Matching the green wave: Growing season length determines embryonic diapause in roe deer
Data files
Apr 22, 2025 version files 75.14 KB
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Matching_green_Wave_Data.csv
61.09 KB
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Matching_The_Green_Wave_Code.R
9.11 KB
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README.md
4.94 KB
Abstract
The roe deer (Capreolus capreolus) is Europe’s most widespread ungulate, notable for its unique trait of embryonic diapause (delayed blastocyst implantation after mating) and an ongoing debate regarding how climate change affects its parturition timing. Given the relatively constant timing of the rut, roe deer could cope with advancing greening by adjusting its diapause end. Here, we bridge the gap on factors influencing roe deer’s diapause by analysing 389 uteri from legally hunted roe deer females in Germany (2017-2020), which we macroscopically examined for the presence of visible embryonic tissue to retrospectively identify the diapause end date. By employing a marginal Cox proportional hazard model, we tested associations between female phenotypic attributes, environmental conditions, and the probability of ending embryonic diapause prematurely. Our results confirmed that high-quality, well-conditioned and prime-aged females tend to terminate embryonic diapause earlier. We also demonstrated for the first time that on a population-averaged level, the growing season length in the year of conception significantly influences the diapause timing, even explaining the much-debated shifts in parturition dates in roe deer over the last seven decades. Increased knowledge of mechanisms involved in embryonic diapause may also help decipher embryo-maternal interactions in general, including in-vitro fertilisation.
https://doi.org/10.5061/dryad.qz612jms1
Description of the data and file structure
The following files accompany the manuscript "Matching the Green Wave: Growing Season Length Predicts End of Embryonic Diapause in Roe Deer"
1. Matching_green_Wave_Data.csv
Dataframe holding the data for the analysis.
2. Matching_The_Green_Wave_Code.R
Code to reproduce the results. The code was run on R version 4.4.2 on Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.5 LTS
Base packages: stats, graphics, grDevices, utils, datasets, methods, base
To run the code, filepath for the data and for the saved pdf file must be changed.
Files and variables
File: Matching_green_Wave_Data.csv
Description:
Variables
- PK: Primary Key
- Lab_Number: Internal reference
- Month_Number: Number of the month
- age_class: Classification of age
- Age: Age as a number
- Corpora_Lutea: Number of corpora lutea
- Successfull_Fertilisation: Number of corpora lutea
- Successfull_Fertilisation_binary: If fertilisation was successful
- Number_embryos: Number of embryos
- mean_CRL_embryo_cm: Mean CRL of Embryos if present
- comment: Lab Comments
- CullingDate: Day of Harvest
- CullingDoy: DOY of Harvest
- year: Year
- year_conception: Year of conception
- DoyImplant: DOY of Implantation (if so)
- days_since_implant: Days since implantation happened
- DateImplant: Date of the implantation
- status: include in analysis
- Start_Date: 1. Octover
- End_Date: Day of Harvest or day of implantation
- days_between: Days between start and day of harvest or implantation
- VEGEND_median: Beginning of the vegetation season (Median per hunting unit)
- VEGBEG_median: End of the vegetation season (Median per hunting unit)
- Sigma_of_Spell_Nov: November temperature spell, given as sigma
- Mandibelength_cm: Mandible length in cm
- Mass_kg: Weight in kg
- Mass_esc_kg: esciverated weight in kg
- Subregions_Gross: greater biogeographic region
- event: used for the cox.ph model
Code/software
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=de_DE.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=de_DE.UTF-8
[6] LC_MESSAGES=en_GB.UTF-8 LC_PAPER=de_DE.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Berlin
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] corrplot_0.92 car_3.1-2 carData_3.0-5 geepack_1.3.12 lmerTest_3.1-3 lme4_1.1-35.5 Matrix_1.7-1 rsq_2.6 NCmisc_1.2.0 survminer_0.4.9
[11] ggpubr_0.6.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[21] tidyr_1.3.1 survival_3.7-0
loaded via a namespace (and not attached):
[1] gtable_0.3.4 xfun_0.42 rstatix_0.7.2 lattice_0.22-5 numDeriv_2016.8-1.1 tzdb_0.4.0 vctrs_0.6.5 tools_4.4.2
[9] generics_0.1.3 fansi_1.0.6 pkgconfig_2.0.3 data.table_1.15.0 lifecycle_1.0.4 farver_2.1.1 compiler_4.4.2 munsell_0.5.0
[17] crayon_1.5.2 nloptr_2.0.3 pillar_1.9.0 MASS_7.3-61 boot_1.3-30 abind_1.4-5 nlme_3.1-165 km.ci_0.5-6
[25] Deriv_4.1.3 tidyselect_1.2.1 stringi_1.8.3 labeling_0.4.3 splines_4.4.2 grid_4.4.2 colorspace_2.1-0 cli_3.6.2
[33] magrittr_2.0.3 utf8_1.2.4 broom_1.0.5 withr_3.0.0 scales_1.3.0 backports_1.4.1 timechange_0.3.0 gridExtra_2.3
[41] ggsignif_0.6.4 zoo_1.8-12 hms_1.1.3 knitr_1.45 KMsurv_0.1-5 mgcv_1.9-1 deming_1.4 survMisc_0.5.6
[49] rlang_1.1.3 Rcpp_1.0.12 xtable_1.8-4 glue_1.7.0 rstudioapi_0.16.0 minqa_1.2.6 R6_2.5.1
Access information
Other publicly accessible locations of the data:
Data was derived from the following sources:
