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
Outline of Presentation
* Background* Research Objectives* Approach and methodology
– Field work– Flow cytometric analysisy y– Microscopic analysis
* Results and on going work
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
Swan River Estuary, WA
Major Bloom Occurrences2000 – Microcystis aeruginosa2003 Karlodinium micrum2003 - Karlodinium micrum
(syn. K. veneficum)
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)
– 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”
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
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
Microbial dynamics in a temperate seasonal estuary
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
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
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
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)
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
Microscopic AnalysisMicroscopic Analysis
• Quantitative/Qualitative enumeration of phytoplankton
Bi l l l i• Biovolume calculation
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
Trends & patterns of various microbial S R Egroups along Swan River Estuary
Seasonal abundance: Autumn and winter
Seasonal Abundance: Spring and summer
Seasonal Carbon Distribution: Summer
Seasonal Carbon Distribution: Winter
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
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
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