NRC fellow, USEPA-MED, Duluth, MN. USEPA-MED, Duluth, MN. ORISE participant, USEPA-MED, Duluth MN. USEPA, NERL/EERD, Cincinnati, OH

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1 The truth is out there: integrating DNA-based data into bioassessments improves our understanding of species distributions and species habitat relationships Christy Meredith 1, Joel Hoffman 2, Anett Trebitz 2, Greg Peterson 2, Julie Lietz 3, Chelsea Hatzenbuhler 3, Erik Pilgrim 4, Sara Okum 3, and John Martinson 4 1 NRC fellow, USEPA-MED, Duluth, MN 2 USEPA-MED, Duluth, MN 3 ORISE participant, USEPA-MED, Duluth MN 4 USEPA, NERL/EERD, Cincinnati, OH

2 Organism identification and enumeration: central to bio-assessment but also a big challenge o Many organisms to sort & count o Extensive bycatch o Some life stages not fully identifiable (lack keys, identifying characteristics) o Poor condition/preservation prevents ID o Many taxa are very small (microscopy, slide mounts, etc.) composition data o Observer sampling bias 2

3 Metabarcoding isolation PCR Mixed environmental sample Bulk DNA Next generation sequencing Full community profile of the sample Match with barcode database 500K to 20 million sequences 3

4 Field Collection Organization of Samples Labeling Preservation Handling Morphological ID

5 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID

6 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination

7 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination DNA Analysis List of OTU s (DNA Codes=Species) Life-Stage % Biomass

8 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination DNA Analysis Species List Closely-Related Species Database Errors Match Species with Database List of OTU s (DNA Codes=Species) Life-Stage % Biomass

9 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination Useable Bioassessment Data DNA Analysis Bringing Together of Morphological and DNA Data Species List Closely-Related Species Database Errors Match Species with Database List of OTU s (DNA Codes=Species) Life-Stage % Biomass

10 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination Useable Bioassessment Data Many Potential Sources of Error! DNA Analysis Bringing Together of Morphological and DNA Data Species List Closely-Related Species Database Errors Match Species with Database List of OTU s (DNA Codes=Species) Life-Stage % Biomass

11 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination Useable Bioassessment Data Many Potential Sources of Error! DNA Analysis Bringing Together of Morphological and DNA Data Species List Closely-Related Species Database Errors Match Species with Database List of OTU s (DNA Codes=Species) Life-Stage % Biomass

12 Study Area

13 Overall Research Goal: Use DNA meta-barcoding to inform knowledge of larval fish distributions and habitat relationships o Why use larvae?

14 Overall Research Goal: Use DNA meta-barcoding to inform knowledge of larval fish distributions and habitat relationships o Why use larvae? -Relatively easy to process in the field, but challenging to identify

15 Overall Research Goal: Use DNA meta-barcoding to inform knowledge of larval fish distributions and habitat relationships o Why use larvae? -Relatively easy to process in the field, but challenging to identify -Susceptible to ballast intake and transfer

16 Overall Research Goal: Use DNA meta-barcoding to inform knowledge of larval fish distributions and habitat relationships o Why use larvae? -Relatively easy to process in the field, but challenging to identify -Susceptible to ballast intake and transfer -Larvae of reproducing invaders often more prevalent than adults

17 Overall Research Goal: Use DNA meta-barcoding to inform knowledge of larval fish distributions and habitat relationships o Why use larvae? -Relatively easy to process in the field, but challenging to identify -Susceptible to ballast intake and transfer -Larvae of reproducing invaders often more prevalent than adults -Presence of larvae indicate a reproducing population

18 Great Lakes Water Quality Agreement (1972, 1978, 1987): The U. S. and Canada pledge to reduce point-source pollution from industrial sources and sewage plants, and virtually eliminate the discharge of persistent toxic chemicals. Great Lakes Water Quality Agreement, revision (2012): A framework is set for addressing additional threats to the Great Lakes, including climate change, aquatic invasive species, habitat destruction, and harmful substances that are not necessarily persistent toxic substances.

19 Ruffe Johnny darter Yellow perch Logperch Ruffe Black Crappie Peterson and Lietz, in press

20 Larval fish sampling protocol I. Probabilistic design II. Field sampling III. Morphological taxonomy larval beach seine light trap key: challenging and incomplete Tucker trawl neuston net Reconstitute sample (with eggs and unknowns) 150 sites (May-June) IV. Molecular taxonomy 1 st run: general barcoding primers (designed to work across vertebrates and invertebrates 2 nd run: cocktail of barcoding primers specifically designed for use with fish

21 Objective: Resolve Mismatches Between DNA and Morphology o What are potential reasons for mismatch between DNA and morphology? 1-Morphological ID error 2-Eggs and unknowns 3-Low biomass (False Absence) 4-Sequencing or contamination (False Presence) o How do species distributions and species-habitat relationships change after resolving mismatches? Automated Program!

22 Step 1:Resolvable Morphological ID Error o Species 1 identified by DNA and Species 2 identified by morphology o Both in same family o Only one possible in-family match Morph Round Goby DNA Tubenose Goby Family DNA Morph (#) Error Type Eggs/Unknowns? Round Goby Gobidae 0 10 False Absence No Tubenose Goby Gobidae 1 0 False Presence No

23 Step 1:Resolvable Morphological ID Error o Species 1 identified by DNA and Species 2 identified by morphology o Both in same family o Only one possible in-family match Morph Round Goby DNA Tubenose Goby Family DNA Morph (#) Error Type Eggs/Unknowns? Round Goby Gobidae 0 10 False Absence No Tubenose Goby Gobidae 1 0 False Presence No Family DNA Morph (#) Error Type Eggs/Unknowns? Tubenose Goby Gobidae 1 10 None No

24 Step 2: Eggs/Unknowns o Eggs and unknowns in sample Morph DNA yellow perch walleye Family DNA Morph (#) Error Type Eggs/Unknowns False Walleye Percidae 1 0 Eggs/Unknowns Yes Presence

25 Step 3: False Presence Case 1: False Presence o Not found in study area o Very few sequences of DNA representative of that species Case 2: Possible False Presence o Potentially found in study area o Very few sequences of DNA representative of that species

26 Step 4: False Absence Case 1: False Absence o No obvious morphological mismatch o < 10% biomass in sample Case 2: Possible False Absence o No obvious morphological mismatch o > 10 % biomass in sample YES Signal Strength Depends on % Biomass in Sample NO

27 RESULTS

28 Morph DNA Rainbow Smelt X X Yellow Perch X X Black Crappie X X Bloater X X Round Goby X X Spottail Shiner X X White Sucker X X Mimic Shiner X X Logperch X X Johnny Darter X X Trout-perch X X Longnose Sucker X X Ruffe X X White Perch X X Pumpkinseed X X Emerald Shiner X X Common Carp X X Brook Silverside X X Fathead Minnow X X Tubenose Goby X X Common Shiner X X Rock Bass X X Smallmouth Bass X X Three-Spined Stickleback X X Longnose Dace X X Bluegill X Walleye X Desert Sucker X Redhorse X Freshwater Drum X Brook Stickleback X Ghost Shiner X 27 Species 31 Species Results: Species Richness

29 Morph DNA Rainbow Smelt X X Yellow Perch X X Black Crappie X X Bloater X X Round Goby X X Spottail Shiner X X White Sucker X X Mimic Shiner X X Logperch X X Johnny Darter X X Trout-perch X X Longnose Sucker X X Ruffe X X White Perch X X Pumpkinseed X X Emerald Shiner X X Common Carp X X Brook Silverside X X Fathead Minnow X X Tubenose Goby X X Common Shiner X X Rock Bass X X Smallmouth Bass X X Three-Spined Stickleback X X Longnose Dace X X Bluegill X ID Error Walleye X eggs Desert Sucker X false presence? Redhorse X eggs/unknowns Freshwater Drum X eggs Brook Stickleback X eggs/unknowns Ghost Shiner X false presence? 27 Species 31 Species Results: Species Richness

30 Morph DNA Rainbow Smelt X X Yellow Perch X X Black Crappie X X Bloater X X Round Goby X X Spottail Shiner X X White Sucker X X Mimic Shiner X X Logperch X X Johnny Darter X X Trout-perch X X Longnose Sucker X X Ruffe X X White Perch X X Pumpkinseed X X Emerald Shiner X X Common Carp X X Brook Silverside X X Fathead Minnow X X Tubenose Goby X X Common Shiner X X Rock Bass X X Smallmouth Bass X X Three-Spined Stickleback X X Longnose Dace X X Bluegill X ID Error Walleye X eggs Desert Sucker X false presence? Redhorse X eggs/unknowns Freshwater Drum X eggs Brook Stickleback X eggs/unknowns Ghost Shiner X false presence? 27 Species 31 Species Results: Species Richness

31 Results: Morph vs DNA Comparison Number of Observations % DNA & Morph DNA Only Morph Only

32 Results: New Morph vs DNA Comparison 18% 10% 65% false absence: 0.2% false presence: 1.2% possible false absence: 4% possible false presence: 1.6%

33 Results: Prevalence of Invasive Species % of Samples Traditional ID Traditional and DNA Combined Invasives Present No Invasives Present Invasives Present No Invasives Present

34 Results: Temporal Patterns Invasive Larval Fish Occurrence in St. Louis River Estuary, 2012 Morphology Only Morphology & DNA

35 Results: Spatial Patterns Morphology Distribution of Ruffe Larvae (Gymnocephalus cernus) St. Louis River Estuary Duluth, MN Morphology & DNA

36 Results: Species-Habitat Relationships Temperature Versus Larval Ruffe Occurrence Original DNA-corrected

37 Field Collection Organization of Samples Labeling Preservation Transfer to DNA Lab Handling Morphological ID DNA Laboratory Primer Selection Potential Contamination C01 or 16s barcode? Useable Bioassessment Data Bringing Together of Morphological and DNA Data Species List Closely-Related Species Database Errors Many Potential Sources of Error! (Hatzenbuhler et al) Match Species with Database DNA Analysis List of OTU s (DNA Codes=Species) Life-Stage % Biomass

38 DNA-based methods can greatly improve the accuracy of bioassessments and increase ability to detect rare species, particularly in combination with morphological methods Benefits o Allowed for better spatial and temporal patterns o Revealed error in ID and key o Revealed needed changes in methodology (separate eggs, unknowns) Challenges o False positives and false negatives o Can t determine % biomass in sample o Some DNA methods aren t well established (long waiting period for results)

39 o o I 2013 and beyond St Louis River Larval Fish: o Improved morphological identification of larvae (e.g., Ruffe and Black Crappie) o o Separation of eggs and unknowns in larvae samples Ultimate goal: Use only DNA-based ID Additional Research: o Other taxa (invertebrates,zooplankton) 2012!( 2013 o DNA methods (CO1 versus 16s) o Compare larval distributions to adult distributions

40 The Truth is Out There