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Dryad

Marmot capture history data and growing season length data

Cite this dataset

Cordes, Line et al. (2023). Marmot capture history data and growing season length data [Dataset]. Dryad. https://doi.org/10.5061/dryad.ht76hdrcd

Abstract

Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots (Marmota flaviventer) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics. We were able to impute body mass for each individual twice during each year following their first capture using a similar approach to Ozgul et al. (2010) (for more details on the modeling procedure see SI Appendix within the main paper). Body mass measurements were log-transformed.

README: Marmot capture history data and growing season length data

https://doi.org/10.5061/dryad.ht76hdrcd

We used live trapping data of yellow-bellied marmots from 1979 to 2018. Individuals were marked with both fur dye and permanent ear tags with unique ID numbers. Individuals were also weighed during each capture. We used data on growing season length which is the number of days between first and last flower.

Description of the data and file structure

The "dryad_capture_histories_age_logbm" datafile contains capture history data for each female marmot included in the analysis. Each row therefore represents an individual marmot. The first column (ch) contains the capture history data starting XXX, followed by the time varying individual covariates of age and body mass (bm).

The "dryad_RMBL_growing_season_length" datafile contains two columns, namely the year and the growing season length in number of days.

Sharing/Access information

Below are links to sources were data used in the article was derived from:

  • The complete flowering phenology data-set used to calculate growing season length is archived at https://osf.io/jt4n5/.
  • The monthly average temperature and total pre-cipitation data for Crested Butte that were used to calculate a calibrateddrought severity index for the RMBL were downloaded from the NCEI Cli-mate Data Online (https://www.ncei.noaa.gov/).
  • RMBL environmental data (including total snowfall, mean minimummonthly temperatures, and snowmelt date) can be downloaded from http://www.gothicwx.org/.

Methods

We used data from the population located in the Upper East River Valley, Colorado, in and around the Rocky Mountain Biological Laboratory (RMBL), which has been studied since 1962. The population comprises four main colonies and 12 satellite colonies distributed between 2,700 and 3,100 m above sea level. We used live trapping data of yellow-bellied marmots from 1979 to 2018 (an interval during which we had high-quality environmental data and extensive trapping effort) to construct capture histories for each individual. Individuals were marked with both fur dye and permanent ear tags with unique ID numbers (59). Individuals were also weighed during each capture.

Usage notes

Body mass data have been log transformed. Growing season length data are in number of days. There are no missing values. The complete flowering phenology dataset used to calculate growing season length is archived at https://osf.io/ jt4n5/.

Funding

Swiss National Science Foundation, Award: 31003A_182286

Swiss National Science Foundation, Award: 31003A_146445

National Geographic Society

University of California, Los Angeles, Faculty Senate

University of California, Los Angeles, Division of Life Sciences

Rocky Mountain Biological Laboratory

National Science Foundation, Award: IDBR-0754247

National Science Foundation, Award: DEB-1119660

National Science Foundation, Award: DEB-1557130

National Science Foundation, Award: DBI-0242960

National Science Foundation, Award: DBI-0731346

National Science Foundation, Award: DBI-1226713

National Institute of Food and Agriculture, Award: Hatch Formula Project 1017848