Inferences About Landbird Abundance from Count Data: Recent Advances and Future Directions

  • James D. NicholsEmail author
  • Len Thomas
  • Paul B. Conn
Part of the Environmental and Ecological Statistics book series (ENES, volume 3)


We summarize results of a November 2006 workshop dealing with recent research on the estimation of landbird abundance from count data. Our conceptual framework includes a decomposition of the probability of detecting a bird potentially exposed to sampling efforts into four separate probabilities. Primary inference methods are described and include distance sampling, multiple observers, time of detection, and repeated counts. The detection parameters estimated by these different approaches differ, leading to different interpretations of resulting estimates of density and abundance. Simultaneous use of combinations of these different inference approaches can not only lead to increased precision but also provides the ability to decompose components of the detection process. Recent efforts to test the efficacy of these different approaches using natural systems and a new bird radio test system provide sobering conclusions about the ability of observers to detect and localize birds in auditory surveys. Recent research is reported on efforts to deal with such potential sources of error as bird misclassification, measurement error, and density gradients. Methods for inference about spatial and temporal variation in avian abundance are outlined. Discussion topics include opinions about the need to estimate detection probability when drawing inference about avian abundance, methodological recommendations based on the current state of knowledge and suggestions for future research.


Home Range Detection Probability Sample Unit Point Count Distance Sampling 
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. Alldredge MW, Pollock KH, Simons TR, Shriner SA (2007b) Multiple species analysis of point count data: a more parsimonious modeling framework. J. Appl. Ecol. 44:281–290.Google Scholar
  2. Alldredge MW, Simons TR, Pollock KH (2007c) Factors affecting aural detections of songbirds. Ecol. Appl. 17:948-955.Google Scholar
  3. Alldredge MW, Pollock KH, Simons TR (2006) Estimating detection probabilities from multiple-observer point counts. Auk 123:1172–1182.Google Scholar
  4. Alldredge MW, Pollock KH, Simons TR, Collazo JA, Shriner SA (2007a) Time of detection method for estimating abundance from point count surveys. Auk 124:653-664.Google Scholar
  5. Alpizar-Jara R, Pollock KH (1996) A combination line transect and capture–recapture sampling model for multiple observers in aerial surveys. Environ. Ecol. Stat. 3:311–327.CrossRefGoogle Scholar
  6. Alpizar-Jara R, Pollock KH (1999) Combining line transect and capture–recapture for mark-resighting studies. Pages 99–114 in Garner GW, Amstrup SC, Laake JL, Manly BFJ, McDonald LL, Robertson DG (eds.) Marine mammal survey and assessment methods. Balkema, Rotterdam.Google Scholar
  7. Anderson DR (2001) The need to get the basics right in wildlife field studies. Wildl. Soc. Bull. 29:1294–1297.Google Scholar
  8. Bart J, Earnst S (2002) Double sampling to estimate density and population trends in birds. Auk 119:36–45.Google Scholar
  9. Besbeas P, Freeman SN, Morgan BJT (2005) The potential for integrated population modeling. Aust. N.Z. J. Stat. 47:35–48.CrossRefzbMATHMathSciNetGoogle Scholar
  10. Blondel J, Ferry C, Frochot B (1970) La methode des indices ponctuels d’abondance (IPA) ou des releves d’avifaune par “stations d’ecoute.” Alauda 41:63–84.Google Scholar
  11. Borchers DL (1999) Composite mark-recapture line transect surveys. Pages115–126 in Garner GW, Amstrup SC, Laake JL, Manly BFJ, McDonald LL, Robertson DG (eds.) Marine mammal survey and assessment methods. Balkema, Rotterdam.Google Scholar
  12. Borchers DL, Buckland ST, Goedhart PW, Clarke ED, Hedley SL (1998a) Horvitz–Thompson etsimators for double-platform line transect surveys. Biometrics 54:1221–1237.Google Scholar
  13. Borchers DL, Buckland ST, Zucchini W (2002) Estimating animal abundance. Springer, New York.CrossRefzbMATHGoogle Scholar
  14. Borchers DL, Efford MG. Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics (in press).Google Scholar
  15. Borchers DL, Laake JL, Southwell C, Paxton CGM (2006) Accommodating unmodeled heterogeneity in double-observer distance sampling surveys. Biometrics 62:372–378.CrossRefMathSciNetGoogle Scholar
  16. Borchers DL, Marques TA, Gunnlaugsson T, Víkingsson GA. Distance sampling with measurement errors. (in prep.).Google Scholar
  17. Borchers DL, Zucchini W, Fewster RM (1998b) Mark-recapture models for line transect surveys. Biometrics 54:1207–1220.Google Scholar
  18. Bravington MV, Hedley SL, Wood SN. A general approach for modelling clustered line transect data using gamma random fields. (in prep.)Google Scholar
  19. Buckland ST (2006) Point transect surveys for songbirds: robust methodologies. Auk 123: 345–357.CrossRefGoogle Scholar
  20. Buckland ST, Anderson DR, Burnham KP, Laake JL (1993) Distance sampling: estimating abundance of biological populations. Chapman and Hall, London, UK.CrossRefGoogle Scholar
  21. Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001) Introduction to distance sampling. Oxford University Press, Oxford, UK.zbMATHGoogle Scholar
  22. Buckland ST, Borchers DL, Johnston A, Henrys PA, Marques TA (2007a) Line transect methods for plant surveys. Biometrics 63:989–998.Google Scholar
  23. Buckland ST, Newman KB, Fernández C, Thomas L, Harwood J (2007b) Embedding population dynamics models in inference. Stat. Sci. 22:44–58.Google Scholar
  24. Buckland ST, Summers RW, Borchers DL, Thomas L (2006) Point transect sampling with traps or lures. J. Appl. Ecol. 43:377–384.CrossRefGoogle Scholar
  25. Burnham KP, Anderson DR, Laake JL (1980) Estimation of density from line transect sampling of biological populations. Wildl. Monogr. 72:1–202.Google Scholar
  26. Burnham KP, Anderson DR, Laake JL (1981) Line transect estimation of bird population density using a Fourier series. Stud. Avian Biol. 6:466–482.Google Scholar
  27. Burnham KP, Buckland ST, Laake JL, Borchers DL, Marques TA, Bishop JRB, Thomas L (2004) Further topics in distance sampling. Pages 307–392 in Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L, eds. Advanced distance sampling. Oxford University Press, Oxford, UK.Google Scholar
  28. Carothers AD (1973) The effects of unequal catchability on Jolly-Seber estimates. Biometrics 29:79–100.CrossRefGoogle Scholar
  29. Carothers AD (1979) Quantifying unequal catchability and its effect on survival estimates in an actual population. J. Anim. Ecol. 48:863–869.CrossRefGoogle Scholar
  30. Carroll RJ, Lombard F (1985) A note on N estimators for the binomial distribution. J. Amer. Stat. Assoc.80:423–426.MathSciNetGoogle Scholar
  31. Cochran WG (1977) Sampling techniques. Third ed. Wiley, New York, USA.zbMATHGoogle Scholar
  32. Conn PB, Bailey LL, Sauer JR (2004) Indexes as surrogates to abundance for low-abundance species. Pages 59–74 in Thompson WL (ed.) Sampling rare or elusive species. Island Press, Washington, DC, USA.Google Scholar
  33. Cook RD, Jacobsen JO (1979) A design for estimating visibility bias in aerial surveys. Biometrics 35:735–742.CrossRefGoogle Scholar
  34. Diefenbach DR, Marshall MR, Mattice JA, Brauning DW (2007) Incorporating availability for detection in estimates of bird abundance. Auk 124:96-106.Google Scholar
  35. Dorazio RM, Royle JA (2005) Estimating the size and composition of biological communities by modeling the occurrence of species. J. Amer. Stat. Assoc. 100:389–398.CrossRefzbMATHMathSciNetGoogle Scholar
  36. Efford MG (2004) Density estimation in live-trapping studies. Oikos 106:598–610.CrossRefGoogle Scholar
  37. Efford MG, Dawson DK. The effect of distant - related heterogeneity on population size estimates from point counts (in prep.)Google Scholar
  38. Efford MG, Warburton B, Coleman MC, Barker RJ (2005) A field test of two methods for density estimation. Wildl. Soc. Bull. 33:731–738.CrossRefGoogle Scholar
  39. Ellingson AR, Lukacs PM (2003) Improving methods for regional landbird monitoring: a reply to Hutto and Young. Wildl. Soc. Bull. 31:896–902.Google Scholar
  40. Emlen JT (1971) Population densities of birds derived from transect counts. Auk 88:323–342.CrossRefGoogle Scholar
  41. Farnsworth GL, Nichols JD, Sauer JR, Fancy SG, Pollock KH, Shriner SA, Simons TR (2005) Statistical approaches to the analysis of point count data: a little extra information can go a long way. Pages 736–743 in Ralph CJ, Rich TD (eds.) Bird Conservation Implementation and Integration in the Americas: Proceedings of the 3rd International Partners in Flight Conference. Volume 2. Gen. Tech. Rep. PSW-GTR-191. Pacific Southwest Research Station, Forest Service, U.S. Dept. Agriculture: Albany, CA.Google Scholar
  42. Farnsworth GL, Pollock KH, Nichols JD, Simons TR, Hines JE, Sauer JR (2002) A removal model for estimating detection probabilities from point count surveys. Auk 119:414–425.Google Scholar
  43. Gilbert RO (1973) Approximations of the bias in the Jolly-Seber capture–recapture model. Biometrics 29:501–526.CrossRefMathSciNetGoogle Scholar
  44. Hedley SL (2000) Modelling heterogeneity in cetacean surveys. Ph.D. thesis, Univ. St. Andrews, St. Andrews, Scotland, UK.Google Scholar
  45. Hedley SL, Buckland ST (2004) Spatial models for line transect sampling. J. Agric. Biol. Environ. Stat. 9:181–199.CrossRefGoogle Scholar
  46. Hedley SL, Buckland ST, Borchers DL (2004) Spatial distance sampling models. Pages 48–70 in Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds.) Advanced distance sampling. Oxford University Press, Oxford, UK.Google Scholar
  47. Hiby L, Lovell P (1998) Using aircraft in tandem formation to estimate abundance of harbour porpoise. Biometrics 54:1280–1289.CrossRefzbMATHGoogle Scholar
  48. Hobson KA, Rempel RS, Greenwood H, Turnbull B, Van Wilgenburg SL (2002) Acoustic surveys of birds using electronic recordings: new potential from an omnidirectional microphone system. Wildl. Soc. Bull. 30:709–720.Google Scholar
  49. Hutto RL, Young JS (2002) Regional landbird monitoring: perspectives from the northern Rocky Mountains. Wildl. Soc. Bull. 30:738–750.Google Scholar
  50. Hutto RL, Young JS (2003) On the design of monitoring programs and the use of population indices: a reply to Ellingson and Lukacs. Wildl. Soc. Bull. 31:903–910.Google Scholar
  51. Jarvinen O, Vaisanen RA (1975) Estimating relative densities of breeding birds by the line transect method. Oikos 26:316–322.CrossRefGoogle Scholar
  52. Jolly GM, Dickson JM (1983) The problem of unequal catchability in mark-recapture estimation of small mammal populations. Can. J. Zool. 61:922–927.CrossRefGoogle Scholar
  53. Kendall WL, Nichols JD, Hines JE (1997) Estimating temporary emigration and breeding proportions using capture–recapture data with Pollock’s robust design. Ecology 78:563–578.Google Scholar
  54. Kery M, Royle JA, Schmid H (2005) Modeling avian abundance from replicated counts using binomial mixture models. Ecol. Appl. 1450–1461.Google Scholar
  55. Kissling ML, Garton EO (2006) Estimating detection probability and density from point-count surveys: a combination of distance and double-observer sampling. Auk 123: 735–752.CrossRefGoogle Scholar
  56. Laake JL, Borchers DL (2004) Methods for incomplete detection at distance zero. Pages 108–189 in Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds.) Advanced distance sampling. Oxford University Press, Oxford, UK.Google Scholar
  57. Lancia RA, Kendall WL, Pollock KH, Nichols JD (2005) Estimating the number of animals in wildlife populations. Pages 106–153 in Braun CE ed. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, MD, USA.Google Scholar
  58. Lancia RA, Nichols JD, Pollock KH (1994) Estimating the number of animals in wildlife populations. Pages 215–253 in Bookhout T (ed.) Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, MD, USA.Google Scholar
  59. Link WA (2003) Nonidentifiability of population size from capture–recapture data with heterogeneous detection probabilities. Biometrics 59:1123–1130.CrossRefzbMATHMathSciNetGoogle Scholar
  60. Link WA, Sauer JR (1997) Estimation of population trajectories from count data. Biometrics 53:499–497.CrossRefGoogle Scholar
  61. Link WA, Sauer JR (1998) Estimating relative abundance from count data.Austrian J. Stat. 27: 83–97.Google Scholar
  62. Link WA, Sauer JR (2002) A hierarchical analysis of population change with application to Cerulean Warblers. Ecology 83:2832–2840.CrossRefGoogle Scholar
  63. Link WA, Sauer JR (2007) Seasonal components of avian population change: joint analysis of two large-scale monitoring programs. Ecology 88:49–55.CrossRefGoogle Scholar
  64. Mackenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA (2002) Estimating site occupancy when detection probabilities are less than one. Ecology 83: 2248–2255.CrossRefGoogle Scholar
  65. Mackenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LA, Hines JE (2006) Occupancy modeling and estimation. Academic Press, San Diego, CA, USA.Google Scholar
  66. MacKenzie DI, Royle JA (2005) Designing occupancy studies: general advice and allocating survey effort. J. Appl. Ecol. 42:1105–1114.CrossRefGoogle Scholar
  67. Manly, B.F.J., McDonald LL, Garner GW (1996) Maximum likelihood estimation for the double-count method with independent observers. J. Agric. Biol. Environ. Stat. 1: 170–189.CrossRefMathSciNetGoogle Scholar
  68. Marques TA (2004) Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models. Biometrics 60:757–763.CrossRefMathSciNetGoogle Scholar
  69. Marques TA (2008) Incorporating measurement error density gradients in distance sampling surveys. Ph.D. thesis, Univ. St. Andrews, St. Andrews, Scotland, UK.Google Scholar
  70. Marques TA, Thomas L, Fancy SG, Buckland ST (2007) Improving estimates of bird density using multiple covariate distance sampling. Auk (124:1229-1245).CrossRefGoogle Scholar
  71. Marsh H, Sinclair DF (1989) Correcting for visibility bias in strip transect aerial surveys of aquatic fauna. J. Wildl. Manage. 53:1017–1024.CrossRefGoogle Scholar
  72. Newman KB, Buckland ST, Lindley ST, Thomas L, Fernández C (2006) Hidden process models for animal population dynamics. Ecol. Appl. 16:74–86.CrossRefGoogle Scholar
  73. Nichols JD, Hines JE, Sauer JR, Fallon FW, Fallon JE, Heglund PJ (2000) A double-observer approach for estimating detection probability and abundance from point counts. Auk 117: 393–408.Google Scholar
  74. Nichols JD, Pollock KH (1983) Estimation methodology in contemporary small mammal capture–recapture studies. J. Mammal. 64:253–260.CrossRefGoogle Scholar
  75. Norris III JL, Pollock KH (1996) Nonparametric MLE under two closed capture–recapture models with heterogeneity. Biometrics 52:639–649.CrossRefzbMATHGoogle Scholar
  76. Okamura H, Minamikawa S, Kitakado T (2006) Effect of surfacing patterns on abundance estimates of long-diving animals. Fish. Sci. 72:631–638.CrossRefGoogle Scholar
  77. Otis DL, Burnham KP, White GC, Anderson DR (1978) Statistical inference from capture data on closed animal populations. Wildl. Monogr. 62:1–35.Google Scholar
  78. Pledger S (2000) Unified maximum likelihood estimates for closed capture–recapture models for mixtures. Biometrics 56:434–442.CrossRefzbMATHGoogle Scholar
  79. Pollock KH (1982) A capture–recapture design robust to unequal probability of capture. J. Wildl. Manage. 46:757–760.CrossRefGoogle Scholar
  80. Pollock KH, Nichols JD, Simons TR, Farnsworth GR, Bailey LL, Sauer JR (2002) Large scale wildlife monitoring studies: statistical methods for design and analysis. Environmetrics 13: 1–15.CrossRefGoogle Scholar
  81. Ralph CJ, Sauer JR, Droege S (eds.) (1995) Monitoring bird populations by point counts. Gen. Tech., Rep. PSW-GTR-149. U.S. Forest Service Pacific Southwest Research Station, Albany, CA, USA.Google Scholar
  82. Ralph CJ, Scott JM (eds.) (1981) Estimating numbers of terrestrial birds. Stud. Avian Biol. No. 6:1–630.Google Scholar
  83. Ramsey FL, Scott JM (1979) Estimating population densities from variable circular plot surveys. Pages 155–181 in Cormack RM, Patil GP, Robson DS (eds.) Sampling biological populations. Statistical Ecology Series, Vol. 5, International Cooperative Publication House, Fairland, MD, USA.Google Scholar
  84. Rosenstock SS, Anderson DR, Giesen KM, Leukering T, Carter MF (2002) Landbird counting techniques: current practices and an alternative. Auk 119:46–53.Google Scholar
  85. Royle JA (2004) N-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–115.CrossRefzbMATHMathSciNetGoogle Scholar
  86. Royle JA, Nichols JD (2003) Estimating abundance from repeated presence absence data or point counts. Ecology 84:777–790.CrossRefGoogle Scholar
  87. Royle JA, Nichols JD, Kery M (2005) Modeling occurrence and abundance of species when detection is imperfect. Oikos 110:353–359.CrossRefGoogle Scholar
  88. Sauer JR, Link WA (2002) Hierarchical modeling of population stability and species group attributes from survey data. Ecology 86:1743–1751.CrossRefGoogle Scholar
  89. Seber GAF (1982) The estimation of animal abundance and related parameters. Second ed. MacMillian Publication Co., Inc., New York, USA.Google Scholar
  90. Simons TR, Alldredge MW, Pollock KH, Wettroth JM (2007) Experimental analysis of the auditory detection process on avian point counts. Auk 124:986-999.CrossRefGoogle Scholar
  91. Skalski JR, Robson DS (1992) Techniques for wildlife investigations. Academic Press, San Diego, USA.Google Scholar
  92. Smith GW (1995) A critical review of the aerial and ground surveys of breeding waterfowl in North America. U.S. Dept. Interior, Biological Science Report 5. Washington, DC, USA.Google Scholar
  93. Spiegelhalter DJ, Thomas A, Best NG, Gilks WR (1995) BUGS: Bayesian inference using Gibbs sampling. Version 0.50. MRC Biostatistics Unit, Cambridge, UK.Google Scholar
  94. Strindberg S, Buckland ST, Thomas L (2004) Design of distance sampling surveys and geographic information systems. Pages 190–228 in Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds.) Advanced distance sampling. Oxford University Press, Oxford, UK.Google Scholar
  95. Thomas L, Borchers DL, Buckland ST, Hammond IE. Estimation of population size from distance sampling data with heterogenous detection probabilities. (in prep.)Google Scholar
  96. Thomas L, Buckland ST, Newman KB, Harwood J (2005) A unified framework for modelling wildlife population dynamics. Aust. N. Z. J. Stat. 47:19–34.CrossRefzbMATHMathSciNetGoogle Scholar
  97. Thomas L, Burnham KP, Buckland ST (2004) Temporal inferences from distance sampling surveys. Pages 71–107 in Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (eds.) Advanced distance sampling. Oxford University Press, Oxford, UK.Google Scholar
  98. Thompson SK (2002a) Sampling. Wiley, New York, USA.Google Scholar
  99. Thompson WL (2002b) Towards reliable bird surveys: accounting for individuals present but not detected. Auk 119:18–25.Google Scholar
  100. Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic Press, San Diego, USA.Google Scholar
  101. Wood SN, Bravington MV, Hedley SL. Soap film smoothing. J. Royal Stat. Soc. Ser. B. (in press).Google Scholar
  102. Yoccoz NG, Nichols JD, Boulinier T (2001) Monitoring of biological diversity in space and time. Trends Ecol. Evol. 16:446–453.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.U.S. Geological SurveyPatuxent Wildlife Research CenterLaurelUSA

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