The fate of a species is a function of interacting environmental and biological processes. Disentangling the roles and interactions of such processes can elucidate the breadth of possible responses to global change, for instance, the potential for phenotypic plasticity or trait evolution to rescue populations from climate change. We explored how environmental and biological factors influenced the timing of emergence post-hibernation, and subsequent oviposition, of the temperate aquatic amphibian, Rana sylvatica. We evaluated how frog phenology changed for 64 populations over 25 years, pairing these observations with 45 years of mechanistic and machine learning simulations of microclimate and frog physiology. Adult frog oviposition dates varied between day-of-year 74 and 135, and on average advanced marginally by 1.6 days per decade. Coupled mechanistic models predicted frog emergence date with a median absolute error of 6.9 days (RMSE: 8.95 days). Sensitivity analyses of the mechanistic simulations demonstrated the importance of vegetation structure and meteorology, and their interactions, for driving variation in emergence dates, while frog behavior played a moderate role. Modeled variation in morphological and physiological traits had little effect on predicted phenological variation, even when trait space was unrealistically inflated. Our study suggests that for this system, interpopulation variability in phenology may be driven more by exogenous factors (the environment), and to a lesser extent behavior, rather than endogenous traits of morphology and physiology, the latter of which may provide little capacity to respond to changing climates over time. Our approach suggests that pairing phenological observations with generalizable mechanistic models can offer an effective platform to understand and predict responses to global change.
Building similarity graph...
Analyzing shared references across papers
Loading...
David H. Klinges
L. Kealoha Freidenburg
Adriana Rubinstein
Ecology
Yale University
Building similarity graph...
Analyzing shared references across papers
Loading...
Klinges et al. (Fri,) studied this question.
synapsesocial.com/papers/69fd7e23bfa21ec5bbf0646d — DOI: https://doi.org/10.1002/ecy.70394