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Fieberg JR, Conn PB. A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies. Ecol Evol. 2014;4(10):1903-12doi: 10.1002/ece3.1066.
Fieberg, J. R., & Conn, P. B. (2014). A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies. Ecology and evolution, 4(10), 1903-12. https://doi.org/10.1002/ece3.1066
Fieberg, John R, and Conn, Paul B. "A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies." Ecology and evolution vol. 4,10 (2014): 1903-12. doi: https://doi.org/10.1002/ece3.1066
Fieberg JR, Conn PB. A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies. Ecol Evol. 2014 May;4(10):1903-12. doi: 10.1002/ece3.1066. Epub 2014 Apr 19. PMID: 24963384; PMCID: PMC4063483.
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Ecol Evol. 2014 May;4(10):1903-12. doi: 10.1002/ece3.1066. Epub 2014 Apr 19.

A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies.

Ecology and evolution

John R Fieberg, Paul B Conn

Affiliations

  1. Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota St. Paul, Minnesota, 55108.
  2. National Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA National Marine Fisheries Service 7600 Sand Point Way NE, Seattle, Washington, 98115.

PMID: 24963384 PMCID: PMC4063483 DOI: 10.1002/ece3.1066

Abstract

An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor-response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.

Keywords: Bayesian; facultative migrator; jags; latent variable; obligate migrator; partially observed state; state-space

References

  1. Ecology. 2009 Oct;90(10):2956-62 - PubMed
  2. Biol Rev Camb Philos Soc. 2002 May;77(2):211-22 - PubMed
  3. Am Nat. 1999 Aug;154(2):220-233 - PubMed
  4. J Anim Ecol. 2009 Jul;78(4):798-806 - PubMed
  5. Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2187-200 - PubMed
  6. Trends Ecol Evol. 1988 Sep;3(9):237-41 - PubMed
  7. J Anim Ecol. 2012 Nov;81(6):1171-1182 - PubMed
  8. Biometrics. 2005 Jun;61(2):442-7 - PubMed
  9. Nature. 1977 Feb 3;265(5593):415-21 - PubMed
  10. Proc Biol Sci. 2004 Sep 22;271(1551):1961-6 - PubMed
  11. Ecology. 2012 Apr;93(4):913-20 - PubMed
  12. Am Nat. 2013 Apr;181(4):520-31 - PubMed
  13. J Anim Ecol. 2010 Mar;79(2):317-26 - PubMed
  14. PLoS One. 2013 Jun 13;8(6):e65368 - PubMed
  15. J Anim Ecol. 2011 Nov;80(6):1196-206 - PubMed

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