DOI: 10.5061/dryad.rfj6q57q0
title: Data from: Saving Bambi from the mower? Using a drone with thermal camera to evaluate a low-tech scaring technique to reduce roe deer fawn mortality during grass harvest

description: Roe deer is a species that hides their neonates as an anti-predator
 strategy. This may prove efficient against mammalian predators, such as
 the red fox; however, it might be an ecological trap as large numbers of
 fawns are killed by tractors with harvesters each year during grass
 harvest. Here, we evaluate a low-tech, low-cost method, using scaring
 flags placed in the field the evening before grass harvest, intended to
 stimulate the roe deer doe to remove any fawns from the field. We evaluate
 the effects of the scaring flags by counting the number of fawns in the
 fields using a multi-rotor drone equipped with a thermal camera. The drone
 proved to be an efficient tool for detecting roe deer fawns. However,
 contrary to other studies, we found limited or no effect of placing the
 scaring flags in the field. This discrepancy highlights the fallacy of
 generalizing based on single case studies. There is still a need to
 develop low-cost, easily applicable tools to reduce roe deer fawn
 mortality during grass harvest. However, with the increasing technological
 development, drones with thermal cameras are a promising tool for wildlife
 management and monitoring in the future.
description: The study was conducted in Nærøy Municipality, Nord-Trøndelag
 County (64° 48’ N, 11° 16’ E,). The landscape features a mixture of
 agricultural fields (mainly grass production for livestock feed), and
 forest patches. The field size of the area is rather small, with an
 average surveyed field size of 2.4 ha. Experimental
 setup In mid-June 2017 and end of May to mid-June 2018
 we surveyed fields prior to grass harvest, using a drone equipped with a
 thermal camera (see “Drone setup” below). In 2017 we surveyed 38 fields
 and in 2018 we surveyed 69 fields. Roe deer fawns leave clearly visible
 heat signatures in the field in the early morning before the sun warms up
 the grass and reduces heat contrast. We utilized an
 experimental design where we first surveyed fields using drones in the
 morning (07:00-10:00), then randomly selected fields for the treatment and
 control groups. We surveyed the same fields with drones a second time the
 following morning (07:00- 10:00). The 2nd flight represents the harvest
 itself during which any remaining roe deer fawns could potentially be
 harmed. This resulted in a total of 52 control fields and 55 treatment
 fields. In the treatment fields, scaring flags (plastic shopping bags,
 approximately 30x40 cm) on 2m sticks were set up in the evening
 (18:00-20:00), control fields were not visited. As such, the scaring flags
 themselves, as well as the action of placing the flags in the field can be
 viewed as part of the experimental treatment. The scaring flags were set
 up 10- 20m from the field edge assuring maximum distance of 50m between
 flags. The scaring flags were left to stand until the second drone survey
 the following morning (07-10:00). This design simulates a grass harvest
 timeline where scaring devices are deployed, the evening before
 harvest. Drone setup We used a DJI
 Phantom 3 Professional multi-rotor drone with an effective flight time of
 &lt; 15 minutes, depending on weather conditions. The drone was
 equipped with a FLIR TAU 2 640 long-wave infrared (LWIR) thermal camera
 (resolution: 640×512 pixels; field of view: 90° horizontal, 69° vertical;
 frame rate: 9 Hz; spectral range: 7.5–13.5 μm). Thermal images were
 recorded onboard via the TeAx module and downloaded post-flight, as no
 live image stream was available. Images were manually analysed in
 ThermoViewer 2.1.6. To avoid double counting, we used visible landmarks
 (e.g. field edges, trees, vehicle tracks, and other features) in
 neighbouring images in combination with the drone’s GPS data to accurately
 identify individual fawns. Quality control
 We utilized an experimental approach to our study, thus
 controlling for any unwanted variation in our data caused by e.g. location
 of the surveyed fields (high versus low roe deer use/abundance) or weather
 conditions that could affect e.g. 1) fawn detectability, 2) roe deer
 activity or 3) scaring flag flapping. However, as an additional quality
 control of the classification of roe deer fawns versus other potential
 objects/animals from thermal images we: 1) Analysed a subset of the survey
 images immediately after the second flight and visited positions at which
 fawns were detected to confirm the image analysis. 2) In cases where we
 discovered fawns while putting out the scaring flags in the evening after
 the first flight – we viewed the thermal images from the prior morning to
 check if the fawns were present in the thermal image. We only did this
 quality control if we were able to identify the exact location of the
 potential object with reference to other structures in the image such as
 shape of the field, powerlines, paths, roads, boulders etc. We defined
 confirmed fawns as either the presence of fawns or distinctive fresh
 bedsites, characterised by completely flattened grass. These bedsites are
 unique to roe deer fawns and differ from resting places of other animals,
 such as hares or birds, commonly found in these fields. The surveyed
 fields would also contain multiple old fawn bedsites, however these would
 1) look different as grass would gradually right itself after the fawn has
 left, 2) would not provide any heat signature on the thermal images as we
 fly in the morning while these bedsites are still shaded by grass and
 therefore not warmed by the sun. Thus, the identified bedsites will
 represent a fawn that was present at the time of the drone flight, but
 that moved during the hours it took to download and review the mages and
 the subsequent visit to the site. In 25 out of 26 such checks, we found 1)
 either a fawn or a fresh bedsite of a fawn when it first was identified in
 the thermal image, or 2) in the case of discovering a fawn in a field, we
 were able to go back to the thermal image and identify the observed
 fawn.
description: # Data from: Saving Bambi from the mower? Using a drone with thermal
 camera to evaluate a low-tech scaring technique to reduce roe deer fawn
 mortality during grass harvest Dataset DOI:
 [10.5061/dryad.rfj6q57q0](https://doi.org/10.5061/dryad.rfj6q57q0) Dataset
 from Vogler et al "Saving Bambi from the mower? - Using a drone with
 thermal camera to evaluate a low-tech scaring technique to reduce roe deer
 fawn mortality during grass harvest" published in Wildlife Biology,
 DOI: 10.1002/wlb3.01450. Study was conducted in Nærøy Municipality,
 Trøndelag County, Norway in May and June of 2017 and 2018. Dataset
 contains a unique grass field ID, with the number of roe deer (Capreolus
 capreolus) fawns detected using a drone with thermal camera. We utilized
 an random experimental design, where we deployed scaring devices (Plastic
 flags on sticks) on half of the fields to deter roe deer does from the
 fields. Each field was flown twice /before and after deployment of scaring
 devices). ### Files and variables #### File:
 Data_from_Vogler_et_al_Wild_Biol_Saving_Bambi_from_the_mower.xlsx
 **Description:**  ##### Variables * Field_ID: unique ID for each field *
 1stflight: Number of fawns detected during first flight * 2ndflight:
 Number of fawns detected during second flight * Exp: Treatment or Control
 fields denoting if scaring flags were deployed or not * Year: Year of data
 collection (2017 or 2018)