Journal of Ornithology

, Volume 152, Supplement 2, pp 409–418 | Cite as

Quantifying changes in abundance without counting animals: extensions to a method of fitting integrated population models

Original Article

Abstract

Integrated population modelling techniques combine information from population surveys and independent demographic studies to estimate population size, survival and productivity rates simultaneously. We review the development of the approach, and investigate further the potential to incorporate sources of population survey data other than those currently employed. Generally, the simpler the field protocol, the more data can be gathered; in the simplest case, only a list of species encountered when a site is surveyed might be recorded. We extend the integrated approach to the case of presence/absence survey data from species lists. We consider specifically the extent to which high-quality demographic data, used in conjunction with an ecologically sound model, may result in credible estimates of change and the drivers of it in the context of either counts or presence/absence survey data. We propose an approach to practical model fitting, applicable in either context, using standard software, and we illustrate its performance in practice. Examples are based on simulated data and records of species with very different trends and ecology, and they are used to compare approaches.

Keywords

Demographic rates Generalized linear modelling Presence/absence data Profile likelihood Species lists 

References

  1. Abadi F, Gimenez O, Ullrich B, Arlettaz R, Schaub M (2010) Estimation of immigration rate using integrated population models. J Appl Ecol 47(2):393–400CrossRefGoogle Scholar
  2. Asher J, Warren M, Fox R, Harding P, Jeffcoate G, Jeffcoate S (2001) The millenium atlas of butterflies in Britain and Ireland. Oxford University Press, OxfordGoogle Scholar
  3. Balmford A, Green RE, Jenkins M (2003) Measuring the changing state of nature. Trends Ecol Evol 18(7):326–330CrossRefGoogle Scholar
  4. Besbeas P, Freeman SN (2006) Methods for joint inference from panel survey and demographic data. Ecology 87(5):1138–1145PubMedCrossRefGoogle Scholar
  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(3):540–547PubMedCrossRefGoogle Scholar
  6. Besbeas P, Lebreton J-D, Morgan BJT (2003) The efficient integration of abundance and demographic data. J R Stat Soc Ser C 52(1):95–102CrossRefGoogle Scholar
  7. Besbeas P, Freeman SN, Morgan BJT (2005) The potential of integrated population modelling. Aust N Z J Stat 47(1):35–48CrossRefGoogle Scholar
  8. Borysiewicz RS, Morgan BJT, Gimenez O, Henaux V, Bregnballe T, Lebreton J-D (2009) An integrated analysis of multisite recruitment, mark-recapture-recovery and multisite census data. In: Thomson DL, Cooch EG, Conroy MJ (eds) Modelling demographic processes in marked populations. Environmental and Ecological Statistics 3. Springer, Berlin, pp 579–591Google Scholar
  9. Brooks SP, King R, Morgan BJT (2004) A Bayesian approach to combining animal abundance and demographic data. Anim Biodivers Conserv 27(1):515–529Google Scholar
  10. Brooks SP, Freeman SN, Greenwood JJD, King R, Mazzetta C (2008) Quantifying conservation concern—Bayesian statistics, birds and the Red Lists. Biol Conserv 141(5):1436–1441CrossRefGoogle Scholar
  11. Cave VM, King R, Freeman SN (2010) An integrated population model from constant effort bird-ringing data. J Agric Biol Environ Stat 15(1):119–137CrossRefGoogle Scholar
  12. Crawley MJ (2006) Statistics—an introduction using R. Wiley, New YorkGoogle Scholar
  13. Eaton MA, Austin GE, Balmer DE, Burton N, Grice PV, Hearn R, Hilton G, Leech D, Musgrove AJ, Newson S, Noble DG, Ratcliffe N, Rehfisch MM, Walker L, Wotton S (2008) The state of the UK’s birds 2007. RSPB, BTO, WWT, CCW, EHS, NE and SNH, Sandy, BedsGoogle Scholar
  14. Fewster RM, Buckland ST, Siriwardena GM, Baillie SR, Wilson JD (2000) Analysis of population trends for farmland birds using Generalized Additive Models. Ecology 81(7):1970–1984CrossRefGoogle Scholar
  15. Freeman SN, Crick HQP (2003) The decline of the Spotted Flycatcher Muscicapa striata in the UK: an integrated population model. Ibis 145(3):400–412CrossRefGoogle Scholar
  16. Freeman SN, Morgan BJT (1992) A modelling strategy for recovery data from birds ringed as nestlings. Biometrics 48(1):217–236PubMedCrossRefGoogle Scholar
  17. Freeman SN, Noble DG, Newson SE, Baillie SR (2007a) Modelling population changes using data from different surveys: the Common Birds Census and the Breeding Bird Survey. Bird Study 54(1):61–72CrossRefGoogle Scholar
  18. Freeman SN, Robinson RA, Clark JA, Griffin BM, Adams SY (2007b) Changing demography and population decline in the Common Starling Sturnus vulgaris: a multisite approach to Integrated Population Monitoring. Ibis 149(3):587–596CrossRefGoogle Scholar
  19. Kéry M, Dorazio RM, Soldaat L, Van Strien A, Zuiderwijk A, Royle JA (2009) Trend estimation in populations with imperfect detection. J Appl Ecol 46(6):1163–1172Google Scholar
  20. King R, Brooks SP, Mazzetta C, Freeman SN, Morgan BJT (2008) Identifying and diagnosing population declines: a Bayesian assessment of Lapwings in the UK. J R Stat Soc Ser C 57(5):609–632CrossRefGoogle Scholar
  21. MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL, Hines JE (2006) Occupancy estimation and modelling. Elsevier, AmsterdamGoogle Scholar
  22. McCrea, RS, Morgan, BJT, Gimenez, O, Besbeas, P, Bregnballe, T, Henaux, V, Lebreton, J-D (2010) Multi-site integrated population modelling. J Agric Biol Environ Stat 15(1):539–561Google Scholar
  23. Nelder JA, Mead R (1965) A simplex method for function maximisation. Comput J 7:308–313Google Scholar
  24. Peach WJ, Siriwardena GM, Gregory RD (1999) Long-term changes in over-winter survival rates explain the decline of Reed Buntings Emberiza schoeniclus in Britian. J Anim Ecol 36(5):798–811CrossRefGoogle Scholar
  25. Pollard E, Yates TJ (1993) Monitoring butterflies for ecology and conservation. Chapman and Hall, LondonGoogle Scholar
  26. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org
  27. Reynolds TJ, King R, Harwood J, Frederiksen M, Harris MP, Wanless S (2009) Integrated data analyses in the presence of emigration and tag-loss. J Agric Biol Environ Stat 14(4):411–431CrossRefGoogle Scholar
  28. Roberts RL, Donald PF, Green RE (2007) Using simple species lists to monitor trends in animal populations: new methods and a comparison with independent data. Anim Conserv 10:332–339CrossRefGoogle Scholar
  29. Roy DB, Rothery P, Brereton T (2007) Reduced-effort schemes for monitoring butterfly populations. J Appl Ecol 44:993–1000CrossRefGoogle Scholar
  30. Royle JA, Nichols JD (2003) Estimating abundance from repeated presence-absence data or point counts. Ecology 84(3):777–790CrossRefGoogle Scholar
  31. Schaub M, Gimenez O, Sierro A, Arlettaz R (2007) Use of integrated modeling to enhance estimates of population dynamics obtained from limited data. Conserv Biol 21(4):945–955PubMedCrossRefGoogle Scholar
  32. Siriwardena GM, Freeman SN, Crick HQP (2001) The decline of the Bullfinch Pyrrhula pyrrhula in Britain: is the mechanism known? Acta Ornithol 36(2):143–152Google Scholar
  33. Tavecchia G, Besbeas P, Coulson T, Morgan BJT, Clutton-Brock TH (2009) Estimating population size and hidden demographic parameters with state-space modelling. Am Nat 173(6):722–733PubMedCrossRefGoogle Scholar
  34. Thomas JA (2007) Guide to butterflies of Britain and Ireland. Philip’s, LondonGoogle Scholar
  35. Ward G, Hastie T, Barry S, Elith J, Leathwick JR (2009) Presence-Only data and the EM algorithm. Biometrics 65(2):554–563PubMedCrossRefGoogle Scholar
  36. White GC, Burnham KP (1999) Program MARK: survival estimation from populations of marked animals. Bird Study 46(Suppl):120–139CrossRefGoogle Scholar
  37. Wright DH (1991) Correlations between incidence and abundance are expected by chance. J Biogeogr 18:463–466CrossRefGoogle Scholar

Copyright information

© Dt. Ornithologen-Gesellschaft e.V. 2011

Authors and Affiliations

  1. 1.Centre for Ecology and HydrologyCrowmarsh Gifford, WallingfordUK
  2. 2.School of Mathematics, Statistics and Actuarial ScienceThe UniversityCanterburyUK
  3. 3.Department of StatisticsAthens University of Economics and BusinessAthensGreece

Personalised recommendations