Multiple drivers of spring migration timing for red deer over the past 16 years in northern Europe
Data files
Nov 14, 2024 version files 43.49 KB
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drivers_data.RData
25.03 KB
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ndvi_patterns_data.RData
12 KB
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README.md
2.85 KB
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trends_data.RData
3.61 KB
Abstract
The timing of migration is fundamental for species exploiting seasonally variable environments. For ungulates, earlier spring migration is expected with earlier vegetation green-up. However, other drivers, such as access to agricultural farmland and variations in local conditions, are also known to affect migration. We investigated the timing of spring migration for 96 male and 201 female red deer (Cervus elaphus) using a long-term dataset (2005-2020). Overall, the timing of migration was mainly characterised by large individual variability between and within years (95% range from April 6th to June 18th). The spring migration timing was, as expected, later with colder winter and spring conditions (NAO winter and April indices) and later peak vegetation green-up (NDVI), with a 5-day delay in green-up causing a migration delay of 1.2 days. The timing was also influenced by local conditions in summer and winter home ranges. Red deer with higher access to farmland and more variable topography (hence variable plant phenology) in winter delayed migration. Similarly, individuals with higher elevation summer ranges (with delayed onset of plant growth) also delayed migration. Our analyses highlight that the timing of red deer migration is formed by multiple drivers affecting foraging conditions in the landscape, indicative of considerable phenotypic plasticity.
https://doi.org/10.5061/dryad.d7wm37q7b
Description of the data and file structure
The data includes three files, one for the analysis of the trend (“trends_data.RData”) one for the analysis of the drivers of spring migration onset (“drivers_data.RData”) for red deer, and one for the analysis of the NDVI patterns within the home ranges of the red deer (“ndvi_patterns_data.RData).
The files include the following information:
- trends_data.RData: id, year, year_sc (= year mean-centred), sex.y (= sex), spring.start.julian (= the day of the year of migration onset), and region2 (= region).
- drivers_data.RData: yr (= year), id, region, spring.start.julian (= the day of the year of migration onset), dens (= population density index, number of shot red deer per km2), pasture (= proportion of available farmland within the winter home range), snow.depth (= mean snow depth in the winter range, mm), nao_djfm (= NAO December-March index), nao_mam (= NAO March-May index), nao_april (NAO April index), nao_may (NAO May index), ndvi.peak.spring (= day of the year of peak spring vegetation green-up in winter range), distance.summer.winter (=migration distance between winter and summer range, km), ele_s_mean (= mean elevation in summer range, m), ele_w_mean (= mean elevation in winter range, m), tri_hr (= terrain ruggedness index in winter range), and dist.coast (=distance to the nearest coastline, m).
- ndvi_patterns_data.RData: yr (= year), mean.peak.spring.win (= mean day of the year of peak spring vegetation green-up in winter range), tri_hr (= terrain ruggedness index in winter range), sd.peak.spring.win (= standard deviation of (day of the year of) peak spring vegetation green-up in winter range), mean.peak.spring.sum (= mean day of the year of peak spring vegetation green-up in the summer range), ele_s_mean (= mean elevation in winter range, m).
The categorical variable sex has two levels; males (m) and females (f). The categorical variable region (or region 2, which is similar) has four levels; SoF (Sogn & Fjordane), Tr (Trøndelag), MoR (Møre & Romsdal), and SW (Hordaland, or the south-west region).
Code/Software
Associated R codes for the two analyses are available, for R version 4.3.0.
The code for the analysis of the trend of spring migration onset is found in the script named “Trend in migration timing2.R”. The code for the analysis of the drivers of spring migration is found in the script named “Drivers of spring migration2.R”. The code for the analysis of the NDVI patterns is found in the script “NDVI patterns.R”.
Packages used in the scripts are:
- ggplot2, version 3.5.1
- ggeffects, version 1.7.2
- lme4, version 1.1-33