Andrew Bissett , Andrew Young, Kelly Hamonts & Kristen Williams BASE – Biomes of Australian Soil Environments
Jul 03, 2015
Andrew Bissett, Andrew Young, Kelly Hamonts & Kristen Williams
BASE – Biomes of Australian Soil Environments
Presentation Outline
Introduction to BASE
Methodology
Progress to date– Environmental sampling
– Amplicon sequencing
– Metagenome sequencing
– Modeling
BASE: Biomes of Australian Soil Environments | Andrew Young2 |
Future steps
Sampling
Sequence Analysis
Modeling
Data Management
INTRODUCTION TO BASE
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BASE: Biomes of Australian Soil Environments
Aim of BASE is to develop a National Framework Data set of Australian soil microbial diversity
Combines metagenomics of environmental samples with soil“contextual” data and modelling at continental scale
TO present a publically available database of soil related diversity information
What is BASE?
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Why BASE?
Very little is known about the identity, abundance or distribution of soil microbial communities. Yet they are ecologically very important.
They: Mediate key ecological processes e.g.
nutrient cycling, carbon cycling, water purification
Form crucial symbiotic associations with plants e.g. nitrogen fixation
Play roles in bioregulation e.g. disease suppressive soils
Etc.
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Soil bacteria
Soil fungi
What will BASE do?
Measure and map microbial community diversity and composition, edaphic and other site specific variables across Australia
Model relationships between microbial community structure and soil environment, vegetation composition and climate
Provide a publically available research database (a National Framework Dataset) for exploration of soil biological diversity and function
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What will BASE do?
Test biogeographical and evolutionary hypotheses regarding microbial ecology and plant-microbe co-evolution
Explore relationships between Australia’s microbial diversity and that of the rest of the world
Examine effects of land management
Inform restoration and management of soil diversity and ecosystem services
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Who is involved in BASE?
BioPlatforms Australia
CSIRO
VicDPI
DOTE and DECWA• Atlas of Living Australia
• Australian Antarctic Division
• Australian National Data Service (ANDS)
• Department of Agriculture, Forestry and Fisheries
• Department of Sustainability Environment Water Population and Communities (SEWPAC)
• Grains Research and Development Corporation
• La Trobe University
• Science and Industry Endowment Fund (SIEF)
• South Australian Research and Development Institute (SARDI)
• Tasmanian Land Conservancy
• Terrestrial Ecosytem Research Network (TERN)
• Monash University
• University of Adelaide
• University of New South Wales
• University of Queensland
• University of Western Australia
• University of Western Sydney
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METHODOLOGY
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Environmental sampling 600+ sampling points in intact natural biomes
across the continent⁻ Stratified by soil type, vegetation composition and
climate
⁻ Mainly National reserve system thus far
25mx25m quadrats⁻ 9 soil cores split into 0-10cm and >20cm depth
⁻ Cores bulked, mixed and stored for soil chemistry and DNA analysis
⁻ Site specific data recorded (Vegetation, slope, aspect, etc.)
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Amplicon sequencing
Amplicon sequencing (Illumina)- Bacteria –16S
- Fungi –ITS
- Archaea – 16S
- Euk – 18S
Bioinformatics pipeline:- BASE database - EMP protocol of closed
reference OTU picking
- Specifc analysis pathways for specifcprojects/questions
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Metagenome sequencing
Use amplicon sequencing data to: Identify “interesting” samples for metagenomic
sequencing
Indentify likely samples for differential binning matches
Similar compositions, different abundances
HiSeq
Incorporate metagenomic information into models Metabolic diversity turnover
Functional distribution models
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Sampling – data –sampling workflow
Quantify diversity and abundance of microbial communities
Model abundance and composition in relation to environmental parameters measured at each site
Model against continental environmental data “surfaces”
Extrapolate to continental scale
Undertake environmental gap analysis
Add new samples and refine model
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PROGRESS TO DATE
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Soil chemistry and vegetation data
Edaphic variables– all samples have data for: Soil moisture, Texture, Ammonium, Nitrate,
Nitrogen, Phosphorus, Potassium, Sulphur, Total Carbon, Organic Carbon, Conductivity, pH, Copper, Iron, Manganese, Zinc, Exc. Aluminium, Exc. Calcium, Exc. Magnesium, Exc. Potassium, Exc. Sodium, Boron, Grain Size (% gravel, sand, silt, clay), project specific data (minerology, MIR, biogeochemical rates,……..).
Non-edaphic/climate/site– all samples have data for: Overlying plant cover (% and identity), slope,
aspect, location, elevation, land-use history, land-management history.
Data for first 400 samples are available online (BPA website), ALA
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Amplicon sequencing
Sequenced 100 samples using 454 pyrosequencing with bacterial and fungal tags Data available online via BPA (although not in such a useful format)
Illumina reads for first 600 samples due next week
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Metagenome sequencing
HiSeq data generated for 6 sites (12 samples)
Test whether amplicon data can inform suitability for sample analysis, binning etc.
Sequences generated from both 350bp and 550bp fragments to assess efficacy of both
Data being analysed now
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BASE database
Developed an offline (access) relational database that allows searching of sequence and contextual data
Currently migrating this to online tool, with AAD, ALA and Centre for Comparative Genomics (BPA)
Raw Sequence data available via BPA data store
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SOME RESULTS
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Model – using site contextual data
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Model (3 & 9)
5778 site pairs
% Deviance
Explained# predictors Main predictor
Presence/
Absence73.0 13 Conductivity
Abundance 74.1 14 Conductivity
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Exploratory models compared OTU presence/absence and abundance
using soil chemistry data (common between sites) using data from first 98
sites for which all data were available
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Antarctica
Xmas Island
Minerology sites
NRS
TERN supersites
Trend transect
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Bacteria Fungi
• Edaphic and spatial components were significant in explaining community structure for
both bacterial and fungal communities
• pH and conductivity most important
• Communities at different soils depths were significantly different
only for bacteria
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NEXT STEPS…
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Extended environmental sampling
Add Temporal component to sampling Long term monitoring sites
Sites with biogeochemical rate data
Sites identified as containing residual information
Sites with NO residual information (to test model)
More metagenomic samples (as guided by amplicon data)
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Agricultural sampling
Cropping, grazing, horticulture⁻ Stratified soil type and by production
system
Measure same parameters as environmental sites
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Modeling
Extend Generalised Dissimilarity modelling to include PhlogeneticDistance metrics
Create species distribution models
Incorporate temporal aspect into models
Test models
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Database availability development
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The BASE Team:CSIRO: Andrew Bissett, Leon Court, Kelly Hamonts,
Shamsul Hoque, Kristen Williams, Andrew Young
Bioplatforms Australia: Anna Fitzgerald, John
Stephens and Catherine Zhang
University of Adelaide: Frank Reith
DEC WA: Margaret Byrne and David Coates
DSEWPaC: Belinda Brown and Parks field staff at:
Kakadu, Christmas Is, Booderee, Uluru
TERN: Supersites
SIEF: Strategic resourcing
Others: Vic DPI, Gondwana Link, Bushblitz, Antarctic
Division
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