A presentation by Prof. Dian Wall: Linking Soil Biodiversity to the Global Soil Map Project

1. 5/27/2010
Linking Soil Biodiversity to the Global Soil
Continental‐scale analysis of total soil Map Project
biodiversity using molecular techniques
Global Digital Soil Map ‐ map soil health conditions of
Sub‐Saharan Africa (see AfricaSoils.net )
Total Soil Biodiversity‐using molecular techniques
Diana Wall1, Ed Ayres1, Uffe Nielsen1, Richard Bardgett2,
, y , , g
B li f i b &i t b t
•Baseline survey of microbes & invertebrates
Jim Garey3 and Tiehang Wu3 – (Invertebrates)
•Fertile and degraded soils at large regional scales
Colorado State U1, U South Florida2, U Lancaster3
•Utilize metadata – edaphic factors, etc of GDSMap
Noah Fierer and Scott Bates – (Bacteria, Archea)
U Colorado
A Globally Integrated African Soil Information
Service
‐freely available, web‐enabled access to an
integrated, evidence‐based, and dynamic soil health
information service
‐information for the non‐desert portions of Sub‐ Randomization of
Saharan Africa. Sentinel Site locations
l l
AfricaSoils.net stratified by climate
CIAT‐TSBF, Earth Institute at Columbia University,
World Agroforestry Centre, International Soil
Reference Information Centre
With support to CIAT‐TSBF from Bill & Melinda
Gates Foundation and Alliance for a Green
AfricaSoils.net
Revolution in Africa
AfricaSoils Sentinel Site AfSIS Soil Characterization
based on the Land Degradation • Infrared
Surveillance Framework spectroscopy on
a spatially stratified, hierarchical,
all samples
randomized sampling framework
• Reference
samples on
subsets
Sentinel site (100 km2)
Sentinel site (100 km • Note modules in
16 Clusters need of
10 Plots development =
soil biology,
4 Sub‐Plots radionucleides,
plant growth
bioassay, soil
classification
• randomization to minimize local biases that might arise from convenience sampling
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2. 5/27/2010
Total Soil Biodiversity‐Why molecular sequencing?
•Less than 1% microbes in soil are culturable
•Invertebrate identification problematic for total biodiversity
specialists, extraction methods and efficiencies, soil types
Next generation of molecular techniques allow rapid processing
(days vs years)
•2 analyses from a composite sample provides all the sequence
•2 analyses from a composite sample provides all the sequence
data for microbes and invertebrates
•Results provide >1000‐10K sequences per sample and all
collected samples simultaneously
•Our 200 samples include fertile and degraded soils covering a
broad geographic scale
•Numerous sequences will allow for intensive community
analyses
Soil Animals
A sequenced based soil animal diversity approach
Standardized Methods
• Sampled at peak biomass, 4 plots on 1 km transect,0‐
10cm
• Bulked samples into ethanol, sieved, DNA extracted
Measurements
•Animals: molecular (18S rRNA gene clone
•Animals: molecular (18S rRNA gene – clone
libraries) & morphological analysis
•NOT next generation ‐ so labor was intensive, time
consuming and yielded far fewer sequences
•Soils: physical, chemical & biological properties
Wu et al., 2009, Soil Biol. Biochem.
Some animals from DNA sequencing of a sample Sequence‐based Rarefaction of Taxonomic Richness
350 AB
Kenya AR
300 Alaska Tundra BZ
CR
Sweden AB Alaska Forest DV
250 KY
ed OTUs (Sobs)
Kansas
Sweden Forest KZ
Costa Rica NZ
200
South Africa PU
Nematoda Tardigrada Platyhelminthes Annelida RE
NZ
NZ
Observe
SA
150
1 0
TK
Peru
100
50 Argentina
Antarctic Dry Valleys
0
Collembola Acari Centipedes Spiders
Gastrotricha
Arthropoda
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3. 5/27/2010
Arthropoda Nematoda Soil Animals
Soil animal composition based Annelida Similar sequences for genera common to four or more sites
Platyhelminthes
on sequence Chordata Rotifera
18
Cnidaria Tardigrada Alaska
‐Bonanza (boreal forest)
Gastrotricha 13
Other ‐Toolik (tundra)
25
Tundra
Tundra
Kansas
Boreal ‐ Konza 20
Forest Boreal
Forest Sweden
9 ‐ Reivo (boreal forest)
22 ‐ Abisko (tundra)
Tallgrass Costa Rica
Costa Rica New Zealand
N Z l d
Arid 14
Tropical
Forest
Grassland
Peru 18
Kenya
Tropical Temperate 4 11
Forest Mediterranean Forest Argentina
Shrub Steppe South Africa
4
10
Cold
Desert Antarctic
Pyrosequencing
DNA extracted from soil samples
PCR amplification with group
specific primers for SSU
rRNA gene
g
Pyrosequencing gives ~1500
sequence per sample across many
samples (>450,000 seq per run)
Taxonomic Information AGCCTTAA… AGCCTTAA… AGCCTTAA…
per sample GCTACCAT…
CGGATCAC…
GCTACCAT…
CGGATCAC…
GCTACCAT…
CGGATCAC…
CTCGATTC… CTCGATTC… CTCGATTC…
Soils from 150 sites at peak of growing season
Sequences aligned
and inserted into a
phylogenetic tree
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4. 5/27/2010
Do bacterial communities vary across a single biome?
Does Biome
type
determine
pattern of Wolf Creek Fairbanks Noatak
Toolik
Dempster
bacterial Taglu Island
diversity ? Daring Lake
Yamba Lake
Aylmer Lake
Artillery Lake
Cambridge Bay
Ramsay Lake
Truelove
Alexandra Fiord
Bylot Island
Circum‐Arctic
Ci A i Bear Island
Kuujjarapik Kangirsujjuaq
Cape Tanfield
heath tundra soils Kangirsuallujjuaq
Torngat Mountains
Svalbard Island
Tropical forest Kangerlussuaq
Skallovaara
Kilpisjarvi village
Temp. conif. forest Laxardalur Valley
Abisko
Lake Raudavatn
Temp. decid. forest
Non Metric Multidimensional Scaling
Prairie
Boreal forest
Temp. grassland
Tundra
Lauber et al. 2009. Appl. Environ. Microbiol. Desert
Mediterranean (Chu et al. In Press. Environ. Microbiol.)
‐ Arctic heath
soils Vegetation Type
Mean Annual Temperature
Soil moisture deficit (PET‐MAP)
% H2O
C:N ratio
pH
Tropical forest
Temp. conif. forest
% silt + clay
Temp. decid. forest
Biome type means nothing to bacterial % organic C
Prairie
communities Boreal forest C mineralization rate
Temp. grassland
Tundra net N mineralization rate
Chu et al. In Press. PNAS Desert
Mediterranean
pH is the best predictor for bacterial communities
Vegetation Type
pH explains
~70% of the MAT
variablility Soil moisture deficit (PET‐MAP)
% H2O
C:N ratio
pH
% silt + clay
% organic C
C mineralization rate
High pH Low
net N mineralization rate
Lauber et al. 2009. Appl. Environ. Microbiol., Fierer and Jackson. 2006. PNAS
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5. 5/27/2010
Sequence data allows for detailed analysis of community The Belowground Biodiversity Project has shown:
shifts • soil diversity is not the same everywhere
• species and communities have biogeography
• diversity decreases with land use change
• certain groups or species are indicators
The next generation pyrosequencing approach will
extend the BGBD and GlobalSoilMap.net particularly
extend the BGBD and GlobalSoilMap.net particularly
for:
Archaea
Bacteria
And together add to
Total eukaryotic communities (fungi +
invertebrates)
Lauber et al. 2009. Appl. Environ. Microbiol.
Soil animal biodiversity Figure 4. “Genera richness” (97% OTUs) observed at four or
more sites.
Nematode families 18
Alaska
‐Bonanza (boreal forest)
100 13 ‐Toolik (tundra)
Everything is NOT
25
everywhere?
Kansas
‐ Konza 20
ccurrence
Sweden
9 ‐ Reivo (boreal forest)
22 ‐ Abisko (tundra)
50 Costa Rica
Costa Rica New Zealand
N Z l d
% oc
14
Peru 18
Kenya
11
4 Argentina
0
South Africa
4
10
Nematode family Antarctic
p
120
Molecular
100 Morphological
r2=0.52
Relative abundance (%)
80
60
40
20
0
Soils collected from >100 sites at
peak of plant growing season
Figure 9b. Relative abundance (%) of arthropods at each SA
AB AR BZ CR DV KY KZ NZ PU RE TK
location using molecular and morphological methods.
Locations
Fierer and Jackson. 2006. PNAS.
Lauber et al. 2009. Appl. Environ. Microbiol.
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