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How to understand the dynamics that shape microbial communities Monitoring Bacteria in the German Bight

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How to understand the dynamics that shape microbial communities

Monitoring Bacteria in the German Bight

AWI Phd-Days 2014 Judith Lucas

Dr. Gunnar Gerdts, Dr. Antje Wichels

(2)

Background

1. Temporal scales

2. Spatial scales

Microbes interact with environment on different scales

• Microscale patterns

• Global distributions

• Vertical and horizontal expansion

• Short-term variation

• Seasonal variation

Lack of spatiotemporal studies !

(3)

1. Describe temporal and spatial diversity and variability of bacterial community composition

2. Determine influencing factors and deconvolute temporal and spatial signals

Research Aims

The bacterial community composition in the German Bight

(4)

Sampling Strategy

March 2012 – Feb 2013

- Temperature - Salinity

- pH - Chl a

- cDOM, DOC

- Dissolved Oxygen - Turbidity, PAR

 Fractionated filtration:

10 µm (plankton attached) 3 µm (particle attached) 0.2 µm (free living)

 Monthly sampling on three transects

P8

Elbe

Eider

 Contextual data

(5)

Sample Processing

Fingerprinting via Automated Ribosomal Intergenic Spacer Analysis (ARISA) Ribosomal Operon of Bacteria

ITS

16S 23S

Infrared labeled tag

Community profiles

M M M M M M M M

1500

1200

1000 900 800 700 650 600 500 400 350 300 200 100 50

Conversion into numeric data

Multivariate statistics

(6)

P8 I P8 II

P8 III

P8 IV P8 V P8 VI

DOC [µmol/l]

50 100 150 200 250 300 350

Elbe I Elbe II

Elbe E3 Elbe III

Elbe IV Elbe V

Elbe VI Elbe VII

Elbe VIII

DOC [µmol/l]

50 100 150 200 250 300 350

Environmental gradients Results

Stable environmental conditions offshore variable environmental conditions nearshore vs

Offshore transect Coastal transect

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Results

-40 -20 0 20 40

dbRDA1 (37,3% of fitted, 15,7% of total variation)

-40 -20 0 20

March April May June August September October December January February

stable winter to spring community

temporal spatial

Distance based multivariate multiple regression model (DISTLM)

dynamic summer community

DO

Turbidity

Chl a

Easting Temp

PAR Salinity

pH

CDOM Northing DO

Turbidity

Chl a

Easting

Temp PAR Salinity

pH

CDOM Northing

I II E3 III IV

V VI VII VIII

-40 -20 0 20 40

dbRDA1 (37,3% of fitted, 15,7% of total variation)

-40 -20 0 20

estuarine marine

dbRDA1 (21,8% of fitted, 9,2% of total variation)

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Summary & Conclusion

• Bacterial community is highly dynamic over time and space

• Temporal and spatial variation triggered by different factors

• Temporal variation superimposes spatial variation

-40 -20 0 20 40

dbRDA1 (37,3% of fitted, 15,7% of total variation)

-40 -20 0 20

March April May June August September October December January February DO

Turbidity

Chl a

Easting Temp

PAR Salinity

pH

CDOM Northing DO

Turbidity

Chl a

Easting

Temp PAR Salinity

pH

CDOM Northing

I II E3 III IV

V VI VII VIII

-40 -20 0 20 40

dbRDA1 (37,3% of fitted, 15,7% of total variation)

-40 -20 0 20

estuarine marine

dbRDA1 (21,8% of fitted, 9,2% of total variation)

temporal spatial

dominated by temperature dominated by turbidity

(9)

Next steps

Prediction of bacterial community composition depending on environmental factors

• Analyses of particle and plankton attached fractions

• 16S rRNA tag sequence analyses

• Linkage with hydrodynamic simulations

• Analyses of bottom water

(10)

Many thanks to:

Microbial Ecology Group Dr. Gunnar Gerdts

Dr. Antje Wichels Hilke Döpke

Sarah Dehne

Crew of FK Uthörn Silvia Peters

Kristine Carstens

Diverse Master students  Friends and colleagues

Thanks for your attention !

(11)

Marginal test Sequential test

Variable Pseudo-F P Proportion of

Variance

Northing 0,88 0,5364 0,056

Easting 5,09 0,0001 0,253

SiO4 5,55 0,0001 0,270

PO4 5,71 0,0001 0,276

NO2 5,00 0,0001 0,250

NO3 5,68 0,0001 0,275

NH4 5,51 0,0001 0,269

pH 4,87 0,0001 0,245

Temperature 2,30 0,0256 0,133

Salinity 4,75 0,0001 0,241

DO 5,00 0,0001 0,250

Chla 6,10 0,0001 0,289

Turbidity 6,72 0,0001 0,309

CDOM 6,73 0,0001 0,310

DOC 6,28 0,0001 0,295

PAR 1,29 0,2174 0,079

Variable Pseudo-F P Proportion of

Variance

CDOM 6,73 0,0001 0,310

Chla 2,99 0,0018 0,121

DO 2,08 0,0328 0,078

PO4 2,14 0,02 0,074

PAR 2,15 0,0105 0,068

Salinity 1,86 0,0413 0,055

Easting 1,78 0,0675 0,048

SiO4 1,42 0,2155 0,037

Temperature 1,17 0,351 0,030

Northing 1,16 0,3607 0,029

NH4 1,32 0,2881 0,031

Turbidity 1,18 0,3472 0,027

NO2 1,12 0,3767 0,025

p < 0.05

Distance based multivariate multiple regression model (DISTLM)

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