Functional traits predict species responses to environmental variation in a California grassland annual plant community

Abstract

O_LITurnover in species composition and community-wide functional traits across environmental gradients is a ubiquitous pattern in ecology, and is generally assumed to reflect shifts in trait optima across these gradients. However, the demographic processes that give rise to these trait turnover patterns at the community level remain unclear. C_LIO_LIWe asked whether shifts in the community-weighted means of three key functional traits across an environmental gradient in a southern California grassland reflect variation in the trait-performance relationship across the landscape. C_LIO_LIWe planted seeds of 17 annual plant species in cleared patches with no competitors, and quantified the lifetime seed production of 1360 individuals. We then asked whether models that included trait-environment interactions help explain interspecific variation in demographic responses to the environment. This allowed us to evaluate whether observed shifts in community-weighted mean traits matched the direction of any trait-environment interactions detected in the plant performance experiment. C_LIO_LIOur results indicate that commonly-measured plant functional traits help explain variation in species responses to the environment - for example, high-SLA species had a demographic advantage in soils with high soil Ca:Mg levels, while low-SLA species had an advantage in low Ca:Mg soils. We also found that shifts in community-weighted mean traits often reflect the direction of these trait-environment interactions, though not all trait-environment relationships at the community level reflect interactive effects of traits and environment on species performance. C_LIO_LIOur results support the value of plant functional traits for predicting species responses to environmental variation, and highlight a need for more detailed evaluation of how trait-performance relationships change across environments to improve such predictions. C_LI