Migration and Movement – The Next Stage

Part of the Environmental and Ecological Statistics book series (ENES, volume 3)

Abstract

The design and analysis of multi-state studies when the states are discrete entities is now well understood with several robust software packages (e.g. M-Surge, MARK) available. However, recent technological advances in radio and archival tags will provide very rich datasets with very fine details on movement. Current methods for the analysis of such data often discretize the data to very coarse states. This paper will review the current state of the art on the analysis of such datasets and make some (bold) forecasts of future directions for the analysis of these data.

References

  1. Arnason AN (1972) Parameter estimation from mark-recapture experiments on two populations subject to migration and death. Researches in Population Ecology 13:97–113.CrossRefGoogle Scholar
  2. Arnason AN (1973) The estimation of population size, migration rates, and survival in a stratified population. Researches in Population Ecology 15:1–8.CrossRefGoogle Scholar
  3. Barker RJ (1997) Joint modeling of live-recapture, tag-resight, and tag-recovery data. Biometrics 53:657–668.CrossRefGoogle Scholar
  4. Barker RJ, White GC (2004) Towards the mother-of-all-models: customised construction of the mark-recapture likelihood function. Animal Biodiversity and Conservation 27:177–185.Google Scholar
  5. Barrowman NJ, Myers RA (2003) Raindrop plots: a new way to display collections of likelihoods and distributions. The American Statistician 57: 268–274.CrossRefMATHMathSciNetGoogle Scholar
  6. Bonner SJ, Schwarz CJ (2006) An extension of the Cormack–Jolly–Seber model for continuous covariates with application to Microtus pennsylvanicus. Biometrics 62:142–149.CrossRefMathSciNetGoogle Scholar
  7. Bonner SJ, Thomson DL, Schwarz CJ (2008) Time-varying covariates and semi-parametric regression in capture–recapture: an adaptive spline approach. In: Thomson DL, Cooch EG, Conroy MJ (eds.) Modeling Demographic Processes in Marked Populations. Environmental and Ecological Statistics, Springer, New York, Vol. 3, pp. 657–675.Google Scholar
  8. Brillinger DR, Preisler HK, Ager AA, Kie JG (2004) An exploratory data analysis (EDA) of the paths of moving animals. Journal of Statistical Planning and Inference 122:43–63.CrossRefMATHMathSciNetGoogle Scholar
  9. Brownie C, Anderson DR, Burnham KP, Robson DS (1985) Statistical inference from band recovery data: a handbook. United States Fish and Wildlife Service Resource Publication Number 15iGoogle Scholar
  10. Brownie C, Hines JE, Nichols JD, Pollock KH, Hestbeck JB (1993) Capture–recapture studies for multiple strata including non-Markovian transition probabilities. Biometrics 49:1173–1187.CrossRefMATHGoogle Scholar
  11. Buckland ST, Newman KB, Thomas L, Koesters NB (2004) State-space models for the dynamics of wild animal populations. Ecological Modeling 171:157–175.CrossRefGoogle Scholar
  12. Burnham KP, Anderson DR, White GC, Brownie C, Pollock KH (1987) Design and analysis methods for fish survival experiments based on release-recapture. American Fisheries Society Monograph 5.Google Scholar
  13. Cappé O, Moulines E, Rydén T (2007) Inference in Hidden Markov Models. Springer, New York.Google Scholar
  14. Chapman DG, Junge CO (1956) The estimation of the size of a stratified animal population. Annals of Mathematical Statistics 27:375–389.CrossRefMATHMathSciNetGoogle Scholar
  15. Choquet RR, Reboulet AM, Pradel R, Gimenez O, Lebreton JD (2005) M-SURGE : new software specially designed for multistate capture-recapture models. Animal Biodiversity and Conservation 27:207–215.Google Scholar
  16. Cormack RM (1964) Estimates of survival from the sighting of marked animals. Biometrika 51:429–438.MATHGoogle Scholar
  17. Darroch JN (1961) The two-sample capture–recapture census when tagging and sampling are stratified. Biometrika 48:241–260.MATHMathSciNetGoogle Scholar
  18. Devineau O, Choquet R, Lebreton JD (2006) Planning capture–recapture studies: straightforward precision, bias, and power calculations. Wildlife Society Bulletin 34:1028–1035.CrossRefGoogle Scholar
  19. Dupuis JA, Schwarz CJ (2007) The stratified Jolly-Seber model. Biometrics 63:1015–1022. Published article online: 14-May-2007. doi: 10.1111/j.1541-0420.2007.00815.xGoogle Scholar
  20. Fitzmaurice G, Laird N, Ware J (2004) Applied Longitudinal Analysis. Wiley-Interscience, New York.MATHGoogle Scholar
  21. Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian Data Analysis. 2 nd edn. Chapman & Hall/CRC, Boca Raton, FL.Google Scholar
  22. Gimenez O, Crainiceanu C, Barbraud C, Jenouvrier S, Morgan BJT (2006) Semiparametric regression in capture–recapture modeling. Biometrics 62:691–698.CrossRefMATHMathSciNetGoogle Scholar
  23. Harvey AC (1991) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.Google Scholar
  24. Henaux V, Bregnballe T, Lebreton JD (2007) Dispersal and recruitment during population growth in a colonial bird, the great cormorant Phalacrocorax carbo sinensis. Journal of Avian Biology 38:44–57.CrossRefGoogle Scholar
  25. 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
  26. Hilborn R (1990) Determination of fish movement patterns from tag-recoveries using maximum likelihood estimators. Canadian Journal of Fisheries and Aquatic Sciences 47:635–643.CrossRefGoogle Scholar
  27. Johnson DS, Hoeting JA (2003) Autoregressive models for capture–recapture data: a Bayesian approach. Biometrics 59:341–350.CrossRefMATHMathSciNetGoogle Scholar
  28. Jolly GM (1965) Explicit estimates from capture-recapture data with both death and immigration – Stochastic model. Biometrika 52:225–247.MATHMathSciNetGoogle Scholar
  29. Jonsen ID, Flemming JM, Myers RA (2005) Robust state-space modeling of animal movement data. Ecology 86:2874–2880.CrossRefGoogle Scholar
  30. Jonsen ID, Myers RA, James MC (2006) Robust hierarchical state-space models reveal diel variation in travel rates of migrating leatherback turtles. Journal of Animal Ecology 75: 1046–1057.CrossRefGoogle Scholar
  31. Kendall WL, Nichols JD (2002) Estimating state-transition probabilities for unobservable states using capture–recapture/resighting data. Ecology 83:3276–3284.Google Scholar
  32. Kendall WL, Nichols JD (2004) Modern statistical methods for the study of dispersal and movement of marked birds. Condor 106:720–731.CrossRefGoogle Scholar
  33. King R, Brooks SP, Morgan BJT, Coulson T (2006) Factors influencing Soay sheep survival: a bayesian analysis. Biometrics 62:211–220.CrossRefMATHMathSciNetGoogle Scholar
  34. 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
  35. Lebreton JD, Pradel R (2002) Multi-stratum recapture models: modeling incomplete individual histories. Journal of Applied Statistics 29:353–369.CrossRefMATHMathSciNetGoogle Scholar
  36. McGarvey R, Feenstra JE (2002) Estimating rates of fish movement from tag recoveries: conditioning by recapture. Canadian Journal of Fisheries and Aquatic Sciences 59:1054–1064.CrossRefGoogle Scholar
  37. Muthukumarana S, Schwarz CJ, Swartz TB (2008) Bayesian analysis of mark-recapture data with travel-time-dependent survival probabilities. Canadian Journal of Statistics 36:5–28.CrossRefMATHMathSciNetGoogle Scholar
  38. Nelson CR (1973) Applied time series analysis for managerial forecasting. Holden-Day, San Francisco.Google Scholar
  39. Newman KB (2000) Hierarchic modeling of salmon harvest and migration. Journal of Agricultural Biological and Environmental Statistics 5:430–455CrossRefMathSciNetGoogle Scholar
  40. Newman KB, Buckland ST, Lindley ST, Thomas L, Fernández C (2006) Hidden process models for animal population dynamics. Ecological Applications 16:74–86CrossRefGoogle Scholar
  41. Nichols JD, Sauer JR, Pollock KH, Hestbeck JB (1992) Estimating transition probabilities for stage based population projection matrices using capture recapture data. Ecology 73:306–312.CrossRefGoogle Scholar
  42. Plante N, Rivest LP, Tremblay G (1998) Stratified capture–recapture estimation of the size of a closed population. Biometrics 54:47–60.CrossRefMATHMathSciNetGoogle Scholar
  43. Pollock KH (1981) Capture–recapture models allowing for age-dependent survival and capture rates. Biometrics 37:521–529.CrossRefMATHGoogle Scholar
  44. Pradel R (1996) Utilization of capture-mark-recapture for the study of recruitment and population growth rates. Biometrics 52:371–377.CrossRefMathSciNetGoogle Scholar
  45. Pradel R (2005) Multievent: an extension of multistate capture–recapture models to uncertain states. Biometrics 61:442–447.CrossRefMATHMathSciNetGoogle Scholar
  46. Schaefer MB (1951) Estimation of the size of animal populations by marking experiments. US Fish and Wildlife Service Fisheries Bulletin 69:191–203.Google Scholar
  47. Schwarz CJ, Ganter B (1995) Estimating the movement among staging areas of the barnacle goose (Branta leucopsis). Journal of Applied Statistics 22:711–725.CrossRefGoogle Scholar
  48. Schwarz CJ, Schweigert J, Arnason AN (1993) Estimating migration rates using tag recovery data. Biometrics 49:177–194.CrossRefGoogle Scholar
  49. Schwarz CJ, Taylor CG (1998) The use of the stratified-Petersen estimator in fisheries management with an illustration of estimating the number of pink salmon (Oncorhynchus gorbuscha) that return to spawn in the Fraser River. Canadian Journal of Fisheries and Aquatic Sciences 55:281–296.CrossRefGoogle Scholar
  50. Seber GAF (1965) A note on the multiple recapture census. Biometrika 52:249–259.MATHMathSciNetGoogle Scholar
  51. Shaffer SA, Tremblay Y, Weimerskirch H, Scott D, Thompson DR, Sagar PM, Moller H, Taylor GA, Foley DG, Block BA, Costa DP (2006) Migratory shearwaters integrate oceanic resources across the Pacific Ocean in an endless summer. Proceedings of the National Academy of Sciences 103:12799–12802Google Scholar
  52. Sibert J, Fournier DA (2001) Possible Models for Combining Tracking Data with Conventional Tagging Data. In: Sibert J, Nielsen J (eds) Electronic Tagging and Tracking in Marine Fisheries Reviews: Methods and Technologies in Fish Biology and Fisheries. Kluwer Academic Press, Dordrecht, pp 443–456.Google Scholar
  53. Sibert JR, Hampton J, Fournier DAA, Bills PJ (1999) An advection–diffusion-reaction model for the estimation of fish movement parameters from tagging data, with application to skipjack tuna (Katsuwonus pelamis). Canadian Journal of Fisheries and Aquatic Science 56:925–938.Google Scholar
  54. Skalski JR, Townsend R, Lady J, Giorgi AE, Stevenson JR, McDonald RD (2002) Estimating route-specific passage and survival probabilities at a hydroelectric project from smolt radiotelemetry studies. Canadian Journal of Fisheries and Aquatic Sciences 59:1385–1393.CrossRefGoogle Scholar
  55. Sugden A, Pennisi E (2006) When to go, where to stop: introduction to special issue of science on migration and movement. Science 313:775–775.CrossRefGoogle Scholar
  56. White GC, Burnham KP (1999) Program MARK: survival estimation from populations of marked animals. Bird Study 46 (suppl):s120–s139.CrossRefGoogle Scholar
  57. Williams BK, Nichols JD, Conroy MJ (2002) Analysis and Management of Animal Populations. Academic Press, New York.Google Scholar
  58. Xiao Y (1996) A framework for evaluating experimental designs for estimating rates of fish movement from tag recoveries. Canadian Journal of Fisheries and Aquatic Sciences 53:1272–1280.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Statistics and Actuarial ScienceSimon Fraser UniversityBurnabyCanada

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