Linking vital rates to the environment is an essential step to understanding the contribution of environmental variation to population dynamics. Ehrlén and Morris (2015) propose extending this approach by incorporating density-dependence into models to jointly predict abundance (equilibrium density) and distribution (locations where low-density population growth rates are positive). Population models that make predictions in terms of population size or distribution can then be validated with empirical data. We describe progress on building, validating, and testing a driver-dependent population model that leverages spatial and temporal variation in data on the climate at and demography of populations of the annual plant Clarkia xantiana ssp. xantiana (Onagraceae) across the geographic range of the subspecies in the southern Sierra Nevada mountains.
Results/Conclusions
We use a hierarchical Bayesian approach to model the statistical relationship between multiple vital rates (seed survival, germination, seedling survival to fruiting, seed set) and the abiotic environment, climate, and density. We show how vital rates depend on covariates for the abiotic environment (slope, azimuth, and rock type), climate (temperature and precipitation), and density (seedling numbers). We describe the effect of spatial variation in the abiotic environment and temporal variation in climate and density on vital rates. We use these analyses to obtain parameter estimates for a population model. We discuss how to validate and test the model using additional years of observations of fruiting plant abundance as well as independently collected estimates of abundance at a set of out-of-sample sites. The model should provide a useful tool for predicting how future environmental change will influence the abundance and distribution of our study species.