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