Christian Kerbiriou
email: christian.kerbiriou@mnhn.fr

Marion Parisot-Laprun

Keywords: bat community, gravel-sand pit, quarry rehabilitation, time of restoration, scale effect

Despite the impacts of extraction activities, quarries have great potential for conservation of biodiversity, and their restoration can be considered a major task in restoration ecology. A particularly important issue is to quantify the roles of the following various factors that may influence biodiversity and restoration success: (i) local variables, (ii) landscape variables and (iii) ecological management. Following a multi-model inference we identify which variables were the most useful predictors of bat activity in gravel-sand pits. We used data from 21 French gravel-sand pits provided by the biodiversity-monitoring scheme, ROSELIERE. Our results suggest that within these restored gravel-sand pits, landscape variables appear to be better predictors of the attractiveness of restored gravel-sand pits for the foraging activity of bats than local variables. Species or community indexes appeared to be more sensitive to landscape variables measured at a large scale (i.e., 1000 m). Among local variables, the time elapsed after operation was a better predictor of bat foraging activity than the description of the habitat itself. The effect was more obvious within a site with an elapsed time since operation greater than 10 years. Our findings suggest that to increase the attractiveness of gravel-sand pits for bat foraging activities, planning policies should consider not only the intrinsic characteristics of a project (i.e. local variables) but also the surrounding environment (i.e. landscape variables).

Introduction

The large number of abandoned quarries represents a challenge for the restoration of these impacted habitats [START_REF] Yuan | Soil Formation and Vegetation Establishment on the Cliff Face of Abandoned Quarries in the Early Stages of Natural Colonization[END_REF]. Quarrying operations have profoundly altered preexisting ecosystems: the original topography has changed, the organic soil has been removed and hydrological regimes could have been perturbed [START_REF] Khawlie | Contribution of remote sensing and GIS in locating quarries for aggregates in Lebanon's coastal area[END_REF][START_REF] Shaban | The relation between water-wells productivity and lineaments morphometry: Selected zones from Lebanon[END_REF]). In many sites, the previous ecological functions have been irreversibly disrupted [START_REF] Milgrom | Environmental aspects of rehabilitating abandoned quarries: Israel as a case study[END_REF], and trying to restore the past state may sometimes be impossible [START_REF] Mcdonald | National standards: Reasserting the ecological restoration framework in uncertain times[END_REF]. By offering substitution habitats, postmining sites may provide a haven for various taxa [START_REF] Benes | Limestone quarries as refuges for European xerophilous butterflies[END_REF][START_REF] Bizoux | Ecology and conservation of Belgian populations of Viola calaminaria, a metallophyte with a restricted geographic distribution[END_REF][START_REF] Brändle | Diversity of surface dwelling beetle assemblages in open-cast lignite mines in Central Germany[END_REF][START_REF] Dekoninck | Forest succession endangers the special ant fauna of abandoned quarries along the river Meuse (Wallonia, Belgium)[END_REF][START_REF] Dolezalova | Technical reclamation and spontaneous succession produce different water habitats: A case study from Czech post-mining sites[END_REF][START_REF] Harabis | High diversity of odonates in post-mining areas: Meta-analysis uncovers potential pitfalls associated with the formation and management of valuable habitats[END_REF], Kerbiriou et al. 2018b[START_REF] Krauss | Habitat area but not habitat age determines wild bee richness in limestone quarries[END_REF][START_REF] Šálek | Spontaneous succession on opencast mining sites: implications for bird biodiversity[END_REF], Tropeck et al. 2010[START_REF] Vojar | Spontaneous succession on spoil banks supports amphibian diversity and abundance[END_REF], particularly in intensively human-dominated landscapes (Tropeck et al. 2010). According to the potential conservation value of these postindustrial sites, restoration management of quarries is now an important task in global restoration ecology [START_REF] Tischew | Implementation of basic studies in the ecological restoration of surface-mined land[END_REF].

Restoration intervention can vary along a gradient from passive approaches based on unassisted regeneration to active approaches, including seeding, planting native species or restoring topography or hydrological regimes [START_REF] Holl | When and where to actively restore ecosystems[END_REF]. In terms of management, several studies have underlined that ecological gains were greater in the case of passive restoration based on natural evolution than when restoration was essentially based on human intervention through reclamation works [START_REF] Brändle | Diversity of surface dwelling beetle assemblages in open-cast lignite mines in Central Germany[END_REF][START_REF] Hodacova | Spoil heaps from brown coal mining: technical reclamation versus spontaneous revegetation[END_REF][START_REF] Prach | Ecological restoration of central european mining sites: a summary of a multi-site analysis[END_REF][START_REF] Šálek | Spontaneous succession on opencast mining sites: implications for bird biodiversity[END_REF][START_REF] Tropek | Can quarries supplement rare xeric habitats in a piedmont region? Spiders of the Blansky les Mts[END_REF][START_REF] Tropek | Technical reclamations are wasting the conservation potential of post-mining sites. A case study of black coal spoil dumps[END_REF]. Some authors highlight expensive costs associated with active restoration (Catteral and Harrison 2006), while others suggest that passive restoration is laden with unrecognized costs and only slowly reaches its objectives [START_REF] Zahawi | Hidden costs of passive restoration[END_REF]). The debate is still ongoing, but [START_REF] Prach | Passive restoration is often quite effective: response to Zahawi et al[END_REF] suggest that criteria to choose between passive, intermediate [START_REF] Uebel | Assisted natural regeneration accelerates recovery of highly disturbed rainforest[END_REF] and active restoration may depend on the landscape. Yet, relatively few studies have simultaneously considered within-site (i.e. local variables such as habitat, age, etc.) and landscape variables (surrounding habitats, distance to habitat source, landscape connectivity…etc.) (see [START_REF] Tropek | Spontaneous succession in limestone quarries as an effective restoration tool for endangered arthropods and plants[END_REF]. A better understanding of the relative importance of local and landscape variables could help to mediate the choice between passive and active restoration and to define targets while taking into account the surrounding landscape.

In addition to the restoration strategy, success also depends on target species and their specific traits: rareness, habitat or food specialization [START_REF] Bezombes | Do biodiversity offsets achieve No Net Loss? An evaluation of offsets in a French department[END_REF], Kraus et al. 2009[START_REF] Tropek | Spontaneous succession in limestone quarries as an effective restoration tool for endangered arthropods and plants[END_REF]. Some studies highlight the importance of the time since the end of the exploitation as a criteria of recovery success on species richness, such as birds (Šálek 2012), terrestrial plants [START_REF] Prach | Ecological restoration of central european mining sites: a summary of a multi-site analysis[END_REF][START_REF] Prach | Spontaneous vegetation succession at different central European mining sites: a comparison across seres[END_REF] or butterflies [START_REF] Benes | Limestone quarries as refuges for European xerophilous butterflies[END_REF], while some others did not detect any age-related effect; see [START_REF] Brändle | Diversity of surface dwelling beetle assemblages in open-cast lignite mines in Central Germany[END_REF] on dwelling beetle richness or [START_REF] Krauss | Habitat area but not habitat age determines wild bee richness in limestone quarries[END_REF] on wild bee richness, which identified a habitat variable as the best predictor of recovery success.

Disentangling the relative effects of local and landscape variables could allow a better account of the cumulative impacts on species and habitats resulting from the additive effects of several projects operating in the same region [START_REF] Bezombes | Ecological equivalence assessment methods: what trade-offs between operationality, scientific basis and comprehensiveness[END_REF]. Such information could improve the sizing of offset measures according to the choice of the restoration processes, their efficiency and the surrounding landscape [START_REF] Bezombes | Do biodiversity offsets achieve No Net Loss? An evaluation of offsets in a French department[END_REF].

We determined which variables, local-(i.e., age, habitat type) or landscape-scale (i.e., surrounding habitat areas or distance to a major resource), were the most useful predictors of bats activity (species relative abundance, richness, community specialization indexes) in 21

French gravel-sand pits. We chose to focus on bats, this group being increasingly threatened worldwide [START_REF] Mickleburgh | A review of the global conservation status of bats[END_REF]. In the European Union, all bat species are strictly protected by the Habitats Directive (Council of the European Union 1992), throughout the life cycle of a development project -including quarries-, developers must demonstrate that they will establish mitigation measures to prevent, reduce and compensate for impacts that result in any loss of bat foraging habitats [START_REF] Bezombes | Ecological equivalence assessment methods: what trade-offs between operationality, scientific basis and comprehensiveness[END_REF]. The purpose of this study is therefore to highlight the most important variables to consider during the rehabilitation of quarry sites to optimize the biodiversity potential.

Materials and methods

Study area

The study was conducted in France between 2009 and 2013 and involved the survey of 21 gravel-sand pits (Figure 1). Data were provided by ROSELIERE, a biodiversity-monitoring scheme implemented in quarries (for more detail see Supporting Information: Appendix S1 and Kerbiriou et al. 2018b).

The exploitation of these gravel-sand pits often results in the formation of a water body due to the underlying presence of the water table. The aquatic surface represents an average of 42% of the total pit area.

The dataset included quarries at different stages of their life cycle, allowing for the testing of the influence of local characteristics: 17% were quarries under operation, while the others were quarries under rehabilitation, with an ecological purpose (Table 1). Rehabilitation was based on passive restoration, and a spontaneous succession process occurred, leading to the presence of various semi-natural habitats, such as grasslands, wetlands, bushes, and woods.

In order to maintain an equal sampling effort between sites, we adjusted the number of sampled points per site (from 1 to 5 points, mean 3.6±0.3) according to the sites size (mean 74.8 ha±2.1). Therefore, the density of points sampled by sites is of the same order of magnitude as that recommended by the French Bat Monitoring Program (for more detail on the FBMP count point survey see Kerbiriou et al. 2019a). Points were spaced out by 200 meters to avoid, as much as possible, contacting the same bat at different points. Sampled points were relatively close to aquatic habitats (mean average distance to water 202 m ± 68 (SE). Between 2009 and 2013, a total of 547 samples were performed.

Local and landscape variables

We tested the effect of environmental variables on bat activity using variables describing local characteristics and variables describing the surrounding landscape.

Local characteristics, which relate to the different stages of the quarries' life cycle and vegetation dynamics, were noted in the field in a homogeneous patch of habitat (within a radius of 50 m around the recording point, in accordance with the maximum distance of detection for the most commonly contacted species in this region, [START_REF] Kerbiriou | Bat Pass Duration Measurement: An Indirect Measure of Distance of Detection[END_REF].

They were defined in two ways: (i) as a categorical variable with five categories of seminatural habitats (bare soil, seminatural grassland, bush (shrubs smaller than 7 m), woodlands (trees taller than 7 m), and water bodies (with water present predominantly over the year)) and (ii) as a categorical variable with four categories describing the gravel-sand pit state (during quarrying and three categories of rehabilitation according to the time elapsed after operation (<5 years; 5 years<<10 years; >10 years)).

At the landscape scale, we selected land-use variables that are known to influence bat activity. We computed the proportion of the following 9 land-use variables in a 1000-meter buffer around the recording points using CORINE Land Cover (http://www.eea.europa.eu/data-and-maps): (1) discontinuous artificial surfaces and ( 2) mineral extraction sites are expected to negatively influence bat activity due to the loss of semi-natural habitat [START_REF] Russo | Sensitivity of bats to urbanization: a review[END_REF]Kerbiriou et al. 2018a); (3) arable land is related to agriculture intensification and is identified as one of the major pressures on bats (Wash and Harris 1996;[START_REF] Jaberg | Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment[END_REF][START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF]); (4) pastures, (5) heterogeneous agricultural areas, (6) land principally occupied by agriculture with significant areas of natural vegetation and (7) broad-leaved forest were included because they include different proportions of semi-natural habitat that could potentially benefit bats (Wash and Harris 1996;Boughley et al. 2011[START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF]; and two variables describing aquatic habitats -(8) water courses (in the study area, the main water course was channeled) and ( 9) bodies of waterbecause they are identified as key habitat for many bat species [START_REF] Rainho | Summer foraging habitats of bats in a Mediterranean region of the Iberian Peninsula[END_REF][START_REF] Salvarina | Using species distribution modelling to predict bat fatality risk at wind farms[END_REF]Wash and Harris 1996). These continuous variables represent the most important habitats of the studied area, covering an average of 96% of the total 1000 m buffer area [min 71% max:100%]. According to the fact that distance to water is among the variables regularly identify for improves drastically the accuracy of bat abundance modelling [START_REF] Rainho | The Importance of Distance to Resources in the Spatial Modelling of Bat Foraging Habitat[END_REF][START_REF] Santos | Using species distribution modelling to predict bat fatality risk at wind farms[END_REF]Sirami et al. 2013), we included in our analysis the distance to nearby water bodies or rivers using BD Cartage (IGN, http://professionnels.ign.fr/bdcarthage).

Design Sampling

Bat sampling

Bats were surveyed using standardized echolocation recordings, a robust method for assessing the relationship between bat activities and the corresponding habitat (Stahlschmidt & Bruhl 2012). We followed a similar protocol to the one designed for the French Bat Monitoring Program (see Kerbiriou et al. 2018a). Echolocation calls were automatically detected using a Tranquility Transect Bat detector (Courtpan Design Ltd, UK) and recorded over 6 min on a Zoom H2 digital recorder (Samson technologies, USA) at a sampling rate of 96 ks/s from the two possible outputs of the detector: time expansion (1.28 s of sound at x10 time expansion) and high frequency. Bat activity was assessed using the high frequency output and the time expansion output was used only for species emitting echolocation calls > 48 kHz. All ultrasound detectors were previously calibrated by the French National Museum of Natural History in order to have the same sensitivity levels and enable the detection of echolocation calls while minimizing background noise due to wind or insects (for more detail see Kerbiriou et al. 2018a). In each plot, echolocation recordings were carried out in two visits corresponding to peaks of bat activity: once during the 15 th June to 31 st July period, during which females are expected to feed their offspring; and second, during the 15 th August to 30 th September period, during which individuals are expected to be less dependent on their reproductive roost. All points of a site were sampled during the same night. Within each site, the sampling of each 6 minutes points occurred early in the night (not earlier than thirty minutes after dusk and no later than three hours after dusk, thus corresponding to peak bat activity, with such design, time effect on bat abundance is drastically reduce, see Pauwels et al., 2019[START_REF] Mimet | Contribution of private gardens to habitat availability, connectivity and conservation of the common pipistrelle in Paris Landscape and Urban Planning in press[END_REF]. Observers sampled bats only when weather conditions were favorable (no rain, temperature higher than 12°C and wind speed lower than 5 m/sec).

Bat identification

We used Adobe Audition 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 and spectral analysis. We manually identified bat echolocation after isolating each bat vocalization and extracting relevant parameters (for more details see Kerbiriou et al. 2018b) using Scan'R (Binary Acoustic Technology, 2010). Species were identify at the species level except for two genera: Plecotus sp., for which there is a lack of consensus about the acoustic criteria for identification at the species level [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][START_REF] Barataud | Acoustic Ecology of European Bats, Species identification, study of their habitat and foraging behavior[END_REF] and Myotis sp. because of the very low occurrence of each single Myotis species and some uncertainties in acoustic identification at the species level [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][START_REF] Barataud | Acoustic Ecology of European Bats, Species identification, study of their habitat and foraging behavior[END_REF].

Bat activity measurements

Distinguish individuals from echolocation calls is currently impossible, so, we measured an index of relative bat activity defined as the mean number of bat passes per species over 6 minutes (following Kerbiriou et al. 2019a for the definition of a bat pass). This definition of bat activity is a commonly used measure of relative bat abundance. Although a bat could be detected more than once, bat activity measured over a short time (6 minutes) is highly correlated with the number of recorded individuals flying is a site [START_REF] Mimet | Contribution of private gardens to habitat availability, connectivity and conservation of the common pipistrelle in Paris Landscape and Urban Planning in press[END_REF] The CSI is a mean trait community index based on the average habitat specialization of each species present in the community. Each species was characterized for habitat specialization through the calculation of a Species Specialization Index (SSI). The SSI varied between 0.54 and 3.86 (Supporting Information: Appendix S2 Table S2). The Community Specialization Index (CSI) is calculated as the arithmetic mean of the SSI of the detected species weighted by their relative abundances (Supporting Information: Appendix S2).

We calculated the Community Specialization to Forest (CSF) index as the arithmetic mean of the species specialization to forest habitat (SSF) weighted by their relative abundances. The SSF was the average density of a species in forest habitats divided by its average density in non-forest habitats. It was calculated using the French Monitoring of Bat Populations data, (see [START_REF] Regnery | Tree microhabitats as indicators of bird and bat communities in Mediterranean forests[END_REF], Supporting Information: Appendix S2).

Data Analysis

We tested the effect of local and landscape variables on bat activities using a generalized linear mixed model (GLMM, R package lme4). The response variable was either species relative abundance (n = 7 taxa) or bat community metrics (total relative abundance, richness, CSI, CSF). The explanatory variables were the two local variables (seminatural habitat and gravel-sand pit state) and the ten landscape variables (9 land-use variables and the distance to closest water). We added as covariables the temperature, the season and the year (as a factorial variable) because we assumed that bat activity might be influenced by weather conditions [START_REF] Ciechanowski | Spatiotemporal variation in activity of bat species differing in hunting tactics: effects of weather moonlight, food abundance, and structural clutter[END_REF]) and because we can expect changes in bat relative abundance among periods of the bat life cycle and between years. We also hypothesized that close points, even if their habitats are different, may likely have a similar bat population density due to similar climatic conditions or large-scale landscape compositions. Thus, we included a site effect, and we added an autocovariate (i.e., a distance-weighted function of neighboring response values; Dormann et al. 2007, function autocov_dist package spdep, Bivand R. et al.) with the aim to account for spatial autocorrelation. According to the hierarchical structure of our sampling design (same sites were sampled year to year), we treated the variable "site" as a random effect, while considering the other explanatory variables as fixed effects [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health[END_REF]). For the total relative abundance, rather than summing in an unique metric the raw data of abundance of each species, we included species identity as random intercepts to account for non-independence of observations corresponding to the same species (see [START_REF] Donald | International conservation policy delivers benefits for birds in Europe[END_REF][START_REF] Gamero | Tracking progress toward EU biodiversity strategy targets: EU policy effects in preserving its common farmland birds[END_REF][START_REF] Pierret | The potential virtue of garden bird feeders: More birds in citizen backyards close to intensive agricultural landscapes[END_REF]. The explanatory variables were centered and standardized so that the regression coefficients were comparable in magnitude [START_REF] Schielzeth | Simple means to improve the interpretability of regression coefficients[END_REF]. According to the nature of the response variable (i.e., count data), we used a negative binomial error distribution [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health[END_REF] to model relative abundance, total relative abundance, and richness. Because the N. noctula model did not fit well, we transformed the response variable (i.e., relative abundance) to presence/absence and then used a binomial error distribution. For CSI and CSF, we used a Gaussian error. Thus, our global models were structured as follows: where bat activity could be the relative abundance of a bat species (n=7) or total relative abundance, or richness or CSI or CSF.

Following a multi-model inference [START_REF] Burnham | AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons[END_REF][START_REF] Grueber | Multimodel inference in ecology and evolution: challenges and solutions[END_REF]), we generated a set of candidate models containing all possible variable combinations. We ranked them using the Akaike information criterion corrected for a small sample using the dredge function (R package MuMIn). We only built models complying with the following conditions: (i) models do not include more than five variables to avoid overparameterization, (ii) models do not include simultaneously correlated covariates (R²>0.5 or non-independent variables such as habitat or gravel-sand pit state, i.e. there is rarely water bodies and woodland at the state "during carrying", while when rehabilitated > 10 years the more common local habitats are water bodies and woodland see Supporting information S1). For each set of candidate models, we did multi-model inference averaging on a delta AICc <2 using the model.avg function (Table S3) to obtain an averaged regression coefficient for each fixed effect (R package MuMIn). Finally, we performed variance-inflation factors (VIF, vif function, package car) on each top model [START_REF] Fox | Generalized collinearity diagnostics[END_REF]. Because all variables showed a VIF value <3 and the mean of VIF values was <2, there was no striking evidence of multicollinearity [START_REF] Chatterjee | Regression analysis by example[END_REF]. All analyses were performed using R statistical software v 3.1.5

Results

A total of 1542 bat passes belonging to 11 species were recorded on the 21 gravel-sand pit sites (Table 2). According to the recording sampling (547 records of 6 minutes each), the mean bat activity was 28 bats passes/hour [min:0max: 850 bat passes/hour]. The majority of echolocation calls from the 11 bat species came from three Pipistrellus bat species (Table 1).

Effects of local variables

Among the two local variables, only the gravel-sand pit state appeared in the set of the top models (Table 3, see also Supporting Information: Appendix S3). When this variable was selected, the relative importance was only of concern for community metrics (richness, CSI and total relative abundance) and not for taxa except for the Myotis spp. group (Table 3). The difference between the "during quarrying" state and the three other states was only significant for the older state (>10 years elapsed after operation), which exhibited greater relative abundance (Figure 2).

Effects of landscape variables

Among the 11 bat activity metrics tested, the proportion of mineral extraction sites within a radius of 1000 m had a strong negative effect on N. noctula, a slight negative but nonsignificant effect on the total relative abundance and a positive effect on Myotis spp. (Table 3, see also Supporting Information: Appendix S3); however, it is noteworthy that the relative importance of this variable for Myotis spp. was very weak (Table 3). For the other highly anthropized habitats present in the sites' surroundings (proportion of discontinuous artificial surfaces), we identified a strong negative effect on N. leisleri and Myotis spp. While the distance to water had a strong negative effect on P. pipistrellus, P. nathusii, P. kuhlii, Myotis ssp., N. leisleri, and N. noctula, (Table 4). Therefore, the farther away the surrounding water was from the site, the lower the relative abundance of those species. However, we did not detect any effect of the distance to water on bat community metrics (Table 4). Surprisingly, the proportion of watercourses exhibited a strong negative effect on P. pipistrellus, P. kuhlii, and all the community metrics (Table 4). Moreover, bodies of water never exhibited any strong effect, except on N. noctula. Arable land negatively influences E. serotinus relative abundance, whereas land principally occupied by agriculture with significant areas of natural vegetation positively influences P. kuhlii, P. nathusii, and E. serotinus, and heterogeneous agricultural areas positively influenced P. pipistrellus, Myotis ssp. and richness. Pastures had a weak positive effect on Myotis ssp. Finally, broadleaved forest exhibited a positive influence on E. serotinus only.

Discussion

Local variable effect

The local variable based on habitat type was not a good predictor of bat activity, whereas the gravel-sand pit state appeared to be a better predictor but mainly for community metrics. This result suggests that within these gravel-sand pit sites in the process of rehabilitation, the time elapsed after operation is a better predictor of bat activity and gives more information than the description of the habitat itself. Whatever the bat activity considered -relative abundance of P. pipistrellus or Myotis spp., richness, Community Habitat specialization index, the total relative abundance of foraging calls -the effect is more obvious within a site with an elapsed time since operation of greater than 10 years. This result found in previous analysis (Kerbiriou et al. 2018b) is still robust when this effect is adjust to landscape variables. This result is consistent with other studies that also highlighted the positive influence of aged quarries on the richness of species such as birds (Šálek 2012) or terrestrial plants [START_REF] Prach | Ecological restoration of central european mining sites: a summary of a multi-site analysis[END_REF][START_REF] Prach | Spontaneous vegetation succession at different central European mining sites: a comparison across seres[END_REF], but it contrasts with studies that found a negative effect, such as [START_REF] Benes | Limestone quarries as refuges for European xerophilous butterflies[END_REF] on butterflies, or studies that did not detect any age-related effects [START_REF] Brändle | Diversity of surface dwelling beetle assemblages in open-cast lignite mines in Central Germany[END_REF] on dwelling beetle richness, [START_REF] Krauss | Habitat area but not habitat age determines wild bee richness in limestone quarries[END_REF] on wild bee richness. In the latter, although the authors tested an age gradient of over 120 years, effective habitats were the best predictors. These contrasting findings among taxa are probably linked to their habitat requirements, and bee and butterfly communities are most likely favored by open habitats such as grasslands present in the early stages of succession; while birds and bats need more wooded habitats that require more time to develop. In our study, the bat guild did not include open specialized species: habitat-specialist species were linked to wetlands (M. daubentonii, P. nathusii) or woodlands (N. noctula). In addition, as trees grow and age, they may have cavities that birds or bats could use to nest and harbor insects that are prey for these insectivores [START_REF] Regnery | Tree microhabitats as indicators of bird and bat communities in Mediterranean forests[END_REF]. The time lag (<10 years) to detect a significant effect on bat activity highlights the need for offset policies to consider the dimension of time between project impacts and gains provided by offset measures [START_REF] Mckenney | Policy development for biodiversity offsets: a review of offset frameworks[END_REF].

Such results highlight the need of proactive approaches that assess ecological possibilities very far upstream of development projects are needed to better adapt development projects to ecological equivalence principles (i.e. equivalence between impact sites and offsets sites, [START_REF] Bezombes | Ecological equivalence assessment methods: what trade-offs between operationality, scientific basis and comprehensiveness[END_REF].

Landscape variable effect

At the landscape scale, distance to the closest water body or river (Dist. water) and the proportion of watercourses channeled in a 1000-meter buffer (Channel) are among those we identified with the greatest number of strong effects and relative importance (Table 3 &4).

Numerous studies based on acoustic methods, and carried out on the species studied here, identified aquatic habitat as favorable and where species relative abundance is greater [START_REF] Rainho | The Importance of Distance to Resources in the Spatial Modelling of Bat Foraging Habitat[END_REF][START_REF] Russ | Habitats associations of bats in Northern Ireland: implications for conservation[END_REF]. However, in our study, the three variables linked to the aquatic habitat showed contrasting effects: (1) the proportion of bodies of water in a 1000-meter buffer (Water bodies) did not exhibit any strong effects on bat activity, (2) the proportion of water courses channeled in a 1000-meter buffer (Channel) exhibited a strong negative effect and no positive effect and (3) the distance to the closest water body or river (Dist. water) exhibited a strong negative effect, i.e. the greater the distance to water, the lower the bat total relative abundance. These results seem somewhat contradictory. The effect of distance to the closest water body or river is congruent with many studies [START_REF] Rainho | The Importance of Distance to Resources in the Spatial Modelling of Bat Foraging Habitat[END_REF]Barré et al. 2019). The apparent contradiction between distance to water (Dist. water) and the proportion of water courses channeled in a 1000-meter (Channel) could be explained by the fact that, in our study, the proportion of water courses was extracted from the CORINE Land Cover shape were the water courses digitalized are mainly wide, channeled rivers (i.e., with artificialized banks) and consequently probably of lesser ecological interest (see [START_REF] Corbacho | Patterns of species richness and introduced species in native freshwater fish faunas of a Mediterranean-type basin: the Guadiana River (southwest Iberian Peninsula)[END_REF]. Second, the accuracy of the two types of variables used was not similar: Channel are assessed using CORINE Land Cover, whose precision is very limited (minimum unit of 25 ha), whereas the distance to water was calculated using BD Cartage with a metric spatial resolution. Thus, differences in response patterns could be linked to spatial data quality or to the lack of precision concerning the ecological characteristics of the water courses or bodies analyzed (small ponds with natural banks are considered by the distance of the water variable vs. the proportion of water habitats variable, which includes only large bodies of water or channeled rivers).

Disentangling the relative importance of local and landscape variables

Among the 11 bat activity measures, local variables performed equally than landscape variables for Myotis spp. and total relative abundance and never performed better than landscape variables (Table 3). Thus, in our study, landscape variables appear to be better predictors of the attractiveness of gravel-sand pits for the foraging activity of bats than local variables. This importance of landscape variables is consistent with the requirements of the species studied. Indeed, their home range matches with a radius ranging from 400 m to 1000 m (see [START_REF] Davidson-Watts | Differences in foraging behaviour between Pipistrellus pipistrellus (Schreber, 1774) and Pipistrellus pygmaeus (Leach, 1825): foraging behaviour in cryptic bat species[END_REF] for P. pipistrellus and Flaquer et al., 2009 for P. nathusii). [START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF] studied the effect of landscapes on bat activity and showed a positive scale effect: the set of species we studied here appeared to be more sensitive to large-scale variables (i.e., 1000 m) rather than ones at a smaller scale. Our findings suggest that, with the aim of increasing the attractiveness of rehabilitated gravel-sand pits for bat foraging activities, planning policies should consider not only the intrinsic characteristics (i.e. local variables) of a new quarrying project but also the surrounding landscape (i.e. landscape variable), particularly when defining the conditions for the restoration process. One way of doing that could consist of adopting a proactive approach for the implementation of offsets. In such frameworks, biodiversity offsets of different projects could be used jointly with the aim to restore connectivity between those postexploitation sites and seminatural or natural habitats at the landscape scale and, in turn, reach a functional level of this restored habitat (i.e., a size in accordance with home-range requirements, see [START_REF] Quétier | No net loss of biodiversity or paper offsets? A critical review of the French no net loss policy[END_REF]. Bearing in mind that different companies finance these individual quarry projects, so the provision of connectivity by aggregation and layout of biodiversity offsets of different projects requires the existence of an efficient coordination at the landscape/regional scale. Such coordination could potentially be effective through the implementation of a "market" type nature mitigation banking system [START_REF] Vaissière | Biodiversity offset markets: What are they really? An empirical approach to wetland mitigation banking[END_REF], however several studies provide empirical evidence of the risks of this system [START_REF] Bendor | Risk and markets for ecosystem services[END_REF][START_REF] Robertson | Evaluation of a market in wetland credits: entrepreneurial wetland banking in Chicago[END_REF][START_REF] Robertson | The Work of Wetland Credit Markets: Two Cases in Entrepreneurial Wetland Banking[END_REF][START_REF] Hannis | Offsetting Nature? Habitat Banking and Biodiversity Offsets in the English Land Use Planning System[END_REF]. Another way could be to mobilize tools dedicated to land-use planning by national/regional governments, such as regulations regarding ecological corridors at national scale (e.g. Polish legislation in an amended Nature Conservation Act of 16 October 1991, [START_REF] Kozieł | Protection of ecological corridors in spatial planning documents in Poland implementation problems[END_REF]and French regional

  . The response variables were (1) relative abundance of bat passes of the most abundant species recorded (Pipistrellus pipistrellus (Schreber, 1774), Pipistrellus kuhlii (Kuhl, 1817), Pipistrellus nathusii (Keyserling & Blasius, 1839), Nyctalus noctula (Schreber, 1774), Nyctalus leisleri (Kuhl, 1818), and Myotis ssp.), (2) total relative abundance of bat passes, (3) species richness, (4) Community Habitat Specialization Index (CSI) and (5) Community Specialization to Forest (CSF).

[

  Bat activity] ~ local variables + landscape variables + season + temperature + year + autocovariate + 1|Site

Figure 1 .

 1 Figure 1. Map of the gravel-sand pits sites of ROSELIERE program and sampled points

Table 4 :

 4 ecological coherence scheme Act of 12 July 2010,[START_REF] Gregoire | Réalisation d'une carte de synthèse nationale des continuités écologiques régionales -Rapport méthodologique[END_REF] or at the EU scale. Conditional average estimate and adjusted SE of the effect of landscape variables on bat activity. For abbreviations, see Table3 legend.(*** P < 0.001, ** P < 0.01, * P < 0.05,. P < 0.1).

		Seminatural habitat
		bare soil
		seminatural grassland	211
		bush
		woodland
		water body	202
		Gravel-sand pit state
		during quarrying
		restoration states <5 years
		restoration states 5 years<<10 years	164
		restoration states >10 years	211
	695
	696
	697	Table 2. Species detected and number of contacts
		Species	Number of contact(s)
		Myotis alcathoe	1
		Myotis brandtii/mystacinus	1
		Myotis emarginatus	2
		Myotis daubentonii	40
		Myotis sp.	83
		Myotis spp. total	127
		Nyctalus leisleri	106
		Nyctalus noctula	82
		Eptesicus serotinus	21
		Pipistrellus kulhii	50
		Pipistrellus nathusii	86
		Pipistrellus pipistrellus	829
		Plecotus spp.	4

This study had financial supports from the French National Union of Quarrying and Materials Industries of Île-de-France (UNICEM IDF), the French National Union of Aggregate Producers (UNPG), operators of extraction sites (A2C Granulats, CEMEX, GSM, Lafarge Granulats, Sables de Brévannes) and Association des Naturalistes de la Vallée du Loing et du massif de Fontainebleau (ANVL).

Supporting Information

The following information may be found in the online version of this article.

Appendix S1: Detailed information on ROSELIERE program and site survey

Appendix S2: Species Specialization Index and details on the assessment of the Habitat Specialization Species Index, Community Specializations Indices and Community Specialization to Forest.

Appendix S3: Additional information on models

Table 1. Local characteristics of sites sampled Table 3. Relative importance of each variable (i.e., relative values of each variable calculated as a sum of the AICc weights over the set of top models in which the term appears). Abbreviations: discontinuous artificial surfaces (Disc. artificial), mineral extraction sites (Min. extraction), arable land (Arable), pastures (Past.), heterogeneous agricultural areas (Hetero. Agri.), land principally occupied by agriculture, with significant areas of natural vegetation (Agri. natural.), broadleaved forest (Wood.), water courses, in the study area, the main water course is channeled (Channel), bodies of water (Water) and distance to closest water bodies or river (Dist. Water).

Supporting information, Appendix S1: Detailed information on ROSELIERE program and site survey

The ROSELIERE program (http://programme-roseliere.fr/node/21), is a biodiversity monitoring scheme, implemented with the aim to (i) assess the level of biodiversity on a large number of quarries nationwide, (ii) assess the success of gravel-sand pits restoration, (iii) understand the basic processes that promote or reduce the conservation effectiveness of these restoration programs. Habitat Specialization species index were assessed using data provided the French Monitoring of Bats population (see Kerbiriou et al., 2018).

Assessment of specializations indices

In

Specialization species index (SSI)

We quantified 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 Julliard et al. 's approach (2006). SSI is thus independent to species habitat preferences. However, it is also possible to assess the species specializations to forest habitats by calculating the average density of a species inventoried in forest habitats divided by the average density in non-forest habitats.

Assessment of bat community specializations indices -The Community Specialisation Index (CSI)

The CSI is calculated as the arithmetic mean of the species specialization index (SSI) of the species detected, weighted by the relative abundances (Julliard et al. 2006) (Eq. ( 1)).

CSIj = (Eq. 1)

where n is the total number of species recorded, aij is the abundance of individuals of species i in plot j, and SSIi its specialization index.

To calculate the Myotis group CSI, we used the sum of identified Myotis species' calls and the average of the CSI known of 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 kept only sites with a non-null abundance because a null CSI does not describe a generalist community.

The Community Specialization to Forest (CSF).

The CSF is calculated as the arithmetic mean of the species specialization to forest habitats (SSF) weighted by the abundances. The SSF is the average density of a species inventoried in forest habitats divided by its average density in non-forest habitats. Contrary to SSI, here we consider species from the most to the least habitat dependent to forest habitats. So far, the degree of habitat specialization was based on expert knowledge in bats, but here we could use species and community indices from standardized monitoring methods. The species specialization index was systematically checked with the literature to confirm the forestdependency gradient among bat species (Table S1). Finally, we calculated the mean specialization to forest habitats using the same equation model as for community specialization index (see Eq. ( 1)).

For CSI and CSF relative abundance, so this measure was corrected by species distance of detection [START_REF] Barataud | Acoustic Ecology of European Bats, Species identification, study of their habitat and foraging behavior[END_REF], species bat activity was weighted by the multiplicative inverse of the distance of detection squared, thus a species with a great distance of detection contributes less for total relative abundance than a species with a small distance of detection.

LITTERATURE CITED


Lara Millon
email: million@gmail.com

Célia Colin

Fabrice Brescia

Christian Kerbiriou

Wind turbines impact bat activity, leading to high losses of habitat use in a biodiversity hotspot

Previous studies have mainly focused on bat mortality through collision by wind turbines, and very few studies have assessed the indirect impacts on bat activity and on foraging habitat availability. Also, there is a global lack of knowledge on the vulnerability of tropical bat fauna due to wind energy production, even though it is well known that windpower can affect bat communities and biodiversity hotspots are widespread in the tropics. We present one of the first studies to quantify the indirect impact of wind farms on insectivorous bats in tropical hotspots of biodiversity. Bat activity was compared between wind farm sites and control sites, via ultrasound recordings at stationary points. The activity of bent winged bats (Miniopterus sp) and wattled bats (Chalinolobus sp) were both significantly lower at wind turbine sites. The result of the study demonstrates a large effect on bat habitat use at wind turbines sites compared to control sites. Bat activity was 20 times higher at control sites compared to wind turbine sites, which suggests that habitat loss is an important impact to consider in wind farm planning We strongly recommend that the loss of the foraging habitat loss is considered in mitigation hierarchy (avoiding, reducing, offsetting) when compensating for negative impacts of wind farms.

Introduction

Global wind capacity grew by 16.1% between mid-2015 and mid-2016 (WWEA 2016, http://www.wwindea.org/2016/). The wind energy industry is well known to impact biodiversity, mainly for birds and bats. The impacts are both direct, such as mortality due to collision or barotrauma [START_REF] Rydell | Bat Mortality at Wind Turbines in Northwestern Europe[END_REF][START_REF] Grodsky | Investigating the causes of death for wind turbine-associated bat fatalities[END_REF][START_REF] Huso | Wind energy development : methods to assess bird and bat fatality rates post-construction[END_REF], and indirect: habitat loss, behavioral changes and reduced population viability (Zimmerling et al., 2013;[START_REF] Arnett | Mitigating wind energy impacts on wildlife: Approaches for multiple taxa[END_REF][START_REF] Frick | Fatalities at wind turbines may threaten population viability of a migratory bat[END_REF]). Yet various factors make it difficult to ascertain bat fatality rates (Zimmerling et al., 2013), while the indirect impacts are often overlooked [START_REF] Minderman | Experimental evidence for the effect of small wind turbine proximity and operation on bird and bat activity[END_REF][START_REF] Arnett | Mitigating wind energy impacts on wildlife: Approaches for multiple taxa[END_REF]. Today, while wind turbines are recognized to be one of the primary causes of bat mortality [START_REF] O'shea | Multiple mortality events in bats: a global review[END_REF], it is still direct mortality that is principally assessed (Arnett et al., 2016;Schusteret al., 2015). Indirect impacts on bats include the destruction of habitat and roosts during the construction of the wind farm and behavioral changes caused during the operational phase [START_REF] Arnett | Mitigating wind energy impacts on wildlife: Approaches for multiple taxa[END_REF][START_REF] Frick | Fatalities at wind turbines may threaten population viability of a migratory bat[END_REF]. Currently, only 4 studies [START_REF] Barré | Impact of wind turbines on bat activity : an omitted long-distance concern[END_REF][START_REF] Millon | Bat activity in intensively farmed landscapes with wind turbines and offset measures[END_REF][START_REF] Minderman | Experimental evidence for the effect of small wind turbine proximity and operation on bird and bat activity[END_REF], 2017) have dealt with the assessment of habitat loss due to wind farms or individual wind turbines. All these studies, carried out in Europe, reported a reduced bat activity within a wind farm compared to outside the wind farm. While most studies on the impact of wind turbines on bats come from Europe or North America (Arnett et al. 2016;[START_REF] O'shea | Multiple mortality events in bats: a global review[END_REF][START_REF] Frick | Fatalities at wind turbines may threaten population viability of a migratory bat[END_REF] a recent review (Arnett et al., 2016) highlighted the need to assess the vulnerability of tropical bat fauna. These assessments should be performed preferably before extensive wind facilities will be planned particularly because the ecological requirements and sensitivity to new human pressure are relatively poorly known, for the species involved. However, wind farm are already operational in tropical islands and many more installations are planned, in order to make such isolated territories energetically autonomous [START_REF] Weisser | On the economics of electricity consumption in small island developing states: a role for renewable energy technologies?[END_REF]). Our study aims to evaluate the indirect impact of wind turbines on bat activity in New-Caledonia. Using a paired-site design sampling, we compared bat activity measured in sites close to wind turbine to control sites (i.e. site outside the wind farm and including the same habitat cover). Bat activity was assessed using ultrasound recordings at stationary points New Caledonia is one of the world's smallest biodiversity hotspots and hosts nine species of bat, six of them endemic [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. Of the Pacific islands, New-Caledonia has the highest electricity needs, mainly due to nickel mining [START_REF] Hourçourigaray | Renewable Energy in the Pacific Islands: An overview and exemplary projects[END_REF]. In 2015, only 2 % of its total energy demand was covered by wind energy, but new projects are underway in order to increase the part of renewal energy in the New-Caledonia electricity production (ISEE 2015, http://www.isee.nc/) thus assessment of wind turbine impact on wildlife is urgently needed.

MATERIALS AND METHODS

Sampling design

The two wind farms studied were localized on the summit of two Neo Caledonian medium mountain (up to 375 m high for Prony and up to 216 m for Mont Mau). The wind farms were made up by 66 wind turbines (Supporting Information 1), 31 of which were 50 meters high, in operation since 2004-2005, and 35 of which were 55 meters high, in operation since [START_REF] Chatterjee | Economic value of the pest control service provided by Brazilian free-tailed bats in south-central Texas[END_REF]-2007[START_REF] Vergnet | Col de Prony wind farm[END_REF]. Wind turbines were placed along the ridges of this mountain ((Supporting Information 1), on ultramafic soil with naturally low or no vegetation (http://explorateur-carto.georep.nc/explorateur-carto/). Remnants of forests and maquis vegetation are found in the valley (http://explorateur-carto.georep.nc/explorateur-carto/). To assess the potential disturbance from the wind turbines, we used a paired survey design to quantify bat activity at sites close to wind turbine (WT) and sites outside the wind farm (control). Each control sites was positioned in the vicinity of a site close to wind turbine (700 ± 140 m from the wind farm, minimum distance between a control site and a wind turbine was 170 m). Recorders (full-spectrum bat detector SM2BAT Wildlife Acoustics Inc. USA) at wind turbine sites were placed 15 meters from the wind turbine. Microphones were placed 2.5 m from the ground. Two sites within a pair were 996 ± 210 m apart (238 m minimum).

Upstream, we have paid a particular attention to ensure that control sites had similar altitude and habitat cover than WT sites (no significant difference was detected between WT sites and control sites: Wilcoxon test : W = 24, p-value = 0. 43 for the altitude, Kruskal test: ks=2.649, P= 0.10; ks =0.820, P= 0.37; respectively for forest cover in a radius of 200 and 1000m; Kruskal test: ks =1.118, P= 0.29; ks=0.365, P= 0.55; respectively for open land cover in a radius of 200 and 1000m). One or two pairs were sampled during the same night. To ensure the independence of the data, the minimum distance between two sampling points was 250 meters. Eight pairs in total were sampled. Fieldwork was carried out during two periods: May-June 2015 (cold season) and November 2015 (dry season). Each pair was sampled once per period thus the dataset included 32 efficient recording samples.

Bat activity

We used echolocation recordings at stationary points to sample bat activity, the most widely used method for a standardized insectivorous bat activity survey (Stahlschmidt and Brühl, 2012). We recorded ultrasounds higher than 8 kHz during the three first hours after sunset, with Song Meter 2 Bat detectors (http://www.wildlifeacoustics.com/). The beginning of the night was chosen because it is known to be when bat activity first peaks (Froidevaux et al., 2014;Griffiths, 2007; see Supporting Information 2). Focusing on the first three hours of the night also allows optimization of the time resources allocated to audio file analysis. We used Kaleidoscope (version 4.0.0, http://www.wildlifeacoustics.com/) and Scan'R (version 1.7.6, http://binaryacoustictech.com/) to isolate bat calls. All files labeled as containing bat calls were manually verified with Batsound (version 4.03, http://www.batsound.com/). Calls higher than 50 kHz were assigned to the genus Miniopterus, and calls between 30 kHz and 40 kHz were assigned to the genus Chalinolobus [START_REF] Kirsch | Contributions à l'inventaire chiroptérologique de la Nouvelle-Calédonie: Chiroptera Pacifica[END_REF]. In southern New-Caledonia, Miniopterus genus is composed of the little bentwinged bat (M. australis) and the small melanesian bentwinged bat (M. macrocneme), considered as least concern and as data deficient respectively (IUCN 2016, https://www.iucn.org/fr). Although the small melanesian bentwinged bat is believed to occur across a very wide range, the difficulties inherent in its identification prevent an accurate evaluation of trends and threats for this species. Locally, bentwinged bats are considered as vulnerable [START_REF] Kirsch | Contributions à l'inventaire chiroptérologique de la Nouvelle-Calédonie: Chiroptera Pacifica[END_REF]. Chalinolobus genus is only composed of the New-Caledonia wattled wat (C. neocaledonicus, [START_REF] Kirsch | Contributions à l'inventaire chiroptérologique de la Nouvelle-Calédonie: Chiroptera Pacifica[END_REF], a New-Caledonian endemic species listed as endangered (IUCN 2016), due to a constant decline in the extent and the quality of its habitat. One other species, the New-Caledonia Long-eared bat (Nyctophilus nebulosus), an endemic critically endangered species (IUCN 2016), may have been present in the study area [START_REF] Parnaby | A new species of long-eared bat (Nyctophilus : Vespertilionidae) from New-Caledonia[END_REF] but was not detected in our recordings. As it is impossible to determine the number of individual bats from their echolocation calls, we calculated a bat activity metric (bat passes), calculated from the total number of contacts per genus during the three first hours after sunset. A bat pass is defined as a single or several echolocation calls during a five second interval [START_REF] Millon | Bat activity in intensively farmed landscapes with wind turbines and offset measures[END_REF]. If it was obvious that several bats emitted calls at the same time, an additional contact was counted.

Statistical analyses

Generalized Linear Mixed Models (GLMM) were used to assess variation in bat activity (i.e., the response variable) as a function of site type (WT versus control, the explanatory variable, R package glmmADMB, Fournier et al., 2012). Due to the nature of the data (i.e.: count data) and its overdispersion, we performed GLMMs (package glmmADMB) with a negative binomial error distribution [START_REF] Crawley | The R book[END_REF]. Due to the difference in detection distance for We performed variance-inflation factors (VIF) on each model [START_REF] Fox | Generalized Collinearity Diagnostics[END_REF]. All variables showed a VIF value of <3 and the mean of VIF values was <2, meaning there was no striking evidence of multicollinearity [START_REF] Chatterjee | Economic value of the pest control service provided by Brazilian free-tailed bats in south-central Texas[END_REF]. All analyses were performed with R statistical software (The R core team, 2016).

RESULTS

In total, more than 3 000 contacts were recorded at wind turbine sites (WT) and sites outside the wind farm, the majority of them emitted by Chalinolobus (80%). Both genera were present during the two seasons, with more contacts recorded during the dry season (72%) than the cold season. For both Chalinolobus and Miniopterus the landscape co-variable selected was the percentage of open area, with a significant negative effect.

For both Chalinolobus and Miniopterus, wind turbines induced a significant difference in bat activity (Table 1, Fig. 1). The mean activity of Chalinolobus was tenfold lower at WT sites than at control sites (mean observed values: Control: 147 [min: 0 -max: 790]; WT: 14 [min: 0max: 120] ; mean predicted values: Control: 265±72 ; WT: 21±4), while the mean activity of Miniopterus was, 20-fold lower at WT than at control sites (mean naïve values: Control: 39 [min: 0 -max: 240]; WT: 2 [min: 0 -max: 14] ; mean predicted values: Control: 44±11 ; WT: 2±1) (Table 1, Fig. 1). The effect size of wind turbine presence was of the same magnitude than open areas. Note that the hierarchical structure or the landscape adjustment provided in our modelling did not change qualitatively naïve (i.e. observed) bat activity (see Fig. S2-1).

DISCUSSION

Indirect effects of wind turbines on bats

This study showed that the activity of two genera of bat species present in New-Caledonia were significantly lower at wind turbines as compared to nearby control sites, and confirmed the displacement effect of the wind farms on foraging bats. Apart from reported bat fatalities in south Australia, there has been no previous attempt to determine to what extent wind turbines impact Oceanian bat populations [START_REF] Bennett | Hepburn Wind Farm bird and bat mortality survey interim report 11th[END_REF][START_REF] Hull | Bat fatalities at two wind farms in Tasmania, Australia: bat characteristics, and spatial and temporal patterns[END_REF]. Here, we showed that Chalinolobus activity was tenfold lower under wind turbines, and Miniopterus activity was 20-fold lower. To our knowledge, this is one of the first studies quantifying the indirect negative impact of wind turbines on bat activity in the tropics.

Although our results were obtained in a tropical biodiversity hotspot, similar results of a displacement effect of wind turbines y have been found in temperate zones in agricultural landscape characterized by open landscape [START_REF] Barré | Impact of wind turbines on bat activity : an omitted long-distance concern[END_REF][START_REF] Millon | Bat activity in intensively farmed landscapes with wind turbines and offset measures[END_REF][START_REF] Minderman | Experimental evidence for the effect of small wind turbine proximity and operation on bird and bat activity[END_REF][START_REF] Minderman | Landscape-scale effects of single-and multiple small wind turbines on bat activity[END_REF]. The lower attractiveness of the foraging habitat under wind turbines, both in a tropical and in a temperate climate, indicates that the indirect impact of wind turbine is a worldwide phenomenon.

Effect of wind turbines on local bats

In a biodiversity hotspot such as New-Caledonia, species face many threats, such as habitat loss and alteration (nickel exploitation, logging, fires), agriculture, hunting, cave disturbance, severe storms and invasive species (e.g. cats, Wiles and Brooke, 2009;[START_REF] Bonvallot | Atlas de la Nouvelle-Calédonie[END_REF].

Here, we provide evidence showing that two genera of insectivorous bat species are also threatened by wind farms. Future studies should also assess the impact on megabats. Few studies have focused on bat species in New-Caledonia, mainly on endemic species [START_REF] Parnaby | A new species of long-eared bat (Nyctophilus : Vespertilionidae) from New-Caledonia[END_REF][START_REF] Bonvallot | Atlas de la Nouvelle-Calédonie[END_REF]. This is the first study showing that insectivorous bats visit ultramafic environments with naturally low or no vegetation, maquis vegetation or remnants of forests.

Conservation implications

Island bats represent 60% of bat species worldwide and the highest proportion of terrestrial mammals on isolated islands, including numerous endemic and threatened species [START_REF] Fleming | Island bats: evolution, ecology and conservation[END_REF]. In addition, wind turbines are a part of alternative energy production that does not contribute to greenhouse gas emissions. In the context of sustainable development of islands the number of windfarm is expected to increase in the future. It is therefore vital for environmental managers to take into consideration the impact of wind turbines, to ensure bat conservation. Eurobat guidelines for consideration of bat in wind farm [START_REF] Rodrigues | Guidelines for consideration of bats in wind farm projects[END_REF] advocate that, for the consideration of bat in wind farms planning, conservation managers need to determine the impact of a wind facility by comparing activity before and after its establishment, within and outside the wind farm, for at least three years after setup. We strongly recommend that the loss of the foraging habitat due to wind farm avoidance is considered potentially a significant threat to bat conservation, and involve specific studies as suggested by [START_REF] Kingston | Research priorities for bat conservation in Southeast Asia: A consensus approach[END_REF] and more particularly during environmental impact assessment (EIA). Indirect effects not only impact the bat's nature heritage value, but also their value in terms of providing ecosystem services, such as pest control (Cleveland et al., 2006). The displacement effect of wind farms on bats, could be more general to other taxa as suggested by [START_REF] Kuvlesky | Wind Energy Development And Wildlife Conservation: Challenges and Opportunities[END_REF] that highlighted that bird populations might be more threatened by habitat loss associated with wind farm development than direct impacts. We strongly recommend that the loss of foraging habitat due to wind farm avoidance is considered in mitigation hierarchy (avoiding, reducing, offsetting impacts) in order to compensate for this negative impact from wind turbines. Loss of the foraging habitat due to wind farm avoidance, could be offset through enhancing the carrying capacity of the habitat (via the creation of ponds, corridors between forest patches, or hedgerows and fallows in the farming landscape, [START_REF] Millon | Bat activity in intensively farmed landscapes with wind turbines and offset measures[END_REF][START_REF] Peste | How to mitigate impacts of wind farms on bats? A review of potential conservation measures in the European context[END_REF]. Despite some opportunities of offsetting, in the context of island biodiversity hotspots, stronger guarantees should be employed, because bat populations are often small and isolated and sometime concern endemic species [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. While switching off turbines at low wind speeds has been shown to be an effective means to reduce bat mortality [START_REF] Martin | Reducing bat fatalities at wind facilities while improving the economic efficiency of operational mitigation[END_REF], it is however necessary, before considering such reduction measures to assess their effectiveness on bat activity.

Wiles G.J., Brooke A.P., 2009. Conservation

  Miniopterus and Chalinolobus, per genus modeling was performed. In view of the hierarchical sampling design (up to two pairs -i.e. a pair means one WT site and one control site -were sampled at the same date) and the repetition of the sampling during two seasons, we used a nested random effect: pairs within recording sessions and season. Since bat activity can depend on landscape variables, we add as fixed effect landscape co-variable: the percentage of open area (with little or no vegetation) and the percentage of forest area each calculated at 2 different buffers of 200 and 1000m (http://www.oeil.nc/geoportail, see Supporting Information 3). To avoid over-parametrization, we only include one co-variable in models and choose the best model (among the 4 model: open land cover and forest cover at 2 radius distance), according to AIC criterion. Thus, our statistical models were structured in the following way: [Bat activity] ~ site type + co-variable + 1 | Season / Date / Pair where bat activity could be either Miniopterus or Chalinolobus activity.

Table 1 :

 1 Threats to Bats in the Tropical Pacific Islands and Insular Southeast Asia pp. 405-459. In Fleming T.H., Racey P.A., 2009. Island bats: evolution, ecology and conservation. University of Chicago Press, Chicago. Zimmerling J.R., Pomeroy A.C., D'Entremont M. V., Francis C.M., 2013. Canadian estimate of bird mortality due to collisions and direct habitat loss associated with wind turbine developments. Avian Conservation and Ecology 8, 10. Results of the Generalised Linear Mixed Models (GLMM) of (A) Chalinolobus activity and (B) Miniopterus activity (number of contacts during first three hours of the night). All Estimates and Std. Errors are expressed with a log link due to the negative binomial distribution used in GLMM. Intercepts represent bat activity in control sites. WF represent the difference in bat activity between Control Sites and Wind Turbine Sites. Prediction of Chalinolobus neocaledonicus activity (A) and Miniopterus spp activity (B) provided by the best model. According that the best model include the percentage of open land within 200 m buffer, we fix the value of this co-variable to its mean in the dataset (i.e. 42%). Bat activity is the number of number of contacts predicted for the first three hours of the night at control sites and wind turbine sites (WT), error bars are standard error. 2.

	A Chalinolobus activity
		Estimate	Std. Error	z value	P-value
	Intercept	4.062	0.455	8.92	P<0.001
	WT	-1.439	0.680	-2.12	P=0.034
	Open area 200	-1.856	0.522	-3.56	P<0.001
	B Miniopterus activity
		Estimate	Std. Error	z value	P-value
	Intercept	2.777	0.437	6.35	P<0.001
	WT	-2.633	0.661	-3.98	P<0.001
	Open area 200	-1.409	0.342	-4.11	P<0.001
	Figure 1:

Observed bat activity (naïve data) without taking account landscape variable or hierarchical sampling structure adjustment.

  Results of the Generalised Linear Mixed Models (GLMM) for Chalinolobus neocaledonicus activity (number of contacts during first three hours of the night). All Estimates and Std. Errors are expressed with a log link due to the negative binomial distribution used in GLMM. Intercepts represent bat activity in control sites. WT represent the difference in bat activity between Control Sites and Wind Turbine Sites, open_200 is the percentage of open area within a 200m buffer, open_1000 is the percentage of open area within a 1000m buffer, forest_200 is the percentage of forest area within a 200m buffer and forest_1000 is the percentage of forest area within a 200m buffer. Results of the Generalised Linear Mixed Models (GLMM) for Miniopterus spp activity (number of contacts during first three hours of the night). All Estimates and Std. Errors are expressed with a log link due to the negative binomial distribution used in GLMM. Intercepts represent bat activity in control sites. WT represent the difference in bat activity between Control Sites and Wind Turbine Sites, open_200 is the percentage of open area within a 200m buffer, open_1000 is the percentage of open area within a 1000m buffer, forest_200 is the percentage of forest area within a 200m buffer and forest_1000 is the percentage of forest area within a 200m buffer.

	random effect)
	Figure S2-1: observed bat activity of Chalinolobus neocaledonicus activity (A) and Miniopterus spp (B) 20 Error 100 150 200 250 (A) Chalinolobus 30 40 50 60 (B) Miniopterus Table S2-3: Model AIC Variable Estimate Std. z value P value
	50 Site type + open_200	178.3 intercept WT	2.777 10 -2.633	0.437 0.661	6.35 -3.98	P<0.001 P<0.001
	0		open_200	0 -1.409	0.342	-4.11	P<0.001
	Control	Wind turbine		Control	Wind turbine
	3. Model selection Site type + open_1000	182.8 intercept	3.285	0.480	6.85	P<0.001
	WT open_1000 -1.143 -3.124 179.3 intercept 2.052 WT -1.624 forest_200 1.500 Site type + forest_1000 188.5 intercept Site type + forest_200 3.755 WT -3.300 forest_1000 -0.231 Table S2-2: Model AIC Variable Estimat Site type 186.6 intercept 3.663	0.735 0.432 0.589 0.724 0.404 0.621 1.012 0.771 Std. Error z value P value -4.25 P<0.001 -2.65 P=0.008 3.48 P<0.001 -2.24 P=0.024 3.71 P<0.001 6.0 P<0.001 -3.26 P=0.001 -0.30 P=0.764 0.530 6.91 P<0.001
			WT	e -3.104	0.772	-4.02	P<0.001
	Site type + open_200 « Null model » (only	274 194.7	intercept	4.062	0.455	8.92	P<0.001
	random effect)		WT	-1.439	0.680	-2.12	P=0.034
			open_200	-1.856	0.522	-3.56	P<0.001
	Site type + open_1000	280.4 intercept	4.916	0.473	10.40	P<0.001
			WT	-2.500	0.686	-3.64	P<0.001
			open_1000 -0.940	0.566	-1.66	P=0.096
	Site type + forest_200	282.4 intercept	4.525	0.841	5.38	P<0.001
			WT	-1.493	1.542	-0.97	0.33
			forest_200	0.567	0.903	0.63	0.53
	Site type + forest_1000 282.8 intercept	5.021	0.505	9.94	P<0.001
			WT	-2.440	0.752	-3.24	P=0.001
			forest_1000 -0.139	0.606	-0.23	P=0.819
	Site type	280.9 intercept	4.987	0.479	10.40	P<0.001
			WT	-2.366	0.681	-3.47	P<0.001
	« Null model » (only 287

Acknowledgments:

We are grateful to Quadran Pacific for permission to perform this study on their wind turbine farms. We also thank N. Heurard-Cuerto for his help in the field. This study was funded by the Institut Agronomique néo-Calédonien (IAC) and did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We are also grateful to Alexander Eriksson, from Ecocom AB, for his helpful comments on an earlier reading of our manuscript and two referee (Jeroen Minderman and an anonymous) for their relevant and useful comments.

Supporting Information

Sampling design (Appendix S1), nightly activity pattern (Appendix S2), and Details on statistical analysis (Appendix S3) are available online. The authors are solely responsible for the content and functionality of these materials. Queries (other than absence of the material) should be directed to the corresponding author.

Appendix S2: Nightly activity pattern

We chose to determine bat activity only during the first three hours of the night at the different sites. The beginning of the night is known to be when bat activity first peaks, probably due to the activity peaks of nocturnal insects (Griffiths 2007;Froidevaux et al. 2014), as has been shown in our study area (Millon, unpublished report;[START_REF] Colin | Caractérisation de l'activité des microchiroptères sous un parc éolien en activité et au sein d'un site naturel sous l'emprise d'une activité minière en Nouvelle-Calédonie[END_REF]; Fig. B1). Bat activity can peak for a second time at the end of the night, when bats return to their roost or go to drink before sleeping [START_REF] Erkert | Ecological aspects of bat activity[END_REF]Taylor & O'Neill 1988). Focusing on the first three hours of the night also allows optimization of the time resource allocated to audio file analysis. In New-Caledonia, very limited human resources have so far been allocated to bat research, and there is no dedicated software to analyze audio files.

. Correlation between the explanatory variables

Because bat activity could depend on landscape variables, the percentages of forest or open area (with little or no vegetation or forest) were calculated within a 200 m buffer area and a 1000m buffer area around each site (http://www.oeil.nc/geoportail). Before performing each GLMM, we systematically evaluated the correlations among explanatory variables using Kruskall Wallis tests between qualitative and quantitative variables (site type, landscape variables, Table C1) to detect any obvious correlation.