Patterns in the microbial community structure in Swan ... Patterns in the microbial community...
Transcript of Patterns in the microbial community structure in Swan ... Patterns in the microbial community...
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Patterns in the microbial community Patterns in the microbial community structure in Swan River Estuary
Alice I. Gedaria, Prof. Tony O’Donnlell and Matthew Alice I. Gedaria, Prof. Tony O Donnlell and Matthew R. Hipsey
School of Earth and Environment, Faculty of Natural and Agricultural Sciences, University of Western Australia
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Outline of Presentation
* Background* Research Objectives* Approach and methodology
– Field work– Flow cytometric analysisy y– Microscopic analysis
* Results and on going work
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Harmful Algal BloomsHarmful Algal Blooms
•May cause harm through the production oftoxins or by their accumulated biomass
•Can affect co-occurring organisms and alterfood-web dynamicsfood web dynamics.
Impacts include human illness and•Impacts include human illness andmortality following consumption of orindirect exposure to HAB toxins
* Substantial economic losses to coastalcommunities and commercial fisheries
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Swan River Estuary, WA
Major Bloom Occurrences2000 – Microcystis aeruginosa2003 Karlodinium micrum2003 - Karlodinium micrum
(syn. K. veneficum)
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Controls on Phytoplankton Controls on Phytoplankton Blooms/Biomass
• Light– TurbidityTurbidity– Colour (DOC)
N i• Nutrients– Nitrogen (N), Phosphorous (P)( ) ( )– micro-nutrients
• Salinity Temperature• Salinity, Temperature• Biological (eg. grazing, viral infection)
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– Hodgkin 1987:
Studies on Swan Primary Production
Hodgkin, 1987:• Salinity as the “master factor”
– John, 1987; Chan and Hamilton, 2001:, ; ,• Freshwater runoff and salt wedge evolution control succession
– Thompson and Hosja, 1996; Thompson 1998:• N as key limiting nutrient
– Kostoglidis et al., 2005; Harris et al., 2008:• CDOM and light climate highly variable• CDOM and light climate highly variable
– Hamilton et al., 2006:• Large spatial and temporal variability in biogeochemistry –a ge spat al a d te po al va ab l ty b ogeoc e st y
“high primary productivity need not be linked to a single limiting factor but were likely a response to coexisting environmental factors”
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Main Objective of the Study
To gain an improved understanding of theinteractions of different microbial groups such asbacteria, phytoplankton and virus-like particles inSwan RiverSwan River
♦ seasonal/spatial timescale♦ seasonal/spatial timescale
♦ relation to hydrodynamics
♦ nutrient loading and cycling processes
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Specific Objectives of the Study:
♦ Develop a novel microbial data set using flow cytometricanalysis- explore population structure and variability- gain insights of its physiological condition
♦ Improve our ability to understand and quantify nutrient flowthrough the ecosystem
i t ti b t i i t i t di l d i- interaction between inorganic nutrients, dissolved organicmatter and the microbial groups
♦Further develop our understanding of microbial ecology in the estuary- improved ability to predict likely impacts of environmental improved ability to predict likely impacts of environmental change to nutrient budgets, algal ecology and river health
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Microbial dynamics in a temperate seasonal estuary
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Microbial Community
Copepods Macro-grazers Micro-grazerslank
ton
Copepods Macro grazers Micro grazers
kton
Zoo-
pl
Chlorophytes
lank
ton
Mic
ro-p
lan
CryptophytesDinoflagellatesDiatoms
ktonN
ano-
plM
Pico-eukaryotes
(mixed, 0.2-2um)
Prochlorococcus(small cyano, <1um)
Synechococcus
(cyano, 0.8-1.5um)
Pico
-pla
nk
Bacteria Viruses/Phages
Pri
o/Vi
rio
ankt
on
/ g
Bact
e-p
la
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Microbial Communityto
n
Copepods Macro-grazers Micro-grazers
Zoo-
plan
kt
on-pla
nkto
nZ
Chlorophytes
ano-
plan
kto
Mic
ro-
CryptophytesDinoflagellatesDiatoms
Pico-eukaryotes Prochlorococcus Synechococcus
plan
ktonN
a
(mixed, 0.2-2um) (small cyano, <1um) (cyano, 0.8-1.5um)
Pic
o-iri
on
Bacteria Viruses/Phages
Bac
terio
/Vi
-pla
nkto
nB
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Methodology
Laboratory Protocol DevelopmentLaboratory definition and optimisation
Field Sampling Routine sample collections and strategic experiments
Laboratory AnalysisFlow Cytometry and MicroscopyFlow Cytometry and Microscopy
Data Analysis Seasonal and spatial trends
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Field SamplingField Sampling
Swan River Estuary
• Microtidal• Microtidal
• Seasonal variability in freshwater inflow
• Stratification: vertically homogenous (summer), two layer stratified system (winter)
• pioneering work in conducting microbial monitoring studies microbial monitoring studies (spatial/temporal)
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Me e e tMeasurements
Properties Measurement
Physical Salinity, Temperature , Dissolved Oxygen, pH
Chemical properties Ammonia (NH3 ‐N), Nitrite and Nitrate , Soluble Reactive
Phosphorous (SRP) , Total Nitrogen/Phosphorous , DissolvedPhosphorous (SRP) , Total Nitrogen/Phosphorous , Dissolved
Organic C arbon (DOC, Silica (SiO2 ‐Si), Diss olved Organi c
Nitrogen (DON), Alkalinity (CaCO3)
Flow Cytometric Counts bacteria, pico and nano phytoplankton, Virus ‐like particles
(VLP’s )(VLP’s )
Microscopic Cell Counts diatoms, dinoflagellates, cryptophytes,
chlorophytes, cyanobacteria, euglenophytes, raphidophytes p y y g p y p p y
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Microscopic AnalysisMicroscopic Analysis
• Quantitative/Qualitative enumeration of phytoplankton
Bi l l l i• Biovolume calculation
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Flow Cytometric Analysis
A B C
Flow Cytometric plots of picophytoplakton populations A) forward scatter vs. chl-a b) side scatter vs. chl-a and c) Accessory pigments (orange fluorescence)
vs. chl-a
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Trends & patterns of various microbial S R Egroups along Swan River Estuary
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Seasonal abundance: Autumn and winter
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Seasonal Abundance: Spring and summer
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Seasonal Carbon Distribution: Summer
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Seasonal Carbon Distribution: Winter
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Data AnalysisCharacterization of general microbial patterns (spatio-temporal)
♦ Pico‐plankton – how many? what are they doing?
♦ Bacteria and virus – how many? what are they doing?
♦ Nano‐plankton – compare with microscopic data
♦ Compare estuary gradients & seasonal changesg
♦ Link to environmental and nutrient driversnutrient drivers
♦ Statistical correlation b t d ibetween groups and various physico‐chemical drivers
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Management Implications
• Dynamics of pico/nano communityHi hl i bl– Highly variable
– Significant contribution to biomass & primary production
• Pico-plankton highly correlated with nutrientsCorrelation with N is high suggesting N as primary driver– Correlation with N is high, suggesting N as primary driver
• Viral patterns indicate potential for bottom up control• Viral patterns indicate potential for bottom up control
• Use to develop quantitative estimation of nutrient flux pathways • Use to develop quantitative estimation of nutrient flux pathways for different seasons/states
• Improved ability to develop ecosystem model
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Acknowledgement♦ Dr. Matthew Hipsey, SEE-UWA
♦ Dr. Tony O’Donnell, FNAS-UWA
♦ Swan River Trust
♦ Drs. Kathy Heel and Paul Rigby- Centre for Microscopy, Characterization and Analysis, UWA
♦ Phytoplankton Ecology Unit, Department of Water
♦ Water Science Branch, Department of Water