DNA Barcoding for Rapid Biodiversity Inventory, Conservation Prioritisation, and control of illegal Logging

1. DNA barcoding for
Rapid Biodiversity Inventory,
Conservation Prioritisation,
and Control of Illegal Logging
Andrew Lowe,
Craig Costion, Hugh Cross, Bernd Degen
Darren Crayn, Jonathan Geach

2. • Barcoding Australian wet tropics trees
– rapid biodiversity inventory
– conservation prioritisation
– biogeographic understanding
• Barcoding Australian plants and soils
– Terrestrial Ecosystem Research Network
– biodiversity surveillance and monitoring
• Barcoding and DNA fingerprinting tropical
timber species for control of illegal logging

3. Costion et al. (2011) Plant DNA barcodes can accurately estimate
species richness in poorly known floras. PLoS ONE: In press
Why barcode plants in the tropics?
• High percentage of flora is undescribed
• Fertile material for accurate field ID’s
• Rapid habitat loss, increasing threats
• Rapid biodiversity assessments needed
DNA barcoding can help

4. Australia’s ever-wet tropical rainforest
Lockerbie Scrub
McIlwraith/Iron Ranges
Wet Tropics
BARCODING PROGRESS
Wet tropics has 2,144 vascular plants
1,200-1,400 species w/ at least 1
barcode
500 species – have 2-3 replicate
barcodes
40% - 50%

5. Cambium extraction
• Rapid and easy tissue collection for DNA extraction
• DNA from cambium shown to be less hampered by
defensive chemicals in leaves (Colpaert et al 2005)

6. Sample all
Costion et al. (2011) PLoS ONE: In press E1
individuals present E15
then contruct F05
F44
distance trees E2
99 F11
E10
F22
• Can we use DNA – 31
F41
F54
F20
barcodes to estimate 33
F67
F26
99 F36
the diversity of an area 99 F45
F53
where species are 23
99
F09
F35
F03
unknown? 38 40
E17
F66
63
F28
99 F01
F02
E9
F40
Discriminate Species: Assessing accuracy of F19
99 F27
barcoding loci to discriminate species F48
26 F51
vs. F38
82 F rbcL57 R A08
F52
Estimate Species: Using plant DNA barcodes 36
F56
E3
to estimate species richness 98
44 F29
F07
98
50 F17
F60
99 F68
37 99 F69

7. Costion et al. (2011) PLoS ONE: In press
Distance
tree of
DNA
samples
Identities
confirmed

8. Costion et al. (2011) PLoS ONE: In press
• No gain in in discrimination accuracy by adding matK
• Estimation accuracy decreases with matK
With addition of 3rd locus trnH psbA discrimination
accuracy remains same ~ 70%
Estimation accuracy increases to 89%

9. Costion et al. (2011) PLoS ONE: In press
89% accuracy of species ID with
rbcL & trnH psbA combination
Biodiversity assessments possible!
~ poorly known areas
~ tree saplings/seedlings
~ high canopy
~ roots or other cryptic samples

10. Using barcode data to assess phylogenetic diversity
Where are the hotspots of evolutionary history?
Plot Network of 250
Costion, C. (PhD Thesis) 0.1 hectare plots

11. All angiosperm genera supertree – largest phylogeny of a
tropical bioregion to date (660 species)
Costion, C. (PhD Thesis)

12. Costion, C. (PhD Thesis)
Rainforest stability index
PD Genus Richness Hilbert et. al (2007)

13. Phylogenetic Diversity (PD)/ Genus Richness (GR)
PD v GR at different spatial resolutions
18
16
14
12
10
PD
8
6
4
2
0
0 100 200 300 400 500
GR
GR0.1 vs PD0.1
GR0.065 vs PD0.065 However, when affects
GR0.125 vs PD0.125
GR0.25 vs PD0.25 of GR are removed
through regression a
biogeographic pattern
emerges
Costion, C. (PhD Thesis)

15. Indomalayan lineages higher frequency
in lowlands. Areas with higher PD than
expected can be explained by higher
proportion of non-Australian
(Gondwanan) elements present.
Extant
rainforest
70 Ancient Gondwana
Indomalayan lineages
60
Laurasian Richness
50
40
30
20
10 Uplands
0
0 200 400 600 800 1000 1200 1400 1600 1800
elevation Gondwanan
lineages
Elevation (m)
Lowlands
Indomalayan
lineages
Costion, C. (PhD Thesis)

16. Australian Centre for Evolutionary Biology and Biodiversity
The Terrestrial Ecosystem Research Network
$45M Research Infrastructure Facility for Australia
The objectives of TERN are to:
• network for terrestrial ecosystem research;
• Coordinate national observation networks;
• Improved access to observational data;
• Identify future needs for research.
Slide 16 Life Impact The University of Adelaide

17. Australian Centre for Evolutionary Biology and Biodiversity
Slide 17 Life Impact The University of Adelaide

18. Australian Centre for Evolutionary Biology and Biodiversity
Rangelands Forestry
plot network plots
Forestry Forestry
plots plots
Multi-scale Plot activities
-AusPlots
Slide 18 Forestry University of Adelaide
Life Impact The
plots

19. Australian Centre for Evolutionary Biology and Biodiversity
NATT
CSIRO plots
transect
SWATT
TREND
transect
transect
Alpine
Multi-scale Plot activities
plots
-AusPlots
-Long Term Ecological Research
Slide 19 Forestry University of Adelaide
Life Impact The
Network plots

20. Australian Centre for Evolutionary Biology and Biodiversity
Multi-scale Plot activities
-AusPlots
-Long Term Ecological Research Network
Slide 20 Life Impact The University of Adelaide
-Supersites

21. Data collection and distribution: Ecoinformatics facility
Australian Centre for Evolutionary Biology and Biodiversity
Multi-Scale Plot Network
Soils Coasts AusCover OzFlux AusPlots Plot networks Supersites Ecoinformatics
Slide 21 Life Impact The University of Adelaide
Scaling/Modelling ACEAS TERN Portal

22. AusPlotsCentre for Evolutionary Biology and Biodiversity
Australian
Continental stratification to group bioregions to establish biodiversity
monitoring plots
Rangelands
plot network
Forestry
plots
Slide 22 Life Impact The University of Adelaide

23. Australian Centre for Evolutionary Biology and Biodiversity
AusPlots – site methodology
1,000 (approx) permanent biodiversity survey plots being
established across the Australian Continent
Combine traditional and cutting edge techniques – modular
– baseline surveys of vegetation and soil diversity and structure
– collect leaf and soil samples for analysis
• Taxonomy, carbon, nutrients, isotopes,
• DNA barcoding, phylogeography, genomics
– Photo points, image interpretation and remote sensing cal/val.
Slide 23 Life Impact The University of Adelaide

24. Australian Centre for Evolutionary Biology and Biodiversity
Long term ecological research network
NATT
CSIRO plots
transect
SWATT
TREND
transect Lindenmayer
transect
and NSW plots
Alpine
plots
Slide 24 Life Impact The University of Adelaide

25. TREND
Australian Centre for Evolutionary Biology and Biodiversity
TREND
Transect for Environmental monitoring and Decision making
How to inform ecosystem management decisions in a
variable and changing climate:
Access historical information on change
Establish monitoring program to track change
Use ‘space as a proxy time’ for predicted changes
Model predictions of changes and compare
Slide 25 Life Impact The University of Adelaide

26. Temperature gradients
Australian Centre for Evolutionary Biology and Biodiversity
Rainfall gradients
Slide 26 Life Impact The University of Adelaide

27. Australian Centre for Evolutionary Biology and Biodiversity
Slide 27 Life Impact The University of Adelaide

28. Australian Centre for Evolutionary Biology and Biodiversity
Plot-based information – flora, veg structure, soils - field & remote sensed
Slide 28 Life Impact The University of Adelaide

29. Australian Centre for Evolutionary Biology and Biodiversity
Plot-based DNA analysis
DNA barcoding to understand
taxonomy, phylogenetic diversity,
community composition and
turnover (IBOL)
Dick and Kress (2009)
Slide 29 Life Impact The University of Adelaide

30. Australian Centre for Evolutionary Biology and Biodiversity
Plot-based DNA analysis
DNA barcoding to understand
taxonomy, phylogenetic diversity,
community composition and
turnover (IBOL)
Genomic analysis to examine soil
communities (metabarcoding,
amplicon COX, RBCL, ITS)
and plant gene expression changes
along selection pressures
(ARC, BGI, BPA)
Slide 30 Life Impact The University of Adelaide
Callistemon teretifolius (2009)
Dick and Kress

31. VERIFYING TIMBER SOURCES

32. Range of levels of
DNA discrimination
 DNA Fingerprinting  Individual log tracking
– Verify integrity of supply chain
 Phylogeography  Regional origin
– Verify country source
 DNA barcoding  Species origin
– Verify species

33. Application to date
Individual log tracking with Certisource
With funding support from the
International Tropical Timber Organisation
 Primary
Lowe et al., 2010

34. Application to date
Individual log tracking with Certisource
With funding support from the
International Tropical Timber Organisation
 Primary
At concession
2627 logs sampled
Lowe et al., 2010

35. Application to date
Individual log tracking with Certisource
With funding support from the
International Tropical Timber Organisation
 Primary
At concession At saw mill
2627 logs sampled 32 logs randomly
sampled
Lowe et al., 2010

36. Application to date
Individual log tracking with Certisource
With funding support from the
International Tropical Timber Organisation
 Matched back
Primary
At concession At saw mill
2627 logs sampled 32 logs randomly
sampled
Lowe et al., 2010

37. Timber Tracking
Forest and sawmill samples profiled with 14 microsatellites
Example Test 1 Test 2
Forest sample 236, 238 240,248
Sawmill sample 236, 238 238,246
No. loci match? Substitution?
Sample 1 6 exact 1 in 50 million
Lowe et al., 2010

38. Timber Tracking
Forest and sawmill samples profiled with 14 microsatellites
Example Test 1 Test 2
Forest sample 236, 238 240,248
Sawmill sample 236, 238 238,246
No. loci match? Substitution?
Sample 1 6 exact 1 in 50 million
Of 32 samples, 27 exact match, 5 did not amplify
Probability of substitution very low
Lowe et al., 2010

39. Range of levels of
DNA discrimination
 DNA Fingerprinting  Individual log tracking
– Verify integrity of supply chain
 Phylogeography  Regional origin
– Verify country source
 DNA barcoding  Species origin
– Verify species

40. Checking country of origin
Mahogany
Score for Guatemala: 100%
33 populations
2038 trees genotyped
Degen et al, subm.
Score for Bolivia: 98.7%
Practical test with 20 mahogany wood samples of German
timber trader + 11 wood samples from South America

41. Checking region of origin
Merbau – valuable timber tree
Intsia bijuga
Singapore and New Guinea
Intsia palembanica
Sabah and Papua
>1000 individuals screened
6 chloroplast loci

42. Checking region of origin
Merbau – valuable timber tree
Intsia bijuga
Singapore and New Guinea
Intsia palembanica
Sabah and Papua
>1000 individuals screened
6 chloroplast loci

43. Range of levels of
DNA discrimination
 DNA Fingerprinting  Individual log tracking
– Verify integrity of supply chain
 Phylogeography  Regional origin
– Verify country source
 DNA barcoding  Species origin
– Verify species

44. Checking species identity
Mahogany
Specific projects with focus on
CITES protected tree species =>
vTI + University of Hamburg (Aki
Höltken and Elisabeth Magel)
Swietenia macrophylla S. mahagoni
Approach:
• sequencing of cpDNA-
Swietenia macrophylla
fragments
• searching for SNPs
• new primer design for
short PCR
amplification products
(< 350 bp)

45. New project in Africa
Seven target countries
Source: http://africamap.harvard.edu/
Center for Geographic Analysis Species identity
Country of origin
Chain of custody
45

46. DNA extraction from wood
DNA + other
compounds
Wood contains many
secondary compounds that
affect success of DNA
extraction and PCR
Including: cellulose, lignin,
hemicellulose, resins,
waxes, trace elements

48. DNA extraction from wood
Boundaries of possibility
Composite
Raw Sawn Solid wood Ancient wood products Pulp and
timber timber product (Mary Rose) (veneer, ply) paper
Technology frontier
Intact DNA Highly
degraded
DNA

49. Acknowledgements
• Wet tropics barcoding
– Australian Tropical Herbarium, James Cook University, TRIN, CSIRO,
– Craig Costion, Darren Crayn, Gary Sankowsky, Andrew Ford,
Dan Metcalfe, Will Edwards, James Richardson, Hugh Cross
• TERN/TREND
– Jeff Foulkes, Ben Sparrow, Andrew White, Nikki Thurgate,
– Greg Guerin, Hugh Cross, Ed Biffin, Kimberly McCallum
• Illegal logging
– von Thunen Institute, Double Helix Tracking Technologies
– Bernd Degen, Hugh Cross, Aki Höltken, Darren Thomas, Jonathan Geach