1 / 47

Monitoring Barcoding Network and Recording

Monitoring Barcoding Network and Recording. Matthew Shepherd Senior Specialist, Soil Biodiversity, Natural England. Why monitor soils?. Soil science has concentrated on agricultural systems, physical and chemical status. Learn from (semi) natural habitats Lessons for managed ecosystems

meli
Download Presentation

Monitoring Barcoding Network and Recording

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Monitoring BarcodingNetwork and Recording Matthew Shepherd Senior Specialist, Soil Biodiversity, Natural England

  2. Why monitor soils? • Soil science has concentrated on agricultural systems, physical and chemical status. • Learn from (semi) natural habitats • Lessons for managed ecosystems • Own interest • A quarter of all biodiversity is found in the soil

  3. Why monitor soils? • Many monitoring efforts (eg. ECN, RSS) have focussed on chemical or physical parameters • yet soil biology does all the work! • New advice from UK SIC, DefraSQuID project • CS2007 – more soil and soil biological parameters than ever before • Try to be compatible, representative and affordable

  4. Why monitor soils? • CS survey in 1998 and 2007 measured soil biological parameters • Measured tRFLP, soil mesofauna

  5. Why monitor soils? • ~12.8 quadrillion soil invertebrates present in the top 8 cm of GB soils • significant increase in total invertebrate catch in all Broad Habitats • except for agricultural areas on mineral soils • Due to increase in the catch of mites in 2007 samples • small reduction in the number of soil invertebrate broad taxa (0-8cm) recorded • different seasonal conditions – more work needed • Needs linking with habitat and chemistry work

  6. Oribatid data – Thanks to Aidan Keith – now have loose locaitons– secret data!

  7. 13 LTMN Soils Method 13 11 • 1 habitat per NNR for soil assessment • 22 so far of ~43 total • 8 broadleaved woodlands • 5 heathlands • 6 calcareous grasslands • 6 neutral grasslands • 2 dune grasslands • 2 blanket bogs • 4 raised bogs • 5 fens • 5 saltmarshes 11 13 13 11 12 12 12 12 11 13 13 12 13 11 11 12 11 11 11

  8. LTMN Soils Method • NE help contribute to fieldwork and Macaulay Scientific Consulting do fieldwork and analysis. • Use aerial photos and veg survey data to choose 5 ~similar points. • Survey from Sept 16th to Oct 16th

  9. LTMN Soils Method • Use GPS to locate veg plot markers and lay out 20m by 20m soil plot to SW using compass • Each contains 100 2m by 2m sub-plots • Same 4 sampled for all plots – change next time.

  10. LTMN Soils Method • Take plot location photos • Sub-plot photos side and above • Vegetation survey • Soil auger assessment

  11. LTMN Soils Method • Cores taken – most bulked • Wrapped, labelled chilled and sent to Scotland. • Different cores are letter coded: • C for “curface” (0-15) • A for “anderneath” (15-30) • Physico-chemical properties • Bulk density • %C, %N • Loss On Ignition • pH • CEC and cations

  12. LTMN Soils Method • B for beasties (0-8 cm mesofauna) • D for DNA (microbial community) – tRFLP, PLFA • E for eelworms (nematodes) • F for fertiliser (N mineralisation)

  13. Baseline Results – Physico-chemical

  14. Baseline Results – Physico-chemical

  15. Baseline Results – Biochemical

  16. Baseline Results – Soil Function: C storage

  17. Baseline Results - Soil function: decomposition

  18. Baseline results - Soil organism communities: tRFLP

  19. Baseline Results - Soil communities: tRFLP

  20. Baseline Results: Overall soil patterns (2011 data) Baseline results – interactions

  21. Baseline Results: implications for future work • Size of change detectable varies site to site... • pH – ~0.4 pH units • ~20% change in bulk density • tRFLP - 7% change in evenness, 12% change in richness • Soil physico-chemical properties change slowly • Soil biological properties may be more sensitive indicator... • Different habitats have distinct soil communities • Soil function – indicators and proxies – more measures needed?

  22. Future analyses and plans • Continue with baseline – comparison over time • Include new analyses - earthworms, root biomass, genetic analysis • Develop new approaches • Metabarcoding project – CEH & NHM – mesofauna • Earthworm DNA? • More multivariate analyses • Write up – plan to present site by site data and full baseline report after 5 years • Comparison with CS2007, CS1998 data – compare agricultural soils • Apply same methodology in experimental work, other monitoring

  23. Answering the big questions... • Soil resistance and resilience to perturbations • Disturbance/fire at Thursley • What are the soil communities in our priority habitats? • Clear microbial (and other?) communities • How do these compare with other habitats? • Extend this method to other sites/experiments & compare CS2007 • Do soil characters/function lag or lead changes? • What will happen to soil carbon in seminatural habitats? • Trends in soil biodiversity – where are changes seen and why? • Wait and see!

  24. But... • Most mesofauna samples are still not sorted and identified • Anyone interested – can borrow NE microscope • 5 samples - probably around 500-1000 beasts in total!

  25. We need a way to identify very large numbers of invertebrate specimens quickly and cheaply DNA metabarcoding?

  26. Barcoding and Metabarcoding • Alternative approach is metabarcoding. • Mitochondrial DNA passed down female line only – no recombination during meiosis • Gradual change by mutations at “regular” rate • Differences and similarities should indicate timings of divergence of species. • Similar story for ribosomal RNA • Sections of these are used as “barcodes” to characterise spp. • COi – cytochromeoxidase 1 gene • Also 18SRNA • Prokaryotes- 16SRNA

  27. Barcode Region for Animals Target Region The Mitochondrial Genome COI

  28. An actual mosquito barcode - a 650 letter word: mosquito-COI: CGCGACAATGATTATTTTCAACTAACCATAAGGATATTGGAACATTATATTTTATTTTTGGAGCTTGAGCAGGAATAGTAGGAACTTCTCTAAGTATTTTAATTCGAGCAGAATTAGGACACCCTGGAGCCTTTATTGGTGATGATCAAATTTATAATGTTATTGTAACAGCTCATGCTTTTATTATAATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTTGGAAATTGACTAGTCCCTCTAATACTAGGGGCCCCAGATATGGCTTTCCCTCGAATAAATAATATAAGATTTTGAATATTACCCCCCTCTTTAACTCTTCTAATTTCTAGAAGTATAGTAGAAAATGGAGCTGGAACAGGGTGAACTGTATATCCTCCTCTATCCTCAGGAATTGCTCATGCAGGAGCTTCAGTAGATTTAGCTATTTTTTCATTACATTTAGCAGGAATTTCTTCAATTTTAGGAGCAGTTAATTTTATTACAACAGTTATTAATATACGAGCACCAGGAATTACTCTTGACCGAATACCGTTATTCGTTTGATCTGTAGTAATTACAGCAGTATTATTATTACTTTCTTTACCAGTATTAGCTGGAGCTATTACTATACTTTTAACAGATCGAAACTTAAATACATCATTC

  29. If you can extract, and amplify barcodes from a community – cross ref with barcodes for known species • Generate spp. List • Not quantitative – differential amplification • Problem – not enough spp. barcoded • Problem – extraction, amplification methods not well developed

  30. Metabarcoding Mass sequencing reduces time and cost Uses CO1 barcode gene

  31. Next-generation sequencers e.g. ‘454’ / Illumina • Produce many parallel sequences • Limited in the length of sequence reads • Error can occur at amplification and sequencing stages – leads to noisy results

  32. ~120,000 reads in two 1/8 plates (~345 bp/sequence) Step 1:Denoise

  33. ~2000 reads Step 2: Cluster similar sequences These are our molecular OTUs

  34. ~2000 reads Step 3:Assign taxonomy Anopheles Mosquito Spider Fruit fly Emerald tree python

  35. Output: species x site table 7 Communities Species

  36. Which treatments work? Agricultural plough Control Swipe Forestry plough Control Forage harvest Turf stripping Disc plough

  37. Pitfall trap data Standard (Spiders, carabids, ants) Metabarcoding (All arthropods) Standard R2=0.76 The best treatments are the most aggressive ones

  38. Pitfall trap data Standard (Spiders, carabids, ants) Metabarcoding (All arthropods) Standard Conclusion: metabarcoding produces useful information for restoration ecology.

  39. NE project to develop method – Dave Spurgeon, Rob Griffiths, Daniel Read at CEH • 3 sites sampled along transects at differing proximites • Old spp. Rich chalk grassland • Improved grassland • Grassland managed to “revert” to chalk grassland • 2 sets of mesofauna extracted • metabarcoding • morpho ID & spp. barcoding

  40. Metabarcoding – problems with primers – will COi work? • 18S RNA better – but good enough for spp? • Morpho ID shows some differences • Communities (PCA) similarities are • old <–> improved <–> reverting • I’m ID’ing samples for NHM – Alfried Vogler – lots of photos • Use for this project and put on BOLD • Challenge for NHM in small size • Must retain voucher specimen!

  41. Barcoding a specimen leaves a “permanent” legacy of an ID, and enables comparison to others (checking or defining) • Link with location – a genetic NBN • Many specimens on current databases wrongly ID’ed • Some barcodes are of foreign material • Correct group-specific primers should help... • Better photos of lots of features.

  42. Issues and Questions • What’s stopping you barcoding things you ID? • Reagents? • Costs? • Lack of knowing where to go? • Would coordination help • Is there a role for NE? Museums? Universities? Biological Record Centres? • Biodiversity groups and networks?

  43. Soil Biodiversity Support Groups • No soil biodiversity society for UK – SES in USA/Canada • Help is out there! • Facebook page • Blogs • Us lot! • What else? • Record centres?

  44. And Recording?

More Related