NSF Award
1550770
Understanding variation in the internal and external drivers of community composition across taxa and systems informs both ecological theory and conservation, particularly regarding the resilience and composition of ecological communities in the face of rapid global change. The proposed research will use National Ecological Observatory Network (NEON) data to determine how assembly processes internal to the community (e.g., biotic interactions, microenvironmental heterogeneity) and large-scale assembly processes external to the community (e.g., climate, land use) combine to affect intraspecific trait variation and community structure at a continental scale. Whether internal or external processes filter how communities respond to their environment will advance the ability to forecast effects of climate change on communities. The proposed work will also contribute to the public through a general science festival; to broad undergraduate education through modules contributed to the Ecological Society of America; and to specific education through the involvement of undergraduates and a postdoc in the research.<br/><br/>The proposed research will address the importance of intraspecific variation of functional morphological traits of plants, ground beetles, and small mammals. Bayesian regression analyses and Bayesian structural equation modeling will be used to uncover the relative importance of intraspecific trait variation in structuring continental-scale biodiversity patterns. Data will be derived from NEON specimens, phylogenies for these taxonomic groups, and NEON environmental and climatic data. The analyses will separate the direct and indirect effect of species richness, phylogenetic relationships, and abiotic variables on the relative influences of internal and external filters that structure communities. Species and environmental data from NEON will provide an unprecedented opportunity to use standardized data to examine continental-scale intraspecific variation on multiple traits across several taxonomic groups. The analyses will contribute new public data for other scientists, and outreach will be developed through existing platforms for science dissemination.
