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Monitoring genetic change in natural populations. Michael S. Schwartz U.S. Forest Service Fred W. Allendorf University of Montana. First Working Group jointly funded by NCEAS & NESCent. Initial meeting in March 2008. Objectives of Working Group

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slide1
Monitoring genetic change in natural populations

Michael S. SchwartzU.S. Forest Service

Fred W. AllendorfUniversity of Montana

slide3
Objectives of 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.

slide5
Trends in Ecology & Evolution 22:25-33. 2007.

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.

slide6
Population Genetics in Space & Time

Space: Describing patterns of genetic variation among geographical populations.

FST,heterozygosity, allelic diversity

Time: Describing changes in patterns of genetic variation among samples collected at different times from a single site.

Fk, etc.

slide9
Class III:

Detect and monitor adaptation (e.g., response to climate change) using candidate genes

Using genetic markers to predict and monitor climate change in insects

Carla Sgro & Ary Hoffmann

slide10
Three Examples
  • Class I: Detecting recolonization of lynx in Minnesota.
  • Class II: Estimation of effective population size in Swedish brown trout.
  • (3) Use of historical DNA to monitor genetic variation and manage grizzly bears in the Yellowstone Ecosystem (YE).
slide12
Genetics of Lynx in

Minnesota

Schwartz et al. 2004

slide13
Lynx Listed as Threatened
  • in March 2000
  • Species occurs on 110
  • Million acres of USFS land
  • Elusive, Patchily Distrib.,
  • cyclic in abundance
  • In MN they were absent
  • from 1993-2001
slide14
Last verified

1993

McKelvey et al. 2000

slide15
How do we confirm lynx’s return

to Minnesota after 10 yr absence?

and

Can we monitor the population

using DNA?

slide16
Our Approach
  • Between 2002 - 2006 backtracked
  • “putative lynx”

Schwartz et al. 2004

McKelvey et al. 2006

slide17
Track Surveys

2002 - 2006 back-tracked

“putative” lynx

slide18
Backtracking

Collected 263 scats and hairs (and tissue)

Goal was to identify to species,

then individual

Schwartz et al. 2004

slide19
Lynx in Minnesota

263 samples

94.1% success scat / 70.0% success hair

157 confirmed lynx

Schwartz et al. Unpub.

slide20
Lynx in Minnesota

But is it 157 Individuals or Less?

Schwartz et al. Unpub.

slide21
Focus on ~100-200bp

PCR

DNA

+

Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

Sample 7

Sample 8

6-Microsatellite

DNA Panel

slide26
All Metrics Suggest that Lynx are

Increasing, But Indices may be

Effort Sensitive

slide28
Lynx Effective Pop. Size (Same Raw Data)
  • Effort less imp.

Methods: Tallmon et al. 2005, Tallmon et al. In Prep.

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EFFECTIVE POPULATION SIZE (Ne) 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

Ne can be estimated by using changes in allele frequencies over time to estimate F.

slide33
Sampled 100 fish from two sites annually from 1980-2000.

Identified cohorts using otoliths to determine age.

Examined 17 polymorphic allozyme loci

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Geography: FST = 0.13 (stable over time)

Temporal changes:

Fk = based upon mean allele frequency change between consecutive cohorts (overlapping generations)

slide35
Site

II Ne=48

I Ne=19

Point estimates of Ne for pairs of consecutive cohorts are relatively stable over time.

slide38
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?
slide40
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.

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And the answer is -

Het: 0.564 0.560 0.544

Ne ~ 75

Ne ~ 50

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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; FST =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.

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1 Feb Introduction – Schwartz & Allendorf8 Feb 15 Feb Monitoring of Ne - Gordon Luikart22 Feb Snake River cutthroat trout - Mark Novak29 Feb Happy Birthday Janean!7 Mar 14 Mar 21 Mar ----4 Apr 11 Apr 18 Apr 25 Apr 2 May
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