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Lynx Genetic Considerations Michael Schwartz John Squires Kevin McKelvey Kristy Pilgrim Big Topic, Turned to October Headlines for Focus Genetics of Disease? Avian Malaria? WNV? Turns out, we dont need to worry.. Lynx Genetic


  1. Lynx Genetic Considerations Michael Schwartz John Squires Kevin McKelvey Kristy Pilgrim

  2. Big Topic, Turned to October Headlines for Focus Genetics of Disease? Avian Malaria? WNV?

  3. Turns out, we don’t need to worry……..

  4. Lynx Genetic Considerations 1) Mini review of lynx population genetic studies 2) Review of lynx hybridization studies 3) Needed genomic data

  5. Population Genetics of Lynx

  6. Stenseth et al. (1999) Suggest Climate Causes Large Scale Cycle Synchrony Continental Atlantic Maritime Pacific Maritime

  7. Lynx Isolated “The conservation of lynx populations is of greatest concern in the western mountains of the conterminous United States at the southern periphery of the species range. Recruitment is low in this region and many lynx populations….are geographically isolated.” - Koehler and Aubry 1994

  8. Lynx Connected: Large Scale Spatial Synchrony We let dispersal between patches be distance-dependent in an exponential fashion and fixed the fraction of migrants leaving each patch each generation. (p.1622 Ranta, Science )

  9. Lynx Trapping Data Suggests Dispersal Common (McKelvey et al. 2000) 1 Montana 0.9 Proportion of Maximum # Lynx Trapped BC/AB +2 yr. 0.8 r = 0.74 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1950 1955 1960 1965 1970 1975 1980 1985

  10. Population Structure and Migration F st - Proportional reduction in heterozygosity due to population subdivision (0-1). - High levels of gene flow drives F st to 0.

  11. F st Results Global Results (17 Populations): F st = 0.033 (+/- 0.002). Pair-wise Results (Extremes): F st / Migrants Fairbanks to Ladue Yukon: 0.001 ≅ High Kenai P. to Seeley Montana: 0.070 ≅ 3.0

  12. Kenai Seeley Lake

  13. Distance Does Not Lead To Structuring 0.0800 Genetic Differentiation (Fst) 0.0700 0.0600 0.0500 0.0400 0.0300 0.0200 0.0100 Mantel: p = 0.42 0.0000 0 500 1000 1500 2000 2500 3000 3500 Distance (km)

  14. 0.3 * Genetic Distance (Fst) Bighorn Sheep 0.2 * Brown Bears 0.1 * Coyote Wolves Lynx 0 2000 4000 Distance (km) *significant p>0.5 Adapted from Forbes and Hogg (1999)

  15. High gene flow across range

  16. Our Initial Conclusions • Ample gene flow continent wide • Limited structure possible at the edges (Kenai,Seeley) • Tide Pool Model • One Evolutionary Significant Unit

  17. • Rockies as barrier to gene flow in western Canada and “invisible barrier” south of Hudson coinciding with ecological Continental and Atlantic regions.

  18. Very, very low Fst Reuness et al. 2003

  19. Mitochondrial DNA Rueness et al. 2003

  20. • 17 microsatellites • Large differentiation on Newfoundland vs. Mainland • Fst – 0.19 between NF and Mainland • “subtle gene flow restriction between Ontario and Manitoba” • Bayesian clustering - 2 clusters NF vs others.

  21. Lynx Sample Distribution Row et al. 2012

  22. Again, very low Fst Row et al. 2012

  23. • Genetic variability correlated with winter climate gradient (snow depth and winter precipitation) – using spca (not with Bayesian clustering) • Stronger relationship than IBD • W-E genetic cline driven by PNO and NAO • Individuals restrict dispersal across climate boundaries in absence of changes in habitat quality. • Imprinting on snow conditions Climate Conditions: min and max temp, snow depth , precip, diff|max-min| Ecological Conditions: open needle-leaved conifer, broad-leaved deciduous, close needle-leaved conifer, closed broad leaved decid.

  24. PNA/NAO snow the “invisible barrier” to gene flow

  25. • 14 microsatellites and 558 lynx to test “riverine barrier hypothesis” • St. Lawrence River is a barrier • Not absolute – 24 indiv. crossing

  26. • 14 microsatellites and 558 lynx to test “riverine barrier hypothesis”

  27. RMRS Genetic Data (2004-2006 only) Principal Coordinates (PCoA) Montana Coord. 2 Minnesota QU_co NE_Lynx BC_co YK_co Washington Colorado Coord. 1

  28. RMRS Genetic Data (2004-2006 only) Principal Coordinates (PCoA) Washington Coord. 2 Minnesota Montana BC_co QU_co Colorado YK_co Coord. 1

  29. Lynx Cycles MN YU (Krebs et al. 2011)

  30. RMRS Genetic Data (2004-2006 only, MN 2001) Principal Coordinates (PCoA) Montana 2004-2006 Coord. 2 BC_co Series1 YK_co QU_co Colorado 2004-2006 Washington Minnesota MN-2001 Coord. 1

  31. Squires 1998-2015 lynx genetic data Purcell Central Seeley Garnet

  32. Lynx Genetic Considerations 1) Mini review of lynx population genetic studies 2) Review of lynx hybridization studies 3) Needed genomic data

  33. Lc 106 Lynx Hybrids Bobcats Bobcats Lc 110 Lynx Schwartz et al. 2004

  34. Schwartz et al. 2004

  35. Bobcat Numbers on the Increase Kapfer 2012

  36. Genetic Monitoring of Lynx in Minnesota

  37. Homyack et al. 2008; placental scars on NB hybrids, kittens on tree.

  38. Canada Lynx – Bobcat Hybridization in North America No Evidence of Hybrids (n=600) Evidence of Hybrids Schwartz et al. 2004 Pilgrim et al. 1998

  39. - bi-directional hybridization (mostly lynx F x bobcat M) - 7 of 2851 individuals hybrids - Backcrossing to both parental types

  40. Lynx Genetic Considerations 1) Mini review of lynx population genetic studies 2) Review of lynx hybridization studies 3) Needed genomic data

  41. What is Genomics? Genomic data: genetic information (e.g. DNA sequences) at thousands to millions of loci across the genome of a sample of organisms. Often focuses on mapping of these sequences and understanding their interactions

  42. #1: Increase Power and Precision Dog-Wolf Hybrid Wolves from 3 Locations in Italy Domestic Dogs 166,000 Molecular Markers

  43. #2 Separate: Neutral vs. Adaptive Genes 8,188 exons from >5,000 genes targeted; Roffler et al. (in prep) Class I histocompatibility antigen

  44. Spatial Distribution of Alleles at Locus Putatively Under Selection Major histocompatibility complex class I, alpha chain BL3-6 Roffler et al. in prep.

  45. Can we find genes under selection with lynx?

  46. Leading Edge of the Range – Drift Wins, Unless Selection is Very Strong or Ne Large Effective population size influences whether a local population can respond to selection = local adaptation (4Ne*s >> 1) selection overpowers drift Drift Wins

  47. First Principles of Population Genetics: Effective Population Size Effective population size influences whether a local population can respond to selection = local adaptation (4Ne*s >> 1) selection overpowers drift

  48. Summary Points • Boreal forest is almost no barrier for lynx • Intriguing results about climate in East • Periphery and some features = limited barrier • Tide pool model • When tide is out – substructure develops • Genomics can address climate and periphery questions while also looking for genes under selection

  49. Where do we go from here? • Sampling (during multiple phases of cycle) • Genomic studies to increase power • Look for genes under selection at range margin, with focus on the NAO

  50. What else should we do? 1)Conserve genetic diversity at the broad scale!!!!!! 2)Recognize that adaptive variation may = reduced gv at leading and trailing edge due to selection or drift. 3) Conserve gradients, and recognize the importance of peripheral populations (where selection occurs)

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