Axelle Tortosa
email: axelle.tortosa@inrae.fr

Brice Giffard

Luc L Barbaro

Jérémy S P Froidevaux

Sylvie Ladet

Jeanne Delhommel

Aude Vialatte

Diverse agricultural landscapes increase bat activity and diversity: Implications for biological pest control

Keywords: Farmland, crop mosaic, Chiroptera, conservation, pest insects, Lepidoptera, Lobesia botrana, Sesamia nonagrioides, Thaumetopoea pityocampa

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The intensification of agricultural production systems, by increased use of chemical inputs, landscape simplification and homogenization of the crop mosaic, has led to a dramatic decrease in farmland biodiversity [START_REF] Benton | Farmland biodiversity: Is habitat heterogeneity the key?[END_REF][START_REF] Flynn | Loss of functional diversity under land use intensification across multiple taxa[END_REF][START_REF] Geiger | Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland[END_REF][START_REF] Tilman | Global food demand and the sustainable intensification of agriculture[END_REF][START_REF] Tscharntke | Landscape perspectives on agricultural intensification and biodiversityecosystem service management[END_REF]. These negative effects are suspected to threaten the long-term stability of ecological processes involved in ecosystem services limiting sustainability and resilience of agroecosystems [START_REF] Cadotte | Beyond species: Functional diversity and the maintenance of ecological processes and services[END_REF]. It is therefore urgent to develop a form of agriculture that is more oriented towards ecological intensification. In particular, farmland biodiversity supports many ecosystem services such as pest control [START_REF] Dainese | A global synthesis reveals biodiversity-mediated benefits for crop production[END_REF][START_REF] Tscharntke | Landscape moderation of biodiversity patterns and processes-Eight hypotheses[END_REF], which has traditionally been a costly challenge for farmers given that pests are responsible for around 20% of yield losses irrespective of crop types [START_REF] Duflot | Farming intensity indirectly reduces crop yield through negative effects on agrobiodiversity and key ecological functions[END_REF][START_REF] Oerke | Crop losses to pests[END_REF]. Arthropod pests are also acknowledged as a major threat to forest health, and are likely to worsen with severe outbreaks due to global change [START_REF] Simler-Williamson | Interacting Effects of Global Change on Forest Pest and Pathogen Dynamics[END_REF].

Pest infestations are known to increase in monoculture landscapes, but it is also greater in more favourable landscapes, i.e., when the proportion of host crop cover is higher within the crop mosaic and/or is increasingly connected [START_REF] Rand | Increased area of a highly suitable host crop increases herbivore pressure in intensified agricultural landscapes[END_REF][START_REF] Veres | Does landscape composition affect pest abundance and their control by natural enemies? A review[END_REF]. Moreover, biological control of pests by their natural enemies tends to be less effective in simplified landscapes, i.e. crop-dominated landscapes, because the effect of local management often interacts with landscape complexity [START_REF] Rusch | Agricultural landscape simplification reduces natural pest control: A quantitative synthesis[END_REF]. While the role of semi-natural habitats on biological control has been intensively studied, the benefits of crop diversity itself are less well understood [START_REF] Veres | Does landscape composition affect pest abundance and their control by natural enemies? A review[END_REF]. It is now widely established that landscape spatio-temporal heterogeneity enhances multitrophic abundance and diversity of natural enemies [START_REF] Sirami | Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions[END_REF] in part by promoting complementary resources for natural enemies, including foraging areas, food sources, shelter, nesting and overwintering sites [START_REF] Bertrand | Spatial and temporal heterogeneity of the crop mosaic influences carabid beetles in agricultural landscapes[END_REF][START_REF] Schellhorn | Time will tell: Resource continuity bolsters ecosystem services[END_REF].

However, the actual consequences in terms of biological pest control remain inconsistent across regions, sites and experiments, probably due to complex interactions between farming practices and landscape context leading to synergistic or antagonistic effects [START_REF] Barbaro | Avian pest control in vineyards is driven by interactions between bird functional diversity and landscape heterogeneity[END_REF][START_REF] Etienne | The limits to ecological limits to diversification[END_REF][START_REF] Muneret | Organic farming at local and landscape scales fosters biological pest control in vineyards[END_REF][START_REF] Ricci | Local pesticide use intensity conditions landscape effects on biological pest control[END_REF].

Although the relationship between natural enemy diversity and the effectiveness of biological pest control is generally positive, it can be substantially disrupted by, e.g., intraguild predation within the natural enemy community [START_REF] Letourneau | Effects of natural enemy biodiversity on the suppression of arthropod herbivores in terrestrial ecosystems[END_REF][START_REF] Martin | Natural enemy interactions constrain pest control in complex agricultural landscapes[END_REF].

Insectivorous bats are efficient natural enemies of key pest arthropods in temperate agroecosystems and forest ecosystems [START_REF] Ancillotto | Bats as suppressors of agroforestry pests in beech forests[END_REF][START_REF] Blažek | Bat aggregational response to pest caterpillar emergence[END_REF][START_REF] Boyles | Economic Importance of Bats in Agriculture[END_REF][START_REF] Mccracken | Bats Track and Exploit Changes in Insect Pest Populations[END_REF]. For instance, they are both efficient in annual crops such as maize fields [START_REF] Aizpurua | Agriculture shapes the trophic niche of a bat preying on multiple pest arthropods across Europe: Evidence from DNA metabarcoding[END_REF][START_REF] Maine | Bats initiate vital agroecological interactions in corn[END_REF][START_REF] Whitby | Agricultural pests consumed by common bat species in the United States corn belt: The importance of DNA primer choice[END_REF] and perennial crops such as vineyards and orchards or in plantation forests [START_REF] Charbonnier | Numerical and Functional Responses of Forest Bats to a Major Insect Pest in Pine Plantations[END_REF][START_REF] Charbonnier | Pest control services provided by bats in vineyard landscapes[END_REF]. In agricultural landscapes, most bat species rely on woody features for commuting and foraging, depending on their wing morphology and echolocation strategy [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Froidevaux | Managing hedgerows for nocturnal wildlife: Do bats and their insect prey benefit from targeted agri-environment schemes[END_REF][START_REF] Garin | Bats from different foraging guilds prey upon the pine processionary moth[END_REF], and some bats can forage above crop fields (e.g., open-space foragers; [START_REF] Heim | The Importance of Landscape Elements for Bat Activity and Species Richness in Agricultural Areas[END_REF].

Higher landscape diversity and shorter distances between roosting sites and other foraging habitats (e.g. water bodies, riparian zones) promote bat activity and species richness [START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF][START_REF] Rainho | The Importance of Distance to Resources in the Spatial Modelling of Bat Foraging Habitat[END_REF]. Bats are also centralplace foragers as they used to return to the same site after foraging, with an average distance between foraging areas and roost for many species most often less than 3 km [START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF], but occasionally up to ten of kilometres to reach high rewarding areas [START_REF] Bruun | Landscape composition affects habitat use and foraging flight distances in breeding European starlings[END_REF]. Most insectivorous bats are generalist predators that are able to maintain vital rates and stable populations by shifting to alternative prey and by feeding on several adult moths. Although their foraging strategy and habitat selection (or use) are different, species belonging to different guilds have been shown to feed on the same pest moths [START_REF] Garin | Bats from different foraging guilds prey upon the pine processionary moth[END_REF].

Thanks to the development of new molecular analysis techniques such as DNA metabarcoding, many moth pests of both annual and perennial crops have been detected in bat diet [START_REF] Aizpurua | Agriculture shapes the trophic niche of a bat preying on multiple pest arthropods across Europe: Evidence from DNA metabarcoding[END_REF][START_REF] Charbonnier | Pest control services provided by bats in vineyard landscapes[END_REF]. Nevertheless, these qualitative approaches only highlight pest consumption and do not allow inference about pest control [START_REF] Russo | Novel perspectives on bat insectivory highlight the value of this ecosystem service in farmland: Research frontiers and management implications[END_REF]. Several studies demonstrated the role of bats as biological control agents in specific crops such as maize or rice, using (semi-) experimental approaches (e.g. [START_REF] Maine | Bats initiate vital agroecological interactions in corn[END_REF][START_REF] Puig-Montserrat | Pest control service provided by bats in Mediterranean rice paddies: Linking agroecosystems structure to ecological functions[END_REF]. Yet, little is known regarding the influence of the landscape and the season on bat activity resulting in more effective biological control.

Crop mosaic diversity at the landscape scale, i.e. agroecosystems mixing trees (forest patches, hedgerows) and crop fields, may be particularly favourable to enhance pest control provided by bats. Crop diversity is expected to increase the co-occurrence of annual and perennial crops offering complementary food and roost resources both temporally and spatially across the entire bat activity period, depending on variation in seasonal energy demands. In addition, crop diversity may benefit a large range of bat species including species with different echolocation, foraging strategies and conservation concerns. Our study focuses on whether the complementarity of three crop types, namely maize, vineyard and pine plantations, at the landscape scale, would affect bat communities and their potential usefulness in terms of biological control, i.e., is the mixture of these three crops at the landscape scale favourable to bats and does it favour biological control of pests?

Our first objective was to evaluate the effects of more diverse agricultural landscapes on bat community and its foraging activity. We predicted that in landscapes mixing the three crops, the presence of higher diversity of landscape elements favourable for bats would provide complementary resources, resulting in higher species richness and bat activity.

Our second prediction is that bat guild-specific responses are expected because cropdiverse landscapes display higher diversity in habitat structures than crop-dominated ones.

Finally, because we predicted that landscapes that are more diverse regarding cooccurring crop types may offer a higher food resource continuity through the succession of each lepidopteran pest biological cycle, we expected a higher foraging activity and less crop damage in these landscapes. To account for the effects of plant resource availability provided by the dominant crop on phytophagous insect abundances, we also directly assessed the abundance of other moths and other alternative bat prey.

Material and methods

Study area and site selection

The study area was located in the south west of France, between Gironde and Lot-et-Garonne counties and near Langon (44° 33' 19.508" N 0° 14' 42.454" W). This area is dominated by vineyards (Sauternes and Graves vineyards), maize fields and pine plantations (mainly Pinus pinaster).

Within this study area, we selected thirty-seven landscapes (a 563-m radius scale; Figure 1) according to the predominance of one or all three target crop types. Thirty landscapes were dominated by only one of the three crops, i.e. 10 simplified landscapes dominated by vineyards (mean: 70%; range 57%-78%), maize fields (mean: 52%; range 21%-76%, without any other dominant crop) or pine plantations (mean: 79 %; range 63%-100%), respectively. Seven landscapes were selected that had all three crop types in varying proportions but their total surface cover was at least 45% of the total area (hereafter referred to as 'diverse landscapes'). Among the 17 vineyards sampled two were organically managed and maize fields were all conventionally managed.

Field sampling of targeted Lepidoptera and potential alternative prey

Vineyards (Vitis vinifera L.), maize (Zea mays L.) fields and pine plantations (mainly Pinus pinaster) dominate agricultural landscapes of South-western France. Key pests include the European grapevine moth Lobesia botrana Denis & Schiffernüller (Lepidoptera: Tortricidae), which cause direct losses in grape production [START_REF] Delbac | Damage to grape flowers and berries by L obesia botrana larvae (Denis & Schiffernüller) (Lepidoptera: Tortricidae), and relation to larval age: Larval age and damages by Lobesia botrana[END_REF]; the Mediterranean corn borer Sesamia nonagrioides Lefèbvre (Lepidoptera: Noctuidae), which is one of the most damaging pests of maize in early stages [START_REF] Maine | Bats initiate vital agroecological interactions in corn[END_REF]; and the pine processionary moth Thaumetopoea pityocampa Denis & Schiffernüller (Lepidoptera: Notodontidae), which is the main defoliator of pines reducing growth of young trees [START_REF] Jacquet | Defoliation by processionary moth significantly reduces tree growth: A quantitative review[END_REF]. The flight peaks of these three species follow one another in time. The corn borer and the grape berry moth have between 2 and 4 generations per year, with a flight peak of the first generation at the end of April and the end of May, respectively. The control of the first generation is key to limit the abundance of the following generations. The pine processionary moth has one generation per year and a flight peak spread out between late June and early September. Current control of moth pests in crops consists of mating disruption, application of insecticide or Bt toxin but their effectiveness tends to decrease in a context of global warming and the appearance of resistance [START_REF] Thiéry | Biological protection against grape berry moths. A review[END_REF].

The data collection was carried out over three sampling periods in April, May and July 2021, according to the flight peaks of the targeted lepidopteran pests in each crop and in the study region. The first sampling period was carried out in April 2021 in the 17 vineyards: 10 vineyards in vineyard-dominated landscapes and 7 vineyards in diverse landscapes. We installed specific pheromone baited-traps of L. botrana alternatively during 7 nights from 12 to 21 April 2021, 30 m inside the fields. We also placed food traps containing diluted apple must in order to collect potential alternative prey (Figure A 1). Food traps were located 50 m away from the pheromone baited-traps in the same fields.

After 7 nights, food traps were refilled and deployed for two more consecutive nights during the bat sampling.

The same experimental set-up was conducted in May in 16 maize fields and in July in 17 pine plantations. The pheromone baited-traps specific to S. nonagrioides were placed from 18 to 27 May 2021 and specific to T. pityocampa from 28 June to 7 July 2021.

All insects collected in food traps were stored in a 70% ethanol solution. Among the collected insects, targeted moths, i.e. L. botrana or S. nonagrioides or T. pityocampa, were counted and added to those counted on pheromone baited-traps. The total relative abundance of moth species was determined in the targeted crop. The collected insects stored in ethanol were then dried for 24 hours at 60° and weighted with a precision balance. The total insect dry biomass for each site was determined.

Bat monitoring

The main activity period of bats ranges from spring to autumn and peaks during periods of high energy demands. For instance, after hibernation, female bats have to fulfil their reserves and ensure the rapid growth of their embryos. Therefore, pregnant females will need more food resources to achieve their pregnancy in June-July [START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF].

The lactation period is also a period of high energy demand, during which the females tend to forage closer to their roost and until the young forage in their turn [START_REF] Arthur | Les chauves-souris de France[END_REF][START_REF] O'donnell | Influence of Sex and Reproductive Status on Nocturnal Activity of Long-Tailed Bats (Chalinolobus tuberculatus)[END_REF].

Bats were recorded using an ultrasound bat detector model Elekon Batlogger A/A+ (Wigum GmbH, Germany); recording was triggered automatically when sounds in the frequency range 8-192 kHz with a signal-noise-ratio level above 6 dB were detected.

Detectors were programmed to record from 30 min before sunset to 30 min after sunrise.

They were installed for two consecutive nights instead of the specific pheromone baitedtraps, i.e. vineyards in both landscape types were sampled between 19 and 23 April 2021; maize fields between 25 and 29 May 2021; pine plantations between 5 and 9 July 2021.

Bats were recorded only in nights without rain, with low wind speed (< 30 km/h) and an average minimum temperature at night of >10°C [START_REF] Parsons | Bat Surveys: Good practice guidelines[END_REF].

Species identification was supported by the Tadarida software, which classified calls to the most accurate taxonomic level with a confidence index value (https://github.com/YvesBas/Tadarida-C/; [START_REF] Bas | Tadarida: A toolbox for animal detection on acoustic recordings[END_REF]. As automated identification can generate significant error rates, we followed the procedure by [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] in order to find the best compromise limiting species identification errors and maintaining a relatively high number of bat passes. Then, we removed acoustic data with a maximum error rate tolerance (MERT) of 0.5 to minimize false positives while keeping a high number of bat passes. We checked the consistency of responses using a more restrictive MERT of 0.1 (detailed results are available in Appendix B), which limited false positives but discarded more true positives. Call identifications were grouped at genus level for Plecotus spp. and we distinguished small and large Myotis spp. (see Table 1). The number of species or complex of species were counted per site and per night. Species of regional conservation concern (SCC) were identified according to the Nouvelle-Aquitaine regional red list (see Table 1; Liste rouge des Chiroptères de Nouvelle-Aquitaine, 2019).

We also grouped bats according to their echolocation range into three guilds [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Schnitzler | Echolocation by insect-eating bats[END_REF]) namely short-, mid-and long-range echolocators (SRE, MRE and LRE respectively -see Table 1).

As acoustic sampling does not allow differentiating individual bats, we used bat activity as a surrogate of bat abundance (e.g. [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][START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF]Froidevaux et al., 2017) which was calculated by summing the number of bat passes per site and per night. Bat pass was defined as the detection of a single or several bat calls during a 5-s interval, which was the best compromise between the risk to miss an individual and to count several times the same one [START_REF] Kerbiriou | Potential of bat pass duration measures for studies of bat activity[END_REF][START_REF] Millon | Bat activity in intensively farmed landscapes with wind turbines and offset measures[END_REF]. Global bat activity (i.e. global activity) was calculated by summing nightly bat activity of each species or complex of species. Guild activity (i.e. LRE , MRE or SRE activities) and SCC activity were also calculated by summing the number of bat passes of species or complex of species that constitute the guild and the number of bat passes of species from SCC mentioned before (Table 1), respectively.

Finally, we quantified bat foraging activity through the mean bat sequence duration BSD [START_REF] Kerbiriou | Potential of bat pass duration measures for studies of bat activity[END_REF] weighted by the number of bat sequences of each species or complex of species and of each guild (i.e. LRE BSD, MRE BSD, SRE BSD). Bat sequence duration corresponds to the total duration of a bat sequence (i.e. a series of bat calls), considering two acoustic events separated by a time interval shorter than 2 seconds as a same bat sequence. Longer bat sequences indicate that a bat is foraging while shorter bat sequences would suggest that a bat is commuting [START_REF] Kerbiriou | Potential of bat pass duration measures for studies of bat activity[END_REF].

Evaluation of specific damage in targeted crops

Specific damage was evaluated in maize and pine plantations to estimate the actual effects of bat predation on target lepidopterans. Damage in vineyards (glomerules on grape bunches) could not be assessed due to a frost episode that occurred at the beginning of April ( 2021) and greatly affected vegetative development of the grapevines.

Maize damage was evaluated the fortnight following bat sampling that corresponded to 10-15 days after 50% of the S. nonagrioides peak flying (maximum of adult emergence)

and to the maximum of larvae at the crawler stage. Three transects of 100 maize plants were observed in each field and the plants showing wilting were counted to assess larvae densities per field and per 100 plants (maize densities equal in all our fields).

In early February 2022, we estimated the density of larval colonies of T. pityocampa by counting the number of larval colonies on all pine trees located 100 m from the forest edge and on the first two rows where the larval colonies are concentrated (Dulaurent et al., 2012).

Landscape metrics

Using ArcGIS Desktop 10.5.1 software (ESRI, Redlands, CA, US), land cover was digitized from OSO 2019 [START_REF] Inglada | Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series[END_REF] and annual crops were identified for the study sites based on direct field observations.

Part of the landscape metrics were then calculated for a 1km² circle (i.e., inside a circular buffer with radius of 563m, centred on the middle of the pheromone baited-trap and acoustic detector position). First, we used a descriptive variable (i.e. landscape type) to distinguish the type of landscape in which the measurements were made, i.e. either simplified (dominance of one targeted crop) or diverse (presence of all three crops).

To describe crop mosaic compositional heterogeneity, the proportion of each targeted crop (vineyards, maize fields and pine plantations) was assessed and the Shannon cropdiversity index was specifically calculated on those categories. Another Shannon diversity index was calculated for the whole landscape based on the proportion of each land cover (other habitats than crops such as deciduous woodlands, water bodies, building, hedgerows with mainly deciduous trees) but it was highly correlated with the Shannon crop-diversity index of specific crops (Pearson's coefficient > 0.9), and was therefore disregarded for the analysis. The compositional heterogeneity of the semi-natural habitats (SNH) was described using the proportion of SNH, which grouped hedgerows, woodlands and water bodies and we also calculated the total length of hedgerows, which represented an indicator of configuration heterogeneity. The proportion covered by artificial spaces (i.e. buildings, roads, commercial and industrial areas) was calculated to consider the potential effect of artificialization.

As bats have a dispersal capacity from a breeding site to a foraging zone that may reach several tens of kilometres depending on the species, we chose to consider the distance to the nearest elements of the landscape with potentially significant effects on the sampled bat community [START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF]. Then we used the distances to the nearest water body, hedgerow, forest edge, riparian zone and building [START_REF] Arthur | Les chauves-souris de France[END_REF][START_REF] Froidevaux | Managing hedgerows for nocturnal wildlife: Do bats and their insect prey benefit from targeted agri-environment schemes[END_REF][START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF][START_REF] Sirami | Artificial wetlands and surrounding habitats provide important foraging habitat for bats in agricultural landscapes in the Western Cape, South Africa[END_REF]. The distance to the Garonne, the main water body of the study area, was also considered [START_REF] Salvarina | Bats and aquatic habitats: A review of habitat use and anthropogenic impacts[END_REF][START_REF] Salvarina | Seasonal bat activity related to insect emergence at three temperate lakes[END_REF]. The distribution of landscape metrics is available in Appendix A (Table A 2).

Statistical analysis

Spearman's correlations were performed on the different response variables in order to evaluate potential redundancies. All rho coefficients were below 0.7, except between total bat activity and MRE activity (Figure A 3).

To assess the influence of landscape type (monoculture vs three-type crop mosaic) on bat community activity and foraging activity, we performed statistical analyses on both (i) the pooled dataset of the three sampling periods, and (ii) each separate dataset corresponding to the sampling in April, May and July respectively. Firstly, we tested independently the relationships between all response variables (i.e. bat species richness, global activity, SCC activity, guild activity and foraging activity) and the landscape type (crop-dominated versus diverse), using Generalized Linear Mixed-Effect Models (GLMMs; lme4 package) with the appropriate distribution (Gaussian or Poisson or negative binomial family) and considering site and night as random effects.

Secondly, independent variables (landscape metrics, biomass and relative pest abundance) were standardized. Pearson's correlations (|r| < 0.7) and collinearity with variation inflation factor (VIF values < 5) were tested using the corvif function [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF] for each period. Then, we modelled relationships between the response variables and different independent landscape variables depending on the landscape context of each targeted crop (Table 2) and co-variables such as biomass or relative abundance of targeted pest. We used GLMMs to create the full model, and then the dredge function (MuMin package) was run with a maximum of three independent variables in the same model to avoid model overparametrization, and site and night as random factors. We selected models with a ∆AICc < 2 and averaged coefficients were calculated using model.avg function (MuMin package). Distance-dependence in all model residuals was assessed using Moran's I test and appeared to be not spatially related (Moran's I test, all p > 0.05).

Finally, Spearman's correlations were calculated between the relative pest abundance and the index of damage for maize and pine plantations sampling periods. We also investigated correlation relationships between variables describing bat community or activity and relative pest abundances and the associated index of damage for each sampling period. Wilcoxon's tests were performed for the different variables responses and between landscape types.

All analyses were performed with R software version 3.6.3 (R Core Team, 2022). *Acoustic recording failed one night in pine plantations, therefore the total number of recording nights is 99 instead of 100.

Results

Bat, pest and plant damage

Bat richness and activity

We recorded a total of 17,786 and 13,386 bat passes, considering a MERT of 0.5 and a MERT of 0.9, that belonged to 16 taxa (Table 3). The average species richness was 7.4

(± 1.8) species in vineyards; 6.4 (± 2.3) in maize fields and 6.1 (± 1.7) in pine plantations.

Bat activity was mainly represented by MRE guild (72%) with the Pipistrellus genus as most frequently genus detected (39% Pipistrellus pipistrellus, 29% Pipistrellus kuhlii).

LRE and SRE guilds represented 22% and 6% of total bat activity; Nyctalus leisleri and Eptesicus serotinus represented each 11% of recorded activity.

Keeping bat passes with an error rate of less than 50%, i.e. a MERT of 0.5, there were 14 bat passes of species of conservation concern compared to only two bat passes when considering an identification error rate of less than 10%, i.e. a MERT of 0.1.

Foraging activity varied from 0.05s to 0.81s (mean for MERT of 0.5: 0.40s; LRE foraging activity: 0.14s ± 0.09; MRE foraging activity: 0.23s ± 0.10; SRE foraging activity: 0.14s ± 0.10).

Pest abundance and alternative prey biomass

The number of L. botrana counted on pheromone baited-traps in vineyards, in April 2021 ranged from zero to six individuals (mean: 0.23 ± 0.19; Figure 2A ). Relative abundance of L. botrana was the lowest among the three targeted lepidopteran pests. The mean dry biomass was 0.25g (± 0.30).

In May 2021, S. nonagrioides counted in maize fields ranged from zero to 22 individuals per site (mean: 3.50 ± 5.56; Figure 2B). The mean dry biomass measured was 0.23 g (± 0.19).

Finally, in July 2021, the number of T. pityocampa ranged from 0 to 46 individuals per site (mean: 8.41 ± 11.11; Figure 2C). The mean dry biomass measured was 0.56 g (± 1.45) and the highest among the three sampling periods.

There was no significant difference between the relative abundances of pests in cropdominated landscapes and in diverse landscapes (Figure 2), for each sampling period.

Plant damage

The damage index measured on maize plants was very low ranging from zero to 0.02, which corresponded to a maximum of six affected plants out of 300 observed.

The density of larval colonies of the pine processionary moth, varied among sites between 0 to 25 nests per site. Depending on the site, the total number of pines observed varied from 21 to 108 trees, with an average around 50 trees per site, leading to an index of damage ranging from zero to 0.61.

The index of damage calculated for pine plantations was significantly lower in diverse landscapes than in pine-dominated landscapes (Wilcoxon's test: W = 198, p-value = 0.019).

Effects of landscape diversity on bat communities

Results exposed in this paragraph correspond to response variables using a MERTof 0.5 in automated identification (for MERT of 0.1 results, see Table B 1).

Bat species richness was always higher in diverse than in simple landscapes (Table 4;

Figure 3).

Total bat activity was significantly influenced by landscape type (Table 4; Figure 3). It was largely driven by the activity of MRE guild, which was also significantly higher in diverse than simple landscapes, as well as activity of species of conservation concern (Table 4).

The total foraging activity, was not significantly influenced by the landscape type.

However when considering guild foraging activity, SRE foraging activity was significantly longer in diverse landscapes.

Effects of crop diversity and pest abundance on bat communities in vineyards 397

Bat activity in vineyards in April increased with crop diversity and decreased with distance 398 to the nearest hedgerow (Figure A 4). Crop diversity had also a positive effect on 399 endangered bat species. 400 A B MRE activity followed the same pattern as total bat activity and peaked in diverse landscapes. LRE activity was positively related to the distance to the largest river and negatively to the distance to the nearest hedgerow.

In addition, the relative abundance of pest L. botrana was negatively correlated with bat species richness (Figure A 5; A 8) and SRE foraging activity.

Effects of crop diversity and pest abundance on bat communities in maize fields, consequences on plant damage

In maize fields in May, bat species richness was influenced positively by the proportion of semi-natural habitats (SNH) and negatively by the total length of hedgerows (Figure A 6, Table B 2).

Total bat activity in maize fields significantly increased with the length of hedgerows and closer to forest edges and decreased closer to the riparian zone (Figure A 6, Table B 2).

Bat guild activity was driven by MRE activity, which was influenced positively by the distance to the nearest forest edge and negatively by the distance to the nearest riparian zone. SRE activity significantly decreased with the total length of hedgerows (Figure A 6;Table B 2).The activity of species of conservation concern in maize fields was significantly and positively related to the distance to the nearest riparian zone. In addition, the proportion of SNH and the crop diversity index had a significant positive effect on SCC activity (Figure A 6; Table B 2).

Bat foraging activity was slightly but significantly positively influenced by the relative abundance of S. nonagrioides and negatively influenced by the distance to the main river.

Considering foraging activity through guild classification, SRE foraging activity was negatively affected by the length of hedgerows and positively affected by the proportion of SNH and relative pest abundance. In addition, insect dry biomass and crop diversity had significant positive effects on LRE foraging activity (Figure A 6;Table B 2).

The abundance of S. nonagrioides in sampled maize fields was positively correlated with bat species richness (Figure A 6; A 8; Table B 3), SRE activity SRE and LRE foraging activities. Total bat activity was not significantly correlated with the index of damage on maize plants (Figure 4) whereas SCC activity and LRE foraging activity were positively correlated to the maize damage index (Table B 4).

Effects of crop diversity and pest abundance on bat communities in pine plantations, consequences for tree damage

In pine plantations in July, bat species richness significantly increased with crop diversity and decreased with the total length of hedgerows. Bat activity increased with crop diversity and distance to the nearest hedgerow, while activity of endangered bats only increased with crop diversity. Bat foraging activity increased with the distance to the nearest hedgerow and crop diversity index. Relative abundance of T. pityocampa in sampled pine plantations was positively correlated with the index of damage measured on pine plantations but only in pine-dominated landscapes (Table B 4). Total foraging activity and especially MRE foraging activity were negatively correlated with the damage index (Figure 4, Table B 4).

Discussion

While more and more studies highlight the importance of landscape complexity for bats (e.g. [START_REF] Allegrini | Insectivorous bats in eastern mediterranean planted pine forests-effects of forest structure on foraging activity, diversity, and implications for management practices[END_REF][START_REF] Krings | Not only hedgerows, but also flower fields can enhance bat activity in intensively used agricultural landscapes[END_REF][START_REF] Ocampo-Ariza | Trait-dependent responses of birds and bats to season and dry forest distance in tropical agroforestry[END_REF], there are still few studies that highlight their potential role in pest control within agricultural landscapes [START_REF] Russo | Novel perspectives on bat insectivory highlight the value of this ecosystem service in farmland: Research frontiers and management implications[END_REF].

This study provides evidence for the positive effect of the diversity of the crop mosaic at the landscape scale on bat richness and activity. The novelty of the study relies in focusing the approach on the biological control of three pests at key periods for bats in agricultural landscapes, and allowed us highlighting their effective role in natural pest control. In line with our hypotheses, our results suggest bat guild-dependent responses.

On the one hand, long-range echolocators foraging activity was higher with pest abundance and damage index in maize fields. On the other hand, damage index on pine trees decreased with mid-range echolocators activity. These findings highlight the importance of landscape scale and pest control-centred approaches together with studies analysing bat diet, in order to quantify the role of bats in agricultural landscapes and to identify conservation actions.

More diverse landscapes foster bat species richness and activity

Our models revealed that species richness and bat activity, especially mid-range echolocators activity (mainly Pipistrellus spp.) were higher in the more diverse landscapes mixing more or less equally the three target crop types. These results corroborate with [START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF], who found that diverse agricultural landscapes improved both bat species richness and activity.

There are three main hypotheses explaining positive effects of crop diversity on species richness, bat activity and to a lesser extent on foraging activity. First, more diverse landscapes typically combined perennial and annual crops that may provide more diverse and abundant prey spatially within the landscape and over the time, especially for highly mobile taxa [START_REF] Bertrand | Spatial and temporal heterogeneity of the crop mosaic influences carabid beetles in agricultural landscapes[END_REF][START_REF] Fahrig | Farmlands with smaller crop fields have higher within-field biodiversity[END_REF][START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF]. Particularly in our study design we hypothesised a temporal continuity in abundance of moth pests, whose flight peaks narrowly followed each other within the three crop types during the entire bat activity period. In diverse landscapes, prey abundance is likely to be more stable through the night as different species are active at different time in different habitats [START_REF] Rydell | Timing of foraging flights of three species of bats in relation to insect activity and predation risk[END_REF]. Second, bats also depend on non-substitutable key resources, such as daylight roosting sites. Diverse landscapes, especially those combining trees and crops may provide these complementary resources year-on-year and then the proximity of foraging and roosting habitats may benefit both species richness and bat activity.

Finally, the combination of annual and perennial crops, which have different phenologies and varying cover heights, offers a three-dimensional structure of the landscape. It is widely accepted that the different bat species do not commute and forage in the same way within the landscape according to their wing morphology and echolocation capabilities [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Fuentes-Montemayor | The effectiveness of agrienvironment schemes for the conservation of farmland moths: Assessing the importance of a landscape-scale management approach[END_REF]. Therefore, landscapes with such three-dimensional structure may be favourable to a larger range of bat species classified into different guilds and will increase their activity. Furthermore, landscape elements such as forest edges or hedgerows contribute to landscape connectivity especially for bats belonging to SRE guild, which includes species that are relatively sensitive to landscape fragmentation and more active in well-connected landscapes [START_REF] Fill | Spatial distribution of bat activity in agricultural fields: Implications for ecosystem service estimates[END_REF][START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF].

In addition to the importance of a more diverse agricultural landscape in terms of crops, our study also showed the influence of the presence and the distance to semi-natural habitats, which is consistent with previous studies (e.g. [START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF]. Surprisingly, we found that a higher length of hedgerows was associated with a bat species richness decrease at the local scale, maybe due to less foraging bats gathering in optimal areas when hedgerow availability is low at the landscape scale. Also, hedgerows may benefit more to some bat species depending on their structure (strata), plant diversity and even height. [START_REF] Lacoeuilhe | The relative effects of local and landscape characteristics of hedgerows on bats[END_REF], showed that bat species preferred wooded hedgerows dominating in agricultural landscapes rather than a diversity of hedgerow types. Hawking bat species such as Pipistrellus spp., benefit from wooded hedgerows due to their vegetation that shelters a large number of insects and allows dispersal away from the wind [START_REF] Lewis | Air movement near windbreaks and a hypothesis of the mechanism of the accumulation of airborne insects[END_REF]. Moreover, gleaning foragers, which are mainly species from SRE guild, were associated with more complex hedgerows with diversified strata. These results also point out that the quality of the hedgerow itself might be critical to explain its differentiated use by the different bat species [START_REF] Froidevaux | Managing hedgerows for nocturnal wildlife: Do bats and their insect prey benefit from targeted agri-environment schemes[END_REF][START_REF] Wickramasinghe | Bat activity and species richness on organic and conventional farms: Impact of agricultural intensification[END_REF].

Our study is therefore in line with several others that highlight both the benefits of the compositional heterogeneity of the crop mosaic but also its arrangement with other seminatural and woodland habitats resulting in promoting bat diversity and activity [START_REF] Fill | Spatial distribution of bat activity in agricultural fields: Implications for ecosystem service estimates[END_REF][START_REF] Maas | Bird and bat predation services in tropical forests and agroforestry landscapes[END_REF][START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF][START_REF] Pedro | The role of the adjacent habitat on promoting bat activity in vineyards: A case study from central chile[END_REF][START_REF] Puig-Montserrat | Pest control service provided by bats in Mediterranean rice paddies: Linking agroecosystems structure to ecological functions[END_REF][START_REF] Rodríguez-San Pedro | Responses of aerial insectivorous bats to landscape composition and heterogeneity in organic vineyards[END_REF].

Crops benefit from each other within the landscape in terms of biological control of pests

Our study adds to the growing body of literature supporting the positive effects of diverse agricultural landscapes and woodlands on biocontrol agents such as bats resulting in higher pest regulation activity [START_REF] Fill | Spatial distribution of bat activity in agricultural fields: Implications for ecosystem service estimates[END_REF][START_REF] Maas | Bird and bat predation services in tropical forests and agroforestry landscapes[END_REF]. We tried to find out the reciprocal benefits of different crop types already implanted in the region and all around the world. It appeared that crop association at the landscape scale offered benefits for each crop in terms of biological control of pests. In our study, grapevines and pine plantations indicate higher levels of biological control by bats in diverse landscapes than in landscapes dominated by a single crop. In vineyards, the abundance of moths decreased with the bat species richness. This represents an indicator of potential biological control, but the low moth population levels and the lack of damage measurements does not allow to affirm that this process is actually at play. However, in French vineyards, [START_REF] Charbonnier | Pest control services provided by bats in vineyard landscapes[END_REF] highlighted the regulation role of bats on grape berry moths. Also the importance of adjacent forested semi-natural habitats near vineyards for prey availability was assessed in Central Chile vineyards which suggests that both semi-natural and vineyards may promote bat conservation and ressources [START_REF] Chaperon | Effects of adjacent habitat on nocturnal flying insects in vineyards and implications for bat foraging[END_REF]. In pine plantations, in July, foraging activity (particularly MRE activity) was not correlated with the relative abundance of T. pityocampa but significantly and negatively with the index of damage on pine trees. These results partly corroborate those from previous studies showing that different species belonging to the three guilds are T. pityocampa predators without being independent on it in their diet [START_REF] Garin | Bats from different foraging guilds prey upon the pine processionary moth[END_REF] and that bats were efficient biological pest control agents for T. pityocampa. Bat foraging plasticity allowing them to concentrate their activity on local prey aggregates [START_REF] Charbonnier | Numerical and Functional Responses of Forest Bats to a Major Insect Pest in Pine Plantations[END_REF], and the high mobility of pine processionary moths through the landscape may explain the non-detection of a relationship between bat activity and relative abundance of T. pityocampa [START_REF] Battisti | Natural history of the processionary moths (thaumetopoea spp.): New insights in relation to climate change[END_REF].

However, no difference of potential moth biological control was observed in maize between maize-dominated and diverse landscapes: while the abundance of moths seems to attract bats (in particular reflected by SRE and LRE foraging activities), the associated predator-prey dynamics does not allow for biological control. It is probably related to the rapid oviposition rate observed in S. nonagrioides, with a maximum of egg-laying on maize as soon as 30% of the moths have emerged. Although the role of bats as predators of corn pests has been demonstrated [START_REF] Maine | Bats initiate vital agroecological interactions in corn[END_REF][START_REF] Whitby | Agricultural pests consumed by common bat species in the United States corn belt: The importance of DNA primer choice[END_REF] bats probably predate moths after most of the eggs have already been laid on maize plants.

While biological control of S. nonagrioides in maize does not seem to be directly and solely controlled by bats, other natural enemies, such as parasitoids, can be involved in the egg stages of this pest and be favoured by diverse landscapes [START_REF] Gardiner | Landscape diversity enhances biological control of an introduced crop pest in the north-central USA[END_REF][START_REF] Landis | Influence of landscape structure on abundance and within-field distribution of european corn borer (lepidoptera: Pyralidae) larval parasitoids in michigan[END_REF]. Additional agricultural practices, not recorded in our study, such as the use of pesticides, may also vary between fields and dampen relationships between moth abundances and observed damage [START_REF] Paredes | Landscape simplification increases vineyard pest outbreaks and insecticide use[END_REF]. Nevertheless, maize appears to provide moth prey that benefit bats at the landscape level, possibly translating into a higher biological pest control by the same bats in the other adjacent crop types.

Our study illustrates that the diversification of agricultural landscapes to favour biological control can be thought of by taking advantage of the dominant crops in the landscapes, as an alternative to the introduction of new crop species, then without fundamentally modifying the local value chains [START_REF] Vialatte | Promoting crop pest control by plant diversification in agricultural landscapes: A conceptual framework for analysing feedback loops between agro-ecological and socio-economic effects[END_REF]. Our study is in line with the results of recent meta-analyses that show that crop diversification promotes biodiversity, biological control and yields [START_REF] Beillouin | Positive but variable effects of crop diversification on biodiversity and ecosystem services[END_REF][START_REF] Paiola | Exploring the potential of vineyards for biodiversity conservation and delivery of biodiversity-mediated ecosystem services: A global-scale systematic review[END_REF][START_REF] Tamburini | Agricultural diversification promotes multiple ecosystem services without compromising yield[END_REF]. It complements this work, which is largely dominated by intra-plot diversification by showing that crop diversification at the landscape scale is also a lever for biological control. While semi-natural elements in landscapes are widely known to support biodiversity and biological control [START_REF] Dainese | A global synthesis reveals biodiversity-mediated benefits for crop production[END_REF][START_REF] Karp | Crop pests and predators exhibit inconsistent responses to surrounding landscape composition[END_REF] and in particular by bats [START_REF] Chaperon | Effects of adjacent habitat on nocturnal flying insects in vineyards and implications for bat foraging[END_REF][START_REF] Pedro | The role of the adjacent habitat on promoting bat activity in vineyards: A case study from central chile[END_REF] crop mosaics appear to be a complementary way of diversification. In the face of the risk of pest outbreaks associated with global warming [START_REF] Klapwijk | Assessing the impact of climate change on outbreak potential[END_REF], our results open up avenues of alternative agroecological management to conventional agriculture for globally important crops such as maize (world's second cereal, FAOSTAT, 2020), planted forests (conifers account for more than 55% of the world's forest plantation resource) and grapevines [START_REF] Brockerhoff | Forest biodiversity, ecosystem functioning and the provision of ecosystem services[END_REF][START_REF] Hannah | Climate change, wine, and conservation[END_REF].

Conservation implications

It is widely recognised that habitat loss driven by the expansion of agriculture has led to biodiversity declines (e.g. [START_REF] Billeter | Indicators for biodiversity in agricultural landscapes: A pan-European study[END_REF]. One of the main challenges for biodiversity conservation is therefore to maintain or enhance biodiversity by taking advantage of existing production types by increasing the diversity of crops, as well as their spatial and temporal arrangement at the scale of the wider landscape mosaic. This study highlights that considering crop diversity and more precisely annual and perennial crops with different ecological characteristics favourable for bats could represent an efficient conservation strategy to promote bat species richness, and favour species of major conservation concern in agricultural landscapes. Bat responses to landscape features vary depending on their home range size, species-specific echolocation call characteristics and foraging strategies that may explain the diversity of species identified and the role of agricultural landscapes as foraging and commuting areas [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Laforge | Landscape composition and life-history traits influence bat movement and space use: Analysis of 30 years of published telemetry data[END_REF].

Finally, the activity of species of conservation concern was significantly and positively related to the most diverse agricultural landscapes. We suggest that the diverse landscapes mixing crops and forests studied here actually have a high conservation potential due to their higher structural complexity [START_REF] Harvey | Agroforestry systems conserve species-rich but modified assemblages of tropical birds and bats[END_REF][START_REF] Schroth | Agroforestry and biodiversity conservation in tropical landscapes[END_REF] allowing a differentiated use by a large range of bat guilds. Moreover, improving landscape compositional heterogeneity through an increase of the amount of key habitats such as hedgerows or riparian zones, their spatial configuration and their connectivity are of main importance (e.g. [START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: Implications for conservation[END_REF][START_REF] Froidevaux | Does organic farming enhance biodiversity in Mediterranean vineyards? A case study with bats and arachnids[END_REF][START_REF] Fuentes-Montemayor | The effectiveness of agrienvironment schemes for the conservation of farmland moths: Assessing the importance of a landscape-scale management approach[END_REF][START_REF] Heim | The Importance of Landscape Elements for Bat Activity and Species Richness in Agricultural Areas[END_REF][START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF]. [START_REF] Mata | Combining DNA metabarcoding and ecological networks to inform conservation biocontrol by small vertebrate predators[END_REF] showed that a limited number of bat species, consisting of both common species such as P. pipistrellus and conservation concern species, are central in pest interaction networks and could be the focus of conservation strategies. The results of our study support that enhancing crop diversity by combining agricultural fields and forestry and also other landscape elements such as linear features promote complementary and more stable resources (e.g. roost, foraging, commuting), which will benefit to a large range of bat species with some positive effects on biological control in crop fields.

Conclusion

Because of their ability to disperse and as generalist predators, bats have been proved

to be particularly important in controlling pest outbreaks [START_REF] Boyles | On estimating the economic value of insectivorous bats: Prospects and priorities for biologists[END_REF]. While more and more studies are focusing on the diet of bats via metabarcoding methods, few studies have been carried out to assess the role of bats for pest control [START_REF] Russo | Novel perspectives on bat insectivory highlight the value of this ecosystem service in farmland: Research frontiers and management implications[END_REF], and even fewer have considered the agricultural and forestry mosaic. Our study adds to the growing body of literature by (i) showing the positive effects of more diverse agricultural landscapes on bat richness and activity and (ii) supporting the efficacy of bats for the biological control of crop and forest pests [START_REF] Charbonnier | Numerical and Functional Responses of Forest Bats to a Major Insect Pest in Pine Plantations[END_REF][START_REF] Maine | Bats initiate vital agroecological interactions in corn[END_REF]. Agricultural landscapes combining a diverse crop mosaic (in a broad sense, including pine forestry) provide more stable resources over space and time for generalist predators such as bats. Thus, more diverse landscapes can allow for win-win strategies in each of the sampled crop increasing bat activity and biological pest control. In addition, more diverse landscapes benefit to a large range of bat species from different guilds, which may have a complementary predation on pests [START_REF] Garin | Bats from different foraging guilds prey upon the pine processionary moth[END_REF]. These results may benefit greatly to biocontrol conservation, especially in the context of diversifying agricultural landscapes facing rapid global changes.

Figure 1 :

 1 Figure 1 : Map of the study area showing the two landscape types: simplified landscapes depending on the targeted crop (P: pine or M: maize or V: vineyards) and diverse landscapes (Div: three crops were represented).

Figure 2 :

 2 Figure 2 : Relative abundance of the three targeted moths in the crops they were specialist about. Respectively

Figure 3 :

 3 Figure 3: Bat species richness (A) and total bat activity (B -data log transformed) depending on landscape type. (** p < 0.01, **** p < 0.0001)

  MRE activity was positively influenced in the same way as global bat activity. SRE activity was positively influenced by the three-crops Shannon diversity index and negatively by the total length of hedgerows (Figure A 7; Table B 2).The three-crops Shannon diversity index affected positively the SCC activity (Figure A 7; Table B 2).

Figure 4 :

 4 Figure 4: Relationships between total bat activity and damage index in maize fields in May (A) and in pine plantations in July (B), especially MRE foraging activity (C). Red points represent sampled points in maize-or pine plantations-dominated landscapes while blue points represent sampled points in diverse landscapes.

Table 1 : List of species sampled in the study and aggregation of species used in the statistical analyses.

 1 Table shows the guild of each different sampled species (according to their echolocation range) and their conservation concern.

	Species	Complex of species	Guild	Status of species on the Nouvelle-Aquitaine red list*
	Nyctalus leisleri	-	LRE	LC
	Nyctalus noctula	-	LRE	VU
	Nyctalus lasiopterus	-	LRE	VU
	Eptesicus serotinus	-	LRE	LC
	Pipistrellus pipistrellus	-	MRE	LC
	Pipistrellus kuhlii	-	MRE	LC
	Pipistrellus nathusii	-	MRE	NT
	Pipistrellus pygmaeus	-	MRE	DD
	Hypsugo savii	-	MRE	LC
	Miniopterus schreibersii	-	MRE	EN
	Barbastella barbastellus	-	SRE	LC
	Plecotus auritus Plecotus austriacus	Plecotus spp.	SRE SRE	LC LC
	Myotis blythii Myotis myotis	Large Myotis spp.	SRE SRE	EN LC
	Myotis emarginatus		SRE	LC
	Myotis crypticus	Small Myotis spp.	SRE	NT
	Myotis mystacinus		SRE	DD
	Rhinolophus ferrumequinum	-	SRE	LC
	Rhinolophus hipposideros	-	SRE	LC

LRE : Long-range echolocators; MRE : Mid-range echolocators; SRE: Short-range echolocators LC: Least concern; NT: Near threatened; VU: Vulnerable; EN: Endangered; DD: Data deficient

Table 2 : Description of the landscape metrics, co-variables and random factors used for each analysis, according to the dataset.

 2

	Dataset	Landscape metrics	Other co-variables	Random factor
	All	Landscape type
	N= 99 (50 sites x 2	(crop-dominated landscapes VS	-	(1|Site) + (1| Night)
	nights*)	landscapes with the three crops)
		Shannon diversity of crops	Biomass	(1|Site) + (1| Night)
		%SNH	Relative pest abundance
		%Artificial	(number of L. botrana)
	Vineyards	Length of hedgerows
	n= 34 (17 sites x 2	Distance to the nearest building
	nights)	Distance to the nearest hedgerow
		Distance to the nearest riparian zone
		Distance to the Garonne
		Distance to the nearest forest edge
		Shannon diversity of crops	Biomass	(1|Site) + (1| Night)
		%SNH	Relative pest abundance
	Maize fields	Length of hedgerows Distance to the nearest building	(number of S. nonagrioides)
	N= 32 (16 sites x 2	Distance to the nearest riparian zone
	nights)	Distance to the Garonne
		Distance to the nearest forest edge
		Distance to the nearest water body
	Pine	%SNH Shannon diversity of crops	(number of T. pityocampa) Relative pest abundance	(1|Site) + (1| Night)
	plantations	Length of hedgerows
	N= 33 (17 sites x 2	Distance to the nearest hedgerow
	nights*)	Distance to the nearest water body

Table 3 : Guild and species bat activity with a maximum error rate tolerance of 0.5 (and a 347 maximum error rate tolerance of 0.1 in brackets), i.e. number of bat passes recorded for 348 each sampling period.

 3

	349
	Taxa	Vineyards	Maize fields Pine plantations	Total
	Long-range echolocators -LRE	810 (619)	531 (358)	2 691 (1 459)	4 032 (2 436)
	Nyctalus leisleri	679 (589)	290 (233)	1 047 (804)	2 016 (1 626)
	Nyctalus noctula	4 (1)	11 (8)	0	15 (9)
	Nyctalus lasiopterus	0	14 (0)	0	14 (0)
	Eptesicus serotinus	127 (29)	216 (117)	1 644 (655)	1 987 (801)
	Mid-range echolocators -MRE	2 167 (1 716)	3 869 (2 834) 6 681 (5 680)	12 717 (10 230)
	Pipistrellus pipistrellus	1 209 (1 104)	2 268 (1 917)	3 415 (3 052)	6 892 (6 073)
	Pipistrellus kuhlii	737 (572)	1 302 (903)	3 138 (2 628)	5 177 (4 103)
	Pipistrellus nathusii	200 (29)	286 (14)	122 (0)	608 (43)
	Pipistrellus pygmaeus	11 (6)	2 (0)	0	13 (6)
	Hypsugo savii	6 (2)	10 (0)	6 (0)	22 (2)
	Miniopterus schreibersii	4 (3)	1 (0)	0	5 (3)
	Short-range echolocators -SRE	250 (152)	456 (323)	331 (205)	1 037 (680)
	Barbastella barbastellus	78 (73)	306 (279)	117 (72)	501 (424)
	Plecotus spp.	81 (15)	123 (31)	110 (44)	314 (90)
	Small Myotis spp.	16 (2)	3 (0)	23 (12)	42 (14)
	Large Myotis spp.	14 (6)	14 (6)	2 (0)	30 (12)
	Rhinolophus ferrumequinum	9 (9)	5 (3)	59 (58)	73 (70)
	Rhinolophus hipposideros	52 (47)	5 (4)	20 (19)	77 (70)
	Total	3 227 (2 487)	4 856 (3 515)	9 703 (7 344)	17 786 (13 386)

Table 4 : Estimates and standards errors (± SE) of the effect of landscape type 389 variable on species richness and bat activity (3 sampling periods combined, n = 390

 4 99). MRE: Mid-Range Echolocators; SRE: Short-Range Echolocators; SCC: Species of Conservation 391 Concern; BSD: Bat Sequence Duration. 392 Marginal R², i.e., variance explained by the fixed effects only, are given for GLMMs and * p <

	393
	394	0.05; ** p < 0.01 and *** p < 0.001.
			Response variable	Estimate (± SE)	z-value or t-value	P-value	Marginal R²
	Bat community	Species richness Gaussian family	1.01 (± 0.43)	2.35	0.023*	0.07
			Total activity Negative binomial family	0.60 (± 0.23)	2.61	0.009**	0.08
	Bat activity	MRE activity Poisson family	0.58 (± 0.28)	2.09	0.037*	0.04
			SCC activity Poisson family	1.23 (± 0.45)	2.75	0.006**	0.14
	Foraging activity		SRE BSD Gaussian family	0.05 (± 0.02)	2.01	0.045*	0.06
	395

Acknowledgements

This study was funded half by INRAE through the Sustainable Management of Crop Health metaprogram and the Occitanie Region.

We thank CC-IN2P3 and PCIA-MNHN for providing computing and storage facilities, Yves and Didier Bas for their help in this process.

We thank Philippe Mouquot of the Gironde Chamber of Agriculture for his expertise on maize fields. We are very grateful to Benjamin Joubard, Olivier Bonnard, Jérôme Molina and Jérôme Willm (INRAE) for their help in collecting data, to François Chiron (CNRS) for the loan of equipment and Yohan Charbonnier (LPO) for his bat expertise. We thank Adrien Rusch and Bastien Castagneyrol for their scientific advices in the sampling design.

We would like to thank all the land-owners who graciously allowed us to work in their fields.


Joffray Guillory
email: joffray.guillory@cnam.fr

Daniel Truong

Christophe Alexandre

Jean-Pierre Wallerand

A prototype of high accuracy telemeter for long-range application

Keywords: Laser diode, Intensity modulation, Phase measurement, Distance measurement, Crosstalk, Geodesy

This paper presents a long-range absolute distance meter working at 1550 nm. This instrument, based on off-the-shelf fiber-optic components from the telecommunications industry, has been evaluated in term of performances thanks to measurement campaigns conducted in the field and to comparisons with interferometric benches. We describe the principle of operation of this prototype and present its performances: resolution, accuracy and range of operation.

I. INTRODUCTION

Accurate long-distance measurements are essential for many applications such as the monitoring of large scale facilities (particle accelerators) or of large structures (dams). However, millimetric uncertainty over 5 or 10 kilometers is still a challenge, either with Global Navigation Satellite Systems (GNSS) or with electro-optical techniques. We present in this paper an all-fibered optical absolute distance meter that we have developed at 1550 nm by using off-the-shelf components. This prototype has demonstrated an accuracy of 2 µm up to 100 m and its range of operation was tested up to 5.4 km.

II. PRINCIPLE OF OPERATION

The telemeter is based on the measurement of the phase shift of a Radio Frequency (RF) modulation applied to the intensity of an optical beam propagating in air.

Figure 1: schematic diagram of the telemeter

As depicted in Fig. 1, a 1550 nm laser diode with a built-in Electro Absorption Modulator (EAM) has been chosen to generate a modulated light beam. The RF carrier used for this modulation around 5 GHz also serves as a phase of reference, φelect, for the phase shift measurement.

The modulated light beam is collimated by an off-axis parabolic mirror for a free-space propagation. After reflection on the distant corner cube, the beam is refocused into the singlemode fiber, then the optical signal is converted in an electrical one by a PIN photodiode. The received signal is used as measurement phase, φopt. The choice of the photodetector is essential to avoid amplitude to phase coupling [START_REF] Guillory | Characterization and reduction of the amplitudeto-phase conversion effects in telemetry[END_REF].

The difference between the two phase states, Φ, is linked to the distance L travelled by the optical beam by the relation:

RF f n c k L              2 2 1 ( 1
)
where c is the speed of light in vacuum, fRF the frequency of the modulation, k the number of time that the phase has rotated of 2 during the propagation, and n the refractive index of the medium travelled by the beam.

In practice, the distance is measured between the position of the distant hollow corner cube and an internal fibered mirror that is periodically illuminated thanks to an optical switch. This internal mirror enables us to remove any drifts of the RF phase that occurs upstream from the optical switch.

The phase difference between the measurement and reference signal is measured at 10.75 MHz after a down-conversion of the RF signals. To this end, a homemade phasemeter based on a Field-Programmable Gate Array (FPGA) has been designed.

III. RESOLUTION AND RANGE OF THE TELEMETER

A. Resolution

At short range, typically some tens of centimeters, in a quiet environment, the standard deviation of a set of data is around 800 nm for 10 ms of integration time for each point (Fig. 2). For longer range, the standard deviation of a measurement depends on the atmospheric conditions. In any case, we have never observed standard deviations larger than 40 µm, even in very harsh weather conditions and over several kilometers of measurement.

B. Range of operation

The maximum range we have tested is 5.4 km. Fig. 3 depicts a measurement over such a distance, with displacement steps of 100 µm applied to the distant corner cube. At this distance, over an urban area, the resolution was around 30 µm. A long-term drift can be observed on the raw dataset as no air index correction is applied, however, we can easily distinguish the distance variations. Between each step, we went back to the zero position to estimate this drift and thus apply a polynomial correction that compensates for the air index fluctuations.

V. ACCURACY OF THE TELEMETER

In addition to an incorrect evaluation of the atmospheric parameters (n index in formula 1), errors can be produces by the instrument itself. For instance, a wrong estimate of the RF carrier value would result in a scale error. To overcome this problem, all the frequencies of the setup are locked on a miniature Rubidium (Rb) clock. This ensures a value better than 10 -9 , well enough for our application. It is more difficult to identify errors due to crosstalks, i.e. the addition of spurious signals at the same frequency as the measurement signal. Such an unwanted signal induces an error in the measured phase that evolves periodically with the measured distance. This is usually called the "non-linearity" of the instrument. To quantify these errors, the distance measured by our telemeter has been compared to the one measured by an interferometer.

For the non-linearity, the comparison has been done with displacement steps of 5 mm, around 10 times less than the expected period of potential non linearities. No cyclic error has been detected at the scale of 2-3 µm (peak-to-peak error).

For the scale error, the comparison has been done over a 50 m long interferometric bench with a folded telemeter beam to double the distance covered. Fig. 4 shows the result for two different alignments of the bench, either with a variation of the RF power detected along the bench of 2 dB (day 1) or 10 dB (day 2). The error corresponds to the difference between the two measured distances, i.e. telemeter and interferometer. No scale error has been detected at the scale of 2.2 µm (standard deviation on the error).

VI. CONCLUSION

The telemeter that we have developed, sufficiently transportable to enable easy measurements in the field, has demonstrated an instrumental standard uncertainty of 2-3 µm up to 100 m (k=1), i.e. with no periodic or scale error at this level. It can also measure beyond this distance: its range of operation is at least 5.4 km, with a resolution below 40 µm in all the cases, but for long-range the measurement accuracy is still limited by the determination of the atmospheric parameters.

We are currently developing a two-wavelength system based on the same principle that should enable distance measurements at the millimeter level without determination of temperature and atmospheric pressure, whatever the measured distance.

Polynomial correction

The long-term drift, mainly due to temperature variations, was removed.

Figure 2 :

 2 Figure 2: short range resolution with standard deviation of 800 nm.

  relative distance (µm) relative distance = distancemean( distance ) mean ~ 244 mm n=1

Figure 3 :

 3 Figure 3: measurement of steps of 100 µm of a corner cube located at 5.4 km away from the telemeter.

Figure 4 :

 4 Figure 4: comparison with an interferometer over a 50 m long bench.

ACKNOWLEDGEMENT

This work was funded in part within the European Metrology Research Programme (EMRP) as JRPs SIB60 Surveying and IND53 Luminar. We thank J. Cali and S. Durand from École Supérieure des Géomètres et Topographes (ESGT) for their advices and the availability of their interferometric bench.