1932

Abstract

Statistical ecology deals with the development of new methodologies for analyzing ecological data. Advanced statistical models and techniques are often needed to provide robust analyses of the available data. The statistical models that are developed can often be separated into two distinct processes: a system process that describes the underlying biological system and an observation process that describes the data collection process. The system process is often a function of the demographic parameters of interest, such as survival probabilities, transition rates between states, and/or abundance, whereas the model parameters associated with the observation process are conditional on the underlying state of the system. This review focuses on a number of common forms of ecological data and discusses their associated models and model-fitting approaches, including the incorporation of heterogeneity within the given biological system and the integration of different data sources.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-022513-115633
2014-01-03
2024-06-17
Loading full text...

Full text loading...

/deliver/fulltext/statistics/1/1/annurev-statistics-022513-115633.html?itemId=/content/journals/10.1146/annurev-statistics-022513-115633&mimeType=html&fmt=ahah

Literature Cited

  1. Abadi F, Gimenez O, Arlettaz R, Schaub M. 2010. An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence. Ecology 91:7–14 [Google Scholar]
  2. Arnold R, Hayakawa Y, Yip P. 2010. Capture-recapture estimation using finite mixtures of arbitrary dimension. Biometrics 66:644–55 [Google Scholar]
  3. Barker RJ. 1997. Joint modelling of live recapture, tag-resight and tag-recovery data. Biometrics 53:666–77 [Google Scholar]
  4. Besbeas P, Borysiewicz RS, Morgan BJT. 2009. Completing the ecological jigsaw. See Thomson et al. 2009 513–39
  5. Besbeas P, Freeman SN, Morgan BJT, Catchpole EA. 2002. Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58:540–47Develops an integrated modeling approach for abundance and ring-recovery data. [Google Scholar]
  6. Besbeas P, Lebreton JD, Morgan BJT. 2003. The efficient integration of abundance and demographic data. J. R. Stat. Soc. C 52:95–102 [Google Scholar]
  7. Besbeas P, Morgan BJT. 2011. A threshold model for heron productivity. J. Agric. Biol. Environ. Stat 17:128–41 [Google Scholar]
  8. Bestley S, Jonsen ID, Hindell MA, Guinet C, Charrassin J-B. 2013. Integrative modelling of animal movement: incorporating in situ habitat and behavioural information for a migratory marine predator. Proc. R. Soc. B 280:20122262 [Google Scholar]
  9. Blackwell PG. 2003. Bayesian inference for Markov processes with diffusion and discrete components. Biometrika 90:613–27 [Google Scholar]
  10. Bonner SJ, Morgan BJT, King R. 2010. Continuous covariates in mark-recapture-recovery analysis: a comparison of methods. Biometrics 66:1256–65 [Google Scholar]
  11. Bonner SJ, Schwarz CJ. 2006. An extension of the Cormack-Jolly-Seber model for continuous covariates with application to Microtus pennsylvanicus. Biometrics 62:142–49 [Google Scholar]
  12. Bonner SJ, Thomson DL, Schwarz CJ. 2009. Time-varying covariates and semi-parametric regression in capture-recapture: an adaptive spline approach. See Thomson et al. 2009 657–76
  13. Borchers DL, Buckland ST, Zucchini W. 2002. Estimating Animal Abundance, Closed Populations London: Springer314 [Google Scholar]
  14. Borchers DL, Efford MG. 2008. Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics 64:377–85Develops a likelihood-based model for spatially explicit capture-recapture data. [Google Scholar]
  15. Borchers DL, Laake JL, Southwell C, Paxton CGM. 2006. Accommodating unmodeled heterogeneity in double-observer distance sampling surveys. Biometrics 62:372–78 [Google Scholar]
  16. Borchers DL, Marques TA, Gunnlaugsson T, Jupp PE. 2010. Estimating distance sampling detection functions when distances are measured with errors. J. Agric. Biol. Environ. Stat 15:346–61 [Google Scholar]
  17. Borchers DL, Zucchini W, Heide-Jørgensen MP, Cañadas A, Langrock R. 2013. Using hidden Markov models to deal with availability bias on line transect surveys. Biometrics 69:703–13 [Google Scholar]
  18. Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L. 2001. Introduction to Distance Sampling Oxford, UK: Oxford Univ. Press448Provides the fundamental ideas for distance sampling methods. [Google Scholar]
  19. Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L. 2004. Advanced Distance Sampling Oxford, UK: Oxford Univ. Press416 [Google Scholar]
  20. Buckland ST, Laake JL, Borchers DL. 2010. Double-observer line transect methods: levels of independence. Biometrics 66:169–77 [Google Scholar]
  21. Buckland ST, Newman KB, Fernández C, Thomas L, Harwood J. 2007. Embedding population dynamics models in inference. Stat. Sci. 22:44–58 [Google Scholar]
  22. Bunge JA. 2013. A survey of software for fitting capture-recapture models. WIREs Comput. Stat. 5:114–20 [Google Scholar]
  23. Burnham KP, Anderson DR. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach London: Springer488, 2nd ed.. [Google Scholar]
  24. Catchpole EA, Freeman SN, Morgan BJT, Harris MP. 1998. Integrated recovery/recapture data analysis. Biometrics 54:33–46 [Google Scholar]
  25. Catchpole EA, Morgan BJT, Coulson T. 2004. Conditional methodology for individual case history data. J. R. Stat. Soc. C 53:123–31 [Google Scholar]
  26. Catchpole EA, Morgan BJT, Coulson TN, Freeman SN, Albon MP. 2000. Factors influencing Soay sheep survival. J. R. Stat. Soc. C 49:453–72 [Google Scholar]
  27. Catchpole EA, Morgan BJT, Tavecchia G. 2008. A new method for analysing discrete life history data with missing covariate values. J. R. Stat. Soc. B 70:445–60 [Google Scholar]
  28. Cave VM, King R, Freeman SN. 2009. An integrated population model from constant effort bird-ringing data. J. Agric. Biol. Environ. Stat 15:119–37 [Google Scholar]
  29. Chandler RB, Royle JA. 2013. Spatially explicit models for inference about density in unmarked or partially marked populations. Ann. Appl. Stat. 7:936–54 [Google Scholar]
  30. Choquet R, Viallefont A, Rouan L, Gaanoun K, Gaillard J-M. 2011. A semi-Markov model to assess reliably survival patterns from birth to death in free-ranging populations. Methods Ecol. Evol. 2:383–89 [Google Scholar]
  31. Cole DJ, Morgan BJT, Titterington DM. 2010. Determining the parametric structure of models. Math. Biosci. 228:16–30 [Google Scholar]
  32. Coull B, Agresti A. 1999. The use of mixed logit models to reflect heterogeneity in capture-recapture studies. Biometrics 55:294–301 [Google Scholar]
  33. Cowen L, Schwarz CJ. 2006. The Jolly-Seber model with tag-loss. Biometrics 62:699–705 [Google Scholar]
  34. de Valpine P. 2012. Frequentist analysis of hierarchical models for population dynamics and demographic data. J. Ornithol. 7152:S393–408 [Google Scholar]
  35. Dorazio RM, Royle JA. 2003. Mixture models for estimating the size of a closed population when capture rates vary among individuals. Biometrics 59:351–64 [Google Scholar]
  36. Dupuis JA. 1995. Bayesian estimation of movement and survival probabilities from capture-recapture data. Biometrika 82:761–72Constructs the hidden Markov model (HMM) formulation for multistate capture-recapture data (without using HMM terminology). [Google Scholar]
  37. Dupuis JA, Badia J, Maublanc ML, Bon R. 2002. Spatial fidelity of mouflon: a Bayesian analysis of an age-dependent capture-recapture model. J. Agric. Biol. Environ. Stat. 7:277–98 [Google Scholar]
  38. Dupuis JA, Schwarz CJ. 2007. A Bayesian approach to the multistate Jolly–Seber capture-recapture model. Biometrics 63:1015–22 [Google Scholar]
  39. Durbin J, Koopman SJ. 2001. Time Series Analysis by State Space Methods Oxford, UK: Oxford Univ. Press253 [Google Scholar]
  40. Efford MG. 2004. Density estimation in live-trapping studies. Oikos 106:598–610 [Google Scholar]
  41. Efford MG. 2011. Estimation of population density by spatially explicit capture-recapture analysis of data from area searches. Ecology 92:2202–7 [Google Scholar]
  42. Efford MG, Borchers DL, Byrom AE. 2009a. Density estimation by spatially explicit capture-recapture: likelihood-based methods. See Thomson et al. 2009 255–69
  43. Efford MG, Borchers DL, Mowat G. 2013. Varying effort in capture-recapture studies. Methods Ecol. Evol. 4:629–36 [Google Scholar]
  44. Efford MG, Dawson DK, Borchers DL. 2009b. Population density estimated from locations of individuals on a passive detector array. Ecology 90:2676–82 [Google Scholar]
  45. Fearnhead P. 2011. MCMC for state-space models. Handbook of Markov Chain Monte Carlo SP Brooks, A Gelman, GL Jones, X-L Meng 513–29 Boca Raton, FL: CRC [Google Scholar]
  46. Ford JH, Bravington MV, Robbins J. 2012. Incorporating individual variability into mark-recapture models. Methods Ecol. Evol. 3:1047–54 [Google Scholar]
  47. Gardner B, Reppucci J, Lucherini M, Royle JA. 2010. Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies. Ecology 91:3376–83 [Google Scholar]
  48. Gardner B, Royle JA, Wegan MT. 2009. Hierarchical models for estimating density from DNA mark-recapture studies. Ecology 90:1106–15 [Google Scholar]
  49. Gimenez O, Choquet R. 2010. Incorporating individual heterogeneity in studies on marked animals using numerical integration: capture-recapture mixed models. Ecology 91:951–57 [Google Scholar]
/content/journals/10.1146/annurev-statistics-022513-115633
Loading
/content/journals/10.1146/annurev-statistics-022513-115633
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error