An integrative approach to quantifying the response of ecological assemblages to anthropogenic stressors
Description: Ecological stressors such as deforestation, climate change, habitat fragmentation, and invasive species are pervasive throughout the world and can be driven in part by human (anthropogenic) activity. The ability to detect and quantify the impact of these stressors on species communities is critical for maintaining and managing biodiversity and the health of ecosystems. The assessment of anthropogenic impacts on these species communities, however, is a challenging problem given the multivariate nature and multiple facets of biodiversity data. Species differ not only in their taxonomic identities but also in their abundances, evolutionary history, and their role and function in ecosystems. Predicting and understanding how species communities are altered by anthropogenic change and how novel communities function is key to sustaining biodiversity and ecosystem function. This project aims to develop a computational approach for quantifying anthropogenic impacts on ecosystems. The application of these tools to pressing ecological questions will generate much needed information regarding how biological communities are responding to multiple globally important anthropogenic stressors
This project aims to develop new computational methods that integrate multiple sources of biodiversity information to delineate constituent units within species assemblages (defined from here onwards as component communities) and quantify the impact of anthropogenic stressors. The project has two specific aims. The first is to integrate phylogenetic information with species interaction and composition data to identify component communities within ecological assemblages in heterogeneous biodiversity data. The second is to apply these integrative methods to diverse datasets to characterize the changes in composition of ecological assemblages based on responses of these component communities to anthropogenic stressors. For more information on this project visit the PI's lab page at http://denisvalle.weebly.com/.
Funder: National Science Foundation
Amount: $896,100
Funding type: grant
Funder scheme: Continuing Grant
Start date: 2015-08-15
End date: 2020-07-31
Investigator: Denis Valle
Institution: University of Florida
Funded outputs count: 17
Funder award ID: 1458034
OpenAlex ID: G7993489469