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Funding provided by:
Sean GrahamUniversity of British Columbia
Plant DNA barcoding --
Advances, applications & limits
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CO-AUTHORS:
Prasad R. Kesanakurti; Aron J. Fazekas; Diana M. Percy; Kevin S. Burgess;
Jeffery M Saarela; Steven G. Newmaster; Brian C. Husband; Mehrdad Hajibabaei;
Spencer C. H. Barrett
Root project: Prasad R. Kesanakurti; Aron J. Fazekas; Kevin S. Burgess
Grasses & Willows: Diana Percy; Jeffery Saarela
Institutions: UBC; U. Guelph, U. Toronto, Canadian Museum of Nature
Plant DNA barcoding --
Advances, applications & limits
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Choosing a multi-locus barcoding system
Are plants harder to barcode than animals?
-- Fazekas & al. 2009 (Mol. Ecol. Resources 2009)
Overview
-- Fazekas & al., 2008 (PLoS ONE 2008)
-- CBOL Plant Working Group (PNAS 2009)
Plant DNA barcoding studies
-- Poaceae & Salix of British Columbia, Canada
-- Eco-applications: Below-ground ecology
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LAND PLANTS• c. 400,000 species
VASCULAR PLANTS
• > 350,000 species
• c. 13,888 genera
• c. 511 families
PLANT
DIVERSITY
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�
Pennisi (2007)
Science 318: 190-191
Plant working group
(Sept 2008)
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CBOL Plant working
group (PNAS 2009)
Core loci (CBOL): rbcL+matK (2 locus)
(plus suppl. loci)
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Plant genus:
ViburnumAnimal genus:
Artibeus
-- Ideally: Intraspecific < Interspecific variation
Genetic distance (K2P)
Intra-
Inter-
What about the barcoding gap?
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Observed monophyly fraction = one criterion for species discrimination
Samples of --
Species A
Species B
= Mutual (reciprocal)
monophyly
-- Intraspecific < Interspecific variation
-- Gene-tree monophyly tracks species boundaries
What about the barcoding gap? Ideally --
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Barcoding a local flora (KSR, Ontario, Canada)
Local flora of Koffler
Scientific Reserve
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Fazekas & al., 2008
(PLoS ONE e2802)
Relation between sequence variation &
percent species resolution
(*PIC = Parsimony Informative Character)
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Plants are harder to barcode precisely
(controlling for amount of variation per species)
7 different animal studies (N = 65-641 species)
„Plateau‟ (upper
limit) observed for
combined plastid
loci
Plant data
(~1 study, 7 loci)
Fazekas & al., 2009
(Mol Ecol Res)(*PIC = Parsimony Informative Character)
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Overview of barcoding gap (across multiple animal genera)
N = 326 animal
genera
Fazekas & al., 2009
(Mol Ecol Res)
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Plants typically have more overlap in inter- vs. intraspecific distances
N = 49 plant
genera
Fazekas & al., 2009
(Mol Ecol Res)
(& smaller scale: plastid genome is slower than animal mt)
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Sister species
mutually monophyletic
Gene-tree paraphyly
(introgression, lineage-sorting,
etc)
Is gene-tree paraphyly more extensive in plants?
Why? More introgression? Shallower gene-tree
coalescences? “Poorer” species-level circumscriptions?
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Fazekas & al., 2009
(Mol Ecol Res)
-- Hybridization in genus --
Lack of „barcoding gap‟ in plants is associated with presence
of hybridization (but not polyploidy)
Inter- minus intra-
specific distance
P < 0.01 (N = 12),
Present Absent
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How problematic is
30%+ error
(impression)
-- Depends on the application:
-- Can think of as question of
resolution:
* Lower resolution „images‟
can work well
e.g. forensics vs. bioinventory
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Species
Genome
(length)Described
speciesUndescribed
species
Arabidopsis thaliana
& other “model” species
DNA barcoding
DNA Barcoding as “horizontal” genomics
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Plant DNA barcoding studies
Choosing a multi-locus barcoding system
Are plants harder to barcode than animals?
-- Poaceae & Salix of BC, Canada/ GrassBoL campaign
-- Eco-applications: Below-ground ecology
Overview
-- Fazekas & al. 2009 (Mol. Ecol. Resources 2009)
-- Fazekas & al., 2008 (PLoS ONE 2008)
-- CBOL Plant Working Group (PNAS 2009)
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Samples collected for grasses –
BOLD specimen map
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Economically important:
Wheat
(Triticum)
Wheat, rice & maize provide > 50% of human calories
Rice
(Oryza)
Identification is often problematic –
Highly reduced reproductive morphology
Maize
(Zea)
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6 subfamilies
(“PACMAD “) incl.
panicoid grasses
Our grass sampling concords with grass family systematics
Bromeae + Triticeae
Poeae
Aveneae
Stipeae
Meliceae
Sub-family Pooideae
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Grasses in British Columbia
Difficult groups: Poa (29 species) Bromus (21 species)
Festuca (15 species)
Agrostis (13 species) Elymus (11 species) Glyceria (10 species)
Calamagrostis (8 species)
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Grasses Willows
rbcL 550 – 94% 541 – 98%
matK 550 – 89% 541 – ~90%
trnH-psbA 419 – 95% 200 – 99%
(Willow herbarium material from 1940s – 2000s
– oldest samples: 1946 & 1947)
Sequence success:
For comparison: Fazekas et al. (2008)
Multiple land plants -- rbcL - 100%, matK – 87.6%, trnH-psbA – 99.2%
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Grasses in British Columbia
190 species (native & introduced)
- incl. 70% of British Columbian species
68% spp. represented by 2-16 samples (mean 3)
73 genera (native & introduced)
rbcL + matK (+ trnH-psbA)
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rbcL matK
Introduced
Native
Introduced
= GenBank
Poa (13 spp.)
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US
UK
rbcL matK
Intro
Native
UK
SA?
(not due to sequence length difference)
= GenBank
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Species monophyletic Genera monophyletic *
rbcL+matK of 128 – 40% (1/2 100% supp) of 27 – 70% (all ≥80% supp)
+trnH-psbA of 128 – 40% (5 diff. spp.) –
NJ distance (K2P) bootstrap ≥70%
Species assignment in Grasses
* Non-monophyletic grass genera:
Ammophila (2 spp.), Calamagrostis (9 spp.), Cynosurus (2
spp.), Elymus (9 spp.), Festuca (9 spp.), Leymus (5 spp.), Poa
(13 spp.)
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Possible GrassBoL objectives
An international initiative to barcode the grasses & grass-like plants
GrassB Larcoding ife
of
• Bring together grass researchers from diverse fields
(taxonomists, ecologists, agronomists, molecular biologists)
& coordinate DNA barcoding efforts
• Identify funding opportunities
• Coordinate protocols & primers for core & supplementary
barcoding loci in Poales
• Develop barcoding applications
GrassBoL Organizers
Andy Lowe Sean GrahamHugh Cross
Adelaide University &State Herbarium of South Australia
University of British
Columbia
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• Substantial genomic resources to develop nuclear/
next-gen markers
• Feasible to DNA barcode --
• Good universality for core & supplementary loci
• Plastid genome has elevated rate
• Phylogenetically discrete (1 major clade, Poales)
• Substantial systematic expertise & interest
• Substantial herbarium resources
• Tractable to get DNA from herb. specimens
• Grasses & rels important economically/ecologically
• „Difficult‟ to ID/ key
GrassB Larcoding ife
of
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Relationships among grasses (Poaceae) & relatives (Poales)
I. Graminids
Poaceae: 11K spp., 600-900
genera, 12 subfams. Cosmopolitan
Ecdeiocoleaceae: 3 spp., W Aust
Joinvilleaceae: 2 spp., SE Asia
Flagellariaceae: 4 spp., S Hemisph
Triticum
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II. Restiids: Southern rushes
Restionaceae: ~500 spp., 55 gen.
Centrolepidaceae: 35 spp., S. Hem.
Anarthriaceae: 10 spp., W Australia
Hypodiscus (Restionaceae)
S Africa, Australia, etc
Relationships among grasses (Poaceae) & relatives (Poales)
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Cyperaceae: ~5000 spp, 120 gen.
Juncaceae: ~440 spp., Cosmopolitan
Thurniaceae: 4 spp., S Am, S Africa
Carex
(Cyperaceae)
Cosmopolitan
III. Cyperids: sedges & rushes
IV. Cattails & bur-reeds
Typhaceae: ~30 spp., 2 gen.
Cosmopolitan
Relationships among grasses (Poaceae) & relatives (Poales)
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GrassBoL: Promoting links with active taxonomy
• DNA from herbarium specimens
• Acrive taxonomic expertise
• Critical funding components
incl. collecting, expert ID/training
& digitization
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Plant DNA barcoding studies
Choosing a multi-locus barcoding system
Are plants harder to barcode than animals?
-- Poaceae & Salix of BC, Canada; GrassBoL campaign
-- Eco-applications: Below-ground ecology
Overview
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UNDERGROUND ECOLOGY -- An “eco-application”
study (Koffler Scientific Reserve, Ontario, Canada)
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STUDY LOCATION: Koffler Scientific Reserve
• Extensive and mature
examples of several
upland forest types and
seepage swamp
communities (Oak
Ridges Moraine)
• 625 plant taxa
• 157 bird species
• 30 mammal species
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Plot APlot B
Plot C Plot D
ROOT BARCODING: Sampling Design
30m
30m
5m
5m
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0 cm
5
10
15
100
20
25
30
.
.
.
Vertical sampling: Section of soil profile
40 soil cubes/plot (each cube is 5
cm3 in size) -- 160 cubes total
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Total: ~3800
Collection & analysis of root fragments
Randomly sampled: ~1503
Barcode region used: rbcL
Barcode library of KSR flora
(450 spp.) used as referencedatabase for root ID
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(* No significant effect of plot or column on root abundance)
Vertical
distribution
(mean number
of roots)
Abundance of root fragments
Soil depth
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Mean sequencing success: 85%
Root barcodes: Sequencing success
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Root barcodes: Soil depth and species diversity
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Root Barcodes: Abundance variation across species
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Poa
Solidago (Asteraceae) Chisquare : 48.337
P<0.0002
Symphyotrichum
(Asteraceae)
Chisquare : 13.518
P<0.0001
Elymus repens (Poaceae) Chisquare: 1.700
P<0.1923
Bromus enermis (Poaceae) Chisquare: 0.585
P<0.4444
Root Barcodes: Some co-existence patterns observed
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Below vs. above-ground abundance poorly correlated
Below Above
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Differences in below-ground vs. above-ground community
Composition & structure
CCA (below ground)RDA (above
ground)
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Plant DNA barcoding studies
Plant barcoding system: rbcL + matK (++)
Plants are harder to barcode, precisely, than animals
-- Regional & monographic campaigns (eg Grasses of BC)
-- Era of eco-applications (e.g. Below-ground ecology)
Summary
-- Causes of greater gene-tree paraphyly?
-- GrassBoL campaign
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Summary
• Barcoding can successfully be used for
identifying roots
• Depth has a significant effect on root
distribution and species number
• Only in few spp. there is correspondence
between above and below ground abundance
• Species from two different families co-existed
more often than expected
• Below ground community structure
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Poaceae:Elymus repens
Lolium
arundinaceum
Phalaris
arundinacea
Bromus enermis
Agrostis stolonifera
Asteraceae:Erigeron philadelphicus
Euthamia graminifolia
Leucanthemum vulgareRudbeckia hirta
Symphyo. x amythestinum
Taraxacum officinale
Tragopogon dubius
Fabaceae:Medicago lupilina
Trifolium aureum
Vicia cracca
Brassicaceae:Alliaria petiolata
Lamiaceae:Clinopodium vulgare
Salicaceae:Populus deltoides
Melanthiaceae:Trillium erectum
Asclepediaceae:Asclepias syriaca
Cyperaceae:Carex aurea
Convolvulaceae:Convolvulus arvensis
Acer sp.
Cirsium sp.
Poa sp.
Solidago sp.
Symphyotrichum sp.
Total: 28 taxa
Taxa identified below ground
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ITS trnH-psbA rbcL rpoC1 rpoB matK ycf5 accD ndhJ
2005
2006
2007
2008
Kress
et al.
Chase et al.
Kress +
Erickson
(1st BoL
Meeting)
Newmaster
et al.
Kew consortium project
Partial
rbcL
Kress +
Erickson
atpF-H
Kim et al.
2nd BoL meeting
psbK-I