Dimensions US-BIOTA - São Paulo: collaborative research: integrating dimensions of microbial biodiversity across land use change in tropical forests

Abstract

Microorganisms drive the ecosystem processes on which all of life depends, yet we know much less about their biodiversity than we do for plants and animals. Microbial ecology is increasingly sophisticated in estimating the phylogenetic, genetic, and functional dimensions of microbial biodiversity, but integration of these dimensions has proven elusive. This project will combine recent advances in nucleic acid sequencing and ecosystem biogeochemistry to address an outstanding question of modern biogeochemistry: what controls methane cycling along gradients of land use in tropical forests? Answering this question will require that we address the fundamental biological challenge of understanding interactions and feedbacks among the three dimensions of microbial biodiversity. To address this challenge, we will assess genetic, phylogenetic, and functional dimensions of bacterial and archaeal biodiversity in two Brazilian tropical forests under threat from development: a western Amazon rainforest in Rondonia and an eastern Amazon National Forest near Santarem. These forests span a range of ecosystem types, soil characteristics and land use histories. We will assess three dimensions of soil microbial biodiversity: (1) microbial community composition and phylogenetic diversity via high throughput sequencing of methane cycling (pmoA and mcrA) genes; (2) whole-community genetic diversity (with a focus on metabolic potential) via targeted gene regions and metagenomic sequencing of functionally relevant genome fragments; and (3) whole-ecosystem functional diversity via observations of production/consumption of methane and its isotopes, which will then be compared to transcript: gene abundance ratios for methane cycling genes. To fully integrate all three dimensions of microbial biodiversity, we will use a two-tiered approach: (1) pairwise statistical integration between datasets of adjacent dimensions; and (2) simultaneous integration of all three dimensions via development of a trait-based computational model (MicroTrait-ME) that combines taxonomic identities, abundances, and trait values (inferred from gene content and expression data) to predict the response of methane cycling to land use change. This model will be useful to a wide community of researchers. (AU)