Data from: Deriving birth timing of roe deer fawns from body measurements to limit mowing mortality
Data files
Dec 11, 2024 version files 180.82 KB
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README.md
1.48 KB
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Stehr_2024_Data_roe_deer_fawn.csv
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Abstract
Every spring, hiding roe deer fawns (Capreolus capreolus L.) in meadows are injured or killed by mowing activities. To assess the temporal overlap between mowing activities and the fawning period, and thus help reduce the risk of mowing death, we have determined the average parturition date in three regions of Bavaria, southern Germany.
Data on physical body parameters were collected from 1387 fawns over four consecutive springs (2020-2024). Across all of the years studied, data collection started at the end of April, independent of spring mowing activities. We searched for fawns mainly using unmanned aerial vehicles (UAV) and recorded the status of the umbilical cord, body mass, hind foot length, total body length, head length, crown-rump length, and presence of siblings of each detected fawn. A total of 118 of the 1387 fawns were found and measured multiple times. To estimate age, we fitted a generalized linear mixed model (GLMM) as a function of multiple physical parameters and subsequently determined the age and birth date of all fawns based on this predictive model. Body mass, head length, crown-rump length, and the presence of siblings were the most important predictors of fawn age (R2 = 0,74). The estimated ages were used to calculate the mean parturition dates for the region. The results show that the first fawns were born as early as April. Across years the mean parturition date was the 135 day of the year (DOY).
Our results confirmed that fawn birth dates and the hiding phase highly overlapped with spring mowing events. However, based on the derived knowledge of the exact birth times, our study can contribute to reducing mowing-related mortalityrates. Our predictive model may also provide researchers in other regions with an approach to accurately determine the ages of roe deer fawns.
README: Deriving birth timing of roe deer fawns from body measurements to limit mowing mortality
https://doi.org/10.5061/dryad.x95x69pvd
Description of the data and file structure
The data file included in the submission consists of Stehr_2024_Data_roe_deer_fawn.csv. The study focuses on developing a robust method for estimating the birth timing of roe deer fawns using body measurements, drawing on a comprehensive dataset of over 1.300 individuals. By achieving greater accuracy in age estimation and identifying a shift in parturition timing, likely linked to environmental adaptability, 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 handling them during the measurements required increased caution, it was not always possible to collect all data. Missing data, which could not be measured, were marked as N/A in the dataset.
Code/software
All statistical analyses were performed using IBM SPSS Statistics version 29.0.1.0.
Methods
Data collection
We systematically searched for roe deer fawns every day during springs (April 25 to June 30), 2020, and 2023, with four teams. Specifically, we systematically scanned agricultural areas at regular spatial intervals using unmanned aerial vehicles (UAV) (DJI Matrice 200 with a DJI Zenmuse XZ2 thermal camera, DJI Mavic 2 Enterprise Advanced with an M2EA 640p thermal camera, and Yuneec H520 with a Yuneec CGOET10 thermal camera). In addition, chains of people were formed to search for fawns in forested areas. Furthermore, in the spring of 2021 and 2023, we searched for fawns of female roe deer equipped with radio-gps collars (LiteTrack 360; LOTEK WIRELESS INC, Newmarket, Canada). Radiocollared does were monitored, and if movement parameters or physical appearance indicated birth (Baur et al., 2024), fawns were identified using thermal drones or thermal binoculars (Pulsar Accolade 2 LRF XP50).
Our search teams, of two people each, were trained in advance to take standardized measurements of physical growth parameters and estimate the ages of the detected roe deer fawns. To estimate age, we evaluated the condition of the umbilical cord according to Jullien et al. (1992). Specifically, we differentiated eight umbilical cord conditions to determine fawn age during the first eight days of life. For fawns determined to be older than eight days, we retrospectively predicted their age based on our predictive model. To build this predictive model, we measured different conditions and constitutional parameters, namely body mass (BM), crown-rump length (CRL), and hind foot length (HFL), for each fawn detected by our search team. From spring 2022 onwards, the total body length (TBL) and head length (HL) were also measured. We used a folding meter stick to measure the HFL and a flexible measuring tape to measure the TBL, HL, and CRL (accuracy of 5 mm). The HL was measured from the tip of the nose to the top of the head between the ears. This point (the top of the head between the ears) was also the starting point for the CRL measurement. To measure this length, the measuring tape was extended along the dorsal line up to the tail stump. The TBL was measured in the same manner. To determine this, a measuring tape was placed along the dorsal line from the tip of the nose to the tail stump. Fawn BM was determined using the Pesola Spring Scale (Macro 5 kg; Macro 10 kg). Finally, we recorded sex and the presence of siblings.