Monitoring and Barcoding

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

– Own interest

• A quarter of all biodiversity is found in the soil

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, Defra SQuID project

• CS2007 – more soil and soil biological parameters than ever

before

• Try to be compatible, representative and affordable

• tRFLP, PLFA, soil mesofauna, mSIR

Why monitor soils?

• CS survey in 1998 and 2007 measured soil biological parameters

• Measured tRFLP, soil mesofauna

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

• Oribatid data – Thanks to Aidan Keith – now have loose locaitons –

secret data!

LTMN Soils Method

• 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

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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

• 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.

LTMN Soils Method

• Take plot location photos

• Sub-plot photos side and above

• Vegetation survey

• Soil auger assessment

LTMN Soils Method

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

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)

Baseline Results – Physico-chemical

y = -0.349ln(x) + 1.6604 R² = 0.9749

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 20 40 60 80 100 120

Dry

Bu

lk D

en

sit

y g

cm

-3

% Organic matter (LOI)

0 - 15 cm

15 - 30 cm

y = 30.422ln(x) - 37.506 R² = 0.6967

0

20

40

60

80

100

120

140

0 20 40 60 80 100

Cati

on

Exch

an

ge C

ap

acti

ty (

mE

q

100g

_1

% Soil organic matter (loss on ignition)

Soil organic matter and cation exchange capacity

Baseline Results – Physico-chemical

y = 395.32ln(x) - 491.93 R² = 0.8434

0

200

400

600

800

1000

1200

1400

1600

1800

0 20 40 60 80 100

To

tal S

oil

PL

FA

co

nte

nt

nm

g-1

% Soil Organic Matter (Loss on Ignition)

Soil organic matter and total soil PLFAs

Baseline Results – Biochemical

Baseline Results – Soil Function: C storage

y = -0.0418x2 + 4.6273x - 23.402 R² = 0.6242

y = -0.0386x2 + 4.3262x - 21.342 R² = 0.6193

0

20

40

60

80

100

120

140

0 20 40 60 80 100

Carb

on

sto

rag

e t

on

nes h

a-1

Field water content %

0-15 cm

15-30 cm

Poly. (0-15 cm)

Poly. (15-30 cm)

Baseline Results - Soil function: decomposition

y = 0.2703x + 14.995 R² = 0.5885

0

5

10

15

20

25

30

35

40

-10 0 10 20 30 40 50 60 70 80

So

il C

:N r

ati

o

% Cover of woody species

Baseline results - Soil organism communities: tRFLP

Baseline Results - Soil communities: tRFLP

Baseline results – interactions

Bulk Density % water

LOI

pH

%N

%C

C:N ratio

Exch_Acidity

Al

Ca

K

Mg Mn

CEC Total P

Olsen P

NH4-N_min

NO3-N min tRFLP richness

tRFLP evenness

tRFLP Shannon Diversity

PLFA_total Bac PLFA

Fungal PLFA

PLFA Fun:Bac ratio

PLFAact Gram +ve

Gram -ve

Gram +ve:-ve ratio

VAM PLFAs

herbs

Fe

Na

grasses

bryophytes and lichens

ericaceae

shrubs and climbers

trees

litter

sedges and rushes

ferns

bare ground

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1 -0.5 0 0.5 1 1.5

PC

A a

xis

2

PCA axis 1

Baseline Results: Overall soil patterns (2011 data)

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?

• 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

Future analyses and plans

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!

But...

• Most mesofauna samples are still not sorted and identified

• Anyone interested – can borrow NE microscope

• 5 samples - probably around 500-2000 beasts in total!

DNA metabarcoding?

We need a way to identify very large

numbers of invertebrate

specimens quickly and cheaply

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 – cytochrome oxidase 1 gene

• Also 18SRNA

• Prokaryotes- 16SRNA

mosquito-COI:

CGCGACAATGATTATTTTCAACTAACCATAAGGATATTGGAACATTATAT

TTTATTTTTGGAGCTTGAGCAGGAATAGTAGGAACTTCTCTAAGTATTTT

AATTCGAGCAGAATTAGGACACCCTGGAGCCTTTATTGGTGATGATCAAA

TTTATAATGTTATTGTAACAGCTCATGCTTTTATTATAATTTTTTTTATA

GTTATACCTATTATAATTGGAGGATTTGGAAATTGACTAGTCCCTCTAAT

ACTAGGGGCCCCAGATATGGCTTTCCCTCGAATAAATAATATAAGATTTT

GAATATTACCCCCCTCTTTAACTCTTCTAATTTCTAGAAGTATAGTAGAA

AATGGAGCTGGAACAGGGTGAACTGTATATCCTCCTCTATCCTCAGGAAT

TGCTCATGCAGGAGCTTCAGTAGATTTAGCTATTTTTTCATTACATTTAG

CAGGAATTTCTTCAATTTTAGGAGCAGTTAATTTTATTACAACAGTTATT

AATATACGAGCACCAGGAATTACTCTTGACCGAATACCGTTATTCGTTTG

ATCTGTAGTAATTACAGCAGTATTATTATTACTTTCTTTACCAGTATTAG

CTGGAGCTATTACTATACTTTTAACAGATCGAAACTTAAATACATCATTC

An actual mosquito barcode -

a 650 letter word:

• 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

• 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

Two sets of six samples were taken from three chalk grasslands: ancient species-rich, agriculturally improved and naturally reverting grassland.

Imagery © 2014 DigitalGlobe, Getmapping PLC, Infoterra Ltd & Bluesky Map data © 2014 Google. Photo: R. Marris, Natural England

Ancient Spp. Rich

Naturally Reverting

Agriculturally Improved

Broad morphological ID - metabarcoding

Metabarcoded for 18S

rRNA and COi

Bulk soil DNA extracted and metabarcoded

Metabarcoded for 18S

rRNA and COi

Bulk soil

sub-

sample

Individual specimens barcoded to create

database

Identified to

species /

genus /

family

~200 individuals of

most common

species

Barcoded 18S rRNA &COi

Compare ability to: • distinguish different

communities • identify realistic community

composition

The future?

• Develop and apply for monitoring – species list but not

biomass/abundance

• Quantitative methods being developed

• Species list from soil – could develop typology for soil fauna

– The NVC of soil life!

• Metabarcoding – problems with primers – will COi work?

• 18S RNA better – but good enough for spp?

• Measure specimen size and relate to DNA harvested?

• Use for this project and put on BOLD

Barcoding

• 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

• Correct group-specific primers should help...

• Better photos of lots of features.

Issues and Questions

• Reagents – ethanol duty, limitations, postage

• 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?

Soil Biodiversity Support Groups

• No soil biodiversity society for UK – SES in USA/Canada

• Help is out there!

– Facebook page

– Scratchpad page