Improving marine management by
implementing DNA-based monitoring
and evaluation strategies
Kick-Off Conference
7-9 March, 2017, Essen, Germany
NAIARA RODRIGUEZ-EZPELETA
EVA AYLAGAS
JON CORELL
ANAÏS REY
IÑAKI MENDIBIL
ENEKO BACHILLER
OIHANE C. BASURKO
ÁNGEL BORJA
UNAI COTANO
XABIER IRIGOIEN
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
Environmental Sample
Analysis pipeline
- Qiime, mothur, …
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!
Quality filtering
- Strict, loose
- Chimera removal
- Singleton removal
Classification strategies
- OTU based, which id treshold
- Taxonomy based
Database
- Which one
- More or less complete
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
Environmental Sample
- Multiple copies per cell
Other issues:
Over-detection
- Need for special taining/equipment
- Non target organisms
- Portability of the assay
- E.g. preys
- Cost-effectiveness
- Non present situation
- Wait time
- Dead organisms
- Comparison with other data
- Remains of DNA carried from other
places
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?
Biodiversity
Assessments
Evaluation &
management
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:
1
sensitive to pressure
indifferent
tolerant
opportunist of second order
opportunist of first order
BIOTIC COEFFICIENT
3
4
5
6
100
90
80
70
60
V
I
50
III
40
30
20
10
0
POLLUTION
WFD
2
AZOIC SEDIMENT
I
II
III
IV
V
PERCENTAGE OF GROUPS
0
IV
II
0
1
UNPOLLUTED
2
3
BIOTIC INDEX
SLIGHTLY POLLUTED
HIGH
GOOD
STATUS
STATUS
4
MEANLY POLLUTED
5
6
HEAVILY
`POLLUTED
7
EXTREM.
POLLUTED
MODERATE
POOR
BAD
STATUS
STATUS
STATUS
INCREASING POLLUTION
AMBI = ((0 * %GI) + (1.5 * %GII) + (3 * %GIII) + (4.5 * %GIV) + (6 * %GV))/100
SPECIES
Manual
classification
Taxonomic
identification
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
METABARCODING (gAMBI)
Towards a 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.)
Type of sample
Bulk extracted or mixed DNAs
Barcode
PCR conditions
Percentage of morphologically identified taxa
found with metabarcoding
All taxonomic levels
level
Only species
AZTI vs gAMBI
Find the conditions at wich gAMBI was most similar to AMBI
Short COI region
Long COI region
Extracellular
DNA
Bulk DNA
46 °C
50 °C
Touchdown
R2=0.15
RMSE=0.61
R2=0.33*
RMSE=0.37
R2=0.49*
RMSE=0.43
R2=0.07
RMSE=0.49
R2=0.42*
RMSE=0.83
R2=0.68*
RMSE=0.28
R2=0.49*
RMSE=0.32
R2=0.21
RMSE=0.39
AZTI vs gAMBI
-
Genetics AMBI can be used in a similar way as the
“traditional” AMBI
-
Calibration of gAMBI based on:
-
-
AMBI used for more than 20 years, can we convince
authorities to change?
-
-
Genetics based abundance estimates
Species not sequenced
….
Yes if same results are obtained using less resources
Before full substitution, some overlapping period with
same samples analyzed with both technologies is
desirable
Development of genetic tools for the early
detection of aquatic Non-Indigenous
Species introduced with ballast water
Ballast water is a major vector of
Non Indigenous Species
introduction
 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
Event of introduction records
100
90
80
70
60
Hull fouling
Ballast Water
Aquaculture
Natural vectors
50
40
30
20
10
0
1900 - 1909 1910 - 1919 1920 - 1929 1930 - 1939 1940 - 1949 1950 - 1959 1960 - 1969 1970 - 1979 1980 - 1989 1990 - 1999 2000 - 2009 2010 - 2019
Years of 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 REQUIRED BY THE BALLAST WATER CONVENTION
Biological analysis
Main output
Currently used methods
Species biogeographical and
-Biological baseline survey of
Visual taxonomical identification
species specific risk assessments
ports
(HELCOM/ OSPAR Guidelines)
- Target species identification
Identification of harmful
Target species identification
None
Estimation of the abundance of
Visual counting, machine counting,
organisms and pathogens in the
area
Sampling for compliance
(Indicative and detailed analysis) viable organisms in 3 different size cultures and kits for bacterial
classes
detection
Tests for approval of Ballast
Estimation of the abundance of
Visual counting, machine counting,
Water Treatment System
viable organisms in 3 different size cultures
class
Risk assessment of biological
Biological baseline survey of the
parameters to design Ballast
area
Water Exchange area
None
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
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)
Genetic tools
Cons: Abundance of organisms not possible, On-board analysis very
limited, Time to a result too long for a reliable indicative method
COMPLIANCE UNDER THE BALLAST WATER CONVENTION
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)
Genetic tools
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
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
Compare DNA
and RNA based
taxonomic
composition
DNA
RNA
Schematic draw of expected OTUs distribution between
RNA and DNA samples
DNA based port baseline survey for granting exemptions
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
Sampling of fouling organisms:
Existing structures
Fender
Metal
Fouling plates
Concrete
Wood
Genetic ballast water management
-
Port baseline surveys through metabarcoding
-
-
Assesing good compliance and effectiveness of BWTS
-
-
Possible, but standardization required
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!
Assessing dietary habits of commercial fish
by metabarcoding of stomach contents
Trophic network
Índice
-
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”
Relaciones
tróficasassessment
- resumen
Genetics
based
of trophic relationships
Índice
-
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?
Evaluation of zooplankton diversity in the
global deep ocean using metabarcoding
MESOZOOPLANKTON
300 to 5,000 µm
0m
Multi-net
Amplicons - Illumina:
18S (115 samples)
mlCOI (89 samples)
folCOI (28 samples)
Metagenome:
miTags (5 samples)
1000 m
2000 m
Amplicons – 454:
18S (21 samples)
Metagenome
miTags (20 samples)
3000 m
MICROZOOPLANKTON
>20 µm
Bottle-net
Reads of the most
abundant groups
per station
Amplicons
miTags
Number of reads
OTUs
Reads
Common taxa among approaches
Same station at
different depths –
taxonomy inferred using
miTags.
A different station is
included for comparison
purposes
Thanks for your atention!
PhD Students:
Anaïs
Rey
Eva
Aylagas
Postdoc:
Jon
Corell
Eneko
Bachiller
Collaborators:
Oihane C.
Basurko
Angel
Borja
Lab technician:
Iñaki
Mendibil
Funding sources
Unai
Cotano
Xabier
Irigoien
www.azti.es | www.alimentatec.com | www.itsasnet.com
T. +34 94 657 40 00 | [email protected]
Txatxarramendi ugartea z/g
Herrera Kaia, Portualdea z/g
Astondo Bidea, Edificio 609
48395 Sukarrieta, Bizkaia (SPAIN)
20110 Pasaia, Gipuzkoa (SPAIN)
Parque Tecnológico de Bizkaia
48160 Derio, Bizkaia (SPAIN)
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