Application of Capture–Recapture to Addressing Questions in Evolutionary Ecology

  • Michael J. Conroy
Part of the Environmental and Ecological Statistics book series (ENES, volume 3)


Capture–recapture (CR) is one of the most commonly used methods in quantitative ecology. Until recently, much of the emphasis of CR was on the estimation of abundance and vital rates, especially survival rates. Here, I discuss several important advances that have enhanced ecologists’ ability to address questions in evolutionary ecology. Generalizations of CR methodology to include group and covariate effects have allowed direct, empirical modeling of the influence of extrinsic and intrinsic factors on demographic rates. Advances in sampling design and software now allow CR modeling to address questions such as dispersal and natal fidelity, tradeoffs between reproductive effort and survival, senescence, and variability in demographic rates in relation to individual traits, among others. Furthermore, complex ecological and evolutionary questions seem to be especially amenable to a paradigm of multiple alternative (vs. single null) hypotheses, which is consistent both with information-theoretic and Bayesian approaches to inference.

Previous CR approaches have emphasized the estimation of averages of demographic parameters for individuals grouped into classes (age, sex, size or other attributes), but evolutionary questions tend to emphasize individual variability, with fitness “parameters” best characterized by frequency distributions. Bayesian approaches are particularly appropriate for modeling individual, temporal, spatial, and other components of variation via random effects models. Finally, Bayesian methods and conditional/hierarchical modeling allow for ready construction of complex models of life history from a variety of data sources. I present selected examples to illustrate each of these major points.


Markov Chain Monte Carlo Apparent Survival Capture History Marked Animal Capture Occasion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petran BN, Csaaki F (eds), Second International Symposium on Information Theory. Akdèemiai Kiadi, Budapest, Hungary, pp 267–281.Google Scholar
  2. Alisauskas RT, Lindberg MS (2002) Effects of neckbands on survival and fidelity of white-fronted and Canada geese captured as non-breeding adults. Journal of Applied Statistics 29:521–537.CrossRefzbMATHMathSciNetGoogle Scholar
  3. Arnason AN (1972) Parameter estimates from mark-recapture experiments on two populations subject to migration and death. Research on Population Ecology. 15:1–8.CrossRefGoogle Scholar
  4. Arnason AN (1973) The estimation of population size, migration rates, and survival in a stratified population. Research on Population Ecology 13:97–113.CrossRefGoogle Scholar
  5. Arnason AN, Schwarz CJ (1999) Using POPAN-5 to analyse banding data. Bird Study 46: S157–S168.CrossRefGoogle Scholar
  6. Bailey LL, Kendall WL, Church DR, Wilbur HM (1995) Estimating survival and breeding probability for pond-breeding amphibians: a modified robust design. Ecology 85:2456–2466.CrossRefGoogle Scholar
  7. Barker RJ (1997) Joint modeling of live-recaptures tag-resight and tag-recovery data. Biometrics 53:666–677.CrossRefzbMATHGoogle Scholar
  8. Blums P, Nichols JD, Hines JE, Lindberg MS, Mednis A (2005) Individual quality survival variation and patterns of phenotypic selection on body condition and timing of nesting in birds. Oecologia 143:365–376.CrossRefGoogle Scholar
  9. Blums P, Nichols JD, Hines JE, Mednis A (2002) Sources of variation in survival and breeding site fidelity in three species of European ducks. Journal of Animal Ecology 71:438–450.CrossRefGoogle Scholar
  10. Brooks SP, Catchpole EA, Morgan BJT, Barry SC (2000) On the Bayesian analysis of ring-recovery data. Biometrics 56:951–956.CrossRefGoogle Scholar
  11. Brown CR, Brown MB (1998) Fitness components associated with alternative reproductive tactics in cliff swallows. Behavioral Ecology 9:158–171.CrossRefGoogle Scholar
  12. Brownie C, Hines JE, Nichols JD, Pollock KH, Hestbeck JB (1993) Capture–recapture studies for multiple state including non-Markovian transition probabilities. Biometrics 49: 1173–1187.CrossRefzbMATHGoogle Scholar
  13. Burnham KP, Anderson DR (2002) Model selection and multi-model inference. Springer, New York.Google Scholar
  14. Burnham KP (1993) A theory for combined analysis of ring recovery and recapture data. In: Lebreton JD, North PM (eds), The study of bird population dynamics using marked individuals. Birkhauser-Verlag, Berlin, pp 199–213.Google Scholar
  15. Burnham KP, White GC (2002) Evaluation of some random effects methodology applicable to bird ringing data. Journal of Applied Statistics 29:245–264.CrossRefzbMATHMathSciNetGoogle Scholar
  16. Cam E, Monnat JY (2000) Apparent inferiority of first-time breeders in the Kittiwake: the role of heterogeneity among age classes. Journal of Animal Ecology 69:380–394.CrossRefGoogle Scholar
  17. Cam E, Link WA, Cooch EG, Monnat J-Y, Danchin E (2002) Individual covariation in life history traits: seeing the trees despite the forest. American Naturalist 159:96–105.Google Scholar
  18. Cam E, Monnat JY, Hines JE (2003) Long-term fitness consequences of early conditions in the kittiwake. Journal of Animal Ecology 72:411–424.CrossRefGoogle Scholar
  19. Cam E, Monnat JY, Royle JA (2004) Dispersal and individual quality in a long lived species. OIKOS 106:386–398.CrossRefGoogle Scholar
  20. Caswell H (2001) Matrix population models:construction analysis and interpretation. Sinauer, Sunderland, MA.Google Scholar
  21. Chamberlin TC (1897) The method of multiple working hypotheses. Journal of Geology 5: 837–848.CrossRefGoogle Scholar
  22. Choquet R, Reboulet AM, Pradel R, Gimenez O, Lebreton JD (2003) User's manual for U-Care. Mimeographed document, CEFE/CNRS, Montpellier, France.Google Scholar
  23. Choquet R, Reboulet AM, Pradel R, Gimenez O, Lebreton JD (2004) M-SURGE: new software specifically designed for multistate capture–recapture models. Animal Biodiversity and Conservation 27:207–215.Google Scholar
  24. Clobert J (1995) Capture–recapture and evolutionary ecology: a difficult wedding? Journal of Applied Statistics 22: 989–1008.CrossRefGoogle Scholar
  25. Conroy MJ, Fonnesbeck CJ, Zimpfer NL (2005) Modeling regional waterfowl harvest rates using Markov chain Monte Carlo. Journal of Wildlife Management 69:77–90.CrossRefGoogle Scholar
  26. Conroy MJ, Senar JC, Domènech J (2002) Analysis of individual- and time-specific covariate effects on survival of Serinus serinus in north-eastern Spain. Journal of Applied Statistics 29:125–142.CrossRefzbMATHMathSciNetGoogle Scholar
  27. Cooch EG, Cam E, Link W (2002) Occam’s shadow: levels of analysis in evolutionary ecology—where to next? Journal of Applied Statistics 29:19–48.CrossRefzbMATHMathSciNetGoogle Scholar
  28. Cormack RM (1964) Estimates of survival from the sighting of marked animals. Biometrika 51:429–438.zbMATHGoogle Scholar
  29. Danchin E, Cam E (2002) Can non-breeding be a cost of breeding dispersal? Behavioral Ecology and Sociobiology 51:153–163.CrossRefGoogle Scholar
  30. Doherty PF, Nichols JD, Tautin J, Voelzer JF, Smith GW, Benning DS, Bentley VR, Bidwell JK, Bollinger KS, Brazda AR, Buelna EK, Goldsberry JR, King RJ, Roetker FH, Solberg JW, Thorpe PP, Wortham JS (2002) Sources of variation in breeding-ground fidelity of mallards (Anas platyrhynchos). Behavioral Ecology 13:543–550.CrossRefGoogle Scholar
  31. Fonnesbeck CJ, Conroy MJ (2004) Application of integrated Bayesian modeling and Markov chain Monte Carlo methods to the conservation of a harvested species. Animal Biodiversity and Conservation 27:267–281.Google Scholar
  32. Grosbois V, Tavecchia G (2003) Modeling dispersal with capture-recapture data: disentangling decisions of leaving and settlement. Ecology 84:1225–1236.CrossRefGoogle Scholar
  33. Hestbeck JB, Nichols JD, Malecki RA (1991) Estimates of movement and site fidelity using mark-resight data of wintering Canada geese. Ecology 72:523–533.CrossRefGoogle Scholar
  34. Hurlbert SH (1984) Pseudoreplication and the design of ecological field experiments. Ecological Monographs 54:187–211.CrossRefGoogle Scholar
  35. Jolly GM (1965) Explicit estimates from capture–recapture data with both death and immigration – stochastic model. Biometrika 52:225–247.zbMATHMathSciNetGoogle Scholar
  36. Karanth KU, Nichols JD (1998) Estimation of tiger densities in India using photographic capture and recaptures. Ecology 79:2852–2862.CrossRefGoogle Scholar
  37. Kendall WL, Conn PB, Hines JE (2006) Combining multistate capture–recapture data with tag recoveries to estimate demographic parameters. Ecology 87:169–177.CrossRefGoogle Scholar
  38. Keyser AJ (2003) Life history, demography, and individual variation in western bluebirds. PhD Disseration, University of Georgia, Athens, USA.Google Scholar
  39. King R, Brooks SP (2001) On the Bayesian analysis of population size. Biometrika 88:317–336.CrossRefzbMATHMathSciNetGoogle Scholar
  40. Lebreton JD, Burnham KP, Clobert J, Anderson DR (1992) Modeling survival and testing biological hypotheses using marked animals – a unified approach with case-studies. Ecological Monographs 62:67–118.CrossRefGoogle Scholar
  41. Lebreton JD, Hines JE, Pradel R, Nichols JD, Spendelow JA (2003) Estimation by capture–recapture of recruitment and dispersal over several sites. OIKOS 101:253–264.CrossRefGoogle Scholar
  42. Lindberg MS, Sedinger JS, Derksen DV, Rockwell RF (1998) Natal and breeding philopatry in a Black Brant, Branta bernicla nigricans, metapopulation. Ecology 79:1893.Google Scholar
  43. Link WA, Cam E, Nichols JD, Cooch EG (2002) Of BUGS and birds: Markov chain Monte Carlo for hierarchical modeling in wildlife research. Journal of Wildlife Management 66: 277–291.CrossRefGoogle Scholar
  44. Link WA, Barker RJ (2004) Hierarchical mark-recapture models: a framework for inferences about demographic processes. Animal Biodiversity and Conservation 27:441–449.Google Scholar
  45. Link WA, Barker RJ (2006) Model weights and the foundations of multimodel inference. Ecology 87:2626–2635.CrossRefGoogle Scholar
  46. Nichols JD, Kendall WA (1995) The use of multi-state capture-recapture models to address questions in evolutionary ecology. Journal of Applied Statistics 22:835–846.CrossRefGoogle Scholar
  47. Nichols JD, Hines JE, Pollock KH, Hinz RL, Link WA (1994) Estimating breeding proportions and testing hypotheses about costs of reproduction with capture–recapture data. Ecology 75: 2052–2065.CrossRefGoogle Scholar
  48. Nichols JD, Hines JE, Lebreton J-D, Pradel R (2000) Estimation of contributions to population growth: a reverse-time capture-recapture approach. Ecology 81:3362–3376.Google Scholar
  49. Nichols JD, Hines JE (2002) Approaches for the direct estimation of lambda, and demographic contributions to lambda, using capture–recapture data. Journal of Applied Statistics 29: 539–568.CrossRefzbMATHMathSciNetGoogle Scholar
  50. Orell M, Belda EJ (2002) Delayed cost of reproduction and senescence in the willow tit Parus montanus. Journal of Animal Ecology 71:55–64.CrossRefGoogle Scholar
  51. Pollock KH (1982) A capture–recapture design robust to unequal probability of capture. Journal of Wildlife Management 46:757–760.CrossRefGoogle Scholar
  52. Pollock KH, Nichols JD, Brownie C, Hines JE (1990) Statistical inference for capture–recapture Wildife Monographs 107:70 pp.Google Scholar
  53. Pollock KH, Solomon DL, Robson DS (1974) Tests for mortality and recruitment in a K-sample tag-recapture experiment. Biometrics 30:77–87.CrossRefMathSciNetGoogle Scholar
  54. Powell LA, Conroy MJ, Hines JE, Nichols JD, Krementz DG (2000) Simultaneous us of mark-recaptuer and radio telemetry to estimate survival, movement, and capture rates. Journal of Wildlife Management 64:302–313.CrossRefGoogle Scholar
  55. Pradel R (1996) Utilization of capture-mark-recapture for the study of recruitment and population growth rate. Biometrics 52:703–709.CrossRefzbMATHMathSciNetGoogle Scholar
  56. Pradel R, Lebreton J-D (1991) Users manual for program SURGE version 4.1. C.E.F.E. C.N.R.S Montpelier France.Google Scholar
  57. Reed ET, Gauthier G, Pradel R, Lebreton J-D (2003) Age and environmental conditions affect recruitment in greater snow geese. Ecology 84:219–230.CrossRefGoogle Scholar
  58. Rivalan P, Prevot-Julliard AC, Choquet R, Pradel R, Jacquemin B, Girondot M (2005) Trade-off between current reproductive effort and delay to next reproduction in the leatherback sea turtle. Oecologia 145:564–574.CrossRefGoogle Scholar
  59. Royle A, Link WA (2002) Random effects and shrinkage estimation in capture–recapture models. Journal of Applied Statistics 29: 329–351.CrossRefzbMATHMathSciNetGoogle Scholar
  60. Runge JP, Runge MC, Nichols JD (2006) The role of local populations within a landscape context: defining and classifying sources and sinks. American Naturalist 167:925–938.CrossRefGoogle Scholar
  61. Schwarz CJ, Schweigert JF, Arnason AN (1993) Estimating migration rates using tag recovery data. Biometrics 49:177–193.CrossRefGoogle Scholar
  62. Seber GAF (1965) A note on the multiple-recapture census. Biometrika 52:249–259.zbMATHMathSciNetGoogle Scholar
  63. Senar JC, Borras A, Cabrera J, Cabrera T, Björklund M (2006) Local differentiaion in the presence of gene flow in the citril finch Serinus citronella. Biology Letters 11:85–87.CrossRefGoogle Scholar
  64. Senar JC, Conroy MJ (2004) Multi-state analysis of the impacts of avian pox on a population of Serins (Serinus serinus): the importance of estimating recapture rates. Animal Biodiversity and Conservation 27:133–146.Google Scholar
  65. Senar JC, Conroy MJ, Borras A (2002) Assymetric exchange between populations differing in habitat quality: a metapopulation study on the citril finch. Journal of Applied Statistics 29: 425–441.CrossRefzbMATHMathSciNetGoogle Scholar
  66. Skalski JR, Hoffman A, Smith SG (1993) Testing the significance of individual and cohort-level covariates in animals survival studies. The study of bird population dynamics using marked individuals. (J.-D. Lebreton & North PM, editors) Birkhauser Verlag, New York.Google Scholar
  67. Smith SG, Skalski JR, Schlecte W, Hoffman A, Cassen V (1994) SURPH.1 Manual. Statistical survival analysis for fish and wildlife tagging studies. Bonneville Power Administration, Portland, OR.Google Scholar
  68. White GC, Burnham, KP (1999) Program MARK: survival rate estimation from both live and dead encounters. Bird Study 46:S120–S139.CrossRefGoogle Scholar
  69. Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Elsevier-Academic, Amsertdam.Google Scholar
  70. Yoccoz NG, Erikstad KE, Butnes JO, Hannsen SA, Tveraa, T (2002) Costs of reproduction in common eiders (Somateria mollissima): an assessment of relationships between reproductive effort and future survival and reproduction based on observational and experimental studies. Journal of Applied Statistics 29:57–64.Google Scholar
  71. Zimpfer NL, Conroy, MJ (2006) Modeling movement and fidelity of American black ducks. Journal of Wildlife Management 70:1770–1777.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.USGS, Georgia Cooperative Fish and Wildlife Research UnitD. B. Warnell School of Forestry and Natural Resources, University of GeorgiaAthensUSA

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