UC Santa Cruz

Biological processes underlying species responses to climate, such as physiology, phenology and demography, can add important information to the prediction of climate change effects on organisms, yet most studies do not consider those processes. On this thesis, I present and evaluate different venues of incorporating such processes on biogeographical analysis. On the first chapter, I show activity time can be a better predictor than environmental temperatures for the distribution a tropical lizard, Tropidurus torquatus. I also determine the best practices for obtaining those estimates. Tropidurus torquatus seems to be restricted in its distributions by colder temperatures and precipitation, thus climate warming could lead to potential range expansion. On the second chapter, I examine the drivers of reproductive seasonality in two tropical lizards, Tropidurus torquatus and Ameiva ameiva. Solar radiation and day length were the main factors determining the reproductive seasonality of T. torquatus, while A. ameiva was more sensitive to precipitation. Solar radiation could be driving T. torquatus breeding phenology through the parietal eye mechanism, while A. ameiva, which lacks such structure, could be more sensitive to immediate weather conditions. This might have important consequences for these T. torquatus adaptation to climate change, since the rapid shift in weather might cause a mismatch between the photoperiodic cue and optimal environmental conditions for reproduction. On the third chapter, I use the estimates of time of activity and breeding phenology o from the previous chapters to spatially extrapolate demographic rates obtained from a 12-year mark and recapture study on a T. torquatus population. Survival was correlated with time of activity and precipitation, both interacting with breeding phenology, while recruitment was correlated with temperature and precipitation, with no breeding season interaction. Population growth projections were not correlated with occurrence records, indicating that spatial predictions were unreliable. Physiology and phenology add important information to the estimation of demographic rates at local scales but proved unreliable predictors for spatial extrapolation of those rates. This could be due to environmental variation, adaptation, plasticity or species interactions. We suggest possible venues for incorporating those processes and improving similar analysis. I provide an R package, Mapinguari, with tools to generate spatial predictors based on the processes described here.