Projected population dynamics for a federally endangered plant under different climate change emission scenarios

Land managers primarily collect population counts to track rare plant population trends. These count-based data sets are often used to develop population viability analysis (PVA) to project future status of these populations. Additionally, practitioners can use this count-based data to project population size changes under different climate change scenarios at both local and regional levels. In this study we developed a count-based PVA for a population of the US federally endangered Dalea foliosa, using annual census data (1997-2008), to determine extinction probability (P _e) at 50 and 80year time points. We determined which weather variables best explained variation in count data and population growth rate using linear regression. Lastly we projected population size for the population location at 50 and 80years using forecasted temperature and precipitation from 16 climate change models under three emission scenarios. Count-based PVA indicated a P _e of 0.2% at both 50 and 80years. However, these estimates of P _e have large confidence intervals, so persistence is not a certainty. Most variation in population size was explained by snowfall (R ²=0.786, p<0.001). Population size projections varied greatly among the 16 climate models due to widely varied weather projections by the models, but little differences were found among emission scenarios for most models. The low P _e projected by count-based PVA represents an estimate based on current conditions remaining the same. However, climate models indicate that current conditions will change over the next century. In particular, mean February temperatures are projected to increase by approximately 2°C. The majority of the models using climate change predictions projected population decline, suggesting that the studied population may not be protected against extinction even under low emissions scenarios. This study demonstrates the usefulness of collecting count-based data and our contrasting results from count-based PVA and climate projections indicate the importance of combining both count-based PVA and climate change models to predict population dynamics of rare and endangered species.