DOI: 10.5061/dryad.g4f4qrg2f
title: Visual-acoustic wildlife deterrents as a supporting measure to prevent mowing-related mortality in roe deer fawns

description: Mowing-related mortality is one of the main causes of death among roe deer
 fawns in agricultural regions, prompting farmers in Germany to implement
 preventative measures. Understanding the effectiveness of different
 deterrent methods is crucial to reducing such mortality. This study
 examined the effectiveness of visual-acoustic wildlife deterrent devices
 in preventing roe deer fawns from entering agriculturally used areas
 before mowing, encouraging those already present to leave, or facilitating
 their removal by their mothers. A total of 76 fawns were fitted with
 GPS-collars between 2022 and 2023, and their movements were analysed using
 a multi-state Markov model.  Our results demonstrate that
 visual-acoustic wildlife deterrents significantly influenced fawn
 behaviour, with 36% of the fawns leaving the trial area and not returning
 at all during the trial period. However, this indicates a substantial risk
 of injury or death for the fawns remaining in the fields during mowing.
 Among the tested devices, electronic acoustic deterrents, particularly
 smoke detectors, were the most effective deterrents, scaring away over 50%
 of the fawns without any significant habituation effect.  The
 probability of fawns leaving the meadows was further influenced by several
 factors such as age, proximity to forest edges, vegetation height, and
 field size. Younger fawns, following a "hider" strategy, were
 less likely to leave the agriculturally used areas, whereas older fawns
 exhibited higher mobility. Fawns more frequently remained in areas with
 tall vegetation and large field sizes, while deterrents proved more
 effective in smaller fields and near forest edges.  Despite some
 success, the limited efficacy of the tested deterrent devices highlights
 the necessity for additional measures, such as using drones equipped with
 thermal imaging cameras, to further reduce fawn mortality during mowing.
 Our findings provide critical insights for improving wildlife management
 practices in agricultural landscapes and reducing roe deer fawn losses
 during the mowing season.
description: # Visual-acoustic wildlife deterrents as a supporting measure to prevent
 mowing-related mortality in roe deer fawns Dataset DOI:
 [10.5061/dryad.g4f4qrg2f](10.5061/dryad.g4f4qrg2f) ## Description of the
 data and file structure **Description of the data** The data file included
 in the submission consists of Stehr_2025_fawn_data_deterrent.csv. This
 study examined the effectiveness of visual-acoustic wildlife deterrent
 devices in preventing roe deer fawns from entering agriculturally used
 areas before mowing, encouraging those already present to leave, or
 facilitating their removal by their mothers. A total of 76 fawns were
 fitted with GPS-collars between 2022 and 2023, and their movements were
 analysed using a multi-state Markov model. By achieving greater knowledge
 about the effectiveness of different deterrent devices, our findings have
 critical implications for mitigating mowing-related mortality — a topic of
 increasing public and scientific concern. The integration of these results
 into wildlife management strategies has the potential to reduce the risk
 to neonate roe deer during agricultural mowing, a recurring issue that
 receives significant media and policy attention. Furthermore, the findings
 contribute valuable insights to the broader understanding of ungulate
 biology under changing environmental conditions. Since we worked with live
 animals and handled them during the measurements, increased caution was
 required, and it was not always possible to collect all data. Missing
 data, which could not be measured, were marked as N/A in the dataset. ###
 Files and variables #### File: Stehr_2025_fawn_data_deterrent.csv
 **Description:**  ##### Variables * study area: name of study area  *
 fawn_name: given name of fawn * Fawn_ID: unique fawn id (Collar ID + fawn
 Eartag) * Collar_ID: id of used collar * Eartag: id of given eartag * Sex:
 fawns sex  * Parturition_date: date of birth of investigated fawn * Age :
 age at trial start [days] * Field size: size of trial area [ha] * N_Start:
 coordinates N of trial area * E_Start: coordinates E of trial area *
 distance forrest edge: shortest distance between trial area and the
 nearest forrest edge [m] * Date Start: Date of Start of the trial  * Time
 start: Time of Start of the trial * date end: Date of End of the trial *
 Time end: Time of End of the trial * kind_of_deterrent: used kind of
 deterrent * Code_kind_of_deterrent: numeric used kind of deterrent
 (1=Carry out/ shooing out; 2=Carry out / shooing out; 3=Plastic bag; 4=
 LARS Wildlife rescuer; 5= Smoke detector; 10= Control) * Hours until
 leaving: Hours passed until the trial area was left for the first time *
 Hours after end until return: Hours passed until the trial area was
 entered for the first time after trialend * Trial: the next 24 variables
 describe the experiment. We have coded the positions of the fawns for the
 evaluation as follows: 1=fawn is in the experimental area; 0=fawn is not
 in the experimental area. * Start: Trial Beginning [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 1.h: first
 trial hour  [1=fawn is in the experimental area; 0=fawn is not in the
 experimental area] * 2.h: second trial hour [1=fawn is in the experimental
 area; 0=fawn is not in the experimental area] * 3.h: Third trial hour
 [1=fawn is in the experimental area; 0=fawn is not in the experimental
 area] * 4.h: Fourth trial hour [1=fawn is in the experimental area; 0=fawn
 is not in the experimental area] * 5.h: Fifth trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 6.h:
 Sixth trial hour [1=fawn is in the experimental area; 0=fawn is not in the
 experimental area]  * 7.h: Seventh trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 8.h:
 Eighth trial hour [1=fawn is in the experimental area; 0=fawn is not in
 the experimental area] * 9.h: Ninth trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 10.h:
 Tenth trial hour [1=fawn is in the experimental area; 0=fawn is not in the
 experimental area] * 11.h: Eleventh trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 12.h:
 Twelfth trial hour [1=fawn is in the experimental area; 0=fawn is not in
 the experimental area] * 13.h: Thirteenth trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 14.h:
 Fourteenth trial hour [1=fawn is in the experimental area; 0=fawn is not
 in the experimental area] * 15.h: Fifteenth trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 16.h:
 Sixteenth trial hour [1=fawn is in the experimental area; 0=fawn is not in
 the experimental area] * 17.h: Seventeenth trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 18.h:
 Eighteenth trial hour [1=fawn is in the experimental area; 0=fawn is not
 in the experimental area] * 19.h: Nineteenth trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 20.h:
 Twentieth trial hour [1=fawn is in the experimental area; 0=fawn is not in
 the experimental area] * 21.h: Twenty-first trial hour [1=fawn is in the
 experimental area; 0=fawn is not in the experimental area] * 22.h:
 Twenty-second trial hour [1=fawn is in the experimental area; 0=fawn is
 not in the experimental area] * 23.h: Twenty-third trial hour [1=fawn is
 in the experimental area; 0=fawn is not in the experimental area] * 24.h:
 Twentyfourth trial hour [1=fawn is in the experimental area; 0=fawn is not
 in the experimental area] * hour in deterrent area: Total number of hours
 the fawn was in the field during the experiment * number of
 deterrents:  Number of deterrents used (as no transportable deterrents
 were used for the carry out/shooing out test type, none are entered here)
 * Objekt: trial objekt * vegetation typ: Vegetation typ of the trial *
 Average vegetation height without drop plate: measured average vegetation
 height [cm] measured vegetation height * Height of vegetation without drop
 plate1: measured vegetation height [cm] * Height of vegetation without
 drop plate2:measured vegetation height [cm] * Average vegetation height
 with drop plate:  measured average vegetation height [cm] measured
 vegetation height * Height of vegetation with drop plate1: measured
 vegetation height [cm] * Height of vegetation with drop plate2: measured
 vegetation height [cm] * vegetation Density % : determined vegetation
 density [%] ## Code/software All hypotheses were tested with a
 significance level of 5%. The statistical analysis was done in SPSS
 Statistics v. 29.0.1.0 (IBM, 2024) and R v. 4.4.1 (RCoreTeam, 2024). In
 order to model the probabilities of the transition over time, we employed
 a 2-D multi-state Markov model using the package “msm” in R (Jackson,
 2011). This model class is particularly useful for panel data, as it
 describes how individuals move between states continuously by calculating
 transition intensities, representing the instantaneous risk of moving from
 one state to another (Jackson, 2011). ## Access information Other publicly
 accessible locations of the data: * none  Data was derived from the
 following sources: * The data were recorded and collected by ourselves