DOI: 10.5061/dryad.hqbzkh1vd
title: Modelling connectivity at a regional scale during seasonal movements of the greater horseshoe bat

description: Connectivity modelling is a powerful tool for strengthening the link
 between landscape and species conservation. This approach often relies on
 expert knowledge of connectivity indicators or limited, small numbers and
 small-scale monitoring data, for example during animal foraging
 activities. However, integrating larger-scale movement data, including
 dispersal or seasonal movements, is crucial to making conservation
 relevant by covering the entire life cycle of species. Using Resource
 Selection Function, the movement patterns of greater horseshoe bats (GHB)
 studied on a local scale were transposed to a regional scale to model the
 connectivity in western France. GHB is highly sensitive to loss of
 connectivity and makes seasonal regional migrations. How the local
 landscape heterogeneity influenced the conductance parameters estimation
 for modelling was examined using gap-crossing method at four different
 sites with variable landscape composition. The inferred parameters were
 used to create a regional connectivity map based on circuit theory. To
 validate this map, acoustic monitoring was conducted during the autumn
 migration to assess its effectiveness in identifying connectivity
 gradients. Finally, the resulting connectivity map was superimposed on the
 Natura 2000 network. Firstly, it can be assumed that connectivity
 parameters are identical whatever the landscape context. Secondly, the
 regional connectivity model identified the main potential corridors
 connecting all the major sites in the region. Finally, based on acoustic
 sampling, the number of GHB in transit was significantly higher in areas
 of higher connectivity. In terms of overlap with conservation, the
 functional connectivity of the GHB have been variously addressed in the
 current Natura 2000 network, with an overall lack of representativeness.
 Studying pathways during high mobility periods is one of the main missing
 elements for effective conservation, particularly for small species such
 as bats. An acoustic, stratified sampling at both local and large scale
 provided sufficient spatial and temporal accuracy to model connectivity
 throughout the life cycle of bats. This framework can easily be applied to
 other bat species to improve our understanding of connectivity, in order
 to explicitly integrate this crucial aspect for highly mobile species into
 the protection network.
description: <strong>Empirical estimation of local-scale RSF in
 different landscapes</strong> Study
 sites To calculate conductance, RSF was estimated using
 the gap-crossing approach, i.e., the probability of crossing a gap in a
 given hedgerow as a function of the width of the gap. To determine whether
 variations in landscape context lead to significant variations in RSF
 estimation, gap-crossing probabilities were estimated at four sites
 differing in hedgerow density (from less than 20 to 200
 m.ha<sup>-1</sup>) (see Fig.S2). Data were recorded when most
 females were lactating, in July 2016 for the Annepont site and late June
 and July 2018 for three other sites: Les Verchers-sur-Layon, Réaumur, and
 Azay-sur-Thouet. Acoustic sampling
 The gap-crossing approach was used to estimate the probability of
 crossing a discontinuity (gap) in a connecting feature (hedgerow) as a
 function of the width of this gap, following Pinaud et al. (2018). At the
 four sites centred on a GHB maternity, recorders were placed overnight
 near the colony (&lt; 2 km) in hedgerows with varying gap widths
 ranging from 5 to 150 m). BatClassify software (Scott, 2012) was then used
 to identify GHB in the recordings, which were then manually confirmed with
 Kaleidoscope software (version 5.3.0, Wildlife Acoustics, USA).
 <strong>Modelling the connectivity
 map</strong> Definition of the conductance
 raster Based on the gap-crossing model, the RSF
 spatially predicted the probability of the presence of GHB as a function
 of the distance to a connecting element such as hedgerows or forests that
 facilitate movement, producing the conductance values in a raster for the
 entire study area. Connecting features (woods, vineyards, orchards,
 hedgerows, peri-urban areas such as villages and farms) were first mapped
 on a 10x10m resolution raster extracted from BD TOPO® provided by the
 French National Geographical Institute (IGN) in 2018. The probability of
 the presence of GHB as a function of the distance to the nearest
 connecting feature was then predicted from the gap-crossing model, giving
 the conductance values. From this model, since the probability of crossing
 at d=0 (intercept) was not equal to 1, the probabilities were rescaled
 from 0 to 1. As important barriers for bats, major roads and large urban
 areas were set to a conductance value of 0. Then, in order to facilitate
 movement within the connecting features (conductance initially set at 1,
 identical at locations a short distance away from them), the conductance
 values of the connecting features were set at 10, by analogy to classical
 studies (see Sawyer et al., 2011). Calculation of the
 connectivity map From this conductance raster, we used
 Circuitscape v5 software to create cumulative current maps (McRae et al.,
 2008) that identify functional connectivity of GHBs at a regional scale.
 For the layer of nodes to connect, we used the main roosts (maternity and
 wintering) with &gt;10 individuals known from long-term monitoring of
 the GHB population in this region. We calculate the connectivity between
 all types of roost because the early results of an ongoing CMR study with
 automatic readers and &gt;7000 GHBs fitted with PIT tags since 2016
 showed that large-scale GHB movements were not unidirectional and occurred
 in both directions between all types of sites during short transit
 periods, both in autumn and spring. When calculating connectivity, for
 computational reasons due to the large number of sites (n = 418 sites: 56
 for maternity and 361 for hibernation), the conductance raster was
 aggregated with 100 × 100 m cells by calculating the median conductance
 per aggregated 10 x 10 m cells. The use of the median for cell aggregation
 was validated using independent acoustic and radiotracking datasets from
 the Annepont study (see Pinaud et al., 2018), showing no difference in the
 ability of the model to predict the presence of GHB based on the flow of
 cumulative currents (i.e., connectivity).
 <strong>Validation of the regional connectivity map using
 independent field data</strong> Sampling
 design To evaluate the biological relevance of the
 regional connectivity map, we aimed to relate the number of GHB detected
 (<em>nbGHB</em>) to connectivity values (cumulative currents)
 calculated from Circuitscape. As <em>nbGHB</em> also depends
 on the probability of GHB presence at a location, we expected:
    <em>nbGHB</em> = P(<em>presence</em>)
 x <em>Connectivity </em>(eq. 1) We used a
 stratified sampling design where strata were chosen to maximise the
 probability of finding a GHB performing regional movements that occur in
 autumn between maternity and hibernation sites. This approach was adopted
 over pure random sampling as it is known to optimize efforts in collecting
 ecological spatial data, particularly with high density heterogeneity
 (see, for example, Smith et al. 2017). In our case, according to studies
 using passive bat recorder networks in this region (i.e., “VigieChiro”,
 Kerbiriou et al. 2018), the probability of contacting a GHB during a whole
 night far from roosting sites throughout the study area is low (between
 0.1 and 0.5), leading to an expected large number of zeros (absence of GHB
 nearby, regardless of connectivity values) and consequently, according to
 a power analysis, to a higher sampling effort. With this design, if at
 least one GHB is detected in a stratum (in a corridor site), it means that
 this stratum is being used (i. e.   in eq. 1), so we will be able to
 directly relate the number of GHB passes to the connectivity predicted by
 the model. Passive ultrasonic recorders were placed at high-intensity
 corridor sites as indicated on the connectivity map. To maximize the
 probability of contacting a GHB in transit rather than foraging close to a
 roost, we placed the recorders at least 10 km away from known maternity
 roosts and hibernation sites, to match the maximum distance bats travel
 when foraging around roosts (Finch et al., 2020; Pinaud et al., 2018). Of
 the 46 corridor sites that met this criterion, 32 were randomly selected
 as strata for sampling. At each corridor site, recorders were placed (143
 locations in total) in hedgerows at locations chosen to cover the entire
 gradient of connectivity values at that site. Acoustic
 survey To evaluate GHB activity around selected
 corridors, passive recorders fitted with ultrasonic microphones were
 placed overnight in the field from 6 pm to 6 am in late August 2020,
 during peak transit periods, with favorable weather conditions (no rain or
 strong wind). The recordings were classified using BatClassify software
 (Scott, 2012) and manually verified. The number of GHB detected at the
 sampling locations was measured as the number of GHB in a 5 seconds pass
 per night.
description: # Modelling connectivity at a regional scale during seasonal movements of
 the greater horseshoe bat Dataset DOI:
 [10.5061/dryad.hqbzkh1vd](10.5061/dryad.hqbzkh1vd) ## Description of the
 data and file structure Using Resource Selection Function, the movement
 patterns of greater horseshoe bats (GHB) studied on a local scale were
 transposed to a regional scale to model the connectivity in western
 France. GHB is highly sensitive to loss of connectivity and makes seasonal
 regional migrations. How the local landscape heterogeneity influenced the
 conductance parameters estimation for modelling was examined using
 gap-crossing method at four different sites with variable landscape
 composition. The inferred parameters were used to create a regional
 connectivity map based on circuit theory. To validate this map, acoustic
 monitoring was conducted during the autumn migration to assess its
 effectiveness in identifying connectivity gradients. ### Files and
 variables #### File: AcousticDetections_WGS84.csv **Description:** The
 description of the ultrasonic files recorded and their species
 classification according to the BatClassify software (Scott, 2012) used
 for validating connectivity modelling ##### Variables * Date_ARU_Channel:
 an ID composed from the date, the Acoustic Recording Unit, and the Channel
 * FileName: the file name with a time stamp * Bbar: probability of
 presence of *Barbastella barbastellus* * Malc: probability of presence of
 *Myotis alcathoe* * Mbec: probability of presence of *Myotis bechsteinii*
 * MbraMmys: probability of presence of *Myotis brandtii / Myotis
 mystacinus* * Mdau: probability of presence of *Myotis daubentonii* *
 Mnat: probability of presence of *Myotis nattereri* * NSL: probability of
 presence of *Nyctalus noctula* /*Nyctalus leisleri* / *Eptesicus
 serotinus* * Paur: probability of presence of *Plecotus auritus* * Ppip:
 probability of presence of *Pipistrellus pipistrellus* * Ppyg: probability
 of presence of *Pipistrellus pygmaeus* * Rfer: probability of presence of
 *Rhinolophus ferrumequinum* * Rhip: probability of presence of
 *Rhinolophus hipposideros* * ARU_type: PassiveRecorder or SM3 * ARU_id:
 ARU id * Stereo: stereo recording? FALSE/TRUE * Channel: channel id (0/1)
 * DateTimeStartTU: Date-Time start of the recording (Universal Time) *
 NightDate: Date of the night of recording * Session: Session Id
 (Date_ARUid) * Location_id: Location Id * ID_Site: Site Id * Long_WGS84: X
 coordinate of the location of recording in decimal degree (rounded at
 0.1), projection EPSG:4326 / WGS84 * Lat_WGS84: Y coordinate of the
 location of recording in decimal degree (rounded at 0.1), projection
 EPSG:4326 / WGS84 * CumCurrent: value of the cumulated currents of the
 location calculated with CircuitScape * GHB_nb: number of *Rhinolophus
 ferrumequinum* detected in the recording #### File:
 ColoniesLocations_BreedWint_epsg2154.csv **Description:** Locations of
 *Rhinolophus ferrumequinum* major sites used in the connectivity analysis
 ##### Variables * ID: Site Id * Dpt: french administrative department *
 Com_Site: french administrative commune * Type: type of site, reproduction
 or wintering * GHB_nb: number of *Rhinolophus ferrumequinum* counted in
 the site. NA: not available. * X_epsg2154: X coordinate of the site,
 projection EPSG:2154 / Lambert-93, rounded at 100m * Y_epsg2154: Y
 coordinate of the site, projection EPSG:2154 / Lambert-93, rounded at 100m
 #### File: GapCrossing_GHBdetection_4sites.csv **Description:** Locations
 used in the gap-crossing study in four sites ##### Variables * GapWidth:
 width of the gap (in meters) * Date: date of the sampling * Site: site of
 the sampling * X_epsg2154: X coordinate of the site, projection EPSG:2154
 / Lambert-93 * Y_epsg2154: Y coordinate of the site, projection EPSG:2154
 / Lambert-93 * GHB_count: cumulated number of *Rhinolophus ferrumequinum*
 counted in this location for one night * GHB_pres: presence / absence of
 *Rhinolophus ferrumequinum* counted in this location for one night ####
 File: rConductance_100m_epsg2154.asc **Description:** raster of
 conductance used for calculating the connectivity with the CircuitScape
 software. Projection EPSG:2154 / Lambert 93. Cell size 100m #### File:
 rConnectivity_CurrentCumul_100m_epsg2154.tif **Description:** raster of
 connectivity (cumulated currents) calculated with the CircuitScape
 software. Projection EPSG:2154 / Lambert 93. Cell size 100m #### File:
 SamplingLocation_AcousticValidation_epsg2154.csv **Description:** The
 description of the location of acoustic sampling used for validating
 connectivity modelling ##### Variables * n_id: location id * loc_id: id
 name of the location * ID_Site: Site Id * Date: Date of sampling * ARU_id:
 Acoustic Recorder Unit id * X_epsg2154: X coordinate of the sampling
 location, projection EPSG:2154 / Lambert-93 * Y_epsg2154: Y coordinate of
 the sampling location, projection EPSG:2154 / Lambert-93 * CumCurrent:
 connectivity (cumulated currents) at location calculated with the
 CircuitScape software. * Conductance: conductance at the location ##
 Code/software * Circuitscape v5 (McRae et al., 2008) * R software V.4.0.3
 (R Core Team, 2020) with packages "dplyr", "readr",
 "rgdal", "gdalUtils", "terra" ## Access
 information Other publicly accessible locations of the data: * N2000 area
 polygons (Inventaire National du Patrimoine Naturel, September 2018
 version [www.inpn.mnhn.fr](http://www.inpn.mnhn.fr)) * habitat cover of
 the 2018 Corine Land Cover ([www.eea.europa.eu](http://www.eea.europa.eu))
 Data was derived from the following sources: BD TOPO® provided by the
 French National Geographical Institute (IGN) in 2018