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.

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.

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/.