DOI: 10.5061/dryad.9s4mw6mvz
title: Managing hedgerows for biodiversity: Disentangling the effects of trimming, structure and connectivity on the use of linear features by bats

description: Hedgerows are key semi-natural elements in European agricultural
 landscapes supporting diverse wildlife, including bats, that depend on
 these linear elements for foraging and commuting. Despite replanting
 initiatives, many hedgerows remain degraded due to intensive management
 practices such as over-trimming. To develop effective and generalisable
 conservation guidelines, more research is needed to understand how both
 management practices and intrinsic hedgerow characteristics influence bat
 species with different foraging strategies. We conducted passive acoustic
 monitoring across 448 hedgerows to assess the influence of six
 characteristics on bat activity: trimming method, height, width, foliage
 density, connectivity, and woody plant diversity. We examined responses
 for two species associated with hedgerow landscapes (Barbastella
 barbastellus, Rhinolophus ferrumequinum), and three echolocation guilds:
 short-, mid-, and long-range echolocators (SRE, MRE, LRE), corresponding
 to clutter, edge, and open space foragers, respectively. Pollarding
 consistently increased bat activity, especially for tree-dwelling species,
 likely by maintaining mature trees with larger crowns and diversified
 microhabitats. Coppicing generally reduced activity for SRE, B.
 barbastellus, and R. ferrumequinum, while increasing LRE activity. Taller,
 tree-filled hedgerows generally supported higher bat activity, whereas
 width and foliage density had more limited effects. Finally, uninterrupted
 hedgerow segments enhanced SRE and MRE activity, highlighting the
 importance of local habitat continuity in creating effective corridors for
 bat movement. Woody plant diversity was also positively associated with
 SRE activity. These results were mostly consistent across simplified open
 croplands and structurally complex bocage landscapes. Synthesis and
 Applications: Our findings underscore the value of targeted hedgerow
 management in promoting on-farm bat activity, and support the integration
 of simple hedgerow quality assessment protocols into agri-environment
 schemes. Consistent with previous research, we confirm the importance of
 tall, tree-filled hedgerows and emphasize the need to preserve mature
 trees. We also provide novel insights: hedgerow continuity and
 low-intensity trimming methods can further improve habitat suitability,
 particularly for tree-dwelling bats. Our management recommendations align
 with evidence from other taxonomic groups, including birds and
 invertebrates, and can contribute to broader biodiversity conservation
 strategies.
description: # Managing hedgerows for biodiversity: Disentangling the effects of
 trimming, structure and connectivity on the use of linear features by bats
 Dataset DOI:
 [10.5061/dryad.9s4mw6mvz](https://doi.org/10.5061/dryad.9s4mw6mvz) ##
 Description of the data and file structure ### Files and variables ####
 File: Dataset_hegderow_bat_activity_2025.csv **Description:**  Using
 passive acoustic monitoring, this study aimed to disentangle the relative
 effects of six hedgerow characteristics (i.e. trimming method, height,
 width, foliage density, connectivity and woody plant diversity) on bat
 activity. We used data from passive acoustic monitoring of bats along 448
 hedgerows across three French regions differing in landscape configuration
 and composition. Hedgerows were selected to cover contrasting structural
 characteristics while ensuring consistent experimental conditions
 (adjacent to crop fields, away from artificial lighting and major
 infrastructures). Bat echolocation calls were recorded over 1,227 nights
 between 2020 and 2023 using passive acoustic devices (Song Meters and
 AudioMoths), following the [French National Bat Monitoring Programme
 guidelines](https://www.vigienature.fr/fr/page/protocole-point-fixe) (e.g.
 under homogenous and favourable weather conditions). Acoustic data were
 processed with automated identification software (Tadarida) and grouped
 into three foraging guilds (short-, mid-, and long-range echolocators),
 with additional focus on two key species strongly associated with hedgerow
 landscapes (*Rhinolophus ferrumequinum, Barbastella barbastellus)*.
 Concurrently, hedgerow structural and management characteristics were
 recorded using a simple standardised field protocol over a 50 m segment.
 All surveys were conducted with landowner and farmer permission. As
 acoustic sampling cannot differentiate individual bats, we used bat
 activity—the number of bat passes per site per night—as a proxy for
 abundance. A bat pass was defined as a five-second sequence containing at
 least one echolocation call. As manual classification would have been
 prohibitively time consuming, we used automated species identification
 with the Tadarida software which assigned each bat call to the most
 accurate taxonomic level along with a confidence index. We excluded data
 activity data with a maximum confidence index below 0.5, reducing false
 positives while retaining a high number of passes. Then, we validated our
 statistical results using a more conservative threshold of 0.9, which
 further limited false positives but excluded more true positives. Thus,
 our dataset includes bat activity variables computed at both thresholds
 (0.5 and 0.9), denoted by the suffixes "05" and "09,"
 respectively. ##### Variables: * hedgerow_ID: Identifier code for each 50
 m hedgerow segment. * lat_y: Geographic coordinates (latitude) in decimal
 degrees (WGS84). * long_x:  Geographic coordinates (longitude) in decimal
 degrees (WGS84). * study_region: Study region (3 levels: "East",
 "West", "North"). * observer: Initials of the observer
 who conducted the hedgerow survey and set up the passive acoustic
 monitoring device. * date: Date of bat monitoring, i.e date at the start
 of the recording (recording spans from 30 after sunset to 30 after
 sunrise). * julian_day: Julian day. * device_type: Type of acoustic
 monitoring device used (2 levels: "AM" = Audiomoth,
 "SM" = SongMeter). * vigiechiro_ID: Acoustic data identifier
 from the [Vigie-Chiro citizen science monitoring
 programme](https://vigiechiro.herokuapp.com/#/accueil) archived by the
 French National History Museum (MNHN). * recording_duration: Acoustic
 recording duration in hours (from 30 min after sunset to 30 after
 sunrise). * night_length: Night duration in hours from sunset to sunrise
 on the date of bat monitoring. * cluster_ID_3km: Cluster variable grouping
 all hedgerows located less than 3 km from one another. *
 cluster_ID_5km: Cluster variable grouping all hedgerows located less than
 5 km from one another. * cluster_ID_8km: Cluster variable grouping all
 hedgerows located less than 8 km from one another. * height: Hedgerow
 height in meters, categorical variable (3 levels: "&lt; 5 m",
 "5-7 m", "&gt; 7 m"). * width: Hedgerow width in
 meters, categorical variable (2 levels: "&lt; 2 m", "&gt; 2
 m"). * woody_plant_diversity: Hedgerow genera-level woody plant
 diversity, categorical variable (2 levels: "low" = less or equal
 to two genera, "high" = more than two genera). *
 trimming_method: Hedgerow trimming method, categorical variable (3 levels:
 "coppicing", "pollarding",
 "vertical_trimming"). * foliage_density: Hedgerow foliage
 density, categorical variable (2 levels: "low" = improved
 manoeuvrability for bat flight through the vegetation, "high" =
 reduced manoeuvrability for bat flight through the vegetation). *
 connectivity: Hedgerow connectivity, categorical variable (3 levels:
 "0" = isolated hedgerow, "1" = hedgerow connected at
 one extremity, "2" = hedgerow connected at both extremities). *
 dist_nearest_water_point_m: Hedgerow distance to nearest water point (e.g.
 pond, lake, river, stream) in meters. * dist_nearest_forest_m: Hedgerow
 distance to nearest forest in meters. * crop_cover_1000m: Crop cover (%)
 within a 1,000 m buffer radius. * crop_cover_1500m: Crop cover (%) within
 a 1,500 m buffer radius. * grassland_cover_1000m: Grassland cover (%)
 within a 1,000 m buffer radius. * grassland_cover_1500m: Grassland cover
 (%) within a 1,500 m buffer radius. * forest_cover_1000m: Forest cover (%)
 within a 1,000 m buffer radius. * forest_cover_1500m: Forest cover (%)
 within a 1,500 m buffer radius. * hedgerow_density_1000m: Hedgerow linear
 density (km/km²) within a 1,000 m buffer radius. * hedgerow_density_1500m:
 Hedgerow linear density (km/km²) within a 1,500 m buffer radius. *
 river_density_1000m: River linear density (km/km²) within a 1,000 m buffer
 radius. * river_density_1500m: River linear density (km/km²) within a
 1,500 m buffer radius. * crop_cover_3000m: Crop cover (%) within a 3,000 m
 buffer radius. * grassland_cover_3000m: Grassland cover (%) within a 3,000
 m buffer radius. * forest_cover_3000m: Forest cover (%) within a 3,000 m
 buffer radius. * hedgerow_density_3000m: Hedgerow linear density (km/km²)
 within a 3,000 m buffer radius. * river_density_3000m: River linear
 density (km/km²) within a 3,000 m buffer radius. * LRE_activity_50: Sum of
 bat activity for all detected long-range echolocators (LRE;
 i.e. *Eptesicus* spp., *Nyctalus* spp.), based on automated species
 identification with a 0.5 (50%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 LRE_activity_90: Sum of bat activity for all detected long-range
 echolocators (LRE; i.e. *Eptesicus* spp., *Nyctalus* spp.), based on
 automated species identification with a 0.9 (90%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * SRE_activity_50: Sum of bat activity for all detected short-range
 echolocators (SRE; i.e. *Barbastella* spp., *Plecotus* spp., *Rhinolophus*
 spp., *Myotis* spp.), based on automated species identification with a 0.5
 (50%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 SRE_activity_90: Sum of bat activity for all detected short-range
 echolocators (SRE; i.e. *Barbastella* spp., *Plecotus* spp., *Rhinolophus*
 spp., *Myotis* spp.), based on automated species identification with a 0.9
 (90%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). * Barbar_09:
 *Barbastella barbastellus* activity, based on automated species
 identification with a 0.9 (90%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). * Eptser_09:
 *Eptesicus serotinus* activity, based on automated species identification
 with a 0.9 (90%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Myo_alc_cap_dau_ema_mys_09: *Myotis* *spp*. activity (i.e. *Myotis
 alcathoe/capaccinii/daubentonii/emarginatus/mystacinus*), based on
 automated species identification with a 0.9 (90%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * MyoGT_09: *Myotis* *myotis* activity, based on automated species
 identification with a 0.9 (90%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Myonat_09: *Myotis* *nattereri* activity, based on automated species
 identification with a 0.9 (90%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Nyc_lei_noc_09: *Nyctalus leisleri/noctula* activity, based on automated
 species identification with a 0.9 (90%) confidence threshold using the
 [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Pip_nat_kuh_09: *Pipistrellus kuhlii/nathusii* activity, based on
 automated species identification with a 0.9 (90%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Pippip_09: *Pipistrellus pipistrellus* activity, based on
 automated species identification with a 0.9 (90%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Plec_aus_aur_09: *Plecotus austriacus/auritus* activity, based on
 automated species identification with a 0.9 (90%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Rhifer_09: *Rhinolophus ferrumequinum* activity, based on
 automated species identification with a 0.9 (90%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Rhihip_Rhieur_09: *Rhinolophus hipposideros/euryale* activity,
 based on automated species identification with a 0.9 (90%) confidence
 threshold using the [TADARIDA software](https://doi.org/10.5334/jors.154)
 (MNHN, 2017). * Barbar_05: *Barbastella barbastellus* activity, based on
 automated species identification with a 0.5 (50%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Eptser_05: *Eptesicus serotinus* activity, based on automated
 species identification with a 0.5 (50%) confidence threshold using the
 [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Myo_alc_cap_dau_ema_mys_05: *Myotis* *spp*. activity (i.e. *Myotis
 alcathoe/capaccinii/daubentonii/emarginatus/mystacinus*), based on
 automated species identification with a 0.5 (50%)confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * MyoGT_05: *Myotis* *myotis* activity, based on automated species
 identification with a 0.5 (50%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Myonat_05: *Myotis* *nattereri* activity, based on automated species
 identification with a 0.5 (50%) confidence threshold using the [TADARIDA
 software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Nyc_lei_noc_05: *Nyctalus leisleri/noctula* activity, based on automated
 species identification with a 0.5 (50%) confidence threshold using the
 [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN, 2017). *
 Pip_nat_kuh_05: *Pipistrellus kuhlii/nathusii* activity, based on
 automated species identification with a 0.5 (50%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Pippip_05: *Pipistrellus pipistrellus* activity, based on
 automated species identification with a 0.5 (50%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Plec_aus_aur_05: *Plecotus austriacus/auritus* activity, based on
 automated species identification with a 0.5 (50%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Rhifer_05: *Rhinolophus ferrumequinum* activity, based on
 automated species identification with a 0.5 (50%) confidence threshold
 using the [TADARIDA software](https://doi.org/10.5334/jors.154) (MNHN,
 2017). * Rhihip_Rhieur_05: *Rhinolophus hipposideros/euryale* activity,
 based on automated species identification with a 0.5 (50%) confidence
 threshold using the [TADARIDA software](https://doi.org/10.5334/jors.154)
 (MNHN, 2017). * min_temperature: Lowest temperature (°C) during the night
 of the recording. * cumulated_rainfall: Cumulative rainfall (mm) recorded
 on the day corresponding to the start of the night (day *n*) and the
 following day corresponding to the end of the night (day *n + 1*). *
 mean_wind_speed: Averaged wind speed (km/h) during the night of the
 recording.