| Autor: |
Raes EJ; CSIRO Oceans and Atmosphere, Hobart, TAS, Australia. eric.raes@dal.ca.; Ocean Frontier Institute and Department of Oceanography, Dalhousie University, Halifax, NS, Canada. eric.raes@dal.ca., Karsh K; CSIRO Oceans and Atmosphere, Hobart, TAS, Australia., Sow SLS; CSIRO Oceans and Atmosphere, Hobart, TAS, Australia.; Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia.; NIOZ Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry, Den Burg, The Netherlands., Ostrowski M; Climate Change Cluster, University of Technology Sydney, Sydney, NSW, Australia., Brown MV; School of Environmental and Life Sciences, The University of Newcastle, Callaghan, NSW, Australia., van de Kamp J; CSIRO Oceans and Atmosphere, Hobart, TAS, Australia., Franco-Santos RM; Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia., Bodrossy L; CSIRO Oceans and Atmosphere, Hobart, TAS, Australia., Waite AM; Ocean Frontier Institute and Department of Oceanography, Dalhousie University, Halifax, NS, Canada. |
| Abstrakt: |
Global oceanographic monitoring initiatives originally measured abiotic essential ocean variables but are currently incorporating biological and metagenomic sampling programs. There is, however, a large knowledge gap on how to infer bacterial functions, the information sought by biogeochemists, ecologists, and modelers, from the bacterial taxonomic information (produced by bacterial marker gene surveys). Here, we provide a correlative understanding of how a bacterial marker gene (16S rRNA) can be used to infer latitudinal trends for metabolic pathways in global monitoring campaigns. From a transect spanning 7000 km in the South Pacific Ocean we infer ten metabolic pathways from 16S rRNA gene sequences and 11 corresponding metagenome samples, which relate to metabolic processes of primary productivity, temperature-regulated thermodynamic effects, coping strategies for nutrient limitation, energy metabolism, and organic matter degradation. This study demonstrates that low-cost, high-throughput bacterial marker gene data, can be used to infer shifts in the metabolic strategies at the community scale. |
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