Identifying Origins and Pathways of Spread of Zebra Mussels using Genetics and dgenomics

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1 Identifying Origins and Pathways of Spread of Zebra Mussels using Genetics and dgenomics Michael McCartney and Sophie Mallez MN AIS Research Center (MAISRC) University of Minnesota, St. Paul Aquatic Invaders Summit Aquatic Invaders Summit St. Cloud MN October 5th, 2016

2 Zebra mussel native range Ponto Caspian region of southern Russia Riverine lakes, rivers flowing into the Black, Caspian and Azov seas Azov Black Sea

3 European zebra mussel invasion Expansion throughout Europe began about 1800 Canal building Downstream spread Commercial shipping traffic Commercial net fishing (e.g. Belarus)

4 North American invasion Several introductions to the Great Lakes in ship ballast water 2011: Brown and Stepien Appeared in Lake St Clair (1988: arrow) Through navigable waters (Great Lakes and Mississippi Basins, Hudson and Susquehanna Rivers) they reached Louisiana to the south, Quebec and New York to the east, Oklahoma and Minnesota to the west in 5 years!

5 The zebra mussel invasion In Minnesota Data sources: US Geological Survey (NAS: 2014) MnDNR (2015) Introduced in 1989, in Lake Superior Upper Mississippi and St Croix Rivers: early 1990 s First natural inland lake infested in 2003: Ossawinnamakee Al l 10 l th A long lag, 10 years longer than other US states. Now invasion rate of inland lakes is very high

6 Minnesota s rate of new inland invasions is now among the highest in the US From Mallez and McCartney (in review) We have the time, the will, and the resources to slow spread and prevent infestation i of many prized water bodies! Prevention works, but must be targeted by Studying boater movements, vectors of spread Using genetics and genomics to directly pinpoint invasion sources and routes

7 Hypotheses for inland spread in MN Longer lag Prevention success Getting out of the rivers [and into an inland hub lake] Post 2009 increase Secondary spread from inland hub (e.g. Mille Lacs Lake)

8 Our approach Preventing relies upon understanding spread Identifying the routes of invasion Geographic path(s) followed by an organism/propagule between source population(s) and destination waterbodies Pinpointing lakes/rivers playing akeyrole Characterizing major vectors of dispersion Improved vigilance and monitoring of key routes and vectors to slow the rate of new infestations W id tif t f We identify routes of invasion using DNA

9 Invasion genetics and genomics DNA markers to track invasion i paths, both past and present DNA markers Variable (ie. polymorphic) genes or DNA fragments whose chromosomal location is known Distinguishes individuals Distinguishes individuals Allows their assignment to population of origin

10 Genetic markers for zebra mussel Microsatellite markers Repeated motifs GTTAGTCCAGAGAG.AGAGAGTTCGATCT Polymorphic numerous alleles Genotyping of 9 microsatellite markers Obtained from the literature Optimized for this study

11 Sampling zebra mussels Sampling of infested waterbodies in WI

12 Analyzing the invasion in Minnesota 1281 individuals genotyped at 9 microsatellite markers 16 lakes, 3 river systems Six lakes (Gull, Mille Lacs, Minnetonka, Prior, Pelican, Superior); Mississippi & St. Croix Rivers at multiple sites Each brand of analysis targets a different invasion process: Analysis of genetic diversity = Number of individuals introduced, severity and duration of bottleneck when lakes were colonized Analysis of differentiation/structure and genetic clusters = Genetic differences between lakes set up by unique histories Analysis of invasion models = Origins and routes of introduction. Contrast of detailed scenarios to describe invasion history. Most lessons for management are generated here.

13 Analysis of genetic diversity High level of polymorphism within populations

14 Analysis of genetic diversity Waterbodies were colonized by a large number of individuals

15 Analysis of genetic diversity x x x x Reduction in allelic diversity bottlenecks during establishment of Carlos, LeHomme Dieu, Mille Lacs, Prior

16 Analysis of differentiation/structure Between lake analyses Some well defined clusters distinguish important lake infestations

17 Analysis of invasion models Comparisons of scenarios of invasion Approximate Bayesian Computation Selection of the most likely scenario of invasion based on probabilities Focus on distinguishable lakes Super spreader lakes Clustered invasion Mille Lacs Lake Pi Prior Lake Alexandria area Lakes

18 Analysis of invasion models Super spreader lakes Mille Lacs Lake A source for spread to other inland lakes infested later (post 2005)? Scenario 1 Scenario 2 Gull Lake Gull Lake vs Mille Lacs Lake Mille Lacs Mille LakeLacs Lake

19 Analysis of invasion models Super spreader lakes Mille Lacs Lake ae A source for spread to other inland lakes infested later (post 2005)? Scenario 1 Scenario 2 Gull Lake Gull Lake vs Mille Lacs Lake Mille Lacs Mille LakeLacs Lake Pr. = 0.89 The answer No was selected in 20 of 22 tests (Posterior Probabilities from 0.86 to 0.99) Mille Lacs Lake did not infest Carlos, LHD, Darling, Gull, Pelican (Otter Tail), Minnetonka, Xmas, Pike (St. Louis), Bass, Sand (Itasca)

20 Analysis of invasion models Clustered Invasion Alexandria area ead aaealakes aes 12 km

21 Analysis of invasion models Clustered Invasion Invasion in Alexandria area a aealakes aes Lake Carlos LeHomme Dieu Lake Scenario 1 Scenario 2 Scenario 2 Lake Carlos Lake Carlos Lake Carlos vs or LeHomme Dieu Lake LeHomme Dieu Lake LeHomme Dieu Lake

22 Analysis of invasion models Clustered Invasion Invasion in Alexandria area a aealakes aes Lake Carlos LeHomme Dieu Lake Scenario 1 Scenario 2 Scenario 2 Lake Carlos Lake Carlos Lake Carlos vs or LeHomme Dieu Lake LeHomme Dieu Lake Pr. = 0.77 Pr. = 0.72 LeHomme Dieu Lake Scenario of successive introductions selected

23 Analysis of invasion models Clustered Invasion Invasion in Alexandria area a aealakes aes LeHomme Dieu Lake Lake Darling Scenario 1 Scenario 2 Scenario 2 vs or LeHomme Dieu Lake LeHomme Dieu Lake LeHomme Dieu Lake Lake Darling Lake Darling Lake Darling

24 Analysis of invasion models Clustered Invasion Invasion in Alexandria area a aealakes aes LeHomme Dieu Lake Lake Darling Scenario 1 Scenario 2 Scenario 2 Pr. > 0.86 Pr. > 0.86 vs or LeHomme Dieu Lake LeHomme Dieu Lake LeHomme Dieu Lake Lake Darling Lake Darling Lake Darling Scenario of independent introductions selected

25 Analysis of invasion models Clustered Invasion Invasion in Alexandria area a aealakes aes LeHomme Dieu Lake Lake Carlos: successive introductions Lake Darling (in tests involving both LeHomme Dieu and Carlos) : independent introductions Stepping stone scenario cannot, alone, account for this clustered invasion

26 Where are we going with this? Expand sampling of water bodies, both in MN and in the region 1. To examine, more comprehensively the causes of clustered invasions 1. To obtain several more recently infested t dlakes to increase confidence in results for hubs Mille Lacs was heavily infested only 2009 Minnetonka 2010

27 Where are we going with this? New lakes sampled in 2016 Chains of lakes Clustered invasions Alexandria area lakes Pelican Rapids area lakes Brainerd area lakes

28 Where are we going with this? New lakes sampled in 2016 for Mille Lacs analyses Brainerd area: geographically proximate to Mille Lacs Many new post 2009 infestations Lakes (e.g. Cass) that are next stop lakes for boaters Alexandria area lakes Pelican Rapids area lakes Brainerd area lakes

29 Where are we going with this? New lakes sampled in 2016 Lower Great Lakes [to understand invasion sources for MN, both historical (and ongoing?)] Lake Huron Lake Michigan Lake St. Clair Lake Erie

30 The Zebra Mussel Genome Project We sequenced the zebra mussel genome using Illumina short read technology 100s of millions of fragments, each base pairs Piled up and stitched together using bioinformatics We used this draft genome to help genotype Single Nucleotide Polymorphism (SNP) markers 5,000 to 200,000 markers per mussel! Genotyped using Sequence Based Genotyping (SBG) new technology at UMGC These thousands of variable SNP markers provide higher resolution for studying sources and pathways of spread

31 Genomic Markers for Studying Spread 24 mussels from Minnetonka and 24 mussels from Gull Lake (2 putative super spreader lakes) Genotyped with 9 microsatellite markers (left panel) Genotyped with > 5,500 genomic (SNP) markers (right panel) Analyzed the same way (Principal Component Analysis) to examine genetic differences between lakes Lake Minnetonka Coord. 2 Gull Lake Coord. 1

32 Higher genetic resolution = more complex scenario testing = more targeted management Lake Michigan Upper Miss Scenario I: Successive invasions inland Time Mille Lacs Minne tonka Management: target boat traffic between inland lakes Lake Michigan Scenario II: Chronic reintroductions Time Mille Lacs Minne tonka Management: increase surveillance of chronic sources (e.g, Mississippi boat ramps, or sources out of state)

33 Analyses of genetic variation show that large numbers of zebra mussels establish new invasions Implications for vectors of spread - Veligers in water, downstream spread? Yes, over short distances. - Veligers in residual water (transient* boats)? Research question. - Adults/juveniles by other pathways (docks, lifts, attached to vegetation, resident** boats)? Research question. *Transient: remain in water 1 4 days, trailered. **Resident: remain in water > 3 months, often commercially hauled.

34 Analysis of clustered invasions show evidence, both for stepping stone and independent introductions Implications: vectors of regional spread 1. Stream connections? Yes 2. Boating? Research question. 3. Movement of equipment? Research question.

35 Analyses of super spreader lakes (Mille Lacs, Prior) have so far indicated a surprising lack of contribution to spread We need to analyze more lakes infested post 2009 for better confidence

36 Lake Michigan Management implications? Upper Miss Mille Lacs Watercraft inspection of boats departing Mille Lacs may be working Invasions are not following patterns of transport of veligers in water in trailered transient boats Lakes infested post 2005

37 Lake Michigan Management implications? Upper Miss Mille Lacs Time is now to consider other pathways (docks, lifts, transport of resident boats from marinas on St. Croix/UMR) How to better monitor and intercept these less frequent but high risk events? Lakes infested post

38 UMN: Thanks to Grace Van Susteren, Sarah Peterson, Maxwell Kleinhaus, Melody Truong for collecting help and lab support UM Genomics Center: Kenneth Beckman, Daryl Gohl, Shea Anderson, Aaron Becker for molecular biology; John Garbe for informatics NPS: Byron Karns, Michelle Prosser for field support on St. Croix & Mississippi MnDNR: Dan Swanson, Rich Rezanka, Keegan Lund for field support and advice on collecting MN waters advice on collecting MN waters USGS: Mary Anne Evans for collections in western Lake Erie Clear Water Fund, ENRTF for funding

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