Increased temperatures due to anthropogenic-induced climate change may raise the threat of extinction for taxa with sessile life histories (e.g., plants) in the near future. Linking climate change models to demographic models may provide useful insights into the potential effects of environmental changes on rare plants, and therefore aid in their current and future conservation. Population demographers generally agree that mechanistic models from a reductionist perspective are necessary to test assumptions in population drivers.
For the first study, I assessed the climate vulnerability of a rare plant species, Pyrrocoma radiata, with a mechanistic model of four climatically-similar populations. I used environmentally-driven demographic models to estimate vital rates and population sizes from a nonlinear, nonparametric regression with local climate variables. I assessed the utility of this environmentally-correlated, stage-structured population matrix model compared to a stationary model of independent and identically-distributed environmental stochasticity. I then simulated future population projections based on climate conditions predicted by General Circulation Models (GCMs) under opposing emission scenarios.
The second study hopes to answer population-level questions using a traditionally community-level method, non-metric multidimensional scaling, which considers correlation structure between response variables and can be used to find environmental correlates of the ordination axes. Demographic data on a threatened perennial, Astragalus tyghensis, were collected from five sites in the Tygh Valley, OR. I considered correlation structure between demographic vital rates to find environmental correlates of the ordination axes.
The search for an environmental driver of population vital rates was successful for the two study species. Previous year dry dormant season precipitation likely affects the fertility rates a year later in P. radiata populations, and dry growing season reference evapotranspiration rates positively correlated with a growth gradient in A. tyghensis. Based on predicted precipitation, P. radiata is expected to rapidly decline by 2050, but this may be due to biases in the two GCMs and reliance on only one environmental factor. The NMS ordination adequately captured most of the variation in transition elements for the years and populations from A. tyghensis demographics. I provided support to the claim that model predictions can improve with the inclusion of mechanistic relationships. The inclusion of abiotic drivers in models used to predict population trends is supported by our study and may enhance predictive power in population viability assessments under changing climates.