Journal of Ornithology

, Volume 152, Supplement 2, pp 561–572

Bayesian analysis of multi-state data with individual covariates for estimating genetic effects on demography

  • Sarah J. Converse
  • J. Andrew Royle
  • Richard P. Urbanek
Original Article

Abstract

Inbreeding depression is frequently a concern of managers interested in restoring endangered species. Decisions to reduce the potential for inbreeding depression by balancing genotypic contributions to reintroduced populations may exact a cost on long-term demographic performance of the population if those decisions result in reduced numbers of animals released and/or restriction of particularly successful genotypes (i.e., heritable traits of particular family lines). As part of an effort to restore a migratory flock of Whooping Cranes (Grus americana) to eastern North America using the offspring of captive breeders, we obtained a unique dataset which includes post-release mark–recapture data, as well as the pedigree of each released individual. We developed a Bayesian formulation of a multi-state model to analyze radio-telemetry, band-resight, and dead recovery data on reintroduced individuals, in order to track survival and breeding state transitions. We used studbook-based individual covariates to examine the comparative evidence for and degree of effects of inbreeding, genotype, and genotype quality on post-release survival of reintroduced individuals. We demonstrate implementation of the Bayesian multi-state model, which allows for the integration of imperfect detection, multiple data types, random effects, and individual- and time-dependent covariates. Our results provide only weak evidence for an effect of the quality of an individual’s genotype in captivity on post-release survival as well as for an effect of inbreeding on post-release survival. We plan to integrate our results into a decision-analytic modeling framework that can explicitly examine tradeoffs between the effects of inbreeding and the effects of genotype and demographic stochasticity on population establishment.

Keywords

Breeding Captive productivity Genotype quality Grus americana Inbreeding coefficient Reintroduction Whooping crane 

References

  1. Barker RJ, White GC, McDougal M (2005) Movement of paradise shelduck between molt sites: a joint multistate-dead recovery mark recapture model. J Wildl Manag 69:1194–1201CrossRefGoogle Scholar
  2. Blouin MS, Parsons M, LaCaille V, Lotz S (1996) Use of microsatellite loci to classify individuals by relatedness. Mol Ecol 5:393–401PubMedGoogle Scholar
  3. Canadian Wildlife Service, and U.S. Fish and Wildlife Service (2005) International recovery plan for the whooping crane. Ottawa, Canada: Recovery of Nationally Endangered Wildlife (RENEW), and U.S. Fish and Wildlife Service, New MexicoGoogle Scholar
  4. Clemen RT (1996) Making hard decisions: an introduction to decision analysis. Duxbury, BelmontGoogle Scholar
  5. Crnokrak P, Roff DA (1999) Inbreeding depression in the wild. Heredity 83:260–270PubMedCrossRefGoogle Scholar
  6. Fischer J, Lindenmayer DB (2000) An assessment of the published results of animal relocations. Biol Conserv 96:1–11CrossRefGoogle Scholar
  7. Frankham R (1995) Conservation genetics. Annu Rev Genet 29:305–327PubMedCrossRefGoogle Scholar
  8. Frankham R (2007) Genetic adaptation to captivity in species conservation programs. Mol Ecol 2007:1–5Google Scholar
  9. Frankham R, Ballou JD, Briscoe DA (2002) Introductiono to conservation genetics. Cambridge University Press, CambridgeGoogle Scholar
  10. Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis, 2nd edn. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  11. Gilks W, Thomas A, Spiegelhalter D (1996) A language and program for complex Bayesian modelling. Statistician 43:169–178CrossRefGoogle Scholar
  12. Glenn TC, Stephan W, Braun MJ (1999) Effects if a population bottleneck on whooping crane mitochondrial DNA variation. Conserv Biol 13:1097–1107CrossRefGoogle Scholar
  13. Green MJ, Medley GF, Browne WJ (2009) Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression. Vet Res 40:30PubMedCrossRefGoogle Scholar
  14. Jones KL, Glenn TC, Lacy RC, Pierce JR, Unruh N, Mirande CM, Chavez-Ramirez F (2002) Refining the whooping crane studbook by incorporating microsatellite DNA and leg-banding analyses. Conserv Biol 16:789–799CrossRefGoogle Scholar
  15. Kendall WL, Conn PB, Hines JE (2006) Combining multi-state capture-recapture data with tag recoveries to estimate demographic parameters. Ecology 87:169–177PubMedCrossRefGoogle Scholar
  16. Lande R (1988) Genetics and demography in biological conservation. Science 241:1455–1460PubMedCrossRefGoogle Scholar
  17. Lebreton J-D, Almeras T, Pradel R (1999) Competing events, mixtures of information and multistratum recapture models. Bird Study Suppl 46:S39–S46CrossRefGoogle Scholar
  18. Link WA, Barker RJ (2006) Model weights and the foundations of multimodel inference. Ecology 87:2626–2635PubMedCrossRefGoogle Scholar
  19. Link WA, Royle JA, Hatfield JS (2003) Demographic analysis from summaries of an age-structured population. Biometrics 59:778–785PubMedCrossRefGoogle Scholar
  20. Millar R (2009) Comparison of hierarchical Bayesian models for over-dispersed count data using DIC and Bayes factors. Biometrics 65:962–969PubMedCrossRefGoogle Scholar
  21. Mirande CM (1995) The genealogy of the whooping crane (Grus americana). International Crane Foundation, BarabooGoogle Scholar
  22. Moore CT, Converse SJ, Folk M, Boughton R, Brooks B, French JB, O’Meara TE, Putnam M, Rodgers J, Spalding M (2008) Releases of whooping cranes to the Florida nonmigratory flock: a structured decision-making approach. Report to the International Whooping Crane Recovery Team. Florida Fish and Wildlife Research Institute In-House Report IHR2008-009. Available at: http://research.myfwc.com/publications/publication_info.asp?id=58528
  23. Moore CT, Converse SJ, Folk MJ, Runge MC, Nesbitt SA (2011) Evaluating release alternatives for a long-lived bird species under uncertainty about long-term demographic rates. J Ornithol (in press)Google Scholar
  24. Queller DC, Goodnight KF (1989) Estimating relatedness using genetic markers. Evolution 43:258–275CrossRefGoogle Scholar
  25. R Development Core Team (2004) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  26. Sturtz S, Ligges U, Gelman A (2005) R2WinBUGS: a package for running WinBUGS from R. J Stat Softw 12:1–16Google Scholar
  27. Urbanek RP, Fondow LEA, Satyshur CD, Lacy AE, Zimorski SE, Wellington M (2005) First cohort of migratory whooping cranes reintroduced to eastern North America: the first year after release. Proc North Am Crane Workshop 9:213–223Google Scholar
  28. Urbanek RP, Fondow LEA, Zimorski SE, Wellington MA, Nipper MA (2009) Winter release and management of reintroduced migratory whooping cranes Grus americana. Bird Conser Int 19:1–12CrossRefGoogle Scholar
  29. Wolf CM, Griffith B, Reed C, Temple SA (1996) Avian and mammalian translocations: update and reanalysis of 1987 survey data. Conserv Biol 10:1142–1154CrossRefGoogle Scholar

Copyright information

© Springer-Verlag (outside the USA) 2011

Authors and Affiliations

  • Sarah J. Converse
    • 1
  • J. Andrew Royle
    • 1
  • Richard P. Urbanek
    • 2
  1. 1.Patuxent Wildlife Research CenterU.S. Geological SurveyLaurelUSA
  2. 2.Necedah National Wildlife RefugeU.S. Fish and Wildlife ServiceNecedahUSA

Personalised recommendations