N = 1235</td><td>N = 736</td><td>N = 575</td></tr><tr><td>Agricultural abandonment</td><td>Pressure</td><td>0.000 [0.000; 0.000]</td><td>-</td><td>0.000 [0.000; 0.000]</td><td>-2.10-4 [-3.10-4; 0.000] <sup>*</sup></td><td>0.000 [-0.001; 0.001]</td></tr><tr><td></td><td>Conservation status</td><td>-0.002 [-0.003; 0.000] <sup>*</sup>,<sup>*</sup></td><td>-</td><td>-0.001 [-0.003; 0.002]</td><td>0.000 [-0.001; 0.001]</td><td>-0.001 [-0.006; 0.005]</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>-0.002 [-0.003; -0.001] <sup>*</sup>,<sup>*</sup></td><td>-</td></tr><tr><td>Surface of AEM (in hectares)</td><td></td><td>N = 832</td><td>N = 0</td><td>N = 832</td><td>N = 204</td><td>N = 546</td></tr><tr><td>Nitrate pollution</td><td>Pressure</td><td>0.001 [0.000; 0.001] <sup>*</sup></td><td>-</td><td>0.001 [0.000; 0.001] <sup>*</sup></td><td>0.000 [0.000; 0.000]</td><td>0.000 [-0.001; 0.001]</td></tr><tr><td></td><td>Conservation status</td><td>0.001 [-0.002; 0.003]</td><td>-</td><td>0.001 [-0.002; 0.003]</td><td>-0.006 [-0.009; -0.002] <sup>*</sup>,<sup>*</sup></td><td>0.003 [-0.008; 0.015]</td></tr><tr><td></td><td>VRC</td><td>-</td><td>-</td><td>-</td><td>0.000 [-0.007; 0.007]</td><td>-0.006 [-0.008; -0.005] <sup>*</sup>,<sup>*</sup>,<sup>*</sup></td></tr></tbody></table>

## 4. Discussion

### 4.1. Adequacy of AES implementation and pressure-state indicators in N2000 network

This study provides to our knowledge the first evaluation of AES targeting inside N2000 network, using the Pressure-State-Response framework. Our objective was to test whether the implementation of agri-environmental schemes within N2000 network could be explained by biodiversity state or anthropogenic pressure variables. Our results are consistent with other studies conducted on AES independently of the distribution of protected areas: globally, AES are poorly correlated with

pressure and state variables: only 37 % of the fitted models showed a significant relationship. More importantly, significant relationships tended to show a negative correlation between AES implementation and agricultural intensification pressure, and, more globally, none of the « good management » hypothesis was verified for most of the significant relationships. Consequently, AES do not seem to be always implemented in accordance with ecological needs within the French N2000 network. This finding is worrisome regarding the ability of N2000 policy to tackle the causes of environmental degradation.

Our results clearly indicated that the pressure level is not a determinant of AES implementation. Among pressure indicators, only nitrate pollution variable was positively correlated with AES implementation.

<footnote>*** P<0.001;</footnote>

<footnote>** P<0.01;</footnote>

<footnote>* P<0.05. ‘-’ indicates models that were not able to fit. Detailed models are available in Appendices S4-S8.</footnote>
<caption>**Table 3**</caption>

Coefficient estimates with their 95 % confidence intervals (in brackets) of models testing for the spatial coordination of the conservation status (CS) of species and/or habitats vulnerable to agriculture-driven pressures with agriculture-driven pressure distribution in French N2000 sites before 2015. AI = agricultural intensification, PEST = use of pesticides, HL = habitat loss due to agricultural intensification, AA = agricultural abandonment, NP = nitrate pollution. N2000 = terrestrial N2000 sites and terrestrial part of mixed sites, SPAs = special protection areas, SACs = special areas of conservation, N = number of observations. Significant estimates are bolded.

<table><thead><tr><th>CS</th><th>N2000</th><th>SPAs</th><th>SACs</th><th>Species</th><th>Habitats</th></tr></thead><tbody><tr><td>AI pressure</td><td>N = 1671, R2 = 0.05<br/>-0.016 [-0.034; 0.001]</td><td>N = 214, R2 = 0.40<br/>-0.027 [-0.077; 0.022]</td><td>N = 1457, R2 = 0.07<br/>-0.014 [-0.032; 0.004]</td><td>N = 940, R2 = 0.10<br/>-0.013 [-0.034; 0.008]</td><td>N = 731, R2 = 0.10<br/>-0.019 [-0.046; 0.009]</td></tr><tr><td>PEST pressure</td><td>N = 1217, R2 = 0.06<br/>0.000 [0.000; 0.000]</td><td>N = 210, R2 = 0.30<br/>0.000 [0.000; 0.000]</td><td>N = 1007, R2 = 0.07<br/>0.000 [0.000; 0.000]</td><td>N = 853, R2 = 0.10<br/>-3.10-6 [-6.10-6; 0.000]<sup>*</sup></td><td>N = 364, R2 = 0.07<br/>0.000 [0.000; 0.000]</td></tr><tr><td>HL pressure</td><td>N = 1540, R2 = 0.04<br/>-0.003 [-0.015; 0.009]</td><td>N = 209, R2 = 0.40<br/>-0.016 [-0.050; 0.019]</td><td>N = 1331, R2 = 0.05<br/>-0.001 [-0.013; 0.011]</td><td>N = 870, R2 = 0.08<br/>-0.002 [-0.014; 0.010]</td><td>N = 670, R2 = 0.06<br/>0.014 [-0.019; 0.048]</td></tr><tr><td>AA pressure</td><td>N = 1444, R2 = 0.06<br/>-0.008 [-0.017; 0.001]</td><td>N = 209, R2 = 0.30<br/>0.012 [-0.015; 0.039]</td><td>N = 1235, R2 = 0.06<br/>-0.010 [-0.019; -0.001]<sup>*</sup></td><td>N = 838, R2 = 0.10<br/>-0.019 [-0.032; -0.005]<sup>**</sup></td><td>N = 606, R2 = 0.05<br/>-0.003 [-0.014; 0.008]</td></tr><tr><td>NP pressure</td><td>N = 832, R2 = 0.05<br/>-0.001 [-0.009; 0.008]</td><td>N = 0</td><td>N = 825, R2 = 0.05<br/>-0.001 [-0.009; 0.008]</td><td>N = 256, R2 = 0.10<br/>-0.006 [-0.022; 0.009]</td><td>N = 569, R2 = 0.05<br/>0.004 [-0.006; 0.013]</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Number of fitted models which validated or invalidated the hypotheses to be tested concerning spatial coordination between response, state and pressure variables in French N2000 site.</figcaption></figure>

This result is consistent with Biffi et al. (2022) who found AES to be positively associated with nitrate surplus at European level, even if the authors found no significant association in France (considering the whole territory). Wiśniewski et al. (2021) showed that more AES were implemented in Polish municipalities with high level of fertilizers. In Germany, while Paulus et al. (2022) found no relation between AES and groundwater nitrate concentrations, a mixed effect had been shown by Früh-Muller et al. (2019), with positive association for nitrous oxide deposition, and negative for ammonium deposition, mainly attributed to intensive livestock production. These results may be explained by a greater motivation of national and regional authorities to focus on environmental objectives corresponding to local public issues as are nitrate pollution and water quality (Dupraz and Guyomard, 2019). The ability of N2000 AES to target nitrate pollution inside the network is particularly interesting as AES with more expenditure in France for the 2015–2018 period was dedicated to the absence of mineral or organic fertilization in grasslands. It is therefore a positive result that the measure which mobilized the most economical resources was also implemented in sites under high pressure. Moreover, AES had recently proved to be effective on reducing nitrate levels on grasslands (Tzemi and Mennig, 2022). However, croplands generate much more nitrate pollution than grasslands (Wick et al., 2012), and consequently AES resource allocation should probably be more balanced between croplands and grasslands to make them even more effective.

Apart from nitrate pollution, our results concerning AES and pressures in N2000 network are in line with the general conclusions of Früh-Muller et al. (2019): AES payments are either decorrelated from

agricultural pressures or concentrated in sites having lowest pressure levels. Agricultural intensification is the most impacting pressure in N2000 sites (EEA 2020), and yet, fewer AES that could address this pressure are implemented in sites where agricultural intensification is greater. Our results are consistent with most studies dedicated to evaluate what drive voluntary AES participation, which have shown that participation in AES was higher in less-intensive farms (Hynes and Garvey, 2009, Zimmermann and Britz, 2016, Biffi et al., 2021). While most existing studies does not differentiate protected and non-protected areas, our analysis showed that this tendency is similar inside the N2000 network. Economic factors are a major driver of AES participation (Lastra-Bravo et al., 2015). Negative correlation between intensive practices and AES participation could be explained by excessive transaction cost: in high-intensity farming areas, the income loss generated by the required change of practices is disproportionate compared to AES subsidy (Barreiro-Hurlé et al. 2010, Früh-Muller et al., 2019). Therefore, AES may only be able to maintain favorable farming practices, resulting in a low capacity of modifying unfavorable ones (Dupraz and Guyomard, 2019).

Another explanation could be that AES are constituted by many different schemes with heterogeneous objectives and targets (Purvis et al., 2009). Some AES strategies consist in targeting sites of high conservation interest (Yang et al., 2014). These sites where rare species or habitats have persisted, like the one protected by N2000, are less likely to be exposed to anthropogenic pressures. This could explain negative correlation between pressures and AES, as we showed that AES were relatively successful in targeting these species or habitats. Such strategy can also be justified by the concern of promoting specialist species, while implementing AES in intensively farmed areas may benefit generalist farmland species. AES would then be a way to maintain favourable practices rather than reversing unfavourable ones. Our methodological framework partly integrated this point: we related each AES category to the pressure it was supposed to deal with, and we included land abandonment, which concerns sector with very low agricultural intensification. However, AES dedicated to land abandonment proved to be disconnected to this pressure or even negatively connected to land abandonment indicators. Consequently, neither of the two strategies, reducing intensives practices or maintaining disappearing extensive ones, seem to really apply. It should be noted that the two are not mutually exclusive and can coexist (European Courts of auditors 2011), but our results show important room for improvement in both areas.

Regarding state indicators, AES implementation is also poorly correlated with conservation status of species and habitats of N2000 sites. Nevertheless, among the few fitted models, most of them validated our “good management” hypothesis: more AES are implemented in more degraded sites. Considering the negative relationship we found between

<footnote>***P<0.001;</footnote>

<footnote>** P<0.01;</footnote>

<footnote>* P<0.05. ‘-’ indicates models that were not able to fit. Detailed models are available in Appendices S4-S8.</footnote>
AES and pressures, this could mean that AES target sites with low con-
servation status but without current pressure, i.e., sites that have been
degraded but may now be free of threat. In this case, AES action would
be remedial, which is consistent with the global need of restoration
actions (Suding et al., 2015, Gann et al., 2019). This hypothesis should
however be taken with caution as poor significant relationship was
detected between conservation status and pressure level variables, when
one might have expected a clear positive relationship (as observed here
with VRC variables). This latter result may be attributed to a low reli-
ability of conservation status obtained from the Standard Data Forms,
and consequently, analysis using these statuses might be interpreted
with caution (Rouveyrol et al., 2018).

We found more significant relationships in accordance with our
"good management" hypothesis using VRC variables. Almost half of the
fitted models showed AES to be more implemented in sites with high
representativeness of habitats and species of Common interest. This
ability of AES to widely target biodiversity had been shown by Princé
et al. (2012) using common birds in France. We confirmed these results
focusing on the N2000 network, where the presence of multiple species
and habitats of Community interest is a driver of AES implementation. It
is an encouraging result, in line with recommendation of multi-species
targeting approach for AES (Bayliss et al., 2005). The ability of AES to
target areas with high level of biodiversity had rarely been tested. It had
been mainly analyzed using data about grassland area: apart from
Kaligarič et al. (2019), studies found that AES were more implemented
in grassland-rich areas (Früh-Müller et al., 2019, Wiwnieski et al., 2021,
Paulus et al., 2022). Biffi et al. (2021) used a broader biodiversity in-
dicator with the biodiversity intactness index derived from Newbold
et al. (2015), but found no relationship.

The distribution of different types of AES within the French N2000
network are also consistent with these conclusions. AES are mainly
dedicated to grassland farming. This information is however not useful
to determine if they target sites with high or low pressure. For example,
the schemes "Absence of meadow fertilization" can subsidize farmers
who would not use fertilization, even if they did not benefit from AES
and therefore target sites with low pressure. On the contrary, this
scheme can generate a change in practices and therefore be a tool to
prevent fertilization. However, grassland farming is known to generate
less pressure and to host more biodiversity than croplands. Conse-
quently, if AES target grasslands, they may target sites with lower
pressure levels and more biodiversity. Predominance of grassland AES is
therefore fully consistent with our results concerning positive relation-
ship between AES and VRC variables and negative relationship between
AES and pressure variables.

## 4.2. Methodological considerations

Our results show the interest to use data concerning all the pressure-
state-response components. Previous studies highlighted the lack of
suitable biodiversity variable, i.e., state indicators (Früh-Müller et al.,
2019, Biffi et al., 2021). Focusing on N2000 network is a solution to get
such information at site level using Natura2000 database. Moreover, this
information reflects directly the European conservation policy objec-
tives and is consequently relevant for PSR analysis.

We enriched the use of state and pressure variables with data con-
cerning the vulnerability of species and habitats to pressures. Use of
vulnerability is recommended for building impact maps using state and
pressure data (Ostwald et al., 2021): it notably allows to restrict the
analyses to specifically threatened species and habitats. It has never
been used at our knowledge with response variables like AES.

We used national dataset, with limited spatial resolution. Notably, it
could be interesting to collect more localized data on species, habitat
and AES distribution within each N2000 site. For example, Paulus et al.
(2021) used data on field parcel, but without comparing it to biodiver-
sity variables. However, we were also restrained by the low spatial
resolution of other datasets: biodiversity ranking provided by the

Standard Data Forms are evaluated at site level and pesticide sales are
available at municipality level. However, they could be combined with
land use data at parcel level (Chayre and Pointereau, 2022, Martin et al.,
2023). Some other pressure data could be used in future works to
improve analyzes using land use dataset from Theia (IGN, 2023).

Apart from spatial resolution issues, some of the data we used suf-
fered limitations as they are mainly based on expert judgment. Assess-
ment of conservation status is notably a complex task (Mehtälä and
Vuorisalo, 2007, Alberdi et al., 2019). Conservation status is evaluated
for each site under the responsibility of local administration at region
level (NUTS 2). Yet, we found a strong effect of region ID in our models,
which could indicate an important bias linked to expert judgement at
regional level. This result argues to a need to reinforce shared and un-
biased methods to assess conservation status at site level (Maciejewski
et al., 2016).

We used an important number of models to reach our conclusions. Given the number of models fitted, one may expect to find some significant results just by chance. However, we did not test every potential relationship but we built models from three predetermined hypotheses. Consequently, it doesn't seem necessary to use correction for multiple comparisons. In any case, various models showed p-value close to 0.05, meaning that the response and explanatory variables were weakly related. More studies would be useful to strengthen our evaluation about theses variables.

4.3. Policy implications

While most studies analyze AES policy at country level, we chose to
analyze how AES targeted environmental needs inside the French N2000
network. Our goal was to understand the ability of agri-environmental
policy to contribute to the achievement of N2000 objectives in France.
Inside the network, more AES were implemented in sites dwelling more
agriculture-sensitive biodiversity and less agriculture-driven pressures.
Therefore, N2000 seems to apply a land-sparing policy (Green et al.,
2005, Phalan et al., 2011) with some part of the network dedicated to
conservation, while others only to agricultural use. This tendency to-
wards spatial segregation concerning AES was described at national
level by Früh-Müller et al. (2019). When it comes to a country, this
strategy can be discussed as a part of the land sparing-land sharing
debate, but it has little sense inside the N2000 network: indeed, all the
sites must be maintained in a good conservation status (Council of the
European Communities, 1992) and applying management only in sites
with low pressure level jeopardizes this objective.

Many studies discussed the factors impeding the effectiveness of AES (Kleijn et al., 2011, Batary et al., 2015, Scheper et al., 2013, Díaz and Concepción (2016), mainly in terms of landscape context and ecological contrast generated by AES. Agri-environmental schemes are generally found to be more effective when they generate a high ecological contrast and when they are implemented in landscape with intermediate complexity: therefore, AES effectiveness is lowest in cleared landscapes, i.e., intensively managed, and in highly complex ecosystems, corresponding to completely unfarmed sites (Tscharntke et al., 2012). N2000 sites were mostly designed in natural and semi-natural ecosystems so cleared landscapes are very poorly represented inside the network (Vandenbroucke et al., 2002, Gaston et al., 2008). Therefore, it is likely that, inside N2000 network, sites with high pressures do not correspond to cleared ecosystems but rather to intermediate ecosystems, which are the one where AES could be the most effective. On the opposite, sites with lowest pressures may correspond to ecosystems with a high level of complexity, where AES are underperforming. As a consequence, current AES implementation inside N2000 network could be a factor of low effectiveness. These conclusions need to be nuanced as other factors influence AES effectiveness. Geographical dispersion of AES implementation, for instance, could reduce this effectiveness (Cullen et al., 2018, Dupraz and Guyomard, 2019) whereas collective AES are more beneficial (Falco et al., 2021). Additionally, a minimal proportion of the
farmed landscape has also to be covered by AES for them to produce an effect (Sharps et al., 2023). These factors would also need to be tested but do not contradict the importance of targeting for AES effectiveness.

Our results are consistent with the assumption that AES are mainly dedicated to maintain existing environmentally-friendly practices, i.e., extensive farming (Alliance Environnement, 2019), although we showed they do not target French sites where these practices are the most threatened of extinction as correlation with land abandonment is also low. Indeed, AES aiming to maintain extensive practices are more likely to be implemented in low-pressure agricultural areas. This can be linked to the proportion of different types of AES actually implemented, which constitute a main factor of AES effectiveness (Llusia and Oñate, 2005): only a small part of available AES measure types is implemented and it concerns only extensive farming with focus on grassland farms. Additional work would be necessary to determine if this incapacity to reverse intensive practices shows the limitations of the contractual approach supported by AES or if it could be overcome by an increase in subsidies for AES in croplands. In any case, our work on AES targeting in N2000 network points out that the policy is not in line with the need of a restoration strategy. We consider that conservation efforts within N2000 sites must be redirected to fulfill the objectives currently discussed in the Nature Restauration Law context, as N2000 is supposed to play a major role in this Law (European Commission, 2022).

Our results may also be useful concerning recent evolutions of the Common Agricultural Policy with notably the introduction of eco-schemes in first pillar. Considering that ecoschemes could be very similar to AES (Horseman 2019 in Dupraz and Guyomard, 2019, Runge et al., 2022), although their level of ambition needs to be assessed (Lassalas et al., 2023), our conclusions may also help to orientate their implementation. Another option would be to make AES and ecoschemes complementary: AES could focus on sectors with rare or endangered species and habitats while ecoschemes could have a wider application by targeting generalist species and habitats (Díaz et al., 2021, Pe'er et al., 2022). In this scenario, we think the PSR approach we developed would be a useful decision-support tool for ecoschemes too. More broadly, our conclusions raise the need of a more strategic approach for N2000 policy: management efforts must be defined and implemented with the help of unbiased data integrating state and pressure indicators to focus the efforts on the causes of biodiversity loss.

## CRediT authorship contribution statement

**Paul Rouveyrol:** Writing – original draft, Project administration, Methodology, Conceptualization. **Marie-Caroline Prima:** Writing – original draft, Methodology, Formal analysis, Data curation.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data Availability

Data will be made available on request.

## Acknowledgments

We thank Karine Princé for proofreading the manuscript, Baptiste Girault and Cedric Gendre from observatory of rural development for providing AES data, and Bastien Coignon from French Ministry for Ecological Transition for his valuable advices. We are also thankful to two anonymous reviewers and one editor who carefully read the manuscript and provided insightful comments.

This work has been funded by French Ministry for Ecological Transition and French Biodiversity Agency.

# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agee.2024.109114.

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