Improving marine management by

implementing DNA-based monitoring

and evaluation strategies

NAIARA RODRIGUEZ-EZPELETA

EVA AYLAGAS

JON CORELL

ANAÏS REY

IÑAKI MENDIBIL

ENEKO BACHILLER

OIHANE C. BASURKO

ÁNGEL BORJA

UNAI COTANO

XABIER IRIGOIEN Kick-Off Conference

7-9 March, 2017, Essen, Germany

Types of samples - Sediment vs water, filtering 2L vs filtering 20 L,

extracellular, environmental or bulk sample DNA?

DNA extraction method - Different protocols/kits are more efficient for

some taxonomic groups than for others

Marker choice - COI, 18S

Primer choice - Leray, Folmer

Barcode amplification conditions - Annealing temperature, number of cylces

Sequencing strategy/platform - How many samples to multiplex

- How long should the reads be - Illumina, Ion Torrent, Minion!

Analysis pipeline - Qiime, mothur, …

Database - Which one - More or less complete

Quality filtering - Strict, loose - Chimera removal - Singleton removal

Classification strategies - OTU based, which id treshold - Taxonomy based

Environmental Sample

Biodiversity Assessments

Evaluation & management

Abundance data (individuals/biomass) - Are our methods able to provide

accurate abundance data? - DNA extraction biases - Primer biases - Sequencing biases

- Is read abundance data meaningful? - Multicellularity - Multiple copies per cell Environmental Sample

Biodiversity Assessments

Evaluation & management

Under detection - How to deal with organisms that

- Are not in the database? - Are never/less amplified?

- Should biotic indices be corrected/calibrated for metabarcoding data?

- How?

Over-detection - Non target organisms

- E.g. preys - Non present situation

- Dead organisms - Remains of DNA carried from other

places

Other issues: - Need for special taining/equipment - Portability of the assay - Cost-effectiveness - Wait time - Comparison with other data

Development of DNA metabarcoding based biotic indices

AZTI´s Marine Biotic Index - AMBI

Based on abundance-weighted pollution tolerances of the species present in a sample, where tolerance is expressed categorically as one of five ecological groups:

I sensitive to pressure II indifferent III tolerant IV opportunist of second order V opportunist of first order

BIOTIC COEFFICIENT

BIOTIC INDEX

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IV

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1 2 3 4 650

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AZ

OIC

SE

DIM

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T

MEANLY POLLUTEDUNPOLLUTED HEAVILY

`POLLUTED

EXTREM.

POLLUTED

INCREASING POLLUTION

SLIGHTLY POLLUTED

BAD

STATUS

POOR

STATUS

MODERATE

STATUS

GOOD

STATUS

HIGH

STATUS

POLLUTION

WFD

BIOTIC COEFFICIENT

BIOTIC INDEX

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IV

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1 2 3 4 650

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RC

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OF

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PS

0 1 2 3 4 65 7

AZ

OIC

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DIM

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MEANLY POLLUTEDUNPOLLUTED HEAVILY

`POLLUTED

EXTREM.

POLLUTED

INCREASING POLLUTION

SLIGHTLY POLLUTED

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POOR

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GOOD

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WFD

BIOTIC INDEX

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IV

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1 2 3 4 650

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OF

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OU

PS

0 1 2 3 4 65 7

AZ

OIC

SE

DIM

EN

T

MEANLY POLLUTEDUNPOLLUTED HEAVILY

`POLLUTED

EXTREM.

POLLUTED

INCREASING POLLUTION

SLIGHTLY POLLUTED

BAD

STATUS

POOR

STATUS

MODERATE

STATUS

GOOD

STATUS

HIGH

STATUS

POLLUTION

WFD

AMBI = ((0 * %GI) + (1.5 * %GII) + (3 * %GIII) + (4.5 * %GIV) + (6 * %GV))/100

Manual

classification

SPECIES ABUNDANCE

Prionospio fallax 2

Spio decoratus 3

Spiophanes bombyx 12

Magelona filiformis 2

Magelona johnstoni 32

Chaetozone gibber 29

Diplocirrus glaucus 1

Armandia cirrhosa 4

Capitella capitata 4

Mediomastus fragilis 3

Taxonomic

identification METABARCODING (gAMBI)

- In silico evaluation of the viability of gAMBI

(Aylagas et al. 2014; PLoS One)

- Developpment of sample processing protocols

(Aylagas et al. 2016; F. in Mar. Sci.)

- Establishment of bioinformatic pipelines

(Aylagas & Rodriguez-Ezpeleta 2016; Meth. in Mol. Biol.)

- Testing of alternative amplification protocols

(Aylagas et al. 2016; F. in Mar. Sci)

- Application to a real monitoring program

(Aylagas et al. in prep.)

Towards a gAMBI

Type of sample

Bulk extracted or mixed DNAs

Barcode PCR conditions

All taxonomic levels Only species

level

Percentage of morphologically identified taxa

found with metabarcoding

Lon

g C

OI r

egio

n

Sho

rt C

OI r

egio

n

Extracellular DNA

R2=0.15 RMSE=0.61

R2=0.42* RMSE=0.83

Bulk DNA

46 °C 50 °C Touchdown

R2=0.33* RMSE=0.37

R2=0.68* RMSE=0.28

R2=0.49* RMSE=0.43

R2=0.49* RMSE=0.32

R2=0.07 RMSE=0.49

R2=0.21 RMSE=0.39

AZTI vs gAMBI

Find the conditions at wich gAMBI was most similar to AMBI

- Genetics AMBI can be used in a similar way as the

“traditional” AMBI

- Calibration of gAMBI based on:

- Genetics based abundance estimates

- Species not sequenced

- ….

- AMBI used for more than 20 years, can we convince

authorities to change?

- Yes if same results are obtained using less resources

- Before full substitution, some overlapping period with

same samples analyzed with both technologies is

desirable

AZTI vs gAMBI

Development of genetic tools for the early detection of aquatic Non-Indigenous Species introduced with ballast water

Up to five billion tonnes of ballast water transferred throughout the world annually Up to 7000 species transferred per day Increasing tendency with globalization of trade

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1900 - 1909 1910 - 1919 1920 - 1929 1930 - 1939 1940 - 1949 1950 - 1959 1960 - 1969 1970 - 1979 1980 - 1989 1990 - 1999 2000 - 2009 2010 - 2019

Eve

nt

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cord

s

Years of introduction

Hull fouling

Ballast Water

Aquaculture

Natural vectors

Ballast water is a major vector of Non Indigenous Species introduction

International regulatory framework to prevent the spread of

harmful organisms including Non Indigenous Species Developed in 2004 by the IMO Will enter in force in September 2017 All ships in international travel must manage their ballast waters

and sediments

Among all requirements … Ships must install on board Ballast Water Treatments Systems which aim

at retaining (with filter) or killing organisms before the discharge Risk assessment for granting exemptions

Rey, Basurko and Rodriguez-Ezpeleta (submitted)

Rey, Basurko and Rodriguez-Ezpeleta (submitted)

Biological analysis Main output Currently used methods

Species biogeographical and

species specific risk assessments

-Biological baseline survey of

ports

- Target species identification

Visual taxonomical identification

(HELCOM/ OSPAR Guidelines)

Identification of harmful

organisms and pathogens in the

area

Target species identification None

Sampling for compliance

(Indicative and detailed analysis)

Estimation of the abundance of

viable organisms in 3 different size

classes

Visual counting, machine counting,

cultures and kits for bacterial

detection

Tests for approval of Ballast

Water Treatment System

Estimation of the abundance of

viable organisms in 3 different size

class

Visual counting, machine counting,

cultures

Risk assessment of biological

parameters to design Ballast

Water Exchange area

Biological baseline survey of the

area

None

BIOLOGICAL ANALYSIS REQUIRED BY THE BALLAST WATER CONVENTION

Testing compliance and evaluating BWTS requires methods that :

-Detect, enumerate, identify organisms, and determine viability -Precise, accurate, economic, fast, portable, worldwide application, no need of expert (for indicative sampling)

COMPLIANCE UNDER THE BALLAST WATER CONVENTION

Testing compliance and evaluating BWTS requires methods that :

-Detect, enumerate, identify organisms, and determine viability

-Precise, accurate, economic, fast, portable, worldwide application, no need of expert (for indicative sampling)

COMPLIANCE UNDER THE BALLAST WATER CONVENTION

Cons: Abundance of organisms not possible, On-board analysis very limited, Time to a result too long for a reliable indicative method

Genetic tools

Testing compliance and evaluating BWTS requires methods that :

Key requirements of used methods:

-Detect, enumerate, identify organisms, and determine viability

-Precise, accurate, economic, fast, portable, worldwide application, no need of expert (for indicative sampling)

COMPLIANCE UNDER THE BALLAST WATER CONVENTION

Pros: Detailed analysis of the discharged community, Early detection of Non Indigenous Species (e.g. larvae,…), Harmful Algae Bloom species (e.g. resting stages,…) and Pathogens, Cost-effectiveness compare to taxonomy identification

Genetic tools

VIABILITY REQUIREMENT

BWM Convention refers only to viable organisms discharged via ballast water

Limit: DNA is a stable and long-lived molecule which can be extracted after cell death

Potential solution: Using RNA instead of DNA The RNA pool of a sample is contributed primarily by those organisms metabolically active and transcribing RNA

T0 T1 T2 T3

DNA RNA

Schematic draw of expected OTUs distribution between RNA and DNA samples

Compare DNA and RNA based taxonomic composition

From RA for Rotterdam Port sampling project

20

Port of Bilbao

• 4 sampling sites and 4 sampling period over one year (Winter-Autumn-Spring-Summer) • Target: hard substrate organisms, soft bottom benthos, phytoplankton, zooplankton, eDNA • To ease complex port baseline surveys, development of a protocol relying on the use of

DNA metabarcoding

DNA based port baseline survey for granting exemptions

Metal Wood

Fender Concrete

Fouling plates

Sampling of fouling organisms:

Existing structures

- Port baseline surveys through metabarcoding

- Possible, but standardization required

- Assesing good compliance and effectiveness of BWTS

- Requires new indicators

- Issue with viability and counting

- Potential of RNA, but more difficult to manipulate than DNA

- Require rapid response (are portable solutions possible?)

- BW management convention – new

- Good opportunity to start with genetics “from scratch”

- Need for a good start!

Genetic ballast water management

Assessing dietary habits of commercial fish by metabarcoding of stomach contents

Índice

Trophic network

- Descriptor 4 of the MSFD

- Reaching MSY in all stocks is dependent on the knowledge on the predator-prey relationships among them

- Multispecific models require data on tropic relationships

- CFP recognizes the need to work from an ecosystemic point of view

- ICES recognizes that data of stomach contets are of “vital importance”

Índice

Relaciones tróficas - resumen Genetics based assessment of trophic relationships

- Allows analyzing many more

stomachs

- Method needs to be optimized

per predator - Blocking primer

- Quantification? - Some testing/calibration needed

- Not possible to combine with

data de visu – starting over

- How to translate genetic data

into model feeding data?

http://www.google.es/url?sa=i&source=images&cd=&cad=rja&docid=REc0EyJihDTXAM&tbnid=n8GSjnm9azMewM:&ved=0CAgQjRwwAA&url=http://www.stripersonline.com/t/606897/stomach-contents&ei=ARzbUaT-He-h7AaxroCgBQ&psig=AFQjCNG-O3pbi05Z-v4VZwqexOFzimxkhw&ust=1373400449573219

Evaluation of zooplankton diversity in the global deep ocean using metabarcoding

1000 m

0 m

3000 m

2000 m

Bottle-net

Multi-net Amplicons - Illumina: 18S (115 samples) mlCOI (89 samples) folCOI (28 samples) Metagenome: miTags (5 samples)

Amplicons – 454: 18S (21 samples) Metagenome miTags (20 samples)

MESOZOOPLANKTON 300 to 5,000 µm

MICROZOOPLANKTON >20 µm

Amplicons

miTags

Reads of the most abundant groups per station

Reads

OTUs Number of reads

Same station at different depths – taxonomy inferred using miTags. A different station is included for comparison purposes

Common taxa among approaches

Thanks for your atention!

Funding sources

PhD Students:

Anaïs

Rey

Eva

Aylagas

Jon

Corell

Oihane C.

Basurko Angel

Borja

Unai

Cotano

Xabier

Irigoien

Collaborators:

Postdoc:

Eneko

Bachiller

Lab technician:

Iñaki

Mendibil

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Compare morphology

and metabarcoding

based taxonomic

composition CLEAR MATCH Morphologically identified sp is in database and in metabarcodig identification CLEAR NO MATCH Lowest morphologically identified taxonomic level in database is not not in metabarcodig identification OTHER POSSIBILITYES Considered POTENTIAL MATCH, but may change to CLEAR MATCH or NO MATCH as database is completed