DOI: 10.5061/dryad.qz612jms1
title: Matching the green wave: Growing season length determines embryonic diapause in roe deer

description: 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.
description: # Matching the Green Wave: Growing Season Length Determines Embryonic
 Diapause in Roe Deer
 [https://doi.org/10.5061/dryad.qz612jms1](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: * DWD [https://opendata.dwd.de/climate_environment/CDC/grids_germany/annual/phenology/](https://opendata.dwd.de/climate_environment/CDC/grids_germany/annual/phenology/) * E-OBS [https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php](https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php)