COS 33-8 - Variance and redundancy of functional trait assemblages in seasonally variable environments

Tuesday, August 13, 2019: 4:00 PM
M111, Kentucky International Convention Center
Elizabeth G. Simpson and William D. Pearse, Department of Biology & Ecology Center, Utah State University, Logan, UT
Background/Question/Methods

As extreme weather events become more prevalent, ecologists need to understand the effect of seasonal climate variation on ecological assemblies. Spatial environmental heterogeneity often supports more diverse functional trait strategies in plant assemblages. However, it is less clear how spatial and temporal variation combined affects the functional variance and redundancy of assemblages. A better understanding of these impacts would help us make better predictions under climate change, since the magnitudes of temporal and spatial variation are not expected to change equally. We mapped current functional trait assemblages onto current seasonal environmental fluctuations to test whether functional redundancy is positively correlated with seasonal variation. This provides both a test of key ecological theory, and a new approach that we outline to comparably measure the mean and variance of an assemblage’s traits and environment.

Results/Conclusions

We collected functional traits that represent competitive (height) and resource acquisitive (specific leaf area; SLA) strategies from species in our fractally-arranged study plots along the Right Hand Fork of the Logan River (UT) during summer 2018. We found statistically significant correlations between the community weighted mean of height and differences in how much soil temperature varies during winter months across aspect. Our results demonstrate the importance of understanding the relationship between seasonal variation in environmental conditions (soil temperature) and patterns in functional trait assemblages (height) for quantifying ecosystem function. We discuss how expanding our understanding of functional diversity’s response to environmental variation may increase our predictive understanding of the resilience of ecological assemblages to increasingly extreme weather events and seasonal variation.