Biotic indices. How to change from conventional to ...inbio-envmetagen.pt/wp-content/uploads/2019/02/10_Jan-PAWLOWSKI.pdf · Biotic indices. How to change from conventional to molecular

Biotic indices. How to change from conventional to molecular approaches?

Jan PawlowskiDepartment of Genetic and Evolution, University of Geneva, Switzerland ID-Gene Ecodiagnostics Ltd, [email protected]

Workshop CIBIO Porto 18.12.18

Bioindication as one of the elements of environmental impact assessment

Environment

Bioindication

Status assessment

Physics Chemistry

RichnessEvennessBiotic Index

Biomass Specific tolerance

Communities composition

NH4

NO3

Cl-

PCBT°

pH

COD

Hg

Cd

Cu

EnvironmentalDNA


Bioindication relies on biotic indices

AMBI index groups (Borja 2000)I – very sensitive speciesII – indifferent speciesIII – tolerant speciesIV – second order opportunistic speciesV – first order opportunistic species

AMBI = ((0 x %GI) + (1.5 x %GII) + (3 x % GIII) + (4.5 x % GIV) + (6 x % GV)) / 100Workshop CIBIO Porto 18.12.18

Traditional biotic indices requires morphologicalidentification of bioindicator species, which are assigned to ecological groups/categories

MORPHOTAXONOMY


Our objective: to infer or predict biotic indices based on high-throughput eDNA metabarcoding data

MORPHOTAXONOMY DNA METABARCODING


Decaying tissues / cells

Extracellular/free molecules

Secretions/mucus

Organisms / cells

What is the environmental DNA (eDNA)?


Workflow of eDNA metabarcoding


Benthic monitoring applied to marine aquaculture

Benthic diatoms monitoring of rivers and streams

Our projects: Testing the application of eDNA metabarcoding to biomonitoring and bioassessment.


Indice Biotique

Morphologie

ADN

Echantillon

Diatom Index: Assessmentof biological quality of riversand streams using benthicdiatoms


Results: The assessment of water quality provides roughly the same results whether the Diatom Index is inferred from morphology, eDNA or eRNA data

Visco et al. 2015Workshop CIBIO Porto 18.12.18

Challenges of taxonomic assignment approach

1) Incompleteness of reference database

2) Cryptic species3) Abundance

biases


Development of taxonomy-free Diatom MolecularIndex (DMI)

Apothéloz-Perret-Gentil et al. Mol Ecol Res 2017Workshop CIBIO Porto 18.12.18

Taxonomy-free approach allows higher coverage of eDNA dataset and better correlation between morphological and molecular indices.


Sieving and manual sorting of benthic macrofauna

Sedimentary eDNA analysis


DNA metabarcoding generates high amount of sequence data (BIG DATA) that cannot be assigned to any taxa of known ecological value

< 1000 specimens / grab

> 500 000 sequences / grab


Sampling sediment eDNA: what we actually get?

Macrofauna

Represented Ecological value

Meiofauna

Micro-eukaryotes

How to take advantages of ecologically unassigned sequences ?


Machine learning applied to metabarcoding data

Machine learning algorithms are trained on macrofauna-based ecological status to predict the ecological status of new samples

Training a modelMaking

predictions


Comparison of indices inferred from assigned sequences and predicted using ML

0 1 2 3 4 5 6

01

23

45

6

AMBI prediction / Inference assigned metazoan - V1V2

Morphology

Mol

ecul

ar

R²=0.504***Kappa=0.448***

AukrasandenBeitveitnesBjørnsvikNedre KvarvStorvika

0 1 2 3 4 5 6

01

23

45

6

AMBI prediction / RF - V1V2 eukaryotes

Morphology

Mol

ecul

ar

R²=0.886***Kappa=0.866***


0 1 2 3 4 5 6

01

23

45

6

AMBI prediction / RF - V1V2 Metazoa

Morphology

Mol

ecul

ar

R²=0.7***Kappa=0.797***


assigned macrofauna macrofauna + meiofauna all eukaryotesWorkshop CIBIO Porto 18.12.18

• 18S V1/V2 – Zoobenthos• 18S V9 – Eukaryotes• 18S 37F - Foraminifera• 16S V3 - Bacteria 0 5 10 15 20 25 30

05

1015

2025

30 foraminifera 37F

Mol

ecul

ar

R =0.82***Kappa=0.832***

0 5 10 15 20 25 30

05

1015

2025

30 bacteria V3V4

R =0.89***Kappa=0.9***

0 5 10 15 20 25 30

05

1015

2025

30 eukaryotes V1V2

Morphology

R =0.914***Kappa=0.893***

0 5 10 15 20 25 30

05

1015

2025

30 eukaryotes V9

R =0.953***Kappa=0.919***

AMBI ISI NSI NQI1 AverageForams 0,834 0,651 0,82 0,822 0,78Bacteria 0,863 0,785 0,89 0,908 0,86Euk V1V2 0,886 0,764 0,914 0,902 0,87Euk V9 0,906 0,79 0,916 0,938 0,89

Machine learning performance depends on selected marker


Advantages of machine learning approach applied to eDNA benthic monitoring: It provides accurate predictive models

for conventional biotic indices It allows using the full dataset of

sequence data It allows extending the range of new

potential bioindicators It does not acquire a prior knowledge

about species ecology


1. Taxonomy-assigned approach – inferring biotic indices from eDNA sequences assigned to indicator species only.

2. Taxonomy-free approach – inferring biotic indices from unknown sequences (ASVs) or OTUs, that were given indicator values based on their occurrence in eDNA data.

3. Machine learning approach - predicting biotic indices based on the comparison of eDNA sequences to reference training dataset

Three approaches to molecular bioindication:


DNA metabarcoding

High-throughput sequencing

Machine learning

All ingredients are there to implement DNA-based biotic indices. They have potential to make biomonitoring:• more sensitive, • more accurate, and• time/cost-effective

Take-home message


AcknowledgmentsUniversity of Geneva / ID-Gene• Tristan Cordier• Franck Lejzerowicz• Laure Apothéloz-Perret-Gentil• Kristina Cermakova• Philippe EslingUniversity of Kaiserslautern (Germany)• Thorsten Stoeck & coUniversity of Aarhus (Denmark) • Tomas Cedhagen


Thank you for your attention!


http://dnaqua.net

DNAqua-net Cost Action: Developing new genetic tools for bioassessment of aquatic ecosystems in Europe



SYNAQUA projectSYNERGIE TRANSFRONTALIÈRE POUR LA BIO-SURVEILLANCEET LA PRÉSERVATION DES ÉCOSYSTÈMES AQUATIQUES

L2

L1

Bioassessment of 40 rivers in CH and 80 rivers in France.

Analysis of 150 sampling sites at coast of Léman

Benthic diatoms and Oligochaetesused as bioindicators

Documents

Biotic indices. How to change from conventional to ...inbio-envmetagen.pt/wp-content/uploads/2019/02/10_Jan-PAWLOWSKI.pdf · Biotic indices. How to change from conventional to molecular