Monitoring genetic change in wild populations of fish &wildlife

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1 Monitoring genetic change in wild populations of fish &wildlife Fred W. Allendorf University of Montana Michael K. Schwartz U.S. Forest Service

2 GeM: Genetic Monitoring First Working Group jointly funded by NCEAS & NESCent.

3 GeM Working Group Members Fred Allendorf University of Montana Michael Schwartz U.S. Forest Service (USFS) C. Scott Baker Oregon State Univ. Michael Hansen Univ. of Aarhus, Denmark Jennifer Jackson British Antarctic Survey Kate Kendall U.S. Geological Survey (USGS) Linda Laikre Stockholm University, Sweden Kevin McKelvey USFS Maile Neel University of Maryland Isabelle Olivieri University of Montpellier, France Nils Ryman Stockholm University, Sweden Ruth Short Bull University of Montana Jeff Stetz USGS David Tallmon Univ. Alaska Christine Vojta USFS Don Waller University of Wisconsin-Madison Robin Waples NOAA Fisheries

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6 Objectives of GeM Working Group (1) Provide practical guidelines for resource managers and policy makers to design genetic programs to monitor population trends and processes. (2) Evaluate potential to use genetic monitoring of candidate genes likely to be affected by climate change, and other types of stress, to understand evolutionary responses to environmental changes.

7 Three levels of diversity in CBD: (1) Ecosystems (2) Species (3) Genes

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9 Population Genetics in Space & Time Space: Describing patterns of genetic variation among geographical populations. F ST, heterozygosity, allelic diversity Time: Describing changes in patterns of genetic variation among samples collected at different times from a single site. F k, etc.

10 Trends in Ecology & Evolution 22: We define genetic monitoring as quantifying temporal changes in population genetic metrics or other population data. We distinguish monitoring, which must have a temporal dimension, from assessment, which reflects a snapshot of population characteristics at a single point in time.

11 Schwartz et al. 2007

12 Schwartz et al. 2007

13 Schwartz et al. 2007

14 Comparing N e and Genetic CMR in Detecting Population Growth A. Wright-Fisher Model

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16 Geographical Framework of GeM Working Group A. Wright-Fisher Model B. Subpopulation Model C. Isolation By Distance Model

17 Continuous Distribution Model (Isolation-by-distance) neighborhoods

18 Class III: Detect and monitor adaptation (e.g., response to climate change) using candidate genes Molecular Ecology (in revision)

19 Examples Class I: Wolverine, lynx, European river otters. Class II: Estimation of effective population size in Swedish brown trout. Use of historical DNA to monitor genetic variation and manage grizzly bears in the Yellowstone Ecosystem and the Northern Continental Divide Ecosystem.

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21 Genetic monitoring can be especially valuable to evaluate the success of reintroductions, natural or managed.

22 Detecting recolonization of lynx in Minnesota. Last verified 1993

23 Track Surveys back-tracked putative lynx Collected 263 scats and hairs.

24 DNA analysis of Minnesota lynx 263 scat & hair samples 94% success scat & 70% success hair 157 confirmed lynx by mtdna Schwartz et al. Unpub.

25 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 How many individuals? DNA + Focus on ~ bp PCR 6-Microsatellite DNA Panel

26 94 unique six-locus genotypes (157 samples identified as lynx) Total Count * Females * * * 2001/2 2002/3 2003/4 2004/5 Winter

27 Capture-Mark-Recapture Estimates (95% CI) Abundance /2 2002/3 2003/4 2004/5 Winter

28 Conservation Genetics 11:

29 Population development 50 Total released 40 # individuals Newborn /03 03/04 04/05 05/06 06/07 07/08 Released W inter period

30 Female Observed progeny Male A04 A05 A06 A08 A12 A15 A18 NB11 NB15 NB42 Total A A A A A A A A A NB NB NB NB NB NB NB NB Total

31 0.8 0 Reproductive Success Frequency Female Male Variance: Female Male NB04 NB15 A # offspring per individual

32 A03 Relatedness - Inbreeding A12 Number of newborn present NB02 A08 NB07 Period F=0 F>0 NB15 NB22 03/ / / / / NBxx Fxx= Fpop = 0.037

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34 Conservation Genetics 4:

35 EFFECTIVE POPULATION SIZE (N e ) is the size of the ideal population (N) that will result in the same amount of genetic drift as in the actual population being considered. per generation: h = -1/2N = F N e can be estimated by using changes in allele frequencies over time to estimate F.

36 Sampled 100 fish from two sites annually from Identified cohorts using otoliths to determine age. Examined 17 polymorphic allozyme loci

37 Geography: F ST = 0.13 (stable over time) Temporal changes: F k = based upon mean allele frequency change between consecutive cohorts (overlapping generations)

38 Site II N e =48 I N e =19 Point estimates of N e for pairs of consecutive cohorts are relatively stable over time.

39 Use of genetic monitoring to manage grizzly bears NCDE YE

40 Why do Yellowstone Ecosystem bears (YE) have 21% less nuclear heterozygosity and 61% less mtdna diversity compared to the nearby Northern Continental Divide Ecosystem (NCDE) bears?

41 ~150 km Continuous habitat

42 Time Travel: The polymerase chain reaction (PCR) allows using historical or ancient DNA to detect genetic changes (or lack thereof) by going back in time using museum specimens or other sources of DNA (seed banks, fish scales, etc.). Miller & Waits (2003) used bone from museum specimens of grizzly bear skulls to extract DNA.

43 Proc. Natl. Acad. Sci. USA 100: A, B, or C???

44 And the answer is - Het: N e ~ 75 N e ~ 50

45 Ongoing Genetic Monitoring Continued genetic screening of YE grizzly bears with microsatellite loci to detect migrant individuals using assignment tests to identify population of origin based upon genotype (YE or NCDE; F ST =0.12) If no genetic exchange is detected by 2020, two bears will be translocated every generation (10 years) from the NCDE to the YE.

46 Grizzly bears in the NCDE Rub tree

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49 Jeff Stetz

50 Jeff Stetz

51 ~ 250 km

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53 Thank you! Questions???