Quantifying how key life-history traits respond to climatic change is fundamental in understanding and predicting long-term population prospects. Age at first reproduction, which affects fitness and population dynamics, may be influenced by environmental stochasticity but has rarely been directly linked to climate change. Here, we use a case study from the highly seasonal and stochastic environment in high-arctic Svalbard, with strong temporal trends in breeding conditions, to test whether rapid climate warming may induce changes in age at first reproduction in barnacle geese, Branta leucopsis. Using long-term mark-recapture and reproductive data (1991-2017), we developed a multi-event model to estimate individual age at first reproduction (i.e., goslings produced). The annual probability of reproducing for the first time was negatively affected by population density but only for two-year olds, the earliest age of maturity. Furthermore, advanced spring onset positively influenced the probability of reproducing and even more strongly the probability of reproducing for the first time. Thus, because climate warming has advanced spring onset by two weeks, this likely led to an earlier age at first reproduction by more than doubling the probability of reproducing at two-years old. This may, in turn, impact important life-history trade-offs and long-term population trajectories.

Long-term individual mark recapture, based on multiple observations per year from 1991-2017.

Covariates used as predictors in the model.

File (1) Individual histories of female barnacle geese over a 27-year study period (1990-2017). 2 =status breeding (observed with goslings), 1 = observed without goslings and 0 = not observed. Individuals histories were fitted with a multi-event model in E-SURGE to model the transitions probilities among states, the multi-event framework allows for estimation of state uncertainty i.e., whether a female was a pre-breeder (never bred before) or a non-breeder.

 

File (1) Scaled covariates, timing of spring onset (SO) and adult population size (POP) used as predictors in the multi-event model.

Model implementation can be found in the supplementary informatoin along with the manuscript.