Transcript prokaryotes
Prokaryotic abundance, activity and community structure in relation to the quality of dissolved organic matter in the deep waters off the Galician Coast (NW Spain). Elisa Guerrero-Feijóo, Mar Nieto-Cid, Xosé-Antón Álvarez-Salgado, Marta Álvarez, Víctor Hernando-Morales, Eva Sintes, Eva Teira, Gerhard J. Herndl, Marta M. Varela I. Introduction The prokaryotes are an important component in marine plankton Arístegui et al. 2009 I. Introduction The prokaryotes are an important component in marine plankton Herndl and Reinthaler, 2013 II. Aims 1. Determine the abundance, activity and the prokaryotic community structure (Bacteria & Archaea) 2. Study the relationship between prokaryotic community structure and environmental variables environmental variables biologic organic matter physico-chemical III: Study area Sampling site (NW Spain) Two cruises: In 2011 BIOPROF-1 In 2012 BIOPROF-2 St 111 St 108 St 16 St 11 IV: Methods Prokaryotic abundance Prokaryotic heterotrophic production (PA) (PHP) Smith and Azam (1992) Prokaryotic community structure CARD-FISH Fingerprinting Water masses EZ ENACW-OMZ V. Results: Properties MW LSW BIOPROF-1 ENADW LDW ENADW LDW LSW MW OMZ BIOPROF-2 V.Results: PA & PHP BIOPROF-1 PHP(umol C m-3 d-1) PA (cell/mL) BIOPROF-2 1.E+00 EZ Water masses Results ENACWOMZ MW LSW ENADW LDW 1.E+02 1.E+04 1.E+06 0.01 0.1 1 10 100 V.Results: CARD-FISH Bacteria EZ EZ ENACW-OMZ ENACW-OMZ Water masses Results Thaumarchaeota MW MW LSW LSW ENADW ENADW LDW LDW 0 20 40 60 % DAPI counts 80 0 5 10 % DAPI counts 15 V.Results: CARD-FISH EZ EZ EZ ENACW-OMZ ENACW-OMZ ENACW-OMZ MW MW MW LSW LSW LSW ENADW ENADW SAR-11 LDW 0 60 0 40 LSW LSW ENADW ENADW LDW LDW 40 % Eubacteria counts 0 60 20 0 40 % Eubacteria counts SAR-324 ENACW-OMZ MW 20 60 EZ MW 0 LDW % Eubacteria counts SAR-202 ENACW-OMZ 20 SAR-406 ENADW Altermonas LDW 20 40 % Eubacteria counts EZ Water masses Water masses Ʃ=97.82% 20 40 % Eubacteria counts 60 60 V.Results: CARD-FISH Relationship: CARD-FISH and quality DOM Bacteria Thaumarchaeota FDOMM -0.68 0.48 FDOMT 0.51 - CDOM254 0.36 - CDOM340 0.23 - CDOM365 0.23 - - 0.33 0.53 - Results Quality DOM DOM275-295 S DOC V.Results: T-RFLPs Archaeal Community Structure (ACS) Euphotic zone Deep waters Results Mesopelagic waters V.Results: ARISA Bacterial Community Structure (BCS) Mesopelagic waters Deep waters Euphotic zone Results: DisTLM analysis For running the model: Sets Physico-Chemical Dissolved Organic Matter Biologic Best procedure Variables Temperature Salinity Oxygen Nitrate Silicate Phosphate FDOMM FDOMT CDOM254 CDOM340 CDOM365 SDOM275-295 DOC Prokaryotic abundance Prokaryotic heterotrophic activity Results: DisTLM analysis For running the model: Sets Physico-Chemical Dissolved Organic Matter Biologic Best procedure Variables Temperature Salinity Oxygen Nitrate Silicate Phosphate FDOMM FDOMT CDOM254 CDOM340 CDOM365 SDOM275-295 DOC Prokaryotic abundance Prokaryotic heterotrophic activity Results: DisTLM analysis For running the model: Sets Physico-Chemical Dissolved Organic Matter Biologic Best procedure Variables Temperature Salinity Oxygen Nitrate Silicate Phosphate FDOMM FDOMT CDOM254 CDOM340 CDOM365 SDOM275-295 DOC Prokaryotic abundance Prokaryotic heterotrophic activity V.Results: DistLM T-RFLPs Results ACS = Physico-chemical (20%) + Organic matter (26%) + Biological (17%) V.Results: DisTLM ARISA Results BCS = Physico-chemical (38%) + Organic matter (30%)+ Biological (18%) VI. Conclusions Prokaryotic abundance and production decrease with depth. prokaryotic heterotrophic Thaumarchaeota relative abundance was higher in deep waters than surface layer, while Bacterial abundance tends to decrease with depth. SAR-11 and Alteromonas dominated the prokaryotic community structure inhabiting surface waters. SAR-202 and SAR-324 increased with depth. SAR-406 did not show any clear trend. The prokaryotic community assemblages clearly clustered according to the different water masses. DisTLM analysis explained that only 29% of the variation of ACS can be modeled by the environmental variables tested in this study. DOM represented the primary factor driving the ACS. On the other hand, the analysis explained 49% of the variation for the BCS to the variables included in this work. The physico-chemical set was the most representative to modeling the BCS. Acknowledgment Elisa Guerrero-Feijóo is supported by Project BIO-PROF Funding: BIO-PROF MODUPLAN Crew of the R/V Cornide de Saavedra Co-authors Marta M Varela Marta Álvarez Mar Nieto Cid Victor Pepe Álvarez Hernando Salgado Morales Eva Teira Eva Sintes Gerhard Herndl THANK FOR YOUR ATTENTION!! Other important members Fátima Eiroa Ángel Lamas Elena Rey Rebeca Alvariño Vladimir Dobal