Laurent Arthur

Michèle Lemaire

Léa Dufrêne

Isabelle Le Viol

Jean François Julien

Christian Kerbiriou
email: kerbiriou@mnhn.fr

Understanding bat-habitat associations and the effects of monitoring on long-term roost success using a volunteer dataset

Keywords: Habitat selection, distribution of colonies, local wildlife rescue networks, protection of above ground roots, Eptesicus serotinus

In gregarious species, the choice of colony location is especially crucial as the costs associated with breeding near conspecifics are important and the quality of breeding patch is known to affect individual fitness. Consequently one could expect robust decision-making rules regarding colony location. The conceptual framework of animal aggregation with regards to habitat selection emphasizes that the presence and success of conspecifics are cues to habitat selection. Based on this, we explored how the distribution of breeding colonies could inform us about how habitat selection operates in bats. The data set we used was provided by a volunteer network whose first aim is to advise citizens facing bats in distress or bats in their homes. The dataset contained information on the locations of 105 serotine (Eptesicus serotinus) breeding colonies in a French region primarily dominated by an agricultural landscape. The methodology used for calculating habitat availability was based on the comparison of habitats surrounding serotine colonies to habitats surrounding random points. We found that serotine bats positively select pastoral and aquatic habitats regardless of the comparison used. The strong correlation between our results and those obtained with radio-tracking or acoustic methods underlines the robustness of this spatial distribution approach. The analysis of the history of the serotine's colonies over a period of nearly 20 years underlined that when attics are restored by the owners without the help of the bat rescue network, the probability of a departure of colony is greater. In addition, monitoring reduces the occurrence of an unfriendly building restoration.

Introduction

Although legally protected in European countries through national or European laws (Council Directive 1992, Convention on Migratory Species (CMS 1985-2008), and Agreement on the conservation of Populations of European Bats), many European bats are endangered throughout much of their range (IUCN 2011). Numerous causes of this decline have been identified and include the availability of suitable foraging habitats [START_REF] Walsh | Foraging habitat preference of vespertilionid bats in Britain[END_REF]Harris 1996, Kunz and[START_REF] Kunz | Bat ecology Chicago[END_REF], agricultural practices that use toxic pesticides [START_REF] Dunsmore | DDT in the bent-winged bat in Australia[END_REF][START_REF] Swanepoel | Energetic effects of sublethal exposure to lindane on pipistrelle bats (Pipistrellus pipistrellus)[END_REF][START_REF] Wickramasinghe | Abundance and Species Richness of Nocturnal Insects on Organic and Conventional Farms: Effects of Agricultural Intensification on Bat Foraging[END_REF]), emerging infectious diseases [START_REF] Frick | An emerging disease causes regional population collapse of a common North American bat species[END_REF][START_REF] Puechmaille | White-Nose Syndrome: is this emerging disease a threat to European bats?[END_REF], and roost destruction and disturbance [START_REF] Bihari | Protecting and managing underground sites for bats[END_REF]). However, land use changes are often considered to be the principal cause of population decline in many bat species [START_REF] Battersby | Guidelines for surveillance and monitoring of European bats[END_REF]. The life history of bats is characterized by low fecundity and high longevity, and this K-strategy has been selected in a stable environment. Thus, bats are particularly vulnerable to environmental changes that lead to reduced demographic parameter values [START_REF] Walsh | Foraging habitat preference of vespertilionid bats in Britain[END_REF]Harris 1996, Kunz and[START_REF] Kunz | Bat ecology Chicago[END_REF]. Therefore, an accurate description of the habitat requirements of threatened species, such as the bat, is a key part of conservation management [START_REF] Walsh | Foraging habitat preference of vespertilionid bats in Britain[END_REF].

The great majority of studies dealing with habitat preferences have been based on intensive protocols, such as radio-tracking [START_REF] Robinson | Home range and habitat use by serotine bat, Eptesicus serotinus, in England[END_REF][START_REF] Goiti | Influence of habitat on the foraging behaviour of the Mediterranean horseshoe bat, Rhinolophus euryale[END_REF][START_REF] Walls | Differential habitat selection by Pipistrellus pipistrellus and Pipistrellus pygmaeus identifies distinct conservation needs for cryptic species of echolocating bats[END_REF][START_REF] Flaquer | Habitat selection by Geoffroy's bats (Myotis emarginatus) in a rural Mediterranean landscape: implications for conservation[END_REF]). These methods often involve animal handling, such as capture during mist-net sessions, which can be controversial approaches when species are endangered and/or sensitive to disturbance. This is particularly the case for bats [START_REF] Punt | Round table discussion on bat conservation[END_REF][START_REF] Dietz | Effects of forearm bands on horseshoe bats (Chiroptera: Rhinolophidae)[END_REF]). In addition, these studies are often limited in space and time and involve a restricted number of individuals (Boughey et al. 2011). Complementary acoustic detection methods have been developed to describe foraging habitats (Carmel and Safriel 1998[START_REF] Shiel | Activity of Leisler's Bat Nyctalus leisleri (Kuhl) in the Field in South-East County Wexford, as Revealed by a Bat Detector[END_REF][START_REF] Rainho | Summer foraging habitats of bats in a Mediterranean region of the Iberian Peninsula[END_REF]). However, despite the quality information they produce, these two approaches (radio-tracking and acoustic) are time-consuming and require highly qualified people.

Due to the awareness of biodiversity loss, there is a pressing need for amateur naturalist volunteers to participate in data collection for biodiversity monitoring programs in Europe [START_REF] Bell | What counts? Volunteers and their organisations in the recording and monitoring of biodiversity[END_REF]. Thanks to citizen scientists, studies can be carried out despite small budgets. Thus, the conservation initiatives and biodiversity studies carried out each year are numerous [START_REF] Henry | Integrating ongoing biodiversity monitoring: potential benefits and methods[END_REF], while the budgets for biodiversity conservation are scattered and scarce. We need to better exploit the information contained in studies and field conservation experiences [START_REF] Sutherland | The need for evidencebased conservation[END_REF]) and judge the effectiveness of conservation interventions in different contexts to ensure that the scarce funds go as far as possible toward improving our knowledge of the function of biodiversity to achieve conservation outcomes [START_REF] Ferraro | Money for nothing? A call for empirical evaluation of biodiversity conservation investments[END_REF].

To date, the studies investigating roost data have dealt with roost characteristics, such as the height of roost openings above ground level [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF], exposure [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF]), external or internal temperature (Churchill 1991, Mitchel-Jones et al. 2007), or hygrometric conditions [START_REF] Hall | The effect of cave microclimate on winter roosting behaviour in the bat, Miniopterus schreibersii blepotis[END_REF]. These works were primarily conducted with the aim of identifying potential roost features and thus developing efficient conservation programs, such as physical protection measures (Mitchel-Jones et al. 2007), the restoration of roosts during the restoration of buildings or even the creation of new roosting opportunities [START_REF] Marnell | Protection of over ground roosts for bats (particularly roosts in buildings of cultural heritage importance)[END_REF]. Surprisingly, however, very few studies have attempted to analyze spatial distribution data with the aim of identifying how habitat selection operates in bats' choice of roost (Lumsdem et al. 2002, Boughey et al. 2011[START_REF] Nicholls | Habitat selection as a mechanism of resource partitioning in two cryptic bat species Pipistrellus pipistrellus and Pipistrellus pygmaeus[END_REF].

The main reason for the lack of research on this topic is most likely related to the fact that, without a doubt, the choice of breeding site is primarily based on intrinsic characteristics (e.g., ease of access, temperature, luminosity, humidity, and so on). In addition to this, many species are highly philopatric (Bogdanowicz et al. 2013); thus, one could expect that bats will return to the same breeding site year after year even if the surrounding landscape suffers some changes. However, there are important costs associated with breeding in a colony such as the increased probability of transmission of parasites (Brown and Bomberger 1996) or diseases [START_REF] Frick | An emerging disease causes regional population collapse of a common North American bat species[END_REF], the competition for space, food and mates. Thus, benefits must exist and must at least balance the cost (for more details, see Danchin and Wagner 1997). Thus, several authors have analyzed colonial species strategies in the framework of habitat selection [START_REF] Dietz | When to use public information for breeding habitat selection? The role of environmental predictability and density dependence[END_REF]. Because the choice of a breeding patch affects individual fitness (i.e., survival and reproductive success), one could expect robust decisionmaking rules. Numerous theoretical, experimental and empirical studies (Danchin and Wagner 1997) have found that for colonial species, the presence and success of conspecifics are cues to habitat selection. For species that have a large number of sites available for reproduction, such as species able to use man-made aboveground structures, we hypothesize that we can detect how habitat selection operates using the spatial distribution of roosts. In contrast, exclusive cave-dwelling species potentially have much lower roost availability; thus, they most likely cannot afford to choose the roost by its surrounding habitat because it is likely a secondary endpoint compared to intrinsic characteristics of the site.

Here, we explored how the breeding colony distribution of a species using over-ground roosts (such as attic of house or church) could inform us of how habitat selection operates We used habitat variables that are available at the European level (Corine Land Cover 2000) and breeding colony data that was provided by a network of local bat rescue volunteers, with the aim of proposing a simple method with a high potential for generalization. Although the primary purpose of these associations is to assist and rehabilitate injured or orphaned bats, the identifying the location of breeding colonies is a natural output of such volunteer network. Many non-governmental nature conservation associations aim to help every citizen facing bat-related issues such as the discovery of a colony in their home and exist in many European states (see [START_REF] Bihari | Protecting and managing underground sites for bats[END_REF][START_REF] Marnell | Protection of over ground roosts for bats (particularly roosts in buildings of cultural heritage importance)[END_REF], Boughey et al. 2011).

We used a dataset of serotine bat (Eptesicus serotinus) breeding colonies to explore how their distribution could inform us about habitat selection by this species. Maternity colonies of serotine bats are known to inhabit large roof spaces [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF][START_REF] Harbusch C | The sessile serotine: the influence of roost temperature on philopatry and reproductive phenology of Eptesicus serotinus (Schreber, 1774) (Mammalia: Chiroptera)[END_REF][START_REF] Vos A | European bat lyssaviruses -an ecological enigma[END_REF]. This data set was provided by a regional volunteer network. We studied the environment surrounding serotine breeding colonies in a 7,300 km² French agricultural landscape and compared it to habitats surrounding randomly selected points. In addition, we assess the effectiveness of this monitoring in terms of serotine roost conservation.

Material and Methods

Study areas

The Museum of Bourges (LA and ML) coordinated the inventory of bat colonies in the Cher from 1988 to 2009. The Cher is an administrative region (7300 km²) in the centre of France (Fig. 1) that is primarily dominated by two contrasting agricultural practices: large patches of arable land (44% of the total Cher area, average patch size: 5.1 km² ± 2.3 km²) and small patches of pasture (23% of the total Cher area, average patch size: 2.7 km² ± 1.2 km²). Forest covers 21% of the Cher's total surface (average patch: 1.9 km² ± 0.7 km²) (sources: Corine Land Cover 2000).

Data collection

Volunteer's network

The Museum of Bourges coordinates a network of volunteers who work on bat surveys and the local bat rescue network of this region. All volunteers were trained in the identification of bats from one of the co-authors (L.A. and M.L., co-director of the Museum of Bourges), and in addition, the vast majority of sites have been visited in the first year of their discovery by the two co-authors. This French network for bat rescue allows citizens to contact a bat expert in their region by calling a unique telephone number when they have question about bats. For example, when someone finds a bat in distress, discovers a bat colony in his home, or decides to restore a home and suspects that some bats are living here, an expert may provide legal information on bat protection status or solutions for home restoration that allow the sustainable maintenance of the bat colony. These experts often conduct private home visits and thus discover a breeding colony (Réseau SOS Chauve-souris 2011). Such networks also exist in many European countries [START_REF] Marnell | Protection of over ground roosts for bats (particularly roosts in buildings of cultural heritage importance)[END_REF]. Most of the time, when a permanent relationship is established with the owner, bat colonies are secured and surveys are performed. In the Cher, this 22-year coordination has supported the development of an impressive database that contains 724 localized breeding colonies for 14 species. Because the aim of this study was to investigate habitat selection, we only considered breeding roosts.

Serotine roosts

Serotine bats primarily choose buildings as breeding sites [START_REF] Schmidt | The occurrence of bats in the town of Hoyerswerda[END_REF][START_REF] Dietz | When to use public information for breeding habitat selection? The role of environmental predictability and density dependence[END_REF]).

The database contains 105 Serotine bat colonies, 96% of which are located in attics, with the others located in bridges, in trees, in flues or behind shutters. The yearly rate of visit is 0.36 ± 0.02SE (i.e., on average, a colony is visited every 3 years). We did not consider colony shifts from one house to another house (in the same village, for example) less than 1 km (average 300 m) away as supplementary colonies. The shift of a colony from one roost to another occurred 6 times in the study period . [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF] also noticed small movements of breeding colonies.

Constitution of the dataset and evaluation of potential bias

Our dataset does not come from a systematic inventory but from opportunistic observations collected over a long period; thus, in our first step, we explored how the database (observations of colonies -locality and date, already known or new colonies) was built up over the years with the aim of evaluating potential bias. We used linear modelling to test the trends of discovery of new colonies ("cumulated number of colonies" as the response variable) along "years" (the explicative variable). We also used a Generalized Additive Model (GAM, Hastie and Tibshirani 1990; R package mgcv) to test the existence of non-linear effects of the yearly trend of new colonies in the database, with "years" as an explicative smoothed variable. As a second step, we assessed whether there were spatial and temporal biases in the implementation of this database. We tested if the discovery of a number of new colonies among regions (the study areas was split in 4 equal areas) was similar among periods (the study period was split in four periods : 1988-1993, 1993-1998, 1999-2003, 2004-2009) using a Chi-squared contingency table.

Habitat selection analyses

Before investigating the links between serotine habitat selection information and the spatial distribution of colonies, we tested the normality of colony distribution using the simple approach proposed by Clark and Evans (1954), which compares the expected mean distance to the nearest neighbor to the observed mean nearest neighbor distance.

We then tested the existence of a habitat selection process by comparing the habitat available in the study area and the habitat surrounding serotine breeding colonies (n=105).

With the aim of evaluating habitat availability, we inventoried habitats surrounding 105 random points used as pseudo-absence (using the random point function in ArcGis 9) by following the approaches of [START_REF] Jiguet | Satellite tracking of breeding black storks Ciconia nigra: new incomes for spatial conservation issues[END_REF] and Boughey et al. (2011). Among these random points, we selected 105 points that included at least one hamlet or building in their 2 km² buffer to attempt to account for serotine colony installation requirements (see Supplementary material -Appendix S1). No obvious bias was detected in the habitat area distribution of the random point data set and global habitat proportion in the Cher region (Kolmogorov-Smirnov test D = 0.36, p-value = 0.078). According to [START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF] we developed an alternative approach based on a target background approach for designing our pseudo absences: we used roost site locations of a species (Pipistrellus pipistrellus) which roosts in similar structures (data provide from the same data set than E. serotinus). Among the 411 breeding colonies of Pipistrellus pipistrellus (see Supplementary material -Appendix S1) we used 105 roosts as pseudo absences. These two species, even if they were not in mixed colonies in our case, are known to have very similar roost requirements [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF][START_REF] Dietz | When to use public information for breeding habitat selection? The role of environmental predictability and density dependence[END_REF][START_REF] Arthur | Les chauves-souris de France, Belgique, Luxembourg et Suisse. Biotope, Mèze[END_REF][START_REF] Marnell | Protection of over ground roosts for bats (particularly roosts in buildings of cultural heritage importance)[END_REF].

Choice of buffer dimension

The choice of the buffer size is an important statement; indeed, with a buffer size that is too small, the buffer may consider only a small part of the space that is potentially used by the individual of the colony. Conversely, with a buffer size that is too large, we may include many areas that are not used for foraging by breeders because of the distance to the colony.

To select a buffer size that is ecologically practical, we relied on telemetry studies that were already conducted on this species. Radio-telemetry from serotine studies indicates that the distance between the breeding colony and foraging areas could reach 11.5 km [START_REF] Degn | Field activity of a colony of Serotine Bat (Eptesicus serotinus)[END_REF], Perez and Ibanez 1991[START_REF] Diehl | Untersuchungen zur biologie der Breitflügelfledermaus in Hessen[END_REF][START_REF] Weidner | Zur Hangplatzwahl der Breitflügelfledermaus Eptesicus serotinus (Schreber, 1774)[END_REF], Catto et al. 1996[START_REF] Robinson | Home range and habitat use by serotine bat, Eptesicus serotinus, in England[END_REF][START_REF] Lubeley | Zur Jagdhabitatnutzung der Breitflügelfledermaus (Eptesicus serotinus)[END_REF], but the mean distance from roost to foraging areas ranges between 1.2 and 3.6 km [START_REF] Hübner | Untersuchungen zur Lebensweise der Breitflügelfledermaus Eptesicus serotinus Schreber, 1774 in Hollingstedt/Schleswig Holstein[END_REF][START_REF] Dense | Telemetrische Untersuchungen zur Habitatnutzung und zum Aktivitätsmuster der Breitflügelfledermaus Eptesicus serotinus Schreber, 1774 im Osnabrücker Hügelland[END_REF][START_REF] Robinson | Home range and habitat use by serotine bat, Eptesicus serotinus, in England[END_REF][START_REF] Kervyn | Foraging and feeding ecology of the serotine bat Eptesicus serotinus[END_REF][START_REF] Lubeley | Zur Jagdhabitatnutzung der Breitflügelfledermaus (Eptesicus serotinus)[END_REF][START_REF] Harbusch | Aspects of the ecology of Serotine bats (Eptesicus serotinus, Schreber 1774) in contrasting landscapes in Southwest Germany and Luxembourg[END_REF][START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF]). Thus, the home range of individually radio-tracked serotines was found to vary between 1.61 km² and 17.05 km² (Perez and Ibanez 1991) and 0.16 km² to 47.58 km² [START_REF] Robinson | Home range and habitat use by serotine bat, Eptesicus serotinus, in England[END_REF]. The size of the home range per colony also varies considerably, ranging from >2 km² in the study carried out by [START_REF] Degn | Field activity of a colony of Serotine Bat (Eptesicus serotinus)[END_REF] to 9.4 km² [START_REF] Rosenau | Untersuchungen zur Quartiernutzung und Habitatnutzung der Breitflügelfledermaus Eptesicus serotinus (SCHREBER, 1774) im Berliner Stadtgebiet[END_REF], 16 km² [START_REF] Diehl | Untersuchungen zur biologie der Breitflügelfledermaus in Hessen[END_REF], 26 km² [START_REF] Harbusch | Aspects of the ecology of Serotine bats (Eptesicus serotinus, Schreber 1774) in contrasting landscapes in Southwest Germany and Luxembourg[END_REF] and 19 km² for the core area of the colony in Robinson and Stebbings's study (1997). Thus, we chose a radius of 2 km around colonies or random points, which corresponded to an approximately 13 km² area surrounding the colonies.

Choice of habitat variables studied

In addition to testing how habitat selection operates using the spatial distribution of roosts, we also wanted to assess the potential to generalize such a study. This is the reason why we used habitat data standardized at a large scale and with free access such as the Corine Land Cover (2000), even if such spatial information is considered relatively coarse. Hereafter, we use the habitat names used in the Corine Land Cover classification. Among the 25 habitat classes present in the Cher region, we selected the 18 most important habitats: those that had areas that represent more than 5% of the territory (see Figure 2). According to the resolution scale of the geographic information layer that we used (Corine Land Cover, CLC), the changes of land use can be considered to be minor during the study period (land cover flows in the studied region less affected than 1% of land cover, source: 1992-2006 Land Cover Flows, http://www.eea.europa.eu).

As underlined by several studies, bats in general and Serotines in particular select linear elements of landscape within their foraging home range [START_REF] Robinson | Home range and habitat use by serotine bat, Eptesicus serotinus, in England[END_REF][START_REF] Verboom | The importance of linear landscape elements for the Pipistrelle Pipistrellus pipistrellus and the serotine bat Eptesicus serotinus[END_REF][START_REF] Waters | Foraging ecology of Leisler's bat (Nyctalus leisleri) at two sites in southern Britain[END_REF][START_REF] Russ | Habitat associations of bats in Northern Ireland: implications for conservation[END_REF][START_REF] Smith | Natterer's bats prefer foraging in broad-leaved woodland and river corridors[END_REF]. Therefore, we also assessed the length of forest edge and the length of rivers in the 2 km² buffer areas in addition to habitat areas.

As we studied the breeding colonies, we expected a great sensitivity to the distance between the colony and foraging habitat in the serotines. The distance clearly appears to be an energetic constraint for breeding bats that must balance their night time budget between foraging in the landscape and feeding their young in the colony [START_REF] Kunz | Bat ecology Chicago[END_REF].

Therefore, we also included the distance to the nearest wood, pasture, and river in our analysis and the number of the confluence of two rivers within the buffer. So, in total, for each buffer, we have 24 variables: 18 variables that are surfaces of habitats (CLC class, m2), 3 variables are distances to habitat (pasture, river, wood), 2 variables that are linear densities (edge forest and river, m) and 1 variable that is the number of river confluences.

Statistical analysis

We tested differences in habitat distribution between the surroundings of serotine colonies and the surroundings of random points using Generalized Linear Models (GLM). According to the nature of our response variable (presence/pseudo-absence), we used a binomial error distribution with logit link. Our explicative variables were habitat areas present in the 2 km² buffer (areas were log transformed). According to numerous correlations between habitats (Spearman rho >0.5), such as the negative correlation between arable land and broad-leafed forest (see Supplementary material -Appendix S2), we analyzed the differences in habitat distribution between Serotine colonies and random points, habitat by habitat (i.e., 18 models), thus avoiding multi-colinearity problems. To account for spatial autocorrelation, we added an autocovariate (i.e., a distance-weighted function of neighboring response values; [START_REF] Dormann | Methods to account for spatial autocorrelation in the analysis of species distributional data: a review[END_REF][START_REF] Penone | Assessing anthropogenic pressures on Orthoptera communities using large scale acoustic monitoring[END_REF] with the autocov dist function in R (package spdep, Roger Bivand). Our statistical models were structured in the following way:

[Presence/pseudo-absence] ~ Habitat i + autocovariate, where i is one of the 18 selected habitat. The results were expressed using a F-test and accounting for over-dispersion by following the Faraway 2006 and Crawley 2009 approaches.

Beyond the identification of differences in habitat distribution between the surroundings of serotine colonies and the surroundings of random points, we tried to assess which differences are the most significant. In this second step, we simultaneously took into account all habitat variables in the analysis using Hierarchical Partitioning (HP, R package hier.part, Chris Walsh), a multiple regression analytical method that allow us to identify the most likely causal factors while alleviating multicollinearity problems (Mac Nally 2000).

Based on Olea et al. ( 2010), commenting on Hierarchical Partitioning usage limits, we limited this hierarchical partitioning to the 9 most important habitats.

In a third step, because the choice of buffer size is an important statement for this study, we evaluated whether the scale choice based on the ecology of the species was efficient and because the species responds to their environment on a range of scales (Boughey et al 2011), we performed a multiscale habitat association study using GLM. We used the same response variable (presence/pseudo-absence of Serotine roost) and a restricted set of habitat variables chosen to be significant for discriminating Serotine roosts and random points (results of the two previous steps) without exhibiting important correlation (see Supplementary material -Appendix S2). Our statistical models were structured in the following way:

[Presence/pseudo-absence] ~ Habitat i + Habitat j + Habitat k + …, autocovariate
We ran these models with different buffer sizes, from a radius of 100 m around colonies or random points to 8000 m. Habitat areas were expressed as a percentage of areas within the buffer. As in the first step, we included an autocovariate function and used a binomial error distribution with logit link. To adjust the effect of each variable to the effects of other variables, test values were expressed using type-II analysis-of-variance (R, package car, John Fox). Type II tests are calculated according to the principle of marginality, each term is tested after all others (Crawley 2009), and we take into account over-dispersion.

Effectiveness of this monitoring in terms of serotine roost conservation

Such networks involving citizen-landowners have resulted in the establishment of frequent and permanent relationships with the owner. Hereafter, we assess the effectiveness of this relationship in terms of the sustainability of serotine roosts in the long term (when landowners plan to work in their attics or the renovation of heritage buildings, etc.).

Using Generalized Linear Models (GLM with binomial error distribution), we attempt to identify which factor could influence the sustainability of serotine roosts: changes in facilities (type of facilities were ranked from soft to important in four classes (i.e., from changes some tiles to the total re-roofing), and a fifth class in cases where there is a deliberate attempt to remove the colony) that plans and designs the work of building restoration (Museum of Bourges vs. landowners) and the year and the size of the serotine population. We used the database of the history of serotine colonies (n=105) over a period of 22 years, including 48 events of work in buildings sheltering serotine colonies. Test values were expressed using type-II analysis-of-variance (R, package car).

All of the analyses were computed with R software (R Development Core Team 2010).

Results

Evaluation of spatial and temporal biases in the establishment of the database

During the colony inventory period , we detect a significant continuous annual growth in the inventoried serotine colonies (an addition of 5 colonies/year, R² = 0.99, F1,20 = 2581.8, P < 0.0001, see supplementary material 1). No non-linear effects were detected (Generalized Additive Model, F1,20 = 1.2, P = 0.33, see Supplementary material -Appendix S3). To evaluate the possibility of a bias in the accumulation of space and time data, we split the Cher region into four equal areas and split the time series into periods of 4 years. We did not detect any obvious bias in the location of new colonies across any time period (Chi sq = 10.9, df = 12, P = 0.54).

Habitat selection analyses

The density of the serotine colonies observed in the study area was 0.01/km². The comparison of the actual mean nearest neighbor distance between Serotine colonies (4.1 km ± 0.3 km) and the expected mean distance to the nearest neighbor (D=√colony density/2 = 42.7 Km) indicated a non-spatial randomness of colonies and a clear aggregation pattern. Thus, it is legitimate to inquire whether this aggregated distribution is related to the distribution of some habitats. Thus, we first explored habitat selection by comparisons with random points, finding that serotine bats appear to positively select pastures (P<0.001) and areas near streams or water bodies (P=0.002) while avoiding heterogeneous agricultural areas (P<0.001), which is land principally occupied by agriculture areas with significant areas of natural vegetation, broadleafed forests (P=0.028), transitional woodland-shrub (P=0.034), and water bodies (P=0.002) (Fig. 2, see Supplementary Material Appendix S3). Undeniably, the greatest difference comes from pasture selection (Fig. 2). In addition, serotine roosts are closer to pastures (P<0.001; average: 304 m ± 38 SE) and rivers (P<0.001; average: 461 m ± 45 m) than are random points (1096 m ± 129 m and 1054 m ± 94 m). Serotine roosts are also located in areas with greater river density (P<0.001) and higher river confluence density (P<0.001) (greater from a factor: one point six and one point eight, see Supplementary material -Appendix S4) than random points. However, no significant differences in the distance to nearest wood or forest edge density were detected.

In addition, comparison between habitats surrounding serotine roosts with those surrounding random points assessed with Hierarchical Partitioning analysis showed that the presence of pastures is the most likely causal factor (Fig. 3). Similar significant results were obtained with pseudo-absence point based on P. pipistrellus roosts: for example serotine bats appear to positively select pastures and areas near streams or water bodies and to avoid heterogeneous agricultural areas and broad-leafed forests. The two main differences are the positive selection of arable land and the avoidance of discontinuous artificial surfaces (See Supplementary material -Appendix S5).

Hierarchical Partitioning analysis for landscape structure showed that the distance to the nearest pasture, the distance to the nearest river and the river density were the most likely causal factors between habitats surrounding serotine roosts compared to random points.

With the aim of assessing the joint effects of the major variables of interest and the influence of the size of the buffer, we performed linear models, including simultaneously considering Pasture (H231), Heterogeneous agricultural areas (H242), Land principally occupied by agriculture with significant areas of natural vegetation (H243) and river density as explicative variables. These four variables exhibit no obvious correlation (see Supplementary material -Appendix S2). We found that serotine bats appear to positively select pastures whatever the scale considered, whereas habitat H243 did not appear as a significant variable whatever the scale considered (Table 1). Habitat H242 and river density appeared to be scale dependent: H242 exhibit a slight trend towards being significant on a small scale, whereas river density appeared only significant at a large scale (Table 1).

According to the AIC value, the best model was obtained with a buffer size of 2000 m.

Effectiveness of this monitoring in terms of serotine roost conservation.

The sustainability of serotine roosts were negatively influence by changes in facilities carried out by landowners without agreement with the Museum of Bourges (GLM χ²=12.450,d.f.=1,P<0.001,, whereas facilities designed by the Museum of Bourges team or in accordance with it did not have a significant effect on the persistence or departure of colonies (GLM χ²=0.024,d.f.=1,P=0.876,β=0.193). We did not detect any significant effect of type of facilities (GLM χ²=0.024,d.f.=1,P=0.530,. In addition, we also detected a significant effect of the year (GLM χ²=36.860, d.f.=22, P=0.025) and the size of the serotine population: larger populations were more stable (GLM χ²=16.088,d.f.=1,P<0.001,β=1.088).

Finally, we observed that over time, building restorations carried out by landowners without agreement of the Museum of Bourges significantly decreased (GLM χ²=7.947, d.f.=1, P=0.005), whereas we did not detect any trend in the occurrence of building restoration carried out by the Museum of Bourges team or in accordance with it (GLM χ²=2.178,d.f.=1,P=0.139). This suggests that the stronger the link (in terms of year) between the museum and the owner of a building is, the more the likelihood of making arrangements that disrupt colonies decreases.

Discussion

Habitat selection.

This study clearly indicates that even species that are considered generalists [START_REF] Kerbiriou | Long term trends monitoring of bats, from biodiversity indicator production to species specialization assessment[END_REF][START_REF] Regnery B | Tree microhabitats as indicators of bird and bat communities in Mediterranean forests[END_REF] with regards to the selection of foraging habitat make obvious choices for the location of their breeding colonies. This choice takes the nature of the surrounding habitat into account. Choosing a specific habitat surrounding colonies is even more unexpected for serotine bats, as this species is known to manage quite a long distance between foraging habitats and breeding colonies (11.5 km).

Land use in the Cher region has resulted in a landscape structure in which arable land is negatively correlated with pasture or forest (see Supplementary material -Appendix S2).

Due to this spatial structure, it is possible to directly identify whether serotine bats make a positive selection for one habitat (such as pasture) or avoid another (such as arable land).

Our study has identified pastures as the most selected habitat, covering on average 37% of the 2 km buffer around roosts. The majority of studies investigating serotine foraging habitats have made this observation (Catto et al. 1996[START_REF] Robinson | Home range and habitat use by serotine bat, Eptesicus serotinus, in England[END_REF], Kervyn, 2001). The study carried out by Boughey et al. (2011) showed a strong relationship between the probability of serotine roosts and the proportion of improved grassland. This affinity to agricultural land is underscored by the proportion of arable land neighboring the roosts (45% on average). However, arable land accounts for the majority of the land use cover of this region; thus, serotine bats appeared to select arable land slightly less frequently than random points. The observed pattern of aquatic habitat selection (% of water stream cover, linear elements) around serotine roosts is in accordance with a large number of studies that have identified the importance of these habitats for serotine foraging activities (acoustic studies: [START_REF] Hübner | Untersuchungen zur Lebensweise der Breitflügelfledermaus Eptesicus serotinus Schreber, 1774 in Hollingstedt/Schleswig Holstein[END_REF][START_REF] Vaughan | Identification of British bat species by multivariate analysis of echolocation call parameters[END_REF][START_REF] Barataud | Inventaire au détecteur d'ultrason des chiroptères fréquentant les zones d'altitude du nord du parc National du mercantour (Alpes, France)[END_REF][START_REF] Lustrat | Les territoires de chasse des chiroptères de la forêt de fontainebleau (France)[END_REF], Ciechanowski 2002[START_REF] Bartonicka | Flight activity and habitat use of four bat species in a small town revealed by bat detectors[END_REF][START_REF] Russo | Use of foraging habitats by bats in a Mediterranean area determined by acoustic surveys: conservation implications[END_REF][START_REF] Kanuch | Relating bat species presence to habitat features in natural forests of Slovakia (Central Europe)[END_REF][START_REF] Rainho | Summer foraging habitats of bats in a Mediterranean region of the Iberian Peninsula[END_REF] or radio-tracking study: [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF]; habitats surrounding roosts: Boughey et al.

2011).

A difference between our results and some previous studies [START_REF] Barataud | Inventaire au détecteur d'ultrason des chiroptères fréquentant les zones d'altitude du nord du parc National du mercantour (Alpes, France)[END_REF][START_REF] Lustrat | Les territoires de chasse des chiroptères de la forêt de fontainebleau (France)[END_REF]Russo andJones 2003, Rainho 2007) is the non-selection of forest habitat, particularly broadleaved woodland. Boughey et al. ( 2011) found a similar result in the UK. Again, it is interesting to consider the habitat surface present close (2 km) to serotine roosts: woodland is the third most abundant habitat and covers more than 13% of the area, so a plausible hypothesis is that the current amount of woodland surrounding roost is sufficient. This possibility explains why we did not detect any selection effect. A second minor difference is the non-selection of urban areas in our study. Due to the selection of roosts in buildings, the proportion of urban areas surrounding serotine roosts is not negligible (6%). However, when comparing this proportion to the proportion of urban areas that surrounded random points, no significant difference could be detected, although some studies have found that foraging serotines choose this habitat [START_REF] Barataud | Inventaire au détecteur d'ultrason des chiroptères fréquentant les zones d'altitude du nord du parc National du mercantour (Alpes, France)[END_REF][START_REF] Gaisler | Habitat preference and flight activity of bats in a city[END_REF][START_REF] Bartonicka | Flight activity and habitat use of four bat species in a small town revealed by bat detectors[END_REF][START_REF] Russo | Use of foraging habitats by bats in a Mediterranean area determined by acoustic surveys: conservation implications[END_REF][START_REF] Rainho | Summer foraging habitats of bats in a Mediterranean region of the Iberian Peninsula[END_REF].

Conservation implications

We found that for the E. serotinus, a species that has a large number of sites available for reproduction because of its ability to use man-made aboveground structures, roost are not distributed randomly. In addition to roost characteristic (not studied here), for their maternity roosts, serotine bats select landscapes with an important proportion of habitats known to be used for foraging (pastoral and aquatic habitats). This result converges with those found by Boughey et al. ( 2011) for P. pipistrellus, P. pygmaeus, Rhinolophus hipposideros, Eptesicus serotinus and Myotis nattereri (sensus lato), species that all selected roosts closer to broadleaved woodland than expected by chance. However, the protection of habitats surrounding maternity roosts is not regularly implemented in conservation measures: as an example, in France, most areas dedicated to bat conservation, such as natura2000 take into account underground cavities used for nesting sites without taking account the foraging range of the colony or its foraging habitat. Another French regulation called "Arrêté préfectoral de protection de biotope, French law of July 16, 1976", when implemented for bats, only protects the attic or the cavity in which the roost is located and again does not consider the hunting ground associated with the colony. For serotine bats, our results suggest that protection of the foraging habitat around the maternity roost should be considered at least within a radius of 2000 meters. In addition, conservation of this species should focus on pasture maintenance, particularly those close to rivers Evaluation of the proposed method.

The strong convergence of our results with those obtained by radio-tracking or acoustic methods for the identification of the most selected habitats or relevant spatial scale, underlines the robustness of an approach that is based on the spatial distribution of roosts.

Moreover, this approach also allows us to study the impact of the landscape structure and clarify some selection processes, such as forest vs. forest edges.

The existence of international biodiversity monitoring networks that are based on highly standardized protocols and have a staff of paid experts would be ideal for providing accurate knowledge of species requirements and population trends, among other things [START_REF] Henry | Integrating ongoing biodiversity monitoring: potential benefits and methods[END_REF]. Therefore, combining the output available from various approaches and ongoing monitoring initiatives is an optimal option. This requires the ability to integrate heterogeneous information on biodiversity, which appears to be more likely due to progress in meta-analysis methodology (Bengtson et al. 2005[START_REF] Sutherland | Predicting the ecological consequences of environmental change: a review of the methods[END_REF][START_REF] Henry | Integrating ongoing biodiversity monitoring: potential benefits and methods[END_REF].

Thus, analysis of the environment surrounding roosts appears to be a complementary and promising approach for implementing knowledge of the habitat requirements of bats and, especially, for species that have a large number of sites available for reproduction. The species concerned may be those that regularly used man-made aboveground structures, such as Pipistrellus species, Plecotus species, Rhinolophus ferrumequinum, R. hipposideros, Barbastella barbastellus, Myotis daubentonii and Myotis myotis [START_REF] Simon | Ecology and conservation of bats in villages and town[END_REF][START_REF] Dietz | When to use public information for breeding habitat selection? The role of environmental predictability and density dependence[END_REF][START_REF] Arthur | Les chauves-souris de France, Belgique, Luxembourg et Suisse. Biotope, Mèze[END_REF][START_REF] Marnell | Protection of over ground roosts for bats (particularly roosts in buildings of cultural heritage importance)[END_REF]. Furthermore, information on roost localization can be accurately gathered by a volunteer network, as in our study.

Fortunately, such a volunteer network is also currently being developed in many countries (see [START_REF] Bihari | Protecting and managing underground sites for bats[END_REF][START_REF] Battersby | Guidelines for surveillance and monitoring of European bats[END_REF][START_REF] Marnell | Protection of over ground roosts for bats (particularly roosts in buildings of cultural heritage importance)[END_REF], Boughey et al. 2011). Public participation in scientific research is recognized as a promising approach to significantly help scientists address biodiversity issues [START_REF] Bell | What counts? Volunteers and their organisations in the recording and monitoring of biodiversity[END_REF], Couvet et al. 2008[START_REF] Henry | Integrating ongoing biodiversity monitoring: potential benefits and methods[END_REF]. It is likely that the use of data from a study dedicated to the systematic inventory of bat colonies in houses in a relatively large area, such as the Cher (7300 km², 290 towns and villages, 313 251 inhabitants), by a strictly professional organization would not have generated an unbiased distribution over time and space. If not for limited resources (financial and human), a professional organization would have planned this inventory effort, and the area most likely would have been surveyed sector by sector over time. Thus, the absence of an obvious spatio-temporal bias is likely linked to this volunteer network-based approach. The benefits of volunteer networks are numerous and not only limited to financial savings [START_REF] Levrel | Balancing state and volunteer investment in biodiversity monitoring for the implementation of CBD indicators: A French example[END_REF], and these benefits include the fact that citizen monitoring systems are likely to have a high resilience (Couvet et al. 2008), which is the key to maintaining the network over time and thus provide an extended time-series [START_REF] Battersby | Guidelines for surveillance and monitoring of European bats[END_REF] or large spatial coverage [START_REF] Jaberg | Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment[END_REF]Guisan 2001, Lepczyk et al. 2005). In addition, this network provides socially legitimate results that contribute to improving the relevance of results, aid in the identification of key biodiversity issues through discussions between citizens and scientists, and orient public policies and actions towards adaptive management (Couvet et al. 2008).

Finally, such networks involving citizen-landowners have resulted in (i) gathering information from private houses, where access is not easy and authorization demands are highly time consuming; (ii) better monitoring of bat colonies (such observations were noticed by [START_REF] Lepczyk | Integrating published data and citizen science to describe bird diversity across a landscape[END_REF] in a bird survey that integrated citizen science); (iii) increasing the availability of a cost-effective data collection method that provides high quality data that are available for conservation biology investigations and actions, and (iv) establishing frequent and permanent relationships with the owner, which contributes to securing bat roosts: this relationship significantly decreased the building restoration carried out by landowners without an agreement with the local wildlife rescue networks, as these types of building restorations have been assessed to have a significant impact on the persistence of bat roosts. H111 H112 H121 H131 H141 H142 H211 H221 H222 H231 H242 H243 H311 H312 H313 H321 H322 H324 H331 H511 H512 H111 0,18 -0,03 -0,01 -0,01 0,50 0,04 -0,02 -0,02 0,03 0,03 -0,09 -0,08 -0,03 -0,03 -0,02 -0,01 -0,04 -0,01 -0,02 -0,03 H112 0,41 0,16 0,18 0,33 0,11 -0,05 0,05 -0,08 0,17 0,15 -0,17 -0,01 -0,02 0,03 0,11 -0,18 0,16 0,08 0,02 H121 -0,03 0,37 0,33 -0,12 -0,06 0,18 -0,11 0,08 0,12 0,02 0,01 -0,10 0,14 -0,04 -0,11 -0,03 0,14 0,26 H131 -0,01 -0,02 0,04 -0,02 -0,02 -0,08 -0,10 0,06 0,02 -0,03 -0,03 -0,02 -0,01 -0,04 1,00 0,51 -0,03 H141 0,51 -0,12 -0,02 -0,02 -0,14 -0,10 -0,09 -0,07 -0,03 -0,03 0,50 -0,01 -0,04 -0,01 -0,02 0,27 H142 -0,14 -0,04 -0,04 0,05 0,00 0,00 -0,02 -0,07 -0,07 0,23 -0,03 -0,08 -0,02 -0,04 0,09 H211 0,00 -0,26 -0,51 0,11 -0,21 -0,47 -0,46 -0,19 -0,01 0,09 -0,28 0,04 -0,07 -0,37 H221 -0,04 -0,01 0,09 -0,09 -0,01 -0,07 -0,07 -0,04 -0,03 -0,08 -0,02 -0,04 -0,07 H222 -0,05 0,13 -0,06 0,21 0,32 0,12 -0,04 -0,02 0,09 -0,02 -0,04 0,31 H231 -0,01 0,25 -0,19 0,00 0,00 -0,13 -0,01 0,10 -0,08 0,03 0,16 H242 0,16 -0,16 -0,20 -0,14 -0,14 0,11 -0,20 -0,10 0,10 -0,05 H243 -0,06 0,06 -0,08 -0,02 -0,04 -0,14 0,06 0,25 -0,02 H311 0,38 0,14 -0,06 -0,07 0,27 0,02 -0,09 0,18 H312 0,23 0,09 -0,05 0,14 -0,03 -0,07 0,28 H313 -0,07 -0,05 0,44 -0,03 0,09 -0,01 H321 -0,03 -0,08 -0,02 -0,04 0,08

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Geographical location of the study area (Cher), land cover use in the Cher region and the localization of serotine breeding colonies (•)

  .P. 2002. On the misuse of residuals in ecology: regression of residuals vs. multiple regression. Journal of Animal Ecology 71: 542-545.for the selection of the linear elements of landscape or distance to habitat, in a

Table 1 :

 1 Joint effects of the major variables of interest and influence of the size of the buffer.Corine Land Cover habitat typology H231: Pastures, H242: Heterogeneous agricultural areas, H243 : Land principally occupied by agriculture, with significant areas of natural vegetation

	Buffer	H231	H242	H243	River	AIC
	Size				density
	100	P=0.036	P=0.027	P=0.78	P=0.27	288.75
	250	P=0.007	P=0.09	P=0.63	P=0.63	287.15
	500	P<0.001	P=0.029	P=0.54	P=0.47	276.40
	750	P<0.001	P=0.006	P=0.32	P=0.46	266.19
	1000	P<0.001	P=0.045	P=0.66	P=0.49	261.69
	1250	P<0.001	P=0.065	P=0.85	P=0.40	266.25
	1500	P<0.001	P=0.081	P=0.76	P=0.41	262.1
	2000	P<0.001	P=0.064	P=0.42	P=0.39	255.43
	2500	P<0.001	P=0.18	P=0.89	P=0.23	263.73
	4000	P=0.005	P=0.90	P=0.62	P=0.05	269.79
	6000	P=0.004	P=0.94	P=0.37	P=0.037	269.92
	8000	P=0.006	P=0.89	P=0.16	P=0.005	270.45

Acknowledgements

We thank deeply volunteers involve for data collection and Julie Marmet for helpful comments on the manuscript.

Literature cited.

 in 1985, 1988, 1991, 1994, 1997, 1999, 2002, 2005 and 2008)Water courses, H512: Bodies of water.


In the '60s -mid '90s: Following Stokoe's (1960) seminal work on American Sign Language (ASL), the visual-gestural language of deaf Americans, the modern study of SL develops world-wide, including research on Italian Sign Language (LIS), pioneered by CNR scholars (Volterra, 1987/2004, Stokoe & Volterra, 1985). Analyses are based primarily on vocal languages (VL) categories. Major outcome: in spite of their gestural substance, SL possess structural properties comparable to those of VL, and must thus be recognized as fully developed natural human languages. Comparative crosslinguistic research highlights primarily the similarities between SL and VL.

Late '90s to present: Novel semiotic models of SL. New discourse-, corpus-based studies of French Sign Language (Cuxac, 1996;2000), LIS (Pizzuto & Volterra, 2000;Russo 2000;2004) and other SL, challenge the view that SL can be easily assimilated to VL. Besides elements broadly comparable to VL word-like units, SL are made of Highly Iconic Structures (HIS), marked by specific manual and non-manual articulators, most notably gaze patterns, where linguistic information is arranged in space and time in a simultaneous, multilinear fashion that has no parallel in VL. We have shown that appropriate written representations of SL are crucial for both basic and applied research (e.g. Pizzuto & al, 2006;Antinoro Pizzuto & al 2008;2009).

Previous descriptions of SL

We have undertaken a novel experimentation of a writing system devised for SL ("SignWriting": SW, Sutton, 1995): deaf signers and doctoral students of our team are using SW for both transcribing LIS and composing for the first time in history written LIS texts. Results have led to significant advancement in the linguistic analysis of SL structure, and in deaf signers' metalinguistic abilities (Di Renzo & al, 2006;Lucioli & al, 2008;Pennacchi 2008;Gianfreda & al, in press;Bianchini & al, 2009).

No modern investigation and description of VL has ever been undertaken without appropriate written representations of VL forms that allow scholars and users to reconstruct form-meaning correspondences even in the absence of 'raw data'.

In most SL research this basic requirement still is not satisfied. The notation systems most widely used either do not represent at all SL units but just 'label' them with VL words, or provide graphic representations that are extremely difficult or impossible to use for representing SL units, and for appropriate analyses of SL discourse.

Within a national project recently funded (MIUR-FIRB RBNE074T5L) we plan to include written LIS in E-Learning environments tailored to the needs of deaf learners, who face very severe difficulties in acquiring literacy skills in spoken/written Italian. We will explore the hypothesis that written LIS may be a powerful pedagogical tool for fostering literacy development in deaf signers.

Past and Novel Directions in SL Research

Word-like sign The child

  and former comparisons of SL and VL are questioned. Research strongly suggest that substantial theoretical-methodological changes in general linguistics are required to account for SL structures, and for the effects engendered by the gestural vs. the vocal modality in shaping the distinctive properties of face-to-face human language (Russo Cardona & Volterra, 2007; Pizzuto, Pietrandrea & Simone, 2007; Cuxac & Antinoro Pizzuto, in press) Most Relevant Projects 2009-2012: National Project "E-learning, Deafness, Written Language: A Bridge Of letters And Signs Towards Knowledge Society -VISEL", MIUR-FIRB Initiative, Grant N. RBNE074T5L, 896.000,00. 2005-to present "Writing Italian Sign Language (LIS) with SignWriting". Co-funded by the Rome State Institute for the Deaf & the Association Progetti Felicità, 26.500. 2004-2007: Joint Italian-French CNR-CNRS Project "Language, its formal properties and cognition: What can be learned from signed languages". In collaboration with Christian Cuxac, Université Paris 8 Unit & CNRS UMR-7023. Travel & Accomodation Grant, ~ 16.000. 1 Part time, also members of the "Gesture and language development and disorders" team and/or other ISTC groups ; 2 Ph.D. Students, ISTC-CNR Co-tutorship; 3 Post-Doc (FIRB Project); 4 Collaborator. Major Challenges: The Written Representation Problem, Deafness, Literacy in Visual vs. Spoken/Written Languages SL dictionary entry via notation (a) graphics (b), word-label (c): .S. "Cross-linguistic Explorations of SignWriting in the Analysis of SL" (in co-tutorship with Université Paris 8 and Università di Perugia). Gianfreda, G. "Conversational Analysis and Italian Sign Language (LIS)", (in co-tutorship with Università di Macerata) Petitta, G. "Units of Analysis in Different LIS Discourse Registers" (in co-tutorship with Università per Stranieri di Siena)