Global climate change increases the likelihood of extreme weather events and may exacerbate variability in inter-annual climate. This heightened variation in weather will likely affect biodiversity and ecosystem function, but we currently lack a general framework for predicting how plant communities will respond to changing environmental conditions. Previous work has characterized broad relationships between plant traits and regional climate, but this work has ignored the population dynamics that lead to observable changes in community structure. The goal of our project is to quantify how plant functional traits mediate demographic responses to inter-annual variation in climate. We synthesized 15 years of demographic data, functional traits, and records of annual climate variability in a short-grass steppe ecosystem in northern Colorado (COSGS). We have calculated the Standardized Precipitation Evapotranspiration Index (SPEI) for each growing season as a measure of drought intensity. Here we focus on leaf dry matter content (LDMC), a metric of plant carbon allocation to leaf structural tissue, and turgor loss point (πTLP), which is indicative of a plant’s ability to maintain cell turgor under dehydration. We predict that species with a more negative πTLP will have higher probability of survival even in dry years. We additionally hypothesize that plants with higher rates of carbon allocation to leaf structure (higher LDMC) will be more resistant to wilting, and thus more drought tolerant. We fit Generalized Linear Mixed Effect Models to determine whether LDMC and πTLP impact the probability that a species will survive following dry years or excessively wet growing seasons.
Results/Conclusions
We found that survival was influenced by a significant interaction between πTLP and SPEI (P < 0.05). Survival is also significantly influenced by an interaction between LDMC and SPEI (P < 0.05) In dry years (low SPEI), individuals with more negative πTLP and higher LDMC had higher survival rates, whereas in wet years (high SPEI), individuals with less negative πTLP and lower LDMC were more likely to survive to the next year. Our long-term goal is to determine which multidimensional trait combinations optimize survival probability in dry years. By focusing on traits that can be measured across species, trait-based models allow us to discern relationships between traits and environmental conditions that can be generalized across ecosystems much more easily than species-based models. The relationships we quantify with our models will be easily transferrable across ecosystems, allowing us to predict how climate change will affect ecosystem productivity and community biodiversity.