Aurélie Lacoeuilhe
email: lacoeuilhe@mnhn.fr

Nathalie Machon

Jean-François Julien

Christian Kerbiriou

Effects of hedgerows on bats and bush crickets at different spatial scales

Keywords: hedgerows, scale effect, Chiroptera, Orthoptera, acoustic monitoring, conservation

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Introduction

Over the last century, agricultural intensification, particularly the increase in agricultural parcel sizes, has had severe consequences for biodiversity [START_REF] Robinson | Post-war changes in arable farming and biodiversity in Great Britain[END_REF].

At the landscape scale, one consequence of such intensification is the widespread removal of linear landscape elements in Europe [START_REF] Robinson | The ecology and conservation of seed-eating birds on farmland[END_REF][START_REF] Sklenicka | Remnants of medieval field patterns in the Czech Republic: Analysis of driving forces behind their disappearance with special attention to the role of hedgerows[END_REF]) and also of large patches of semi-natural habitats, such as forests and grasslands, even though the ecological importance of hedgerows and linear landscape elements has been known for decades (i.e., [START_REF] Burel | Effect of landscape structure and dynamics on species diversity in hedgerow networks[END_REF]Beier and Noss 1998;Bennett 2003). Hedgerows play a role in the control of water flow, water level and water quality [START_REF] Mérot | The influence of hedgerow systems on the hydrology of agricultural catchments in a temperate climate[END_REF]Baudry et al. 2000). They provide breeding habitat, food resources and dispersal pathways for many species of birds [START_REF] Hinsley | The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review[END_REF], amphibians [START_REF] Scribner | Environmental correlates of toad abundance and population genetic diversity[END_REF][START_REF] Rosenberg | Compensatory behaviour of Ensatina eschscholtzii in biological corridors: a field experiment[END_REF][START_REF] Brown | Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia[END_REF], mammals [START_REF] Henderson | Patchy environments and species survival: chipmunks in an agricultural mosaic[END_REF][START_REF] Pardini | The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape[END_REF] and invertebrates [START_REF] Burel | Effect of landscape structure and dynamics on species diversity in hedgerow networks[END_REF][START_REF] Hannon | Hedgerows in an agri-natural landscape: potential habitat value for native bees[END_REF]).

However, to our knowledge, the effects of the quality of linear elements, such as hedgerows (e.g., their diversity of structure and wood production), at different scales, on biodiversity have been poorly considered in previous studies, except [START_REF] Pywell | Assessing habitat quality for butterflies on intensively managed arable farmland[END_REF] and [START_REF] Dainese | Testing scale-dependent effects of semi-natural habitats on farmland biodiversity[END_REF]. Nevertheless, these effects are of interest because many agri-environmental schemes provide financial support for environmentally sensitive hedgerow management. The effectiveness of such initiatives in terms of both financial costs and biodiversity gains can be improved through a better understanding of the benefits provided by different types of hedgerows in different landscape contexts [START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF].

In this paper, we present a study of bats and bush crickets, both of which are known to take advantage of the presence of hedgerows, and which have been detected and identified based on their calls, recorded by detectors. Bats (Chiroptera) and bush crickets (Orthoptera) represent complementary ecological indicators of biodiversity quality. Bazelet and Samways (2011) identified bush crickets as good bioindicators for the assessment of the habitat quality of ecological networks because they respond strongly to management practices, such as grazing intensity [START_REF] Jauregui | Effects of stocking density and breed of goats on vegetation and grasshopper occurrence in heathland[END_REF]) and mowing regime [START_REF] Gardiner | Does microclimate affect grasshopper populations after cutting of hay in improved grassland?[END_REF].

Bush crickets belong to an invertebrate group that is abundant in grasslands, have a short life cycle and are at a low level in the food chain (mainly herbivore species and some omnivores) compared to bats. Although the importance of linear landscape elements is poorly understood for bush crickets, they have nevertheless been described to be useful for their dispersal behavior (Berggren et al. 2002) and colonization success (Berggren et al. 2001). Thus, we hypothesized that bush crickets could be sensitive to hedgerows at small spatial scales in accordance with [START_REF] Reinhardt | Low dispersal ability and habitat specificity promote extinctions in rare but not in widespread species: the Orthoptera of Germany[END_REF]. Bat species have a long life cycle relative to bush crickets. In the study areas, bats are all nocturnal insectivores at the top of the food chain [START_REF] Dietz | L'encyclopédie des chauves-souris d'Europe et d'Afrique du Nord : Biologie, caractéristiques, protection[END_REF]. They are considered to be bioindicators [START_REF] Jones | Carpe noctem: the importance of bats as bioindicators[END_REF]) because they react to several stressors, including the loss of landscape elements, which impacts their abundance, distribution and activity (e.g., [START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF]. Moreover, all bat species are protected in Europe (IUCN 2011). The primary predictor of bat abundance is the quality of the habitat, which is positively related to the availability of vegetation corridors [START_REF] Walsh | Factors determining the abundance of vespertilionid bats in Britain: Geographical, land class and local habitat relationships[END_REF][START_REF] Hein | Site-occupancy of bats in relation to forested corridors[END_REF]) and to the density of linear elements [START_REF] Verboom | The importance of linear landscape elements for the pipistrelle Pipistrellus pipistrellus and the serotine bat Eptesicus serotinus[END_REF]. Based on the size of the foraging home range of the bat (see Davidson-Watts andJones 2005, Perez-Jorda and[START_REF] Perez-Jorda | Preliminary results on activity rhythms and space use obtained by radio-tracking a colony of Eptesicus serotinus[END_REF], we hypothesized that bats could be sensitive to hedgerows at large spatial scales, in accordance with Bellamy et al. (2013) and [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF].

Many studies have been performed on the effects of the density and connectivity of linear landscape elements on the movement and dispersal of species [START_REF] Erickson | Influence of linear versus network corridors on the movement and dispersal of the bush-cricket Metrioptera roeseli (Orthoptera: Tettigoniidae) in an experimental landscape[END_REF]Berggren et al. 2001;Berggren et al. 2002;[START_REF] Diekötter | Effects of landscape structure on movement patterns of the flightless bush cricket Pholidoptera griseoaptera[END_REF][START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF]. However, little is known about how the quality of linear elements affects their use by bats because the only studies that show such effects are [START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF]) and [START_REF] Verboom | The importance of linear landscape elements for the pipistrelle Pipistrellus pipistrellus and the serotine bat Eptesicus serotinus[END_REF], and even less is known about bush crickets; thus, we need to better understand the relationship between biodiversity and the quality of linear landscape elements [START_REF] Hein | Site-occupancy of bats in relation to forested corridors[END_REF]).

To study linear element quality, we examined three hedgerow characteristics: (1) density within the landscape (density) (2) potential wood production (production) and (3) structural diversity (diversity). Indeed, each of these three indices provides information at different land levels and involves different stakeholders. Density reflects the history of successive agricultural policies (changing the division of lands) at the landscape scale, whereas production and structural diversity are more linked to local farming practices. Thus, these three indices provided information on the management of the land at different scales, which is important because spatial processes are known to influence the structure and dynamics of animal populations and communities [START_REF] Cottenie | Integrating environmental and spatial processes in ecological community dynamics[END_REF].

In this study, we used two approaches: 1) a species approach in which we tested the effects of the linear element quality on the activity of the species and 2) a trait based approach in which we tested the same effects on several traits (e.g., habitat specialization, bat foraging strategy and bush cricket mobility ability). The trait approach can provide information on the mechanisms involved in the use of different types of hedgerows by bat and bush cricket communities. Such information is essential for developing effective conservation plans and can be used to improve forest and agriculture management strategies.

Materials and methods

Study area

The study was conducted in western France, in the Loire delta, between the cities Nantes and Saint-Nazaire, in a European network of the protected area "Natura 2000" (Figure 1). The site is mainly composed of extensively managed grassland grazed by cattle and surrounded by a dense network of hedgerows. The grassland was identified as having a high nature conservation value by Veen et al. (2009).

Sampling design and scale approach

We employed a random stratified design in which 51 point counts were sampled in the grassland, the dominant habitat in the studied site. The point counts were positioned according to two criteria: 1) more than 50% of grassland in the 500 m buffer and 2) at three classes of distance from the hedgerow: 23 point counts at 0 meters (i.e., in the hedgerow), 17 at 25 and 11 at 50 meters from the hedgerow. These two criteria, "proportion of grassland in the buffer" and "distance to the hedgerow", were not correlated (rho = 0.09, p value = 0.378) and were therefore included as covariables. To measure the effects of the characteristics of the linear landscape elements at different scales, we used ArcGIS 9.3 and a local landuse database [START_REF] Geffray | Le bocage en vallée de la Loire: mise à jour cartographique et estimation des capacités en bois-énergie[END_REF]. Previous studies linked environmental variables at a few spatial scales with the abundance of bush crickets (Batáry et al. 2007;[START_REF] Braschler | Effects of small-scale grassland fragmentation and frequent mowing on population density and species diversity of orthopterans: a long-term study[END_REF][START_REF] Diekötter | Effects of landscape structure on movement patterns of the flightless bush cricket Pholidoptera griseoaptera[END_REF], Penone et al. 2013b) and bats (Bellamy et al. 2013;[START_REF] Lookingbill | Influence of wetland networks on bat activity in mixed-use landscapes[END_REF][START_REF] Dixon | Relationship between land cover and insectivorous bat activity in an urban landscape[END_REF][START_REF] Hale | Habitat composition and connectivity predicts bat presence and activity at foraging sites in a large UK conurbation[END_REF]), and we defined 11 circular buffers (radii of 50 m and every 100 m until 1000 m) around each point count that defined our sampling plots (a correspondence scale of areas depending on the buffer size used in this study and areas obtained with a doubling of surfaces is provided in Appendix C).Within these circular buffers, we extracted landscape characteristics: the proportion of semi-natural habitat area (grasslands) in each plot (for 500 m buffers around the 51 point counts, mean = 73.6% ± 1.6% SE) and the 3 indices of the quality of linear landscape elements in each plot: 1) the density, i.e., length per unit of studied area, 2) the structural diversity and 3) the wood production.

Linear landscape element and hedgerow indices

We calculated the three indices for each study plot. To describe the density of linear elements, we calculated the sum of their lengths in meters within the buffer zones. To describe the structural diversity, we used Shannon's diversity index [START_REF] Shannon | The mathematical theory of communication[END_REF] and a database [START_REF] Geffray | Le bocage en vallée de la Loire: mise à jour cartographique et estimation des capacités en bois-énergie[END_REF]) that described the linear landscape elements in six categories: (1) alignment of trees, (2) riparian vegetation, (3) shrub hedgerows with only shrubs, (4) wooded hedgerows without the presence of shrubs, (5) three-strata hedgerows (tree, shrub and herb).

The production index is a measure of the wood volume contained by the linear element (in m 3 ). Hereafter in this study, linear landscape elements will be termed hedgerows in accordance with Baudry and Jouin (2003), who defined a hedgerow as a line of trees and / or shrubs. The production index is calculated from the length of the hedgerow and a coefficient of density for the woody elements given by the database (Geffray 2010) (see Appendix A).

The diversity is not correlated with the production (for 500 m buffer: rho = 0.124, p value = 0.215) or the density (for 500 m buffer: rho = 0.117, p value = 0.241), whereas there is a correlation between density and production (for 500 m buffer: rho = 0.961, p value <0.001), which makes sense because the more trees present in an area, the greater the timber production. The high value of the last correlation implies the necessity of performing separate analyses of hedgerow indices.

Bat sampling

Bat activity was assessed by recording bat calls using a Tranquility Transect Bat detector (Courtpan Design Ltd., Cheltenham, UK) with direct and continuous recording on a Zoom H2 digital recorder (Zoom Corporation, Tokyo, Japan) at a sampling rate of 96 ks/s in .wav format. From the two possible outputs of the detector, time expansion and high frequency, we only used the second output, which did not include a trigger on sound volume. We placed one detector at each station at a height of 1.50 meters above the ground and facing away from the hedgerow. Each station was monitored twice in 2011. The first monitoring session was from June 15 th to July 31 st , during which female bats are known to give birth and to feed their offspring. The second monitoring session was performed from August 15 th to September 30 th , during which the young are flying and individuals are suspected to be less dependent on their reproductive roost. For each station, we recorded two 30-minute sound samples (one per session) . This sampling occurred during the bat activity peak, which begins 30 minutes after sunset and spans 4 hours [START_REF] Roche | Development of a Car-Based Bat Monitoring Protocol for the Republic of Ireland[END_REF]. Correlation between sampling point characteristics (density/diversity/ production) and covariables are presented in Appendix D.

The sampling was only performed when weather conditions were favorable, i.e., no rain, low wind speed and temperature higher than 12°C. Hourly cloud cover, temperature and wind speed data were retrieved from a local weather station (Météo France 2012). We studied bat calls within bat passes [START_REF] Thomas | Sampling methods for bats[END_REF]. Species calls were identified at the species level by the authors using Scan'R (Binary Acoustic Technology, 2010) to isolate each bat vocalization and automate the measurement of relevant parameters and Syrinx software version 2.6 [START_REF] Burt | Syrinx a software for real time spectrographic recording, analysis and playback of sound[END_REF] for spectrogram analyses (for more details see Appendix E and [START_REF] Lacoeuilhe | The influence of low intensities of light pollution on bat communities in a semi-natural context[END_REF]; exceptions included vocalizations of Plecotus austriacus and Plecotus auritus, which were pooled as Plecotus spp., and those of Myotis myotis, Myotis daubentonii, Myotis mystacinus, Myotis nattereri and Myotis bechsteinii, which were pooled as Myotis spp., due to their very low occurrence and some uncertainties in identification. We used the number of calls per 30 minutes as a proxy for the measure of bat activity. We distinguished two guilds according to their foraging traits: "gleaning bats" which include Myotis and Plecotus species that mainly eat diurnal brachyceran Diptera and non-volant arthropods, such as weevils, lepidopteran larvae, harvestmen and spiders (Audet 1990;[START_REF] Swift | Gleaning as a foraging strategy in Natterer's bat Myotis nattereri[END_REF][START_REF] Dietz | L'encyclopédie des chauves-souris d'Europe et d'Afrique du Nord : Biologie, caractéristiques, protection[END_REF], and "aerial hawking bats" which include Pipistrellus species, Eptesicus serotinus and Nyctalus species that mainly search the sky for prey [START_REF] Schnitzler | From spatial orientation to food acquisition in echolocating bats[END_REF][START_REF] Holderied | Echolocation range and wingbeat period match in aerial-hawking bats[END_REF] (Appendix B1).

Bush cricket sampling

Because bush crickets produce mating calls [START_REF] Ragge | The Songs of the Grasshoppers and Crickets of Western Europe[END_REF], it is possible to collect large standardized data sets using recording devices (Penone et al. 2013a). Bush cricket calling songs have a role in pre-mating isolation, and their structure is an important component of their mate recognition system [START_REF] Paterson | The recognition concept of species[END_REF]. Therefore, the analysis of calling songs may allow identification to the species level [START_REF] Ragge | The Songs of the Grasshoppers and Crickets of Western Europe[END_REF] and even provide reliable information on species abundances [START_REF] Fischer | Quantitative assessment of grassland quality: acoustic determination of population sizes of orthopteran indicator species[END_REF]). Nevertheless, it does not give an exact estimation of species abundances mainly because only adult males stridulate. However, this method can provide relative measures of abundance (rather than absolute abundance), which is adequate for detecting spatial changes in species abundances and for detecting anthropogenic pressures on bush cricket communities (Penone et al. 2013b).

For species with uninterrupted calls such as Tettigonia viridissima, Ruspolia nitidula, Metrioptera roeselii and Phaneroptera falcata, it was not possible to determine a value of abundance because their syllables are emitted at a continuous and quick rate (10-100 per second), which does not allow the easy distinguishing of several individuals singing simultaneously. Thus, we only noted their presence/absence, while for other species, with interrupted calls, we counted the number of calls of each species in each sample point as a proxy for relative measures of their activities.

We detected 11 species from the Tettigoniidae community and distinguished two mobility traits according to [START_REF] Reinhardt | Low dispersal ability and habitat specificity promote extinctions in rare but not in widespread species: the Orthoptera of Germany[END_REF] and [START_REF] Marini | Disentangling effects of habitat diversity and area on orthopteran species with contrasting mobility[END_REF]: mobile (mostly with wings) and sedentary (Appendix B2).

Mean trait community index 1.6.1 Habitat specialization index

Each species was characterized for habitat specialization through the calculation of a Species Specialization Index (SSI) following the Julliard approach (Julliard et al. 2006). To define habitat specialization for bush crickets, we used the SSI indices assessed by Penone et al. (2013a). For bat SSI calculation, we used an independent data set provided by the national biodiversity monitoring scheme coordinated by the French National Museum of Natural History (http://vigienature.mnhn.fr/; for more details on the data set used, see Appendix F).

The SSI was calculated for each species using the coefficient of variation of the species' abundance across habitats (Appendix B1, B2 and F). We calculated the Community Specialization Index (CSI) as the arithmetic mean of the SSI of the detected species weighted by the abundances (Julliard et al. 2006) (see Appendix F). Because of the lack of SSI for some species, CSI values were calculated on 85% of the total of bush cricket calls, whereas for the bats, all calls contribute to the assessment of CSI.

Mobility trait

For dispersal ability of bush crickets, according to [START_REF] Reinhardt | Low dispersal ability and habitat specificity promote extinctions in rare but not in widespread species: the Orthoptera of Germany[END_REF] and [START_REF] Marini | Disentangling effects of habitat diversity and area on orthopteran species with contrasting mobility[END_REF] we conducted analyses by summing, on the one hand, the abundances of mobile species, and on the other hand, the abundances of sedentary species. Two species (P. albopunctata and M. roeselii) had intermediate dispersal ability; thus, they were removed from the analyses on this trait, but they represented only 14.4% of the total calls.

Foraging strategy trait

Based on [START_REF] Dietz | L'encyclopédie des chauves-souris d'Europe et d'Afrique du Nord : Biologie, caractéristiques, protection[END_REF], we conducted analyses by summing, on the one hand, the abundances of aerial hawking species, and on the other hand, the abundances of gleaner species. As B.barbastellus (only 0.9% of total calls) had a mixed foraging type, this species was removed from the analyses of this trait.

For the bat community approach (foraging strategy trait and CSI), we corrected the bat activity by their distance of detection (see coefficients of detection in Barataud 2012) before calculating the community indices.

Statistical analysis 1.7.1 The influence of hedgerow characteristics on bat and bush cricket species.

We assessed the influence of the three indices of hedgerows (density, diversity and production) on bat and bush cricket species using a generalized linear model (GLM). The response variables were (i) abundance of bush crickets' stridulations (n = 11 species) or bat foraging calls (n = 9 taxa) and (ii) community traits (for bush crickets: species dispersal abilities and CSI and for bats: foraging type and CSI) per site (n = 51).

In our model, the explanatory variables were density, structural diversity and potential of wood production. Using some of the some correlations (between production and density, see 2.3), we constructed a model of each explanatory variable.

We considered a set of covariables, date, temperature, wind speed, and time after sunset (in minutes), because they could also influence bush cricket or bat activities. To test the effect of hedgerow characteristics in taking into account the landscape structure, we also included in the model the distance of each station to the nearest hedgerow and the proportion of seminatural habitat within the 11 buffer zones described above. We took into account the influence of covariables to study the hedgerows'characteristic variables. First, we systematically evaluated the correlations among explanatory variables using Spearman's rho for quantitative variables [START_REF] Crawley | The R book[END_REF]) (see Appendix D) to detect obvious correlation. Secondly, we performed variance-inflation factor (VIF) on the full models [START_REF] Fox | Generalized collinearity diagnostics[END_REF]; all variables had VIF<5, indicating no problem of multicollinearity in the explanatory variables of our models. To account for spatial autocorrelation, we added an autocovariate (i.e., a distance-weighted function of neighboring response values; Dormann et al. 2007) with the autocov_dist function in R (package spdep, Bivand et al. 2011).

Thus, for each species of bat and bush cricket and for each of the 11 buffer zones, our full statistical models were structured as follow:

[species activity] i ~ date +time after sunset + temperature + wind speed+ distance to the nearest hedgerow + proportion of grassland i + hedgerow characteristic i + autocovariate i where i is the buffer size considered (from 50 m to 1000 m radii) and hedgerow characteristic is the characteristic considered (density, structural diversity or potential of wood production).

Because of the expected non-normal distribution of bat and bush cricket species calls, and the possible high frequency of zero, we followed [START_REF] Potts | Comparing species abundance models[END_REF][START_REF] Potts | Comparing species abundance models[END_REF]Vandelvelde et al.( 2014) performing four GLMs according to the nature of the response variable (count of bat and bush cricket calls) for each tested species: one with a Poisson error distribution (GLM-P), one with a negative binomial distribution (GLM-NB) and two with a zero-inflated GLMs [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF]): one with a Poisson error distribution [ZAP] and one with a negative binomial [ZANB]; R package pscl, Jackman 2012). The zero inflated models were hurdle models (ZAP and ZANB) that consider presence and absence data (with a binomial function) and analyze the presence data in a second step with a count model (Poisson or a negative binomial) [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF]. Finally, based on the patterns ofresiduals (as recommended by [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF] to choose the appropriate modeling), we selected ZANB for all species, except for Plecotus spp., the gleaner group and community specialization indices, for whish we used GLM-NB (see Appendix G). For all species, we used the count model [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF]) of the ZANB to account for their activity; for species with uninterrupted calls (Appendix B2) and species with a very low occurrence (B. barbastellus), we focused on the presence/absence and used the binomial model of the ZANB [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF]. From the full model, using a buffer size of 500 meters, we performed a backward selection based on Akaike's information criterion (AIC). The final models for each species and community are presented in Appendix G, ; note that, exploratory treatment indicate that the procedure of covariable selection is not sensitive to buffer size. Finally, based on the foraging or mobility traits for each bat and bush cricket species and community, we tested the same model taking into account the covariables and measures of hedgerow quality in the landscape (density, diversity and productivity). We can compare the different explanatory variables (indices) using AIC comparisons.

Effect of hedgerow characteristics at different landscape scales 1.7.2.1 Step 1: calculation at a given scale

For a given species and for each of the eleven buffer sizes, from 50 meters to 1000 meters, we used the same final model to test the effect of each hedgerow index (density, diversity or production) on the activity of each species or the community index (forage, mobility and CSI), taking into account significant co-variables. Because we used the same model to test the effect of each index at all scales, we were able to directly compare the value of the estimate of the effect or goodness of fit of model (AIC) of indices for one species or community between different spatial scales. However, we could not perform direct comparisons between species or communities (see Appendix G).

Step 2: calculation across scales

Next, we tested whether the slope of the relationship between the dependent variable (species activities and community traits) and the hedgerow indices changed with the scale. Thus, we tested the effect of the buffer sizes on estimates from previous models using a GLM with a normal error structure because the response variable was normally distributed (hereafter referred to as 'scale analysis') (see Appendix G3). Following the approach of Penone et al.

(2013a), we assigned the response variable (i.e., estimate of slope) different weights according to the associated standard error (1 per SE²) obtained from the previous analysis.

Buffer size effects were adjusted to variables using a type II ANOVA. When we did not detect a linear effect across the buffer size range (from 50 to 1000), we used the exploratory approach of broken-line models.Broken-line models are regression models in which the relationships between the response and explanatory variables are piecewise linear, represented here by two straight lines connected at unknown values: these values are usually called breakpoints (Package segmented, [START_REF] Muggeo | Segmented: an R package to fit regression models with broken-line relationships[END_REF]) (see Appendix H). All p values were corrected for potential over-dispersion according to the [START_REF] Faraway | Extending the linear model with R, Generalized linear, mixed effects and nonparametric regression models Chapman & Hall/CRC[END_REF] approach. All analyses were performed with R software (R Development Core Team 2011).

Results

A total of 35 263 bat calls belonging to 14 species were recorded at the 51 point counts during the two periods. The majority of echolocation calls from the 14 bat species came from three Pipistrellus bats (Pipistrellus pipistrellus (48.5%), Pipistrellus kuhli (24.4%) and Pipistrellus nathusii (21.2%)). For bush crickets, we detected 101 419 calling songs belonging to 11 species from the Tettigoniidae family (Appendix B2). For more details on average activities of bats and bush crickets, see Appendix I.

Effect of the wood production of hedgerows 2.1.1 The effect of the wood production of hedgerows on bats

We obtained 104 positive estimates of which 44 were significant and 28 negative estimates of which 3 were significant (see Appendix J3). The activity of bat species was generally positively correlated with the wood production of hedgerows, as shown in Figure 2, and the effect was stronger at larger spatial scales (see Appendix J3). In the graph e (Figure 2), we present the effect of the density of hedgerows on the aerial bat species. We showed that 1) for example, with the 1000 m buffer, the activity of aerial species is positively correlated with the density of the hedgerow (the estimate reported from the modeling at 1000 meters -with its standard error) is positive, i.e., above zero, and this effect is significant, as indicated by the asterisk; and 2) the scale effect, obtained from the linear regression, from 50 meters to 1000 meters was significant and is shown by the dotted line. The activity of aerial bat species was significantly and positively correlated with the hedgerow density in a 1000 m buffer. The scale effect obtained from linear regressions was significant and there is a positive scale effect for these communities (above zero). When we did not detect a linear scale effect, we assessed a potential breakpoint value (see also Appendix H).

The effect of the wood production of hedgerows on bush crickets

We obtained 108 positive estimates, of which 16 were significant and 56 negative estimates of which 15 were significant (see Appendix J3). The wood production of hedgerows had a positive influence on the activity of all bush cricket species, (see Figure 3) and the influence was greater when the spatial scale was large, except for the sedentary species and Uromenus rugosicollis, for which results were the inverse of the other species: wood production was negatively correlated with their activities and this effect was stronger when the spatial scale was large (see Appendix J3).

Effect of the density of hedgerows

The effect of the density of hedgerows on bats

The density of hedgerows had a generally positive effect on the foraging activity of the studied bat species and communities based on the studied traits. Among the 132 estimates ((9 taxa + 3 community indices) x 11 buffer sizes) we generated for bats, we obtained 106 positive estimates, of which 33 were significant, and 26 negative estimates, of which only 4 were significant (see Appendix J1). The greater the hedgerow density around the point count, the more numerous were the bat calls. We observed this effect at almost all the spatial scales we studied. Moreover, we found that the larger the scale, the stronger the effect (observed in 9 out of 12 cases, see Table 1 andAppendix J1); therefore, we suggest that the density of hedgerows significantly and positively influenced the activity of the bat species and the two guilds at increasingly large spatial scales (see Appendix K1).

The effect of the density of hedgerows on bush crickets

The effect of density on bush crickets' activity was less marked than for bats and depended on the species studied and on their mobility. Among our 154 estimates (((11 taxa + 4 community indices)-(1 non-convergent model)) x 11 buffer sizes), we obtained 96 positive estimates, of which 15 were significant and 68 negative estimates, of which 11 were significant (see Appendix J1).

Moreover, the obtained positive effect increased at a large scale (> 500 m) for Tettigonia viridissima, Ruspolia nitidula and Pholidoptera griseoaptera, whereas the effect was negative and became stronger at a large scale for Uromenus rugosicollis. Two contrasting patterns were observed for the two communities based on their mobility traits. The activity of mobile species of bush crickets appeared to increase with the density of the hedgerows and to increase more strongly at large scales, whereas the opposite effect was observed for sedentary species whose activity decreased in the presence of high-density hedgerows, especially at large scale (see Appendix K2).

Effect of the diversity of hedgerows 2.3.1 The effect of the diversity of hedgerows on bats

We obtained 91 positive estimates, of which 30 were significant and 41 negative estimates, of which 12 were significant (see Appendix J2). This effect was weaker at larger scales and had a break point at approximately 500 m (see Appendix L1 and J2). Moreover, we obtained contrasting and opposing patterns for aerial and gleaner species. The latter showed decreased activity with increasing hedgerow diversity at all scales, whereas the activity of the aerial species increased until 500 m and then decreased at larger spatial scales. Unlike other gleaner species, the activity of Myotis spp. increased with hedgerow diversity, especially at large spatial scales (see Appendix J2 and L1).

The effect of the diversity of hedgerows on bush crickets

Among the 110 estimates (((11 taxa + 4 community indices)-(5 non convergent models)) x 11 buffer sizes), we obtained 88 positive estimates, of which 12 were significant, and 72 negative estimates, of which 24 were significant (see Appendix J2). Tettigonia viridissima and Uromenus rugosicollis activities increased with diversity at scales larger than 500 m (see Appendix L2). However, the activities of Ruspolia nitidula and Pholidoptera griseoaptera decreased with hedgerow diversity at all spatial scales, and the effect was stronger at large spatial scales (see Appendix L2). Hedgerow diversity had a positive effect on the activity of the mobile species, but the effect decreased and became negative for buffers of 800 m and larger, whereas the activity of sedentary species increased with the diversity of hedgerows, and this effect was stronger at larger spatial scales (see Appendix L2).

Comparison between the indices

According to the AIC value (Table 1), the best model, for each bat and bush cricket species and for communities based on foraging, mobility and habitat specialization, was the model with the productivity index.

Discussion

To our knowledge, our study is the first to assess the influence of hedgerow quality using three characteristics (density, structural diversity and potential of wood production) on the activity of bats and bush crickets, as well as on communities, based on foraging, mobility and habitat specialization. The results of the present study demonstrate the importance of hedgerow quality for bats and bush crickets, but the strength of the association between taxa and hedgerows varied greatly among species and spatial scales. From management and conservation perspectives, we need to understand the characteristics of hedgerows and spatial scales that must be considered to assess the hedgerows' quality for conservation of the different taxa. Indeed, many European bat species use linear features of the landscape for commuting and foraging [START_REF] Downs | The use by bats of habitat features in mixed farmland in Scotland[END_REF][START_REF] Entwistle | Habitat exploitation by a gleaning bat, Plecotus auritus[END_REF][START_REF] Limpens | Bats, their behaviour and linear landscape elements[END_REF]Kapteyn 1991, Glendell and[START_REF] Glendell | Foraging activity of bats in historic landscape parks in relation to habitat composition and park management[END_REF], and thus the density of hedgerows was particularly correlated with the activity of bats such as Pipistrellus spp. [START_REF] Verboom | The importance of linear landscape elements for the pipistrelle Pipistrellus pipistrellus and the serotine bat Eptesicus serotinus[END_REF][START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF]. Some studies have shown that occupancy of bat species was positively associated with corridor overstory height [START_REF] Hein | Site-occupancy of bats in relation to forested corridors[END_REF]. [START_REF] Merckx | Hedgerow trees and extendedwidth field margins enhance macro-moth diversity: implications for management[END_REF] showed that in agricultural landscapes, trees in hedgerows increase the diversity of nocturnal Lepidopterawhich are some of the prey of certain European batsand can also provide microhabitats, such as dead wood, that also increase invertebrate abundance and diversity (DEFRA 2010;[START_REF] Winter | Microhabitats in lowland beech forests as monitoring tool for nature conservation[END_REF]. Moreover, production reflected richly structured habitats with microhabitats [START_REF] Regnery | Tree microhabitats as indicators of bird and bat communities in Mediterranean forests[END_REF], that are preferred by many bat species such as P. pipistrellus [START_REF] Davidson-Watts | Differential habitat selection by Pipistrellus pipistrellus and Pipistrellus pygmaeus identifies distinct conservation needs for cryptic species of echolocating bats[END_REF]) and especially by highly specialized and threatened species such as B. barbastellus [START_REF] Kusch | Foraging habitat preferences of bats in relation to food supply and spatial vegetation structures in a western European low mountain range forest[END_REF]). Moreover, to reach hunting grounds, species such as Myotis daubentonii forage and commute along wood edges and hedgerows and avoid crossing open areas [START_REF] Limpens | Bats, their behaviour and linear landscape elements[END_REF]. Our results were congruent with the literature but showed this relationship at different spatial scales for 9 bat taxa. We also identified a positive effect on groups based on foraging traits, mobility and habitat specialization. However, concerning bush cricket activity, few studies have documented hedgerow effects (Berggren et al. 2002). For this group, there were nearly as many significantly negative indices as significantly positive indices. The species we studied includedspecies predicted to prefer grasslands and others predicted to prefer forest, which may explain this result.

However, our study has several potential limitations. The ultrasound recording time per site may be relatively short for identifying accurate absolute animal activity or abundance.

Sampling using recording throughout the entire night would be more appropriate but also more time-consuming. Still, our design focus on the beginning of the night occurred during the bat and bush cricket activity peak. Moreover, our sampling design allows unbiased measurement of animal activity among the hedgerow characteristics tested.

Scale effect

Based on the intensities of bat calls and bush cricket stridulations and on the technical characteristics of the microphones, we did not expect to detect individuals at a distance greater than 100 meters from the recording point. However, in our study, the strengths of the correlations tended to be strongest at large scales, suggesting that hedgerows could exert an influence at the scale of the landscape. However, it is worth noting that the influence of hedgerows on activity seems to vary depending the index characterizing the hedgerows and the species, most likely because several processes may be involved at different spatial scales (Bellamy et al. 2013). In fact, as already shown by [START_REF] Hale | Habitat composition and connectivity predicts bat presence and activity at foraging sites in a large UK conurbation[END_REF] for bats, the organisms are sensitive to landscape composition and structural connectivity at multiple spatial scales, and the impact of forest harvesting on habitat use by foraging bats varies with spatial scale [START_REF] Grindal | Impact of forest harvesting on habitat use by foraging insectivorous bats at different spatial scales[END_REF].

Based on homerange studies of bats using radio-tracking methods (see ref. [START_REF] Drescher | Radiotracking of Myotis myotis (Chiroptera, Verspertilionidae) in south Tyrol and implications for its conservation[END_REF], we expected to detect an effect of hedgerow quality at large spatial scales, and this is what we found. For instance, the positive effect of a hedgerow network was detected in buffers whose radii ranged from 400 m to 1000 m, a range that is comparable to the home range of some individuals of Pipistrellus pipistrellus (Davidson-Watts and Jones 2005) and, Eptesicus serotinus [START_REF] Perez-Jorda | Preliminary results on activity rhythms and space use obtained by radio-tracking a colony of Eptesicus serotinus[END_REF] or to the mean distance between roost and feeding sites for species such as Pipistrellus nathusii [START_REF] Flaquer | Habitat selection in Nathusius' pipistrelle (Pipistrellus nathusii): the importance of wetland[END_REF] and Eptesicus serotinus [START_REF] Robinson | Home range and habitat use by the serotine bat, Eptesicus serotinus, in England[END_REF].

In addition, our results showed that gleaner species were significantly influenced by the potential production of hedgerow at small scales (see Figure 2 and Appendix J3), whereas the effect for aerial species was significant only at large scales (Figure 2 and Appendix J3). [START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF] showed that hedgerows of all types were associated with a similar increase in P. Pipistrellus incidence; it is interesting to note that in our study, density and production indices had a positive increasing influence with spatial scales on the activity of the aerial hawking guild and the gleaning guild except for the diversity index for this last group (Table 1).

Based on habitat area requirements and the individual bush crickets' perception of the landscape, we hypothesized that the bush crickets were more sensitive to small-scale changes.

Arak and Eiriksson (1992) found that males of T. viridissima were regularly spaced with a mean distance of 6 m between nearest-neighbors. However, our results showed that the bush cricket species (i.e., abundances) and community indices were also influenced by landscape structures at large scales (Figure 3 and Table 1).

In addition, we did not identify thresholds beyond which the scale effect no longer increased. This result was congruent with Penone et al. (2013a) and [START_REF] With | Critical thresholds in species responses to landscape structure[END_REF] who showed that some bush cricket species were sensitive to large-scale effects. We obtained a contrasting effect for the two bush cricket communities based on mobility: a significant positive scale effect of the production index on the activity of mobile species and a significant negative effect on the activity of sedentary species (See Appendix J and Figure 3 e and . and Table 1).

The habitat of specialized species appeared to benefit from the density of hedgerows composed of old and large trees. For bush crickets and bats, we detected a positive scale effect of the density and production indices on the two communities based on indices of habitat specialization (CSI), whereas the effect of the structural diversity index was not significant (Table 1). The comparison of the AIC of the models, for each quality index and for each species or community, showed that the scale effect of the potential wood production best explained the activity of the bats and bush crickets we studied.

For some taxa (i.e., N. noctula, Fig. 2) and communities (i.e., gleaners, Fig. 2), we detected a slope change of approximately 600 m with the scale analysis. This change may be specific to our study site and could be linked to the landscape structuring at the regional scale linked to the Loire River. A non-exclusive hypothesis could be linked to specific biological features of the taxa studied, indicating scale variations of landscape perception, which emphasizes the need to confirm this observed trend with future studies.

Comparison among the 3 indices: density, structural diversity and wood production

There was no correlation between diversity and wood production or between diversity and density, whereas a correlation between the density and wood production was detected. This relationship was logical according to the construction of the production index (see section 2.3) which includes hedgerow density. However, this index includes the information contained in the density index. Even if the wood production index appears globally to be a better predictor of bat and bush cricket activities (according to AIC value, see Table 1), the strong correlation between these two indices prevents identify, with certainty, the best predictor.

We showed that from the landscape perspective, the quality of the hedgerow network was better represented by the potential wood production that generally positively influenced the activity of bush crickets and bats. Concerning bats, the production index showed a frequent significant positive effect on bat activity. Concerning bush crickets, the production index showed a more frequent significant positive effect (see Appendix J) than the density or diversity indices. We assumed two non-exclusive hypotheses to explain the differences in the patterns observed in Figure 3 between the mobile and sedentary species (Figures 3.e and 2.f.).

First, the community of a mobile species would be more related to forest edges (i.e., T. viridissima), whereas the community of a sedentary species (C. dorsalis, U. rugosicollis) would be more related to the open areas such as grasslands [START_REF] Voisin | Atlas des Orthoptères et des Mantides de France[END_REF]. Thus, we would have congruence between the traits mobility and habitat selection. Second, the hedgerows may contribute to colonization success in the grasslands, and therefore play a functional role, as Berggren et al. (2001) showed in the case of Metrioptera roeselii, which showed higher activity in landscapes with more hedgerows. This result was congruent with our results

showing that the activity of this species was influenced positively by the density, diversity, and productivity indices and increased with spatial scale.

Through the diversity index, our study provided new evidence regarding the importance of hedgerows. The diversity of the structure of the hedgerows reflected from an ecological perspective, the heterogeneity of habitats in the hedgerows and thus the heterogeneity of ecological niches. Although this index shows a generally positive effect on the activity of bats and bush crickets compared to the two other indices (see Appendix J), its influence appeared to be less linear: few linear scale effects were detected (see Appendix L) and broken line analysis indicated a change at approximately 400-600 m. These patterns may be linked to the nature of this variable: the diversity index is calculated using a Shannon index (see 2.3).

Indeed, the effects of the diversity index can be reversed according to the scales considered.

At a small scale, a high structural diversity indicates a strong heterogeneity of the hedgerow types at a given location, whereas at larger scales, high structural diversity implied a good spread of different hedgerows types and a large quantity of each type.

Conclusion and implications for conservation

If habitat is the primary predictor of bat abundance [START_REF] Müller | Aggregative response in bats: prey abundance versus habitat[END_REF][START_REF] Walsh | Factors determining the abundance of vespertilionid bats in Britain: Geographical, land class and local habitat relationships[END_REF], hedgerows may play a role in optimal foraging by promoting the dispersal of individuals because, they improved the general quality of habitats by improving the access to resource areas and the dispersion between roosts (particularly maternities) and foraging sites.

To manage protected areas and to maximize management efforts (the latter point often focuses on the preservation of habitat patches), efforts should focusing more on the preservation of connected habitats [START_REF] Lookingbill | Influence of wetland networks on bat activity in mixed-use landscapes[END_REF]. [START_REF] Mimet | Assessing functional connectivity: a landscape approach for handling multiple ecological requirements[END_REF] and [START_REF] Pellissier | Birds are also sensitive to landscape composition and configuration within the city centre[END_REF] suggested that, for land use management, both the composition and the configuration of habitats must be taken into account to maintain suitable conditions for biodiversity [START_REF] Zeale | Home range use and habitat selection by barbastelle bats (Barbastella barbastellus): implications for conservation[END_REF] underline the need to improve the quality and the numbers of ecological corridors. Optimizing the biodiversity gain provided by linear features will maximize the effectiveness of these schemes [START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF]. We showed that the wood production index positively influences the variation in the activities of bats and bush crickets. According to [START_REF] Geffray | Le bocage en vallée de la Loire: mise à jour cartographique et estimation des capacités en bois-énergie[END_REF], the wood production index is potentially correlated to tall and old trees and also to a potential ecosystem service of high timber production for land managers and farmers. Moreover, hedgerows provide habitats for auxiliary crops (Van Emden & Dabrowski Z.T. 1994), shelter against the wind and cold for crops, and wood production for firewood (linked to the European and French energy transition) (Château & Rossetti di Valdalbero, 2011). Thus, hedgerows play important roles in the ecosystem and provide many ecosystem services (Altieri 1999). They reconcile agricultural practices and biodiversity. Moreover, treefilled hedgerows could provide substitute environments for forest species; thus, in grassland habitats, such as our study site, we need to encourage hedgerows with large and old trees.

Usually, it is recognized that hedgerow density is a heritage of old historical agricultural practices that shaped this landscape structure. Although agricultural practices have been largely modified, this network of hedgerows persists today. Currently, this network is influenced by regional, national and even European (common agricultural policy (CAP) agricultural policy. The wood production of hedgerows, however, is linked to management by local farmers. Thus, territorial policy decisions or local practices could influence the quality of the biodiversity. The present study examined different scales and different hedgerow indices and its results suggest that landscape planning should be considered both :1) at large scales because they are the ones that are guided by agricultural policies that can increase the density of hedges and 2) at small scales, such as the agricultural parcel level, because it is at this scale that agro-ecosystem schemes offer subsidies or/and financial tax shelters that could be distributed to farmers and landowners to create, conserve and manage hedgerows to promote longer cutting cycles for old trees with high production. The distribution of agrienvironmental measures (AEM) may be conditional on the hedgerow quality, rather than simply their presence.

However, the current European context, with its political pressure regarding wood energy, may not be favorable to shortening cutting cycles for large timber. Because hedgerows provide valuable habitat for many species [START_REF] Boughey | Improving the biodiversity benefits of hedgerows: How physical characteristics and the proximity of foraging habitat affect the use of linear features by bats[END_REF], the diversity index reflected the different types of hedgerows and therefore must be linked with AEM for management.

The reintroduction of structural elements to increase habitat heterogeneity should become part of agro-ecosystem schemes [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF]). Our study site, as with many other sites, was in spaces with multiple pruposed including agriculture, hunting, and even wood production through which hedgerows contribute to ecosystem services (Baudry et al. 2000).

Acknowledgments

This study was supported by the company EDF. We certify that the funding sources had no influence on the collection, analysis or interpretation of the data. We gratefully acknowledge CORELA (Conservatoire Régional des Rives de la Loire et de ses Affluents: http://www.corela.org/) for the land use database and the hedgerow database. We thank Lucille Brianceau for her assistance with fieldwork and to Maud Deniau and Léa Noël for their help with the sonogram analyses. We thank deeply the Vigie-Nature volunteers for data collection allowing the calculation of a Species Specialization Index for bats. We thank Agathe Le Bocq and Violaine Brochier for their comments on an earlier version of the manuscript.

Altieri, M.A., 1999. The ecological role of biodiversity in agroecosystems. Agric. Ecosyst. Environ. 74, 19-31 Audet, D., 1990. Foraging

Buffer size (meters)

Nyctalus noctula

Aerial species

Pipistrellus pipistrellus

Gleaner species

Barbastella barbastellus

Myotis spp.

Estimates of the relationships with production of hedgerows

Buffer size (meters)

Pholidoptera griseoaptera Uromenus rugosicollis

Mobile species Sedentary species

Tettigonia viridissima Ruspolia nitidula

Bat activity records

We used Syrinx software version 2.6 [START_REF] Burt | Syrinx a software for real time spectrographic recording, analysis and playback of sound[END_REF] for spectrogram and Adobe Audition for spectral analysis, we used Scan'R (Binary Acoustic Technology, 2010) to isolate each bat vocalization and automatethe measurement of relevant parameters [START_REF] Gannon | Call character Lexicon and analysis of field recorded bat echolocation calls[END_REF][START_REF] Obrist | Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach[END_REF], Barataud 2012). Species identification of sounds was performed by Lea Noel and Aurelie Lacoeuilhe, and verified by coordinators of the French Bat Monitoring Program (Jean-François Julien & Christian

Kerbiriou)

The main relevant parameters used were as follows:

Call duration (msec) Time elapsed since previous detection (msec)

Maximum frequency detected (Fmax, KHz)

Minimum frequency detected (Fmin, KHz)

Total Bandwidth [Fmax -Fmin] (KHz)

Frequency at strongest sound pressure level (KHz)

Location of Dominant Frequencies (% of total duration) not overlap itself. The second requirement is to design a circuit that crosses, as proportionally as possible, the different habitats present in the area. To avoid biases in habitat sampling (we assume that observers would tend to follow rich habitats), the MNHN validates the circuit and randomly defines a starting point on the circuit. Starting from this point, the circuit is divided into ten 2-km road segments, separated by 1-km road segments in which no recording is performed. These sampling designs allow quite good correlations between the habitat proportions sampled and the habitat proportions present at the national scale (R² = 0.94), except for urban areas which were slightly more represented in our sampling.

Assessment of specializations indices

The French national bat monitoring volunteers were involved in collecting habitat variables on a detailed and adapted basis. Habitat information is recorded on the first reconnaissance visit to the circuit. The appropriate habitat codes are chosen from an established hierarchical system allowing the description of more than 950 habitat types, which is very similar to the widely used habitat codes in bird monitoring schemes (see [START_REF] Crick | A bird-habitat coding system for use in Britain and Ireland incorporating aspects of land-management and human activity[END_REF] for the UK and [START_REF] Barnagaud | Relating Habitat and Climatic Niches in Birds[END_REF] for the French monitoring)but is adapted to account for bat foraging specifics and particularly linear element (for more details about the French Bat monitoring see the website [http://vigienature.mnhn.fr/page/releves-d-habitats]). Habitat classes are collected in a radius of 100 m around the sampled point and grouped into 18 classes to obtain sufficient numbers of samples per class (for more methodological information see Julliard et al., 2006;[START_REF] Devictor | Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation[END_REF][START_REF] Kerbiriou | Long term trends monitoring of bats, from biodiversity indicator production to species specialization assessment[END_REF]).

Specialization species index (SSI)

We calculated the species specialization index (SSI), which is the degree of habitat specialization for a species, as the coefficient of variation (SD/mean) of its densities across habitats following the approach of Julliard et al.(2006). SSI is thus independent of species habitat preferences. However, it is also possible to assess the species specializations for forest habitats by calculating the average density of a species inventoried in forest habitats divided by the average density in non-forest habitats.

Rhinolophus hipposideros NA Assessment of bat community specializations indices

The Community Specialisation Index (CSI) is calculated as the arithmetic mean of the species specialization index (SSI) of the species detected, weighted by the abundances (Julliard et al., 2006) (Eq. ( 2)).

CSI j =

(2)

where n is the total number of species recorded, aij is the abundance of individuals of species i (with a specialization index SSI) in segment j [START_REF] Devictor | Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation[END_REF].

To calculate the Myotis group CSI, we used the total number of calls identified as from Myotis species and the average of the CSI values calculated for identified Myotis species.

The CSI reflects the relative abundance of more or less specialized species in local assemblages and is therefore expected to decrease following the relative declines of specialists (species with a high SSI). For the analyses, we used only sites with a non-null abundance (n = 44 for bats and n=33

for Orthoptera, i.e., 65% and 85% of the respective data sets) because a null CSI does not describe a generalist community.

15 Appendix H: Inflection points and slopes for species and groups with segmented method for linear landscape element density, diversity and production f. e.

Sedentary species Mobile species

Tettigonia viridissima

Nyctalus noctula

Aerial species

Pipistrellus pipistrellus

Gleaner species

Buffer size (meters) Estimates of the relationships with diversity of linear landscape elements

Tettigonia viridissima Ruspolia nitidula

Pholidoptera griseoaptera Uromenus rugosicollis

Mobile species Sedentary species

  behavior and habitat use by a gleaning bat, Myotis myotis (Chiroptera : Vespertilonidae). J. Mammal.. 71: 420-427. Arak A & T Eiriksson 1992. Choice of singing sites by male bushcrickets (Tettigonia viridissima) in relation to signal propagation. Behavioral Ecology and Sociobiology, 30: 365-372. Barataud, M., 2012. Ecologie acoustique des chiroptères d'Europe. Identification des espèces, études de leurs habitats et comportements de chasse. Biotope, Mèze ; Muséum national d'histoire naturelle, Paris (collection Inventaires et biodiversité), 344 p. Batáry, P., Orci, K.M., Báldi, A., Kleijn, D., Kisbenedek, T., Erdős, S., 2007. Effects of local and landscape scale and cattle grazing intensity on Orthoptera assemblages of the Hungarian Great Plain. Basic Appl. Ecol. 8 (3), 280-290. Baudry, J., Bunce, R.G.H., Burel, F., 2000. Hedgerows: An international perspective on their origin, function and management. J. Environ. Manage. 60 (1), 7-22. Baudry, J., Jouin, A., 2003. De la haie aux bocages. Organisation, dynamique et gestion. INRA Editions, France. 435 p. Bazelet, C.S., Samways, M.J., 2011. Identifying grasshopper bioindicators for habitat quality assessment of ecological networks. Ecol. Indic. 11 (5), 1259-1269. Beier, P., Noss, R.F., 1998. Do habitat corridors provide connectivity? Cons. Biol.12 (6), 1241-1252. Bellamy, C., Scott, C., Altringham, J., 2013. Multiscale, presence-only habitat suitability models: fine-resolution maps for eight bat species. J. App. Ecol.50 (4), 892-901. Bennett, A.F., 2003. Linkages in the landscape: The role of corridors and connectivity in wildlife conservation. IUCN, Gland, Switerland, & Cambridge, UK. 254 p.Berggren, Å., Carlson, A., Kindvall, O., 2001. The effect of landscape composition on colonization success, growth rate and dispersal in introduced bush-crickets Metrioptera roeseli. J. Anim. Ecol.70 (4),663-670. Berggren, Å., Birath, B., Kindvall, O., 2002. Effect of corridors and habitat edges on dispersal behavior, movement rates, and movement angles in Roesel's bush-cricket (Metrioptera roeseli). Cons.Biol. 16 (6), 1562Biol. 16 (6), -1569. .

Figure 1 :Figure 2 :

 12 Figure 1: Positions of point counts in the study site (A) and an enlargement showing the 3 distances from the hedgerow (0 m, 25 m and 50 m) (B)

Figure 3 :

 3 Figure3: Effect on the bush crickets of the hedgerow production at different spatial scales.

Figure I1 :

 I1 Figure I1: Bat activities (mean numbers of contacts per minute and per site). Barbastella barbastellus (barbar), Eptesicus serotinus (eptser), Myotis spp. (myossp), Nyctalus leisleri (nyclei), Nyctalus noctula (nycnoc), Pipistrellus kuhlii (pikul), Pipistrellus nathusii (pinat), Pipistrellus pipistrellus (pippip), Plecotus spp (plesp) Mean numbers of contacts per minute and per site

Table 1 :

 1 Spatial scale effect of density, diversity and productivity (estimate of GLM) and the 861 significance of the GLM (anova, F test) at each spatial scale from 50 to 1000 m. 862

					Hedgerow density			Hedgerow diversity			Hedgerow production
			Species	Estimated GLM	SE GLM	P value ANOVA	F ANOVA	AIC GLM	Estimate GLM	SE GLM	P value ANOVA	F ANOVA	AIC GLM	Estimate GLM	SE GLM	P value ANOVA	F ANOVA	AIC GLM
		Continuous	T. viridissima	0.028	0.015	0.093	3.52	96.10	0.001	0.001	0.492	0.51	34.70	2.65E-06	4.63E-07	<0.001	32.80	-132.00
		calls
			R. nitidula	-0.005	0.034	0.884	2.25E-02	114.00	-0.004	0.001	0.012	9.82	38.20	8.49E-08	1.30E-06	0.950	0.00	-110.00
			M. roeselii	0.082	0.026	0.013	9.64	109.00	0.004	0.002	0.069	4.27	49.00	2.75E-06	1.34E-06	0.071	4.20	-109.00
			P. falcata	0.003	0.043	0.947	4.62E-03	120.00	0.007	0.002	0.010	10.40	48.80	-1.92E-06	1.96E-06	0.352	0.97	-99.30
	Bush crickets	Non-continuous calls	P. griseoaptera U. rugosicollis P. nana L. punctatissima P. albopunctata	0.394 -0.662 0.052 0.094 1.076	0.072 0.050 0.097 0.022 0.487	<0.001 <0.001 0.610 0.002 0.069	30.10 175.00 0.282 18.50 4.87	133.00 114.00 127.60 108.00 131.14	-0.012 0.005 -0.050 -0.009 -0.074	0.004 0.002 0.014 0.001 0.010	0.009 0.070 0.005 <0.001 0.020	10.80 4.24 13.61 48.70 49.50	68.40 56.80 90.79 41.00 19.00	2.47E-05 -4.21E-06 8.93E-06 4.54E-06 6.02E-05	5.80E-06 1.59E-06 7.43E-06 7.57E-07 4.18E-05	0.002 0.026 0.260 <0.001 0.188	18.20 7.04 1.44 35.90 2.07	-74.60 -103.00 -72.85 -118.00 -30.05
			C. dorsalis	-0.032	0.039	0.428	0.69	115.00	-0.002	0.002	0.416	0.73	48.40	-7.70E-07	1.29E-06	0.565	0.36	-112.00
			P. tessellata	-0.070	0.017	0.003	16.10	96.90	0.015	0.004	0.007	12.70	60.40	-6.15E-06	1.27E-06	0.001	23.30	-105.00
		Orthoptera	Mobility	0.022	0.001	<0.001	1610.00	31.00	-2.64E-04	1.71E-04	0.158	2.37	-0.25	1.27E-06	4.81E-08	<0.001	700.00	-176.00
		mobility
		trait	Sedentary	-0.066	0.020	0.008	11.40	103.00	0.001	0.001	0.021	7.74	19.20	-6.15E-06	8.36E-07	<0.001	54.10	-119.00
			Intermediate	-0.036	0.027	0.208	1.84	108.57	-0.001	0.001	0.38	0.86	40.54	-2.05E-06	1.12E-06	0.100	3.35	-112.95
			CSI	0.018	0.009	0.088	3.65	85.33	4.51E-04	3.51E-04	0.23	1.65	9.59	6.10E-07	4.98E-07	0.251	1.50	-131.38
			B. barbastellus	0.158	0.044	0.006	12.90	120.00	0.001	0.001	0.188	2.03	35.23	7.13E-06	1.96E-06	0.005	13.18	-100.17
			Myotis spp.	0.450	0.052	<0.001	73.60	113.00	0.017	0.003	<0.001	23.20	64.62	2.13E-05	2.16E-06	<0.001	97.83	-93.06
			N. noctula	0.064	0.018	0.006	12.50	99.50	-0.001	0.002	0.411	0.74	50.37	2.45E-06	6.14E-07	0.003	15.91	-125.13
			P. pipistrellus	0.051	0.007	<0.001	52.80	78.30	0.003	0.001	0.060	4.64	39.20	3.03E-06	3.75E-07	<0.001	65.20	-138.00
			P. kuhlii	0.134	0.030	0.002	20.10	111.00	0.003	0.001	0.043	5.55	35.20	9.69E-06	6.02E-07	<0.001	258.00	-126.00
	Bats		P. nathusii Plecotus spp.	0.154 -0.003	0.008 0.031	<0.001 0.920	411.00 1.07E-02	81.60 113.48	-0.005 -0.004	0.002 0.002	0.047 0.057	0.05 4.74	50.90 48.16	8.11E-06 -2.85E-06	2.64E-07 3.31E-06	<0.001 0.412	946.00 0.74	-144.00 -88.43
			E. serotinus	-0.056	0.026	0.060	4.63	113.00	0.006	0.001	<0.001	93.70	30.60	-8.08E-07	1.47E-06	0.596	0.30	-103.00
			N. leisleri	-0.121	0.034	0.006	12.60	114.00	0.008	0.001	<0.001	46.40	41.20	-4.28E-06	1.46E-06	0.017	8.57	-108.00
		Bat	Aerial	0.218	0.015	<0.001	220.30	95.75	0.003	0.002	0.161	2.33	46.28	1.11E-05	2.88E-07	<0.001	1495.18	-142.59
		foraging
		trait	Gleaner	0.273	0.084	0.010	10.60	133.69	-0.007	0.002	0.019	8.15	53.11	1.17E-05	3.23E-06	0.005	13.22	-90.37
			CSI	0.015	0.001	<0.001	124.83	48.25	-2.17E-04	3.69E-04	0.57	0.34	10.90	6.20E-07	8.98E-08	<0.001	47.76	-168.98

: Potential production of hedgerows in Loire Valley between Saint-Nazaire and Montsoreau in 2010.

	The database (Geffray 2010) described the linear landscape elements in five categories:
	(1) Alignment of trees,
	(2) Riparian vegetation,
	(3) Shrub hedgerow (which only correspond to weak productivity)
	(4) Wooded hedgerow without the presence of shrubs
	(5) Three strata hedgerow (tree, shrub and herb)
	Productivity in m 3 /1 hm
	Typology (CORELA 2010)	/year
		(CORELA prediction)
	Shrub hedgerow and alignment of trees with weak	0.22
	productivity
	Hedgerow with trees and riparian vegetation adjacent to	0.74
	rivers with weak productivity
	Wooded hedgerow and alignment of trees with high	1.11
	productivity
	Wooded hedgerow and alignment of trees with medium	0.93
	productivity
	Riparian vegetation adjacent to rivers with high	1.67
	productivity
	Riparian vegetation adjacent to rivers with medium	1.11
	productivity
	Three strata hedgerow with high productivity	1.11
	Three strata hedgerow with medium productivity	0.74
	Note: 1 hm=100 linear meters; MAP = meter apparent of wood planking (2.7 MAP sec/m 3 = 1 m 3 of wood).

Reference

Geffray, Olivier, 2010

. Le bocage en vallée de la Loire: mise à jour cartographique et estimation des capacités en bois énergie. Conservatoire Régional des Rives de la Loire et de ses Affluents (CORELA), Rapport d'étude, 63 p. 8 Appendix

B1: Indices used for bat community assessment. For assessment of Habitat Specialization Index, see Appendix F and Figure2.6.

	9 Appendix B2:
	Bat species	Habitat Specialization	Foraging type
		Index(SSI)
	Barbastella barbastellus	3.6	Mixed strategist
	Eptesicus serotinus	1.5	Aerial hawking
	Myotis spp.(mainly daubentonii)	3.2	Gleaner
	Nyctalus leisleri	0.9	Aerial hawking
	Nyctalus noctula	1.6	Aerial hawking
	Pipistrellus kuhlii	0.7	Aerial hawking
	Pipistrellus nathusii	3.1	Aerial hawking
	Pipistrellus pipistrellus	0.5	Aerial hawking
	Plecotus spp (mainly austriacus) 1.4	Gleaner

Bush cricket species specialization and traits: *dispersal ability according to (Reinhardt et al. 2005; Marini et al. 2010); ** the SSI calculation is explained in Appendix F, and see 2.6 and Appendix E for explanations of the identification processes.

				Species with
	Species name	Dispersal ability* SSI**
				uninterrupted calls
	Tettigonia viridissima	Mobile	0.42	yes
	Phaneroptera nana	Mobile	0.27	no
	Leptophye punctatissima	Sedentary	0.55	no
	Platycleis tessellata	Sedentary	1.42	no
	Platycleis albopunctata	Intermediate	0.49	no
	Conocephalus dorsalis	Sedentary	NA	no
	Ruspolia nitidula	Mobile	0.52	yes
	Metrioptera roeselii	Intermediate	NA	yes
	Pholidoptera griseoaptera Sedentary	0.91	no
	Uromenus rugosicollis	Sedentary	NA	no
	Phaneroptera falcata	Mobile	0.27	yes

10 Appendix C: Correspondence scale of areas (m²) depending on the buffer size (in meters) 11 Appendix D: Correlations between variables

Table D1 :

 D1 Correlations between characteristics of hedgerows (density, structural diversity and potential wood production) and covariables at each spatial scale. The important correlations between variables (correlation coefficient |rho|≥0.5;Freckleton, 2002) are indicated in bold

			time after sunset	date	temperature	wind	distance from hedgerow	Proportion of grassland
		Density	0.023	0.149	0.058	0.156	-0.055	-0.221
	50 m	Diversity	-0.193	0.092	0.157	0.142	0.064	0.006
		Production	-0.020	0.132	0.116	0.085	-0.011	-0.277
		Density	0.028	0.269	0.099	0.106	0.156	-0.227
	100 m	Diversity	0.061	0.217	0.019	0.117	0.078	-0.171
		Production	-0.069	0.226	0.106	0.042	0.126	-0.254
		Density	-0.020	0.274	0.085	0.037	0.154	-0.025
	200 m	Diversity	0.201	0.073	-0.015	-0.008	0.114	0.023
		Production	-0.123	0.198	0.173	0.006	0.219	0.099
		Density	-0.075	0.273	0.192	-0.007	0.139	0.159
	300 m	Diversity	0.092	-0.119	-0.014	-0.108	0.055	0.254
		Production	-0.130	0.157	0.209	-0.015	0.177	0.136
		Density	-0.041	0.203	0.159	-0.053	0.131	0.150
	400 m	Diversity	0.191	-0.017	0.040	-0.121	0.032	0.418
		Production	-0.083	0.108	0.196	-0.065	0.150	0.175
		Density	-0.051	0.156	0.159	-0.066	0.088	0.208
	500 m	Diversity	0.339	0.005	-0.039	-0.092	0.018	0.569
		Production	-0.096	0.072	0.191	-0.095	0.119	0.190
		Density	-0.050	0.183	0.173	-0.060	0.044	0.276
	600 m	Diversity	0.389	-0.003	-0.130	-0.093	-0.035	0.535
		Production	-0.088	0.095	0.177	-0.065	0.089	0.234
		Density	-0.100	0.173	0.196	-0.055	0.067	0.219
	700 m	Diversity	0.388	-0.020	-0.169	-0.053	-0.075	0.472
		Production	-0.104	0.122	0.187	-0.052	0.088	0.227
		Density	-0.082	0.174	0.164	-0.015	0.077	0.276
	800 m	Diversity	0.418	0.005	-0.039	-0.092	0.018	0.584
		Production	-0.103	0.072	0.191	-0.095	0.119	0.166
		Density	-0.029	0.186	0.138	-0.002	0.064	0.387
	900 m	Diversity	0.376	0.040	-0.211	0.040	0.060	0.605
		Production	-0.079	0.161	0.159	-0.051	0.045	0.345
		Density	-0.015	0.188	0.106	0.012	0.046	0.496
	1000 m	Diversity	0.413	0.055	-0.182	0.043	0.058	0.579
		Production	-0.085	0.172	0.143	-0.048	0.042	0.415

Table D2 :

 D2 Spearman correlations between characteristics of hedgerows (density, structural diversity and potential wood production) for a 500 m buffer.

	12 Appendix E
		Density	Diversity	Production
	Density	1	rho= 0.117, p value =0.241 rho= 0.961, p value < 0.001
	Diversity	rho= 0.117, p value =0.241	1	rho= 0.124, p value =0.215
	Production rho= 0.961, p value < 0.001 rho= 0.124, p value =0.215	1

: Sonograms and information on sound files

	Species name	File name
	Leptophyes punctatissima	LEPPUN.wav
	Phaneroptera nana	Orthoptera activities records PHANAN.wav
	Pholidoptera griseoaptera	PHOGRI.wav
	Platycleis tessellata	PLATES.wav
	Ruspolia nitidula	RUSNIT.wav
	Tettigonia viridissima	TETVIR.wav

Table F1 :

 F1 Habitat Specialization Index

	Bat species	Habitat Specialization Index
	Barbastella barbastellus	3.64
	Eptesicus serotinus	1.50
	Myotis myotis	NA
	Myotis daubentonii	3.48
	Myotis mystacinus	3.86
	Myotis nattereri	2.40
	Nyctalus leisleri	0.95
	Nyctalus noctula	1.58
	Pipistrellus kuhlii	0.75
	Pipistrellus nathusii	3.06
	Pipistrellus pipistrellus	0.54
	Pipistrellus pygmaeus	1.79
	Plecotus spp (mainly austriacus)	1.36

Average activities of bats and bush-crickets

	16 Appendix I:
		Linear landscape element density		Linear landscape element diversity		Linear landscape element productivity
	Species	Estimat e	SE	t value	CI (95%). l	CI (95%). u	inflectio n point	Estimat e	SE	t value	CI (95%).l	CI (95%). u	inflectio n point	Estimat e	SE	t value	CI (95%).l	CI (95%).u	inflectio n point
	T. viridissima R. nitidula	slope1 -2.90E-04 slope2 0.109 slope1 -1.052 slope2 0.069	0.02 7 0.10 0 0.22 5 0.02 4	-0.011 -0.064 0.064 1.091 -0.127 0.344 -4.681 -1.583 -0.521 2.879 0.012 0.126	613.67 110.61	-0.001 0.005	0.002 -0.295 -0.006 0.004 0.004 1.262 -0.004 0.014 M e a n	479.33	-7.06E-07 3.10E-06	2.57E-06 1.04E-05	-0.275 0.299	-6.79E-06 -2.14E-05	5.38E-06 2.76E-05	649.03
	U. rugosicollis	slope1 slope2						0.001 0.029	0.002 0.309 -0.005 0.007 n 0.009 3.049 0.006 0.051 u	490.53
	Mobility	slope1 slope2						0.001 -0.001	1.82E-04 m b 3.46E-04 e r	3.299 -4.108 -0.002 -0.001 1.70E-04 0.001	526.39
	B. barbastellus	slope1 slope2						0.011 -0.001	s 0.010 1.117 -0.012 0.035 0.001 -0.916 -0.004 0.002 o	198.95
	N. noctula	slope1 slope2						0.008 -0.010	0.004 1.778 -0.003 0.018 f 0.006 -1.796 -0.023 0.003 c	428.84
	P. pipistrellus Aerial	slope1 slope2 slope1 slope2						0.006 -0.013 0.006 -0.009	0.001 6.843 0.004 o 0.005 -2.702 -0.025 -0.002 0.008 n 0.001 5.399 0.003 t 0.008 a 0.003 -2.903 -0.016 -0.002 c	633.41 530.93
	Gleaner	slope1 slope2						-0.005 0.005	0.003 -2.079 -0.011 0.001 t s 0.012 0.442 -0.023 0.033 p	629.60	6.60E-05 -1.79E-05	1.27E-05 4.61E-06	5.217 3.61E-05 9.60E-05 -2.87E--6.96E--3.876 05 06	234.34
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Table J2 : Effect of Diversity (estimate (Est.), SE and p value)

 J2 Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue

				Species /traits	50 m	100 m	200 m	300 m	400 m	500 m	600 m	700 m	800 m	900 m	1000 m
		continuous calls	T. viridissima -0.77 0.86 0.37 0.61 0.77 0.43 0.70 0.92 0.45 -0.48 0.95 0.61 -0.41 1.28 0.75 -0.55 1.94 0.78 0.37 2.27 0.87 0.74 2.66 0.78 0.61 3.13 0.84 0.97 3.47 0.78 2.70 3.74 0.47 R. nitidula -0.11 1.01 0.92 0.40 0.99 0.69 0.58 1.08 0.59 0.13 1.15 0.91 -0.31 1.59 0.85 -0.10 2.53 0.97 -4.05 3.19 0.20 -3.77 3.76 0.32 -4.62 4.68 0.32 -2.89 5.15 0.58 -3.95 5.79 0.49 M. roeselii -0.79 0.85 0.36 1.75 0.96 0.07 3.16 1.22 0.01 1.72 1.32 0.19 2.06 1.61 0.20 3.48 2.40 0.15 1.61 2.56 0.53 1.85 3.06 0.55 1.34 3.93 0.73 2.54 4.68 0.59 5.92 4.58 0.20 P. falcata -0.28 2.49 0.91 0.52 1.95 0.79 -0.42 2.04 0.84 0.07 1.96 0.97 2.02 2.53 0.43 5.05 3.24 0.12 5.23 3.88 0.18 5.77 4.72 0.22 5.63 6.57 0.39 3.17 8.04 0.69 0.14 6.59 0.98
				L. punctatissima 1.30 0.52 0.01 -0.77 0.70 0.27 0.70 0.77 0.36 -0.21 0.80 0.79 -1.31 1.22 0.28 -0.57 1.85 0.76 -4.50 1.62 0.01 -5.51 1.46 0.00 -5.82 1.88 0.00 -8.36 2.32 0.00 -7.59 2.05 0.00
	Orthoptera	non-continuous calls	C. dorsalis P. griseoaptera -2.44 1.74 0.16 -2.43 2.32 0.29 3.97 3.74 0.29 1.48 5.59 0.79 1.37 5.67 0.81 1.04 6.62 0.88 -4.72 5.84 0.42 -8.38 5.65 0.14 -9.71 5.55 0.08 -14.23 7.16 0.05 -16.70 4.40 0.00 -0.44 0.84 0.60 1.13 0.89 0.20 0.84 0.88 0.34 0.80 1.64 0.63 0.22 2.61 0.93 -1.18 4.42 0.79 1.78 4.42 0.69 -1.69 5.64 0.76 -4.54 6.19 0.46 -5.25 5.82 0.37 -3.32 7.42 0.65 P. nana -4.93 6.53 0.45 6.51 3.31 0.05 3.32 2.31 0.15 5.17 2.28 0.02 -21.27 9.79 0.03 13.71 7.56 0.07 -27.09 7.35 0.00 -24.22 6.79 0.00 -31.48 13.32 0.02 -43.48 14.91 0.00 -54.84 11.08 0.00 U. rugosicollis -2.71 0.38 0.00 -0.27 0.06 0.00 -0.22 0.09 0.01 -0.58 0.29 0.05 -3.24 1.50 0.03 -3.12 1.18 0.01 5.16 1.66 0.00 3.70 0.91 0.00 6.08 1.24 0.00 14.04 6.55 0.03 12.30 1.37 0.00 P. tessellata -0.63 NA NA 0.42 1.01 0.68 0.26 1.21 0.83 0.23 1.79 0.90 0.64 2.11 0.76 3.08 3.79 0.42 4.19 4.46 0.35 8.84 7.79 0.26 15.25 10.81 0.16 10.91 12.17 0.37 22.67 4.64 0.00
				P. albopunctata -0.77 1.96 0.69 -5.74 0.34 0.00 -9.35 3.42 0.01 -12.23 4.04 0.00 -19.08 3.75 0.00 -18.13 3.50 0.00	NA NA NA	NA NA NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
				CSI	-0.09 0.76 0.91 -0.18 0.68 0.79 -0.11 0.73 0.88 0.25 0.82 0.76 0.38 1.07 0.72 0.43 1.62 0.79 0.32 1.93 0.87 0.17 2.22 0.94 -0.20 2.63 0.94 -0.44 3.16 0.89 -0.73 3.64 0.84
		Mobility trait	Mobility Sedentary Intermediate	-0.02 0.22 0.94 0.15 0.24 0.55 0.12 0.26 0.64 0.09 0.30 0.75 0.25 0.39 0.53 0.40 0.56 0.47 0.19 0.64 0.76 0.10 0.76 0.90 -0.09 0.92 0.92 -0.18 1.02 0.86 -0.36 0.38 0.34 0.38 1.08 0.73 0.09 0.69 0.89 0.10 0.91 0.91 -0.24 1.09 0.83 0.03 1.31 0.98 1.23 1.93 0.52 0.51 2.23 0.82 1.19 2.65 0.65 1.91 2.93 0.51 1.44 3.39 0.67 1.57 2.59 0.54 0.66 0.32 0.04 1.29 0.38 0.00 1.05 0.68 0.13 -0.89 0.65 0.17 -0.96 0.80 0.23 -0.81 1.18 0.49 -0.73 1.27 0.57 0.63 1.63 0.70 1.64 1.73 0.34 2.46 1.96 0.21 2.01 2.08 0.33
				B. barbastellus 0.74 1.32 0.58 1.15 1.28 0.37 2.94 1.99 0.14 2.50 2.26 0.27 1.45 2.07 0.48 1.55 2.90 0.59 1.35 3.67 0.71 0.73 4.27 0.87 2.34 5.03 0.64 1.98 5.51 0.72 3.81 5.70 0.50
				E. serotinus	-0.28 0.46 0.54 0.78 0.41 0.06 1.32 0.53 0.01 3.97 1.01 0.00 3.04 1.26 0.02 3.57 1.51 0.02 3.41 1.22 0.00 3.72 1.07 0.00 4.33 0.82 0.00 6.19 0.77 0.00 6.00 1.25 0.00
				Myotis spp.	1.49 0.51 0.00 1.16 0.86 0.18 3.84 1.91 0.04 5.80 1.62 0.00 5.29 1.40 0.00 7.59 1.96 0.00 8.66 2.20 0.00 10.89 2.99 0.00 31.51 8.46 0.00 15.36 8.10 0.06 46.68 7.53 0.00
				N. leisleri	0.25 1.71 0.88 1.56 1.01 0.12 1.63 1.52 0.28 0.94 2.33 0.68 0.98 2.45 0.69 4.19 4.23 0.32 4.50 3.82 0.24 5.29 3.11 0.09 6.76 2.73 0.01 6.73 3.63 0.06 10.65 3.11 0.00
	Chiroptera			N. noctula P. kuhlii P. nathusii P. pipistrellus -0.56 0.50 0.27 -0.66 0.43 0.13 -0.39 0.49 0.43 0.47 0.63 0.46 1.68 0.95 0.08 2.07 1.31 0.11 3.45 1.79 0.05 1.73 1.97 0.38 0.81 2.14 0.70 -1.05 2.43 0.67 -2.11 2.33 0.37 0.44 0.57 0.44 -0.20 0.51 0.70 2.03 0.51 0.00 -0.81 1.35 0.55 4.29 2.32 0.06 3.48 2.26 0.12 0.18 2.32 0.94 0.22 2.23 0.92 -0.80 1.96 0.68 -1.63 1.80 0.36 -2.39 1.64 0.14 1.04 0.84 0.22 0.57 0.92 0.53 1.60 0.74 0.03 1.43 1.03 0.17 3.46 1.70 0.04 3.60 2.45 0.14 3.35 3.05 0.27 2.04 3.17 0.52 1.82 3.56 0.61 0.24 4.01 0.95 2.79 3.80 0.46 1.21 0.52 0.02 -0.44 0.63 0.49 1.73 0.67 0.01 1.36 0.95 0.15 1.33 1.35 0.32 1.98 1.92 0.30 -1.67 1.93 0.39 -4.56 2.10 0.03 -5.07 2.46 0.04 -6.57 2.94 0.03 -4.57 2.93 0.12
				Plecotus spp. -3.40 0.39 0.00 0.07 0.17 0.67 0.31 0.22 0.17 0.19 0.23 0.40 -0.97 0.27 0.00 -2.97 0.49 0.00 -4.89 0.68 0.00 -5.11 0.76 0.00 -3.42 0.75 0.00 -3.74 0.81 0.00 -3.33 0.85 0.00
				CSI	-0.05 0.30 0.86 0.05 0.29 0.87 0.09 0.33 0.77 0.28 0.40 0.48 0.31 0.53 0.56 0.36 0.80 0.66 0.04 0.95 0.97 -0.32 1.12 0.77 -0.90 1.35 0.50 -0.91 1.56 0.56 -0.93 1.77 0.60
		Foraging	trait	Aerial Gleaner	0.45 0.71 0.53 0.48 0.78 0.53 1.35 0.69 0.05 1.76 0.88 0.05 3.84 1.37 0.01 4.61 1.87 0.01 4.63 2.49 0.06 1.89 2.64 0.47 0.69 2.88 0.81 -1.42 3.27 0.67 -0.65 3.40 0.85 -2.13 1.19 0.07 0.41 0.95 0.67 0.30 1.06 0.78 -0.13 1.06 0.90 -1.24 1.41 0.38 -3.32 2.39 0.17 -5.96 3.25 0.07 -7.12 3.79 0.06 -9.58 4.30 0.03 -7.94 4.63 0.09 -6.83 4.95 0.17
				68 69

Table J3 : Effect of Production (estimate (Est.), SE and p value)

 J3 Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue Est. SE Pvalue

			Species /traits		50 m			100 m			200 m			300 m			400 m			500 m			600 m			700 m			800 m			900 m		1000 m
		continuous calls	T. viridissima R. nitidula M. roeselii P. falcata	-2.94E-05 1.67E-03 1.83E-04 1.27E-03	1.07E-03 1.37E-03 1.09E-03 2.74E-03	9.78E-01 2.24E-01 8.67E-01 6.42E-01	1.21E-04 -3.89E-04 7.95E-04 1.26E-04	1.41E-03 1.81E-03 1.46E-03 3.76E-03	9.32E-01 8.30E-01 5.86E-01 9.73E-01	9.07E-04 -3.25E-04 2.52E-03 -7.14E-04	2.12E-03 2.56E-03 2.03E-03 6.73E-03	6.69E-01 8.99E-01 2.14E-01 9.16E-01	1.38E-03 -1.34E-04 2.61E-03 -2.78E-03	2.68E-03 3.16E-03 2.53E-03 8.14E-03	6.07E-01 9.66E-01 3.02E-01 7.33E-01	6.64E-04 4.11E-04 2.49E-03 -2.54E-03	3.05E-03 3.77E-03 2.94E-03 7.91E-03	8.28E-01 9.13E-01 3.96E-01 7.48E-01	5.64E-04 1.85E-03 3.22E-03 -2.63E-03	3.56E-03 4.51E-03 3.53E-03 8.94E-03	8.74E-01 6.82E-01 3.61E-01 7.69E-01	6.98E-04 2.01E-03 4.23E-03 -3.09E-03	3.94E-03 5.19E-03 4.20E-03 1.03E-02	8.59E-01 6.99E-01 3.14E-01 7.64E-01	1.19E-03 9.00E-04 3.47E-03 -3.37E-03	4.12E-03 5.44E-03 4.40E-03 1.08E-02	7.72E-01 8.69E-01 4.31E-01 7.55E-01	1.63E-03 3.51E-04 1.81E-03 8.80E-05	4.23E-03 5.59E-03 4.50E-03 1.14E-02	6.99E-01 9.50E-01 6.87E-01 9.94E-01	2.70E-03 9.25E-04 1.32E-03 2.37E-03	4.48E-03 6.00E-03 4.74E-03 1.16E-02	5.47E-01 8.77E-01 7.80E-01 8.39E-01	3.35E-03 1.75E-03 8.85E-05 2.70E-03	4.11E-03 5.57E-03 4.60E-03 1.02E-02	4.15E-01 7.53E-01 9.85E-01 7.91E-01
			L.	-2.87E-	8.83E-	9.74E-	-1.09E-	1.22E-	3.73E-	-1.38E-	2.53E-	9.96E-	6.51E-	2.59E-	9.80E-	-6.72E-	3.10E-	8.28E-	2.08E-	3.55E-	5.59E-	3.10E-	3.54E-	3.81E-	2.71E-	3.46E-	4.34E-	2.51E-	3.35E-	4.54E-	3.34E-	3.27E-	3.07E-	4.18E-	2.68E-	1.19E-
			punctatissima	05	04	01	03	03	01	05	03	01	05	03	01	04	03	01	03	03	01	03	03	01	03	03	01	03	03	01	03	03	01	03	03	01
	Orthoptera	non-continuous calls	C. dorsalis P. griseoaptera P. nana U. rugosicollis P. tessellata	2.36E-03 1.75E-04 9.29E-03 -9.14E-04 1.13E-03	1.06E-03 3.35E-03 1.55E-02 1.37E-04 6.43E-03	2.64E-02 9.58E-01 5.49E-01 2.83E-11 8.60E-01	1.27E-03 3.43E-03 5.66E-03 4.63E-03 2.34E-03	8.93E-04 5.02E-03 4.73E-03 1.10E-03 NA	1.55E-01 4.95E-01 2.31E-01 2.47E-05 NA	1.85E-03 1.11E-02 5.07E-03 -1.23E-03 2.36E-03	1.15E-03 6.19E-03 5.79E-03 4.65E-04 3.17E-03	1.09E-01 7.26E-02 3.81E-01 8.46E-03 4.56E-01	1.47E-04 1.95E-02 6.46E-03 -1.16E-03 1.75E-03	1.55E-03 6.13E-03 7.41E-03 5.63E-04 3.62E-03	9.24E-01 1.46E-03 3.83E-01 3.96E-02 6.29E-01	3.42E-04 2.41E-02 1.15E-03 -1.54E-03 1.81E-03	2.14E-03 7.97E-03 9.42E-03 7.61E-04 4.73E-03	8.73E-01 2.49E-03 9.03E-01 4.24E-02 7.02E-01	6.16E-04 2.55E-02 -1.13E-03 -1.91E-03 1.57E-03	3.13E-03 8.53E-03 1.17E-02 7.27E-04 5.79E-03	8.44E-01 2.79E-03 9.23E-01 8.72E-03 7.87E-01	8.19E-04 2.27E-02 -6.12E-04 -3.38E-03 3.66E-04	4.43E-03 7.35E-03 1.37E-02 1.03E-03 6.13E-03	8.53E-01 1.99E-03 9.64E-01 9.82E-04 9.52E-01	6.03E-04 2.37E-02 6.77E-04 -4.93E-03 -6.38E-04	4.82E-03 7.87E-03 1.41E-02 1.31E-03 6.50E-03	9.01E-01 2.63E-03 9.62E-01 1.58E-04 9.22E-01	7.24E-04 2.12E-02 5.40E-03 -9.45E-03 -2.09E-03	5.03E-03 7.31E-03 1.53E-02 2.23E-03 8.06E-03	8.86E-01 3.69E-03 7.25E-01 2.18E-05 7.95E-01	2.68E-03 2.01E-02 2.55E-02 -9.78E-03 -3.08E-03	5.86E-03 7.19E-03 1.48E-02 2.77E-03 1.00E-02	6.47E-01 5.27E-03 8.57E-02 4.26E-04 7.59E-01	3.51E-03 1.98E-02 3.04E-02 -4.47E-03 -5.12E-03	3.54E-03 6.98E-03 1.72E-02 1.29E-03 1.06E-02	3.21E-01 4.60E-03 7.70E-02 5.54E-04 6.29E-01
			P. albopunctata	-5.32E-04	3.05E-03	8.61E-01	-9.66E-03	3.53E-03	6.14E-03	-1.87E-02	8.33E-03	2.51E-02	-2.10E-02	6.85E-03	2.18E-03	-1.76E-02	6.12E-03	4.10E-03	-2.75E-02	1.03E-02	7.73E-03	-5.80E-02	3.09E-02	6.06E-02	7.79E-02	7.47E-02	2.97E-01	1.21E-01	1.05E-02	1.09E-30	5.75E-02	1.75E-02	1.02E-03	NA	NA	NA
			CSI	5.18E-04	9.35E-04	5.80E-01	2.39E-04	1.29E-03	8.54E-01	-2.27E-04	1.83E-03	9.01E-01	-1.21E-05	2.23E-03	9.96E-01	-6.96E-05	2.65E-03	9.79E-01	2.47E-04	3.08E-03	9.36E-01	5.73E-04	3.51E-03	8.70E-01	6.18E-04	3.80E-03	8.71E-01	1.14E-03	4.03E-03	7.78E-01	1.61E-03	4.39E-03	7.14E-01	1.72E-03	4.80E-03	7.19E-01
		Mobility trait	Mobility Sedentary Intermediate	7.55E-05 -2.06E-04 9.20E-04	2.73E-04 1.17E-03 3.68E-04	7.82E-01 8.60E-01 1.23E-02	9.60E-05 -1.85E-03 1.63E-03	3.81E-04 1.98E-03 5.48E-04	8.01E-01 3.51E-01 2.92E-03	2.91E-04 -2.44E-03 2.24E-03	5.65E-04 2.36E-03 1.01E-03	6.07E-01 3.01E-01 2.69E-02	4.62E-04 -3.32E-03 1.67E-03	7.10E-04 2.72E-03 1.35E-03	5.15E-01 2.22E-01 2.15E-01	6.32E-04 -3.78E-03 1.82E-03	8.13E-04 3.23E-03 1.57E-03	4.37E-01 2.42E-01 2.47E-01	7.92E-04 -3.60E-03 2.06E-03	9.64E-04 3.67E-03 2.06E-03	4.11E-01 3.27E-01 3.15E-01	8.94E-04 -2.99E-03 -2.42E-05	1.12E-03 3.68E-03 2.37E-03	4.24E-01 4.17E-01 9.92E-01	8.85E-04 -4.12E-03 -8.79E-04	1.16E-03 3.86E-03 2.40E-03	4.45E-01 2.87E-01 7.14E-01	9.83E-04 -5.03E-03 -1.43E-03	1.19E-03 4.11E-03 2.36E-03	4.09E-01 2.21E-01 5.46E-01	1.11E-03 -5.67E-03 -1.78E-03	1.25E-03 4.42E-03 2.38E-03	3.76E-01 2.00E-01 4.54E-01	1.26E-03 -6.75E-03 -2.10E-03	4.76E-04 4.49E-03 2.52E-03	8.34E-03 1.32E-01 4.04E-01
			B. barbastellus	-9.63E-06	1.80E-03	9.96E-01	3.03E-03	2.23E-03	1.73E-01	5.57E-03	3.25E-03	8.67E-02	5.11E-03	3.82E-03	1.81E-01	4.72E-03	4.44E-03	2.88E-01	5.21E-03	5.36E-03	3.31E-01	5.32E-03	5.83E-03	3.61E-01	6.62E-03	6.17E-03	2.83E-01	6.21E-03	6.04E-03	3.04E-01	6.44E-03	6.32E-03	3.08E-01	6.46E-03	6.04E-03	2.85E-01
			E. serotinus	-2.01E-03	6.44E-04	1.81E-03	-4.35E-03	1.66E-03	8.86E-03	1.59E-03	2.63E-03	5.47E-01	-1.33E-03	2.74E-03	6.26E-01	-2.47E-03	3.12E-03	4.28E-01	-3.01E-03	2.78E-03	2.79E-01	-3.01E-03	2.58E-03	2.43E-01	-2.98E-03	2.52E-03	2.35E-01	-4.11E-03	2.49E-03	9.86E-02	-4.22E-03	2.84E-03	1.37E-01	1.16E-03	3.41E-03	7.34E-01
			Myotis spp.	-2.54E-03	1.24E-03	4.04E-02	2.79E-03	1.57E-03	7.49E-02	6.46E-03	1.43E-03	6.51E-06	7.91E-03	2.43E-03	1.13E-03	9.92E-03	2.68E-03	2.16E-04	1.27E-02	3.29E-03	1.07E-04	1.41E-02	3.56E-03	7.77E-05	1.41E-02	3.55E-03	7.51E-05	1.50E-02	3.73E-03	5.66E-05	1.84E-02	4.45E-03	3.72E-05	1.97E-02	1.57E-03	4.19E-36
	Chiroptera		N. leisleri N. noctula P. kuhlii	-6.22E-04 6.86E-04 2.04E-03	2.22E-03 4.58E-04 8.79E-04	7.80E-01 1.34E-01 2.02E-02	5.47E-04 -2.58E-05 2.58E-03	1.53E-03 1.87E-03 1.17E-03	7.21E-01 9.89E-01 2.73E-02	7.12E-04 6.93E-04 4.22E-03	2.23E-03 9.96E-04 1.91E-03	7.50E-01 4.86E-01 2.70E-02	1.07E-04 1.72E-03 5.43E-03	3.26E-03 8.65E-04 2.50E-03	9.74E-01 4.68E-02 2.96E-02	-5.22E-04 2.02E-03 5.33E-03	5.12E-03 9.99E-04 3.18E-03	9.19E-01 4.33E-02 9.34E-02	-4.19E-03 2.68E-03 4.91E-03	6.66E-03 1.13E-03 3.83E-03	5.29E-01 1.76E-02 2.00E-01	-5.04E-03 3.13E-03 6.79E-03	6.40E-03 1.53E-03 4.25E-03	4.31E-01 4.11E-02 1.11E-01	-3.63E-03 2.87E-03 8.11E-03	5.67E-03 1.77E-03 4.42E-03	5.22E-01 1.05E-01 6.64E-02	-3.93E-03 2.12E-03 9.08E-03	6.10E-03 1.72E-03 4.74E-03	5.19E-01 2.17E-01 5.53E-02	-2.24E-03 1.82E-03 1.08E-02	6.90E-03 1.76E-03 4.96E-03	7.45E-01 3.00E-01 2.89E-02	-1.23E-03 1.85E-03 1.08E-02	6.50E-03 1.78E-03 4.96E-03	8.50E-01 2.97E-01 2.89E-02
			P. nathusii	1.25E-03	6.77E-04	6.39E-02	1.25E-03	6.77E-04	6.39E-02	2.93E-03	1.38E-03	3.38E-02	3.82E-03	1.72E-03	2.59E-02	4.15E-03	1.94E-03	3.27E-02	4.92E-03	2.23E-03	2.76E-02	6.13E-03	2.43E-03	1.15E-02	6.87E-03	2.54E-03	6.78E-03	7.45E-03	2.67E-03	5.27E-03	8.13E-03	2.81E-03	3.87E-03	8.68E-03	2.69E-03	1.29E-03
			P. pipistrellus	-7.49E-05	6.77E-04	9.12E-01	-5.01E-04	8.28E-04	5.45E-01	-3.03E-04	1.10E-03	7.84E-01	4.73E-04	1.40E-03	7.35E-01	9.21E-04	1.68E-03	5.84E-01	1.14E-03	1.96E-03	5.61E-01	1.47E-03	2.30E-03	5.23E-01	1.62E-03	2.51E-03	5.20E-01	1.91E-03	2.77E-03	4.90E-01	2.24E-03	2.98E-03	4.53E-01	2.59E-03	2.67E-03	3.32E-01
			Plecotus spp.	03 7.23E-	03 3.90E-	02 6.36E-	02 1.15E-	03 5.54E-	02 3.82E-	03 6.87E-	03 7.35E-	01 3.50E-	03 4.65E-	04 8.14E-	08 1.10E-	04 6.59E-	04 9.31E-	01 4.79E-	03 -1.38E-	03 1.10E-	01 2.13E-	03 -1.94E-	03 1.11E-	02 8.12E-	03 -1.35E-	03 1.16E-	01 2.45E-	05 -4.70E-	03 1.14E-	01 9.67E-	03 2.41E-	03 1.13E-	02 3.27E-	03 2.88E-	03 1.18E-	1.45E-

Buffer size (meters) Estimates of the relationships with density of linear landscape elements

	a.	b.
	c.	d.

Appendix list

Appendix A: Proposition of potential production of hedgerows in Loire Valley between Saint-Nazaire and Montsoreau in 2010. Appendix B1 Index uses for bat community assessment Appendix B2: Bush cricket species specialization and traits Appendix C: Correspondence scale of areas depending on the buffer size Appendix D: Correlations between variables Table D1: Correlations between characteristics of hedgerows (density, structural diversity and: potential wood production) and covariables at each spatial scale Table D2: Spearman correlations between characteristics of hedgerows (density, structural diversity and potential wood production) for a 500 m buffer Appendix E: Sonograms and information on sound files Appendix F: Assessment of Habitat Specialization species index and community specialization indices Table F1: Habitat Specialization Index Appendix G: Details on statistical analysis Appendix G1: Graphical abstract of the calculation at a given scale Appendix G2: Models used (= final models) for each species and community to assess the influence of the variables density, structural diversity and potential wood production Appendix G3: Graphical abstract of the calculation across scales Appendix H: Average activities of bats and bush-crickets To make the recordings audible, the sampling rate has been divided by ten (from 96000 ks/s to 9600 ks/s). Volunteer-based standardized monitoring schemes have been widely implemented in Europe and North America [START_REF] Jiguet | French citizens monitoring ordinary birds provide tools for conservation and ecological sciences[END_REF]. In France, the National Museum of Natural History (MNHN) initiated the French Bat Survey (BS) in 2006. This scheme offers a dataset of 6774 sites with both habitat characterizations and bat identifications (28 species and 960 500 bat calls).

Counting methods

The French BS recruits keen volunteers to count bats twice per year: once from 15th June to 31st

July, and a second time from 15th August to 30th September. Two different versions are used: road survey by car and count point. Count point: this method consists of a square of 2 km per-side randomly chosen (by the MNHN) within a radius of 10 km from the observer's home, (i.e., in average one square randomly chosen from among 80 possible squares). Within this square ten point counts are chosen by the observer with at least five points counts representative of the habitats of the square and the other being located in 'favorable' places for bats such wetlands, wood edges, etc.

Road survey: the choice of the circuit is delegated to the volunteers because the main constraint is related to their safety. The circuit must be performed at night at low speed (recording at constant speed: 30 ± 5 km/h), and this excludes non-paved roads, roads with heavy traffic and high-speed For all the species and communities, we used a zero-inflated GLM with a negative binomial distribution (ZANB) except for Plecotus spp., both CSI analyses, and the gleaner group for which we used a GLM with a negative binomial distribution (GLM.nb) [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF] For some species such as B. barbastellus and the Orthopera species that had uninterrupted calls, we reported (pres/abs) for presence/absence and used the binomial model of the ZANB;for the others, we used the count model [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF].

Thus, our statistical models were structured as follows: Sedentary -7.29 25.37 0.77 -41.86 40.14 0.30 -49.08 48.76 0.31 -60.38 53.40 0.26 -69.44 63.91 0.28 -65.94 73.13 0.37 -49.77 72.24 0.49 -60.22 75.04 0.42 -71.39 78.79 0.36 -77.70 85.93 0.37 -49.94 86.88 . pipistrellus -21.20 16.90 0.21 -16.86 20.64 0.41 -9.93 25.69 0.70 1.52 29.97 0.96 10.78 34.44 0.75 12.95 40.61 0.75 13.42 47.43 0.78 11.41 51.72 0.83 14.91 57.47 0.80 13.97 64.24 0.83 17.55 57.63 .48 8.80 0.87 3.05 12.14 0.80 3.05 16.50 0.85 5.92 19.67 0.76 4.17 22.73 0.85 6.44 26.76 0.81 10.86 30.14 0.72 12.02 31.45 0.70 14.56 33.20 0.66 15.43 36.01 0.67 13.84 38.99 Gleaner -35.78 39.79 0.37 162.07 61.85 0.01 238.21 87.94 0.01 270.35 97.83 0.01 160.65 97.26 0.10 134.25 115.76 0.25 174.39 132.45 0.19 244.90 148.52 0.10 298.81 157.62 0.06 328.65 176.86 0.06 336.35 194.37 estimates of the relationships between density and activity of 4 different species and community specialization and activity of aerial and gleaner species. We present 6 graphs: 4 species (a, b, c, d) with various ecologies and 2 traits (e, f). In graph 1.e., we present the effect of the density of linear landscape elements on the aerial bat species. We show that 1) using in the 1000 m buffer as an example, the activity of aerial species is positively correlated with the density of linear elements (the estimate reported from the modeling at 1000 meters -with its standard error -is positive, i.e., above zero, and this effect is significant as indicated by asterisks ; and 2) the scale effect from 50 meters to 1000 meters was significant and is visualized by the dotted line. The activity of aerial bat species was significantly and positively correlated with the linear landscape element density in a 1000 m buffer. The scale effect was significant, and there is a positive scale effect for these communities (above zero).

When we did not detect a linear scale effect, we assessed a potential breakpoint value (see Appendix I).