Global wildfire regimes are changing rapidly, with widespread increases in the size, frequency, duration, and severity of wildfires. Whereas the effects of wildfire on ecological state variables such as occupancy, abundance, and species diversity are relatively well-documented, changes in population vital rates (e.g., survival, recruitment) and individual responses (e.g., growth, movement) to wildfire are more limited because of the detailed information needed on the same individuals both pre- and post-fire. We capitalized on the 2018 Roosevelt wildfire, which occurred during our six-year (2015–2020) capture-mark-recapture study of boreal toads (Anaxyrus boreas boreas; n = 1415) in the Bridger-Teton National Forest, USA, to evaluate the responses of population vital rates and individual metrics to wildfire. We employed robust design capture-recapture models to compare the growth, dispersal, survival, and recruitment of adult boreal toads pre- and post-fire at burned versus unburned sites. At burned locations, growth increased two-years post-fire compared with the year directly following wildfire and was higher two-years post-fire than any other interval during our study period. Boreal toads dispersed to alternative breeding patches more at burned sites than unburned sites and dispersal increased two-years post-fire compared with the year directly following wildfire. Annual survival and recruitment did not differ between pre- and post-fire years nor among pre-fire years, the year following wildfire, and two-years post-fire. We demonstrate that, in certain contexts, dispersal can play a major role in changes to state variables (e.g., abundance) after wildfire, as opposed to other vital rates such as survival and recruitment. Our study represents an important step towards understanding the biological processes that underlie observed patterns in state variables following wildfire, which ultimately will be critical for the effective management of species in landscapes experiencing shifts in fire activity.

Please refer to the README files associated with each dataset for information on how the data were collected and processed.

No specific programs are needed to open and view the data files. We used the RMark package in Program R to analyze and interpret the datasets, though other software may be used and should produce the same results.