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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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We developed a genome-to-ecosystem (G2E) framework linking microbial genomics to ecosystem models. This approach accurately predicts greenhouse gas emissions, showing genomic data

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Area of Science:

  • Microbial ecology
  • Ecosystem modeling
  • Genomics

Background:

  • Microbial activities are crucial for Earth's biogeochemical cycles.
  • Integrating genomic data with ecosystem models for accurate climate change projections remains challenging.

Purpose of the Study:

  • To develop a general genome-to-ecosystem (G2E) framework.
  • To link genome-inferred microbial traits to mechanistic ecosystem models.
  • To improve predictions of ecosystem functioning and greenhouse gas emissions.

Main Methods:

  • Developed a G2E framework integrating microbial kinetic traits from genomic data into ecosystem models.
  • Applied the framework to an Arctic wetland ecosystem.
  • Benchmarked model predictions against observed greenhouse gas emissions.

Main Results:

  • Variations in genome-inferred microbial traits significantly impacted simulated methane emissions.
  • Using genome relative-abundance-weighting improved methane emission predictions by up to 54% compared to ignoring abundances.
  • Demonstrated the consequential impact of genomic variations on ecosystem functioning.

Conclusions:

  • The G2E framework successfully integrates microbial genomics with mechanistic ecosystem modeling.
  • Abundance-weighted genomic traits enhance the accuracy of ecosystem process predictions.
  • This approach offers a pathway to utilize vast microbial metagenomics data for improved predictions in diverse ecosystems.