Sampling strategies for estimating brook trout effective population size

Andrew R. Whiteley, Jason A. Coombs, Mark Hudy, Zachary Robinson, Keith H. Nislow, Benjamin H. Letcher

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


The influence of sampling strategy on estimates of effective population size (Ne) from single-sample genetic methods has not been rigorously examined, though these methods are increasingly used. For headwater salmonids, spatially close kin association among age-0 individuals suggests that sampling strategy (number of individuals and location from which they are collected) will influence estimates of Ne through family representation effects. We collected age-0 brook trout by completely sampling three headwater habitat patches, and used microsatellite data and empirically parameterized simulations to test the effects of different combinations of sample size (S = 25, 50, 75, 100, 150, or 200) and number of equally-spaced sample starting locations (SL = 1, 2, 3, 4, or random) on estimates of mean family size and effective number of breeders (Nb). Both S and SL had a strong influence on estimates of mean family size and N̂b, however the strength of the effects varied among habitat patches that varied in family spatial distributions. The sampling strategy that resulted in an optimal balance between precise estimates of Nb and sampling effort regardless of family structure occurred with S = 75 and SL = 3. This strategy limited bias by ensuring samples contained individuals from a high proportion of available families while providing a large enough sample size for precise estimates. Because this sampling effort performed well for populations that vary in family structure, it should provide a generally applicable approach for genetic monitoring of iteroparous headwater stream fishes that have overlapping generations.

Original languageEnglish
Pages (from-to)625-637
Number of pages13
JournalConservation Genetics
Issue number3
StatePublished - Jun 2012


  • Brook trout
  • Effective number of breeders
  • Effective population size
  • Genetic monitoring
  • Headwater streams
  • LDNe
  • Linkage disequilibrium


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