Climate change consists of gradual shifts in broader climate regimes and alterations to local weather patterns across the globe. Changes to regional climate can affect species distributions, limiting persistence in available habitat and forcing migration to new, previously unsuitable habitat beyond historic climate envelopes. Additionally, changes to weather patterns result in increased variability and higher incidence of extreme weather events that can exceed historic norms. Both components of climate change can negatively impact species and natural populations, precluding shifts in distributions toward suitable climate and potentially resulting in local population extinctions. An understanding of how natural populations respond to both climate and weather is essential to identifying the different ways climate change will impact species. Alpine areas are unique study systems for climate change as species inhabiting these ecosystems generally experience more variable weather patterns and may be limited in the extent to which they can emigrate to new habitat in response to broader climate shifts. Thus, incorporating the direct and relative effects of climate and weather on alpine species provides a powerful opportunity to differentiate the ways these two components of climate change will impact population dynamics. In this study, we used abundance records along 17 meadows from 2003 to present of the lance-leaf stonecrop, Sedum lanceolatum Torr. (Crassulaceae), in the front ranges of the Rocky Mountains, along Jumpingpound Ridge in Alberta, Canada. Climate, as measured using Pacific Decadal Oscillation (PDO), and local weather, measured as temperature and precipitation, were combined with local topographic parameters and biotic variables to identify statistical models that best describe population growth of S. lanceolatum along these meadows.
Results/Conclusions
We used randomForest, rpart, and statistical modeling to identify which of over 230 variables were most important to describing changes in population growth. These relevant predictors were combined in three separate models: including a climate-only, a weather-only, and a combination model to identify which suite of predictors were most valuable in describing population growth. While climate and weather described a significant amount of variation in population growth of this species, each explained variation that the other model did not, indicating independent effects of climate and weather. Developing a better understanding of how species respond to gradual variation in climate and extreme weather can provide the predictive power to describe changes in alpine plant species and determine the mechanisms that drive year to year variation in population growth.