Robert Griffin-Nolan
Speaker's Institution
Santa Clara University
Bio 136
Mixer Time
Mixer Time
Calendar (ICS) Event
Additional Information

Grasses are a highly successful family of plants (Poaceae) with > 11,000 species worldwide. They are the dominant growth form of grasslands, which cover 52 million km², or roughly 40% of Earth’s terrestrial land surface, and make a substantial contribution to the terrestrial carbon sink. Humans are heavily dependent on grasses for food (e.g. cereal grains), building materials (e.g., bamboo), and forage for livestock. Despite their ecological, economic, and cultural importance, grasses have received relatively little attention in the field of plant functional traits. Plant traits are useful for understanding how species respond to environmental change and influence ecosystem processes. Much of our understanding of how plant traits respond to their environment stems from interspecific comparisons, although traits can vary significantly within species. In this talk, I will explore both local and global patterns of intra-specific trait variation of grasses.

In local grasslands of the San Francisco Bay Area, I measured the relative abundance, maximum height, and specific leaf area (SLA; leaf area / leaf dry mass) of 19 grass species across 117 unique plots spanning a steep precipitation gradient. Using this dataset, I show how traits can be predictive of an individual’s abundance and vice versa, and that these relationships depend on biotic interactions more than climate. I then added these data to a global dataset of grass traits spanning six continents to explore variation in trait-climate relationships worldwide. Overall, I find that traits do not respond consistently to either temperature or precipitation. However, while intraspecific trait-climate relationships may at first appear idiosyncratic, variation in the magnitude (i.e., slope) and direction of trait-climate relationships is linked to a species’ lifespan and its typical form and function. Finally, I discuss the utility of these trait-climate relationships for training predictive models of trait variation, particularly in regions that are not easily accessible for field work.