DOI: 10.5061/dryad.bk3j9kdnn
title: Fluttering in a changing world: Effects of urbanization and nectar plants on butterfly movement patterns

description: We aimed to answer the general question whether urbanization affects
 butterfly movement patterns and more specific, whether (1) the mobility of
 the investigated small white (Pieris rapae) and the small heath butterfly
 (Coenonympha pamphilus) is affected differently and (2) these butterflies
 show altered tortuosity-patterns along a rural-urban gradient. The study
 sites were situated along a rural-urban gradient in the Berlin-Brandenburg
 metropolitan region (Germany). We recorded GPS-movement trajectories of
 two common butterfly species differing in territoriality, agility and
 habitat requirements. Movement trajectories were analyzed in terms of
 mobility (flight speed and time investment in stopping, resting and
 nectaring) and tortuosity measures and the effect of urbanization on the
 derived variables was investigated.
description: The fieldwork was carried out from June to September 2020,
 between 9:00 and 15:00 h on sunny and calm days. To gain spatial data of
 butterfly movements, we tracked two to five butterflies per species and
 study site if the species was present (<em>P. rapae</em>:
 3,7±0,9 and <em>C. pamphilus</em>: 3,4±0,8 mean±SD of
 butterflies per study site), by constantly keeping a distance of about
 2.5±0.5 m between observer and animal. Movements of the observer were
 logged by a DGPS-Receiver with real-time differential correction (“Trimble
 R10”), mounted on a GPS-backpack and operated via handheld control. The
 GPS-position was recorded with a temporal resolution of one second and an
 average horizontal accuracy of 0.02 m. The high
 accuracy of the DGPS-Receiver allowed for the tracking of both, large
 scale fast flight, as well as small scale fluttering flight. In order to
 be able to identify stops, we noted the respective second on a prepared
 data sheet every time the observed butterfly was landing. Additionally, we
 noted the respective action of the butterfly during a stop, being either
 nectaring, resting, basking or oviposition. The recording stopped, when
 the observer lost sight of the butterfly or after a maximum of 12 minutes
 (00:05:36 ±00:02:38; mean ±SD). Since the high volatility of the butterfly
 movements often made it impossible to write all information down
 immediately, comments regarding the tracks were recorded as audio files
 via “AGPTEK” Lavalier microphone on a smartphone. The audio-recording
 started simultaneously with the GPS-recording in order to achieve initial
 temporal synchronization. This allowed for a subsequent completion of the
 data sheets and facilitated the identification of stops or data gaps (the
 latter sometimes occurred when butterflies were flying through groves and
 the GPS-signal was insufficient to locate the observers
 position). The meteorological parameters wind speed
 (m/s), air temperature (°C) and relative humidity (%) were measured with a
 Thermo-Hygro-Anemometer („PCE-THA 10“) approximately 2 m above ground
 subsequently to each GPS-tracking.  To attain an
 approximation for food availability, a transect was drawn through the
 respective movement area and the coverage of flowering nectar plants was
 estimated for each 1-m segment of the transect within a margin of 0.5 m to
 the left and right side respectively. The mean coverage of nectar plants
 was then calculated for each transect.  The recorded
 movement patterns were exported as point features in an ESRI shapefile and
 imported into ArcGIS 10.3.1 (Esri Inc. 2018) for processing. Line
 geometries were drawn along the subsequent GPS-positions, using the time
 information (seconds since start of the track) as guidance. Due to small
 movements of the observer, even when trying to stand perfectly still, the
 butterfly landings were not recorded as perfect stops but rather as point
 clouds. For each point cloud, previously confirmed as a stop via
 comparison with the data sheet information, the median center was
 calculated with the ArcGIS tool “Median Center”. The computed coordinate
 was then attributed to all affected GPS-points and used for further
 analyses. For the calculation of the tortuosity however, all but the first
 (adjusted) point of each stop were excluded to avoid problems with the
 computation of the mean cosine. Stops during initiation and termination of
 the GPS-logging were excluded from all analyses. To
 describe the butterfly movements, we derived several parameters from the
 spatial GPS-data and assigned them to one of the categories (1) mobility
 or (2) tortuosity. As measures for butterfly
 <strong>mobility</strong>, the mean flight speed (“flight
 speed” in m/s) and the share of time spent stopping (“stopping time”),
 nectaring (“nectaring time”) and resting (“resting time”) were calculated.
 Due to low sample sizes, the time spend ovipositing and the time spent
 basking were not analyzed. As
 <strong>tortuosity</strong> measure, the sinuosity of the
 flight path, as defined by Benhamou (2004) was calculated with the R
 package trajr and the function TrajSinuosity2 (McLean & Skowron
 Volponi 2018). High values of sinuosity indicate a high tortuosity, while
 low values indicate a virtually straight flight path. In cases of split
 tracks (due to data gaps), the sinuosity was computed for each segment
 individually and the mean sinuosity, weighted by relative track segment
 length was then calculated for the entire butterfly track. 
 As a measure for urbanization, the percentage of sealed soil
 surface around each study site was calculated. Within the boundaries of
 Berlin, this calculation was based on the Berlin impervious soil coverage
 map (BISCM, SenUDH 2016). Since such data was not available for
 Brandenburg, approximations based on the BISCM (SenUDH 2016) and the
 Berlin biotope type map (SenUDH 2014) were computed. For this purpose, the
 average percentage of surface sealing was calculated for each biotope type
 via the Zonal Statistics tool in QGIS 2.18.11 (QGIS-DT 2018) based on the
 available rasterized data for Berlin. The calculated values were then
 attributed to the biotope patches of the Brandenburg biotope type map (LfU
 2009). The vector data gained was merged with the BISCM and then
 transformed into raster data (resolution: 2 x 2 m). This map was used to
 calculate the mean percentage of sealed surface within a 500, 1000 and
 2000 m radius around the center point of each study site via the Zonal
 Statistics tool in QGIS 3.4.12 (QGIS-DT 2018). To
 attain an approximation for the size of the habitat patch, the area (ha)
 of the habitat site was calculated using the “calculate geometry” tool of
 ArcGIS Version 10.3.1 (Esri Inc. 2018). To do so, we first digitalized the
 continuous grassland site (not interrupted by urban matrix, forest,
 agricultural crops, waterbodies, ornamental lawns or other flower-free
 biotopes as reed beds) based on orthophotos of the Berlin-Brandenburg area
 in summer 2020 and winter 2021 (Geoportal Berlin 2020, 2021; LGB 2020) and
 supported by the Berlin and Brandenburg biotope type map (LfU 2009, SenUDH
 2014).
 <strong>References: </strong>
 Benhamou, S. (2004). How to reliably estimate the tortuosity of
 an animal’s path: straightness, sinuosity, or fractal dimension?
 <em>Journal of Theoretical Biology, 229</em>, 209–220.
 https://doi.org/10.1016/j.jtbi.2004.03.016 Esri Inc.
 ArcMap™. Version 10.3.1.4959. 18 June 2015 [27.04.2018]. Available online:
 https://www.esri.de/de-de/home. Geoportal Berlin (2020). Digitale farbige Orthophotos 2020 (DOP20RGB). Datenlizenz Deutschland – Namensnennung – Version 2.0. URL: https://fbinter.stadt-berlin.de/fb/wms/senstadt/k_luftbild2020_rgb [19.04.2021]. Geoportal Berlin (2021). Digitale farbige Orthophotos 2021 (DOP20RGBI). Datenlizenz Deutschland – Namensnennung – Version 2.0. URL: https://fbinter.stadt-berlin.de/fb/wms/senstadt/k_luftbild2021_rgb [19.04.2021]. LfU (Landesamt für Umwelt Brandenburg) (2009). CIR-Biotoptypen 2009 – Flächendeckende Biotop- und Landnutzungskartierung im Land Brandenburg (BTLN). dl-de/by-2-0, URL: https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https://geoportal.brandenburg.de/gs-json/xml?fileid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412 [19.04.2021]. LGB (Landesvermessung und Geobasisinformation Brandenburg) (2020). Digitale Orthophotos 20cm Bodenauflösung Farbe Brandenburg mit Berlin (WMS). GeoBasis-DE/LGB, dl-de/by-2-0, URL: https://geobroker.geobasis-bb.de/gbss.php?MODE=GetProductInformation&PRODUCTID=253b7d3d-6b42-47dc-b127-682de078b7ae [19.04.2021]. McLean, D.J., Skowron Volponi, M.A. (2018). trajr: An R package for characterisation of animal trajectories. <em>Ethology, 124</em>, 440–448. https://doi.org/10.1111/eth.12739 QGIS-DT (QGIS Development Team) (2018). “QGIS Geographic Information System: Open Source Geospatial Foundation.” http://qgis.osgeo.org. SenUDH (Senate Department for Urban Development and Housing) (2014). Geoportal Berlin. 05.08 Biotope Types. dl-de/by-2-0, URL: https://www.berlin.de/umweltatlas/en/biotopes/biotope-types/2013/maps/artikel.970219.en.php [02.03.2020].
description: # Fluttering in a changing world: Effects of urbanization and nectar
 plants on butterfly movement patterns
 [https://doi.org/10.5061/dryad.bk3j9kdnn](https://doi.org/10.5061/dryad.bk3j9kdnn) ## Dataset Overview This dataset contains the data required to replicate the analyses in Gamrath et al. (in review), investigating whether urbanization and nectar plont abundance affect butterfly movement patterns and more specific, whether (1) the mobility of the investigated small white (*Pieris rapae*) and the small heath butterfly (*Coenonympha pamphilus*) is affected differently, and (2) these butterflies show altered tortuosity-patterns along a rural-urban gradient. ### Timeframe of Data Collection The fieldwork was carried out from June to September 2020, between 9:00 and 15:00 h on sunny and calm days. ### Study sites The study was conducted at 29 grassland sites in the metropolitan area of Berlin and its rural surroundings within the federal state of Brandenburg, Germany (Fig. 1; 52°20' to 52°40' N and 12°59' to 13°37' E). We utilized the infrastructure of the CityScapeLabs Berlin (von der Lippe et al. 2020), which were established to investigate urbanisation effects on biodiversity and biotic interactions within the framework of the BIBS-Project (Bridging in Biodiversity Science; [https://www.tu.berlin/en/oekosys/research/projects/past-projects](https://www.tu.berlin/en/oekosys/research/projects/past-projects)). The CityScapeLab sites were preselected regarding their size (more than 1 ha), location along the rural-urban gradient (covering a wide span of urbanization), biotope type (dry or mesophilic grassland) and probable accessibility, leaving a total of 28 apparently suitable grassland sites, but only 16 of these sites were actually accessible and occupied by at least one of the investigated species. Therefore, we randomly selected and visited additional sites from the Berlin biotope type map (SenUDH 2014) and the Brandenburg biotope type map (LfU 2009) matching the same criteria, to obtain a sufficient sample size. ### References: LfU (Landesamt für Umwelt Brandenburg) (2009). CIR-Biotoptypen 2009 – Flächendeckende Biotop- und Landnutzungskartierung im Land Brandenburg (BTLN). dl-de/by-2-0, URL: [https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https://geoportal.brandenburg.de/gs-json/xml?fileid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412](https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https://geoportal.brandenburg.de/gs-json/xml?fileid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412) [19.04.2021]. Pallmann, A. (2020). Tagfalter-Mobilität: Welchen Einfluss hat die Urbanisierung auf die Mobilität von Pieris rapae? Bachelor’s Thesis, Institute for Ecology, TU Berlin. QGIS-DT (QGIS Development Team) (2018). “QGIS Geographic Information System: Open Source Geospatial Foundation.” [http://qgis.osgeo.org](http://qgis.osgeo.org). SenUDH (Senate Department for Urban Development and Housing) (2014). Geoportal Berlin. 05.08 Biotope Types. dl-de/by-2-0, URL: [https://www.berlin.de/umweltatlas/en/biotopes/biotope-types/2013/maps/artikel.970219.en.php](https://www.berlin.de/umweltatlas/en/biotopes/biotope-types/2013/maps/artikel.970219.en.php) [02.03.2020]. von der Lippe, M., Buchholz, S., Hiller, A., Seitz, B., Kowarik, I. (2020). CityScapeLab Berlin: A Research Platform for Untangling Urbanization Effects on Biodiversity. Sustainability, 12, 2565. [https://doi.org/10.3390/su12062565](https://doi.org/10.3390/su12062565) ### Files and variables #### File: Butterfly_Data.csv **Description:** All variables analyzed in Gamrath et al. (in review) as well as some additional/supporting variables. Missing data are coded as NA. The file format is 'CSV UTF-8'. Seperator is ";", decimal marker is ".". ##### Variables * Site: study site identifier * BID: butterfly identifier * spec: observed butterfly species (CP - Coenonympha pamphilus; PR - Pieris rapae) * sex: sex of the observed butterfly (f - female; m - male; NA - not identified) * Date: date of the survey * Date_num: day of the year (1-365) of the survey * Time_from: start time of the survey hh:mm:ss) * Time_to: end time of the survey (hh:mm:ss) * dur_survey: survey duration (hh:mm:ss) * Time_fin: finishing time of the GPS-tracking (hh:mm:ss) * Time_fin_num: finishing time of the GPS-tracking in decimal fraction of the day * dur_track: track duration (hh:mm:ss) * Hab_area_ha: area of the habitat patch (ha) * NP_min: minimum coverage of nectar plants at the transect * NP_max: maximum coverage of nectar plants at the transect * NP_mean: mean coverage of nectar plants at the transect * NP_sd: standard deviation of nectar plant coverage at the transect * HP_min: minimum coverage of host plants at the transect * HP_max: maximum coverage of host plants at the transect * HP_mean: mean coverage of host plants at the transect * HP_sd: standard deviation of host plant coverage at the transect * wind_speed: mean wind speed (m/s) 2 m above ground * rel_hum: relative humidity (%) 2 m above ground * temp: air temperature (°C) 2 m above ground * Seal_500: sealed surface within a 500 m radius around the sample site (%) * Seal_1000: sealed surface within a 1000 m radius around the sample site (%) * Seal_2000: sealed surface within a 2000 m radius around the sample site (%) * c_IC: count if interactions with conspecifics * c_IX: count of interactions with other butterfly species * c_I: total count of interactions * C_I.min: interaction frequency (count of interactions per minute) * c_stop: total count of stops * c_stop/min: count of stops per minute * t_stop: total time spent stopping * t_stop_mean: mean stop duration * sh_stop: share of time spent stopping * sh_F: share of time spent nectaring * sh_R: share of time spent resting * sh_B: share of time spent basking * L: butterfly track length (m) * Dnet: net distance moved (beeline distance between start and finish of the GPS-recording) (m) * sec_TakeOff: second (since start of GPS-recording) of first take-off (stops during initiation of the GPS-logging were excluded from the analyses) * sec_End: last second of the track (stops during termination of the GPS-logging were excluded from the data) * dur_track_s: track duration (s) * vF: flight speed (m/s) * Deff: effective distance (m) calculated as the length of the MBG (Minimum Bounding Geometry) of the individuals' GPS-Points * Straightness: straightness of a trajectory, calculated as Deff/L * Sinuosity: measure for tortuosity, defined by Benhamou (2004) * HR_area_ha: activity range area (ha) * LSI: "Landscape Shape Index; Relation between perimeter of the activity range and the perimeter of a perfect circle with the area of the activity range (after Patton 1975) #### File: Geodata_Butterfly_Movement.7z **Description:** Spatial data required to replicate the analyses in Gamrath et al. (in review). Spatial reference is ETRS 1989 UTM Zone 33N, but .prj-filed are included. The geodata files are described in 'README_Geodata_ButterflyMovement.txt' All the shapefiles (.shp) used in this study contain the geometry and attributes of geospatial features (e.g., points, lines, polylines, polygons). The file bundle contains the main file .shp and companion files including: .cpg, .dbf, .prj, .sbn, .sbx, .shx. Description of these file extensions is given as follows: .shp: The main geospatial data file that contains feature geometry.\ .cpg: The file specifying the codepage to identify the characterset.\ .dbf: The dBASE that contains the attributes of features.\ .prj: The file that contains the coordinate system and map projection information.\ .sbn: The file containing the spatial index of features.\ .sbx: The file containing the spatial index of features.\ .shx: The file containing the index of feature geometry. The main .shp file can be opened and analyzed by Python, R, and many other programming languages, and open-source geospatial software such as QGIS, SAGA GIS, GRASS GIS, GeoDa, etc. #### File: Statistics_and_Graphs_Butterfly_Movement.Rmd **Description:** R (markdown) code of the statistical analyses performed for Gamrath et al. (in review). ## Code/software Spatial analysis was performed using:  Esri Inc. ArcMap™. Version 10.3.1.4959. 18 June 2015 [27.04.2018]. Available online: [https://www.esri.de/de-de/home](https://www.esri.de/de-de/home). QGIS-DT (QGIS Development Team) (2018). “QGIS Geographic Information System: Open Source Geospatial Foundation.” [http://qgis.osgeo.org](http://qgis.osgeo.org). Statistical analysis (of 'StudySites_Data.csv') was performed using:  R Core Team. R for Windows 3.6.1. July 5 2019 [08.12.2019]. Available online: [https://cran.r-project.org/bin/windows/base/old/3.6.1/](https://cran.r-project.org/bin/windows/base/old/3.6.1/). RStudio Inc. RStudio™ 1.1.423. February 2 2018 [16.02.2018]. Available online: [https://www.rstudio.com/products/rstudio/download/](https://www.rstudio.com/products/rstudio/download/). ## Access information Data was derived from the following sources: Most data were collected in the field by the authors or subsequently calculated from those data. The percentage of sealed soil surface around the study sites was calculated via QGIS 2.18.11 (QGIS-DT 2018) based on the Berlin impervious soil coverage map (BISCM, SenUDH 2016), the Berlin biotope type map (SenUDH 2014) and the Brandenburg biotope type map (LfU 2009). The habitat patch area was calculated via ArcGIS Version 10.3.1 (Esri Inc. 2018) based on orthophotos of the Berlin-Brandenburg area in summer 2020 and winter 2021 (Geoportal Berlin 2020, 2021; LGB 2020) and supported by the Berlin and Brandenburg biotope type map (LfU 2009, SenUDH 2014) References: Esri Inc. ArcMap™. Version 10.3.1.4959. 18 June 2015 [27.04.2018]. Available online: [https://www.esri.de/de-de/home](https://www.esri.de/de-de/home). Geoportal Berlin (2020). Digitale farbige Orthophotos 2020 (DOP20RGB). Datenlizenz Deutschland – Namensnennung – Version 2.0. URL: [https://fbinter.stadt-berlin.de/fb/wms/senstadt/k_luftbild2020_rgb](https://fbinter.stadt-berlin.de/fb/wms/senstadt/k_luftbild2020_rgb) [19.04.2021]. Geoportal Berlin (2021). Digitale farbige Orthophotos 2021 (DOP20RGBI). Datenlizenz Deutschland – Namensnennung – Version 2.0. URL: [https://fbinter.stadt-berlin.de/fb/wms/senstadt/k_luftbild2021_rgb](https://fbinter.stadt-berlin.de/fb/wms/senstadt/k_luftbild2021_rgb) [19.04.2021]. LfU (Landesamt für Umwelt Brandenburg) (2009). CIR-Biotoptypen 2009 – Flächendeckende Biotop- und Landnutzungskartierung im Land Brandenburg (BTLN). dl-de/by-2-0, URL: [https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https://geoportal.brandenburg.de/gs-json/xml?fileid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412](https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https://geoportal.brandenburg.de/gs-json/xml?fileid=B57B9F35-AFFF-49F2-BA32-618D1A1CD412) [19.04.2021]. LGB (Landesvermessung und Geobasisinformation Brandenburg) (2020). Digitale Orthophotos 20cm Bodenauflösung Farbe Brandenburg mit Berlin (WMS). GeoBasis-DE/LGB, dl-de/by-2-0, URL: [https://geobroker.geobasis-bb.de/gbss.php?MODE=GetProductInformation&PRODUCTID=253b7d3d-6b42-47dc-b127-682de078b7ae](https://geobroker.geobasis-bb.de/gbss.php?MODE=GetProductInformation&PRODUCTID=253b7d3d-6b42-47dc-b127-682de078b7ae) [19.04.2021]. QGIS-DT (QGIS Development Team) (2018). “QGIS Geographic Information System: Open Source Geospatial Foundation.” [http://qgis.osgeo.org](http://qgis.osgeo.org). SenUDH (Senate Department for Urban Development and Housing) (2014). Geoportal Berlin. 05.08 Biotope Types. dl-de/by-2-0, URL: [https://www.berlin.de/umweltatlas/en/biotopes/biotope-types/2013/maps/artikel.970219.en.php](https://www.berlin.de/umweltatlas/en/biotopes/biotope-types/2013/maps/artikel.970219.en.php) [02.03.2020].