Dimensions: Collaborative Research: Can Evolutionary History Predict How Changes in Biodiversity Impact the Productivity of Ecosystems?

Project: Research project

Project Details

Description

Scientists seek to understand how the current accelerating loss of biodiversity will affect the productivity and sustainability of Earth's ecosystems. The goal of this project is to predict which species extinctions will have the greatest effect on primary production, the fundamentally important process of photosynthetic capture of biomass. This project tests the novel hypothesis that evolution leads to genetic divergence and niche differences among species. In turn, niche differences lead to a 'division of labor' that determines how efficiently biological communities capture the limited resources needed to produce new biomass. To test this hypothesis, the project bridges the fields of ecology, phylogenetics, and genomics to examine how one of the most widespread and ecologically important groups of algae impacts the productivity of lakes throughout North America. The goals are to 1) Create a new molecular phylogeny that can be used to test whether assemblages of freshwater algae are more genetically diverse than expected by chance. 2) Experimentally manipulate the evolutionary and genetic divergence of species to assess how these aspects of biodiversity control niche differences and primary production. 3) Conduct analyses of gene expression data to identify the genetic basis of niche differences among species, and relate these to rates of primary production by phytoplankton communities.

The project will train a variety of students from high school interns to postdoctoral researchers in multidisciplinary work blending genomics, phylogenetics, and ecology. Two K-12 exercises will broaden students' appreciation for biological diversity in important freshwater habitats. The information from the project will provide important information for the management of freshwater resources.

StatusFinished
Effective start/end date10/1/109/30/16

Funding

  • National Science Foundation: $779,589.00

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