1. Biomonitoring, but not as we know it: meeting the challenge of DNA-based observation in bioassessment Donald Baird Environment & Climate Change Canada / Canadian Rivers Institute, University of New Brunswick, Fredericton, NB Photo: Kristie Heard

2. Roadmap The quality of biodiversity & biomonitoring data DNA as a unit of ecosystem observation 5 challenges to the adoption of metabarcoding as a routine observation method Do we need a ‘Benthos Genome Project’ ?

3. Biodiversity data quality: questionable… d’oh!

4. An ongoing data tragedy Local-scale studies have high-level taxonomy, but are idiosyncratic, and generally lack QA/QC Regional or national monitoring studies are more consistently observed, with QA/QC, but are generally at low taxonomic level Large-scale analyses employing mashups of both types of information are therefore broken by design, due to unacknowledged pseudoabsences resulting from inconsistent observation

5. Eastern Screech Owl. Photo: G McGeorge Pseudo-absence

6. Pseudo-presence Loch Ness ‘object’. Photo: Col. R. Wilson

7. ‘Flora’s Wagon of Fools’ (1637) by Hendrik Gerritsz Pot (Frans Haals Museum, Haarlem, Netherlands)

8. Gibson et al (2015) PLoS One: e0138432 Biodiversity data from morphology versus DNA

9. Gibson et al (2015) PLoS One: e0138432 Peace delta Athabasca delta Distinguishing local faunas

10. Why does DNA give better delta-level discrimination? DNA = entire sample (17,000 - 156,000 organisms) CABIN = subsample (ca. 300-500 organisms) Gibson et al (2015) PLoS One: e0138432

11. Challenge 1: DNA is an unfamiliar unit of observation DNA is ubiquitous in the environment Need to make a distinction between DNA extracted from bulk environmental samples containing living organisms (e.g. benthic samples) and ‘eDNA’ DNA fragments in a sample do not necessarily correspond to whole organisms or living organisms Benthic sample processing routinely ignores non-head fragments DNA may lurk in areas not normally observable

12. Individual as DNA microcosm

13. Marker gene (COI / ITS / 18s etc) PCR amplification (primer design, protocol) Sequence read length & region Identification using sequences from voucher specimens in curated (e.g. BOLD) versus non- curated (GenBank) databases Challenge 2: DNA metabarcoding lacks a consensus approach

14. If a taxon has never been barcoded with sequences deposited in a library/database, it is not identified Identification is also possible using phylogenetic relatedness (e.g. Porter et al 2014 MER) Barcode libraries are expanding, but remain incomplete (Curry et al SFS Platform S06 1645) Guidance is needed on the number and nature of library sequences required for reliable identification. Challenge 3: Pseudoabsences abound in metabarcoding data

15. DNA metabarcoding provides presence/absence information Yields of sequence reads are also generated While there is a relationship between biomass and the numbers of sequences observed for a taxon, this is not consistent, and requires calibration DNA capture arrays and PCR-free methods can reduce variability and facilitate quantification Challenge 4: DNA metabarcoding does not support quantification

16. Currently, NGS studies in ecology are in Wild West mode (madly in all directions, guns blazing) Much awesomeness, yet many conflicting results, with little generality emerging Proofs of concept have yet to make the leap to routine implementation (e.g. federal agencies) Challenge 5: Lack of co-ordination among studies

17. What is Needed? Co-ordination of efforts both nationally and internationally for library development Greater openness in sharing of curated sequence libraries (e.g. BOLD) Parallel studies co-ordinated to demonstrate breadth of applicability of methods (field/lab studies) Laboratory intercalibration of methods and approaches Focus on use cases (macroinvertebrates, algae) with clear management application