Sample Survey Strategies

Part of the Springer Series on Environmental Management book series (SSEM)

The goal of wildlife ecology research is to learn about wildlife populations and their use of habitats. The objective of this chapter is to provide a description of the fundamentals of sampling for wildlife and other ecological studies. We discuss a majority of sampling issues from the perspective of design-based observational studies where empirical data are collected according to a specific study design. We end the chapter with a discussion of several common model-based sampling approaches that combine collection of new data with parameters from the literature or data from similar studies by way of a theoretical mathematical/statistical model. This chapter draws upon and summarizes topics from several books on applied statistical sampling and wildlife monitoring and we would encourage interested readers to see Thompson and Seber (1996), Thompson (2002b), Thompson et al. (1998), Cochran (1977), and Williams et al. (2002).

Typically, the availability of resources is limited in wildlife studies, so researchers are unable to carry out a census of a population of plants or animals. Even in the case of fixed organisms (e.g., plants), the amount of data may make it impossible to collect and process all relevant information within the available time. Other methods of data collection may be destructive, making measurements on all individuals in the population infeasible. Thus, in most cases wildlife ecologists must study a subset of the population and use information collected from that subset to make statements about the population as a whole. This subset under study is called a sample and is the focus of this section. We again note that there is a significant difference between a statistical population and a biological population (Chap. 1).


Simple Random Sample Line Transect Adaptive Sampling Resource Selection Aerial Survey 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alldredge, J. R., D. L. Thomas, and L. McDonald. 1998. Survey and comparison of methods for study of resource selection. J. Agric. Biol. Environ. Stat. 3: 237–253.CrossRefGoogle Scholar
  2. Alpizar-Jara, R., and K. H. Pollock. 1996. A combination line transect and capture re-capture sampling model for multiple observers in aerial surveys. J. Environ. Stat. 3: 311–327.CrossRefGoogle Scholar
  3. Amstrup, S. C., T. L. McDonald, and B. F. J. Manly. 2005. Handbook of Capture–Recapture Analysis. Princeton University Press, Princeton.Google Scholar
  4. Anderson, D. R. 2001. The need to get the basics right in wildlife field studies. Wildl. Soc. Bull. 29: 1294–1297.Google Scholar
  5. Bart, J. and S. Earnst. 2002. Double sampling to estimate density and population trends in birds. Auk 119: 36–45.CrossRefGoogle Scholar
  6. Beavers, S. C., and F. L. Ramsey. 1998. Detectability analysis in transect surveys. J. Wildl. Manage. 62(3): 948–957.CrossRefGoogle Scholar
  7. Block, W. M., and L. A. Brennan. 1992. The habitat concept in ornithology: Theory and applications. Curr. Ornithol. 11: 35–91.Google Scholar
  8. Borchers, D. L., S. T. Buckland, and W. Zucchini. 2002. Estimating Animal Abundance. Springer, Berlin Heidelberg New York.Google Scholar
  9. Borgman, L. E., M. Taheri, and R. Hagan. 1984. Three-dimensional, frequency-domain simulations of geologic variables, in G. Verly, M. David, A. G. Journel, and A. Marechal, Eds. Geostatistics for Natural Resources Characterization, Part I. Reidel, Dordrecht.Google Scholar
  10. Borgman, L. E., C. D. Miler, S. R. Signerini, and R. C. Faucette. 1994. Stochastic interpolation as a means to estimate oceanic fields. Atmos.-Ocean. 32(2): 395–419.Google Scholar
  11. Bormann, F. H. 1953. The statistical efficiency of sample plot size and shape in forest ecology. Ecology 34: 474–487.CrossRefGoogle Scholar
  12. Brownie, C., J. E. Hines, J. D. Nichols, K. H. Pollock, and J. B. Hestbeck. 1993. Capture–recapture studies for multiple strata including non-Markovian transitions. Biometrics 49: 1173–1187.CrossRefGoogle Scholar
  13. Buckland, S. T. 1987. On the variable circular plot method of estimating animal density. Biometrics 43: 363–384.CrossRefGoogle Scholar
  14. Buckland, S. T., D. R. Anderson, K. P. Burnham, and J. L. Laake. 1993. Distance Sampling: Estimating Abundance of Biological Populations. Chapman and Hall, London.Google Scholar
  15. Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling. Oxford University Press, Oxford.Google Scholar
  16. Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2004. Advanced Distance Sampling. Oxford University Press, Oxford.Google Scholar
  17. Burnham, K. P. 1993. A theory for combined analysis of ring recovery and recapture data, in J. D. Lebreton and P. M. North, Eds. Marked Individuals in the Study of Bird Population, pp. 199–214. Birkhäuser-Verlag, Basel, Switzerland.Google Scholar
  18. Burnham, K. P., and W. S. Overton. 1978. Estimation of the size of a closed population when capture probabilities vary among animals. Bometrika 65: 625–633.CrossRefGoogle Scholar
  19. Burnham, K. P., D. R. Anderson, and J. L. Laake. 1980. Estimation of density from line transect sampling of biological populations. Wildl. Monogr. 72: 1–202.Google Scholar
  20. Burnham, K. P., S. T. Buckland, J. L. Laake, D.L. Borchers, T. A. Marques, J. R. B. Bishop, and L. Thomas. 2004. In S. T. Buckland, D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas, Eds. Advanced Distance Sampling, pp. 307–392. Oxford University Press, Oxford.Google Scholar
  21. Byth, K. 1982. On robust distance-based intensity estimators. Biometrics 38: 127–135.CrossRefGoogle Scholar
  22. Canfield, R. H. 1941. Application of the line intercept method in sampling range vegetation. J. Forest. 39: 388–394.Google Scholar
  23. Caughley, G. 1977. Sampling in aerial survey. J. Wildl. Manage. 41: 605–615.CrossRefGoogle Scholar
  24. Caughley, G., and D. Grice. 1982. A correction factor for counting emus from the air and its application to counts in western Australia. Aust. Wildl. Res. 9: 253–259.CrossRefGoogle Scholar
  25. Chao, A. 1987. Estimating the population size for capture–recapture data with unequal catchability. Biometrics 43: 783–791.PubMedCrossRefGoogle Scholar
  26. Chao, A. 1988. Estimating animal abundance with capture frequency data. J. Wildl. Manage. 52: 295–300.CrossRefGoogle Scholar
  27. Chao, A. 1989. Estimating population size for sparse data in capture–recapture experiments. Biometrics 45: 427–438.CrossRefGoogle Scholar
  28. Christman, M. C. 2000. A review of quadrat-based sampling of rare, geographically clustered populations. J. Agric. Biol. Environ. Stat. 5: 168–201.CrossRefGoogle Scholar
  29. Cochran, W. G. 1977. Sampling Techniques, 3rd Edition. Wiley, New York.Google Scholar
  30. Collier, B. A., S. S. Ditchkoff, J. B. Raglin, and J. M. Smith. 2007. Detection probability and sources of variation in white-tailed deer spotlight surveys. J. Wildl. Manage. 71: 277–281.CrossRefGoogle Scholar
  31. Conroy, M. J., J. R. Goldsberry, J. E. Hines, and D. B. Stotts. 1988. Evaluation of aerial transect surveys for wintering American black ducks. J. Wildl. Manage. 52: 694–703.CrossRefGoogle Scholar
  32. Cook, R. D., and J. O. Jacobson. 1979. A design for estimating visibility bias in aerial surveys. Biometrics 35: 735–742.CrossRefGoogle Scholar
  33. Cressie, N. A. C. 1991. Statistics for Spatial Data. Wiley, New York.Google Scholar
  34. Darroch, J. N. 1958. The multiple recapture census: I. Estimation of a closed population. Biometrika 45: 343–359.Google Scholar
  35. Dell, T. R., and J. L. Clutter. 1972. Ranked set sampling theory with order statistics background. Biometrics 28: 545–553.CrossRefGoogle Scholar
  36. Deutsch, C. V., and A. G. Journel. 1992. GSLIB Geostatistical Software Library and User’s Guide. Oxford University Press, New York.Google Scholar
  37. Diggle, P. J. 1983. Statistical Analysis of Spatial Point Patterns. Academic, London.Google Scholar
  38. Dinsmore, S. J., G. C. White, and F. L. Knopf. 2002. Advanced techniques for modeling avian nest survival. Ecology 83: 3476–3488.CrossRefGoogle Scholar
  39. Drummer, T. D. 1991. SIZETRAN: Analysis of size-biased line transect data. Wildl. Soc. Bull. 19(1): 117–118.Google Scholar
  40. Drummer, T. D., and L. L. McDonald. 1987. Size bias in line transect sampling. Biometrics 43: 13–21.CrossRefGoogle Scholar
  41. Eberhardt, L. L. 1978. Transect methods for populations studies. J. Wildl. Manage. 42: 1–31.CrossRefGoogle Scholar
  42. Eberhardt, L. L., and M. A. Simmons. 1987. Calibrating population indices by double sampling. J. Wildl. Manage. 51: 665–675.CrossRefGoogle Scholar
  43. Erickson, W. P., T. L. McDonald, and R. Skinner. 1998. Habitat selection using GIS data: A case study. J. Agric. Biol. Environ. Stat. 3: 296–310.CrossRefGoogle Scholar
  44. Flowy, T. J., L. D. Mech, and M. E. Nelson. 1979. An improved method of censusing deer in deciduous–coniferous forests. J. Wildl. Manage. 43: 258–261.CrossRefGoogle Scholar
  45. Foreman. K. 1991. Survey Sampling Principles. Marcel Dekker, New York.Google Scholar
  46. Fretwell, S. D., and H. L. Lucas. 1970. On territorial behavior and other factors influencing habitat distribution in birds. I. Acta Biotheoret. 19: 16–36.CrossRefGoogle Scholar
  47. Gaillard, J. M. 1988. Contribution a la Dynamique des Populations de Grands Mammiferes: l’Exemple du Chevreuil (Capreolus capreolus). Dissertation. Universite Lyon I, Villeurbanne, France.Google Scholar
  48. Gasaway, W. C., S. D. DuBois, D. J. Reed, and S. J. Harbo. 1986. Estimating Moose Population Parameters from Aerial Surveys. Institute of Arctic Biology, Biological Papers of the University of Alaska, Fairbanks, AK 99775, No. 22.Google Scholar
  49. Gilbert, R. O. 1987. Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, New York.Google Scholar
  50. Gilbert, R. O., and J. C. Simpson. 1992. Statistical methods for evaluating the attainment of cleanup standards. Vol. 3, Based Standards for Soils and Solid Media. Prepared by Pacific Northwest Laboratory, Battelle Memorial Institute, Richland, WA, for U.S. Environmental Protection Agency under a Related Services Agreement with U.S. Department of Energy, Washington, DC. PNL-7409 Vol. 3, Rev. 1/UC-600.Google Scholar
  51. Graham, A., and R. Bell. 1969. Factors influencing the countability of animals. East Afr. Agric. For. J. 34: 38–43.Google Scholar
  52. Grosenbaugh, L. R. 1952. Plotless timber estimates–new, fast, easy. J. For. 50: 532–537.Google Scholar
  53. Guenzel, R. J. 1997. Estimating Pronghorn Abundance Using Aerial Line Transect Sampling. Wyoming Game and Fish Department, Cheyenne, WY.Google Scholar
  54. Hasel, A. A. 1938. Sampling error in timber surveys. J. Agric. Res. 57: 713–736.Google Scholar
  55. Hendricks, W. A. 1956. The Mathematical Theory of Sampling. The Scarecrow Press, New Brunswick.Google Scholar
  56. Hornocker, M. G. 1970. An analysis of mountain lion predation upon mule deor and elk in the Idaho Primitive Area. Wildl. Monogr. 21.Google Scholar
  57. Horvitz, D. G., and D. J. Thompson. 1952. A generalization of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47: 663–685.CrossRefGoogle Scholar
  58. Hosmer Jr., D. W., and S. Lemeshow. 1999. Applied Survival Analysis. Wiley, New York.Google Scholar
  59. Huggins, R. M. 1989. On the statistical analysis of capture experiments. Biometrika 76: 133–140.CrossRefGoogle Scholar
  60. Huggins, R. M. 1991. Some practical aspects of a conditional likelihood approach to capture experiments. Biometrics 47: 725–732.CrossRefGoogle Scholar
  61. Hurlbert, S. H. 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54: 187–211.CrossRefGoogle Scholar
  62. Isaaks, E. H., and R. M. Srivastava. 1989. An Introduction to Applied Geostatistics, Oxford University Press, New York.Google Scholar
  63. Johnson, D. H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61: 65–71.CrossRefGoogle Scholar
  64. Johnson, D. H., Ed. 1998. J. Agric. Biol. Environ. Stat. 3(3) Special Issue: Resource Selection Using Data from Geographical Information Systems (GIS).Google Scholar
  65. Johnson, D. H. 2002. The importance of replication in wildlife research. J. Wildl. Manage. 66: 919–932.CrossRefGoogle Scholar
  66. Journel, A. G., and C. J. Huijbregts. 1978. Mining Geostatistics. Academic, London.Google Scholar
  67. Kaiser, L. 1983. Unbiased estimation in line-intercept sampling. Biometrics 39: 965–976.CrossRefGoogle Scholar
  68. Kalamkar, R. J. 1932. Experimental error and the field plot technique with potatoes. J. Agric. Sci. 22: 373–383.CrossRefGoogle Scholar
  69. Kaplan, E. L., and P. Meier. 1958. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53: 457–481.CrossRefGoogle Scholar
  70. Kendall, W. L. 1999. Robustness of closed capture–recapture methods to violations of the closure assumption. Ecology 80: 2517–2525.Google Scholar
  71. Kendall, W. L., and K. H. Pollock. 1992. The robust design in capture–recapture studies: A review and evaluation by Monte Carlo simulation, in D. R. McCullough, and R. H. Barrett, Eds. Wildlife 2001: Populations, pp. 31–43. Elsevier, London.Google Scholar
  72. Kendall, W. L., J. D. Nichols, and J. E. Hines, 1997. Estimating temporary emigration using capture–recapture data with Pollock’s robust design. Ecology 78: 563–578.Google Scholar
  73. Kern, J. W. 1997. Data analysis techniques for point prediction and estimation of spatial means. Technical Research Work Order Dan Goodman, Department of Biology, Montana State University, Bozeman, MT. Contract 290801.Google Scholar
  74. Kery, M., J. A. Royle, and H. Schmid. 2005. Modeling avian abundance from replicated counts using binomial mixture models. Ecol. Appl. 15: 1450–1461.CrossRefGoogle Scholar
  75. Kleinbaum, D. G. 1996. Survival Analysis: A self-learning text. Springer, New York.Google Scholar
  76. Krebs, C. J. 1989. Ecological Methodology. Harper Collins, New York.Google Scholar
  77. Krebs, C. J. 1999. Ecological Methodology, 2nd Edition. Addison-Welsey, Menlo Park.Google Scholar
  78. Krige, D. G. 1951. A Statistical Approach to Some Mine Valuation and Allied Problems at the Witwaterstrand. Unpublished Masters Thesis, University of Witwaterstrand.Google Scholar
  79. Kuehl, R. O. 2000. Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd Edition. Brooks/Cole, Pacific Grove.Google Scholar
  80. Laake, J. L., K. P. Burnham, and D. R. Anderson. 1979. User’s Manual for Program TRANSECT, 26 pp. Utah State University Press, Logan, Utah.Google Scholar
  81. Laake, J. L., S. T. Buckland, D. R. Anderson, and K. P. Burnham. 1993. DISTANCE User’s Guide. Version 2.0. Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO.Google Scholar
  82. Lancia, R. A., W. L. Kendall, K. H. Pollock, and J. D. Nichols. 2005. Estimating the number of animals in wildlife populations, in C. E. Braun, Ed. Research and Management Techniques for Wildlife and Habitats, pp. 106–153. Wildlife Society, Bethesda, MD.Google Scholar
  83. Lebreton, J. -D., K. P. Burnham, J. Clobert, and D. R. Anderson. 1992. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62: 67–118.CrossRefGoogle Scholar
  84. Lee, S. -M., and A. Chao. 1994. Estimating population size via sample coverage for closed capture–recapture models. Biometrics 50: 88–97.PubMedCrossRefGoogle Scholar
  85. Lincoln, F. C. 1930. Calculating waterfowl abundance on the basis of banding returns. US Dept. Agric. Circ. No. 118: 1–4.Google Scholar
  86. Link, W. A., and R. J. Barker. 2005. Modeling association among demographic parameters in analysis of open population capture–recapture data. Biometrics 61: 46–54.PubMedCrossRefGoogle Scholar
  87. Lucas, H. A., and G. A. F. Seber. 1977. Estimating coverage and particle density using the line intercept method. Biometrika 64: 618–622.CrossRefGoogle Scholar
  88. Ludwig, J. A., and J. F. Reynolds. 1988. Statistical Ecology. Wiley, New York.Google Scholar
  89. Lukacs, P. M. 2002. WebSim: Simulation software to assist in trapping web design. Wildl. Soc. Bull. 30: 1259–1261.Google Scholar
  90. Lukacs, P. M., A. B. Franklin, D. R. Anderson. 2004. In S. T. Buckland, D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas, Eds. Advanced Distance Sampling, pp. 260–278. Oxford University Press, Oxford.Google Scholar
  91. Lukacs, P. M., D. R. Anderson, and K. P. Burnham. 2005. Evaluation of trapping-web design. Wildl. Res. 32: 103–110.CrossRefGoogle Scholar
  92. MacKenzie, D. I. 2005. What are the issues with presence–absence data for wildlife managers? J. Wildl. Manage. 69: 849–860.CrossRefGoogle Scholar
  93. MacKenzie, D. I., and J. A. Royle. 2005. Designing occupancy studies: General advice and allocating survey effort. J. Appl. Ecol. 42: 1105–1114.CrossRefGoogle Scholar
  94. MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248–2255.CrossRefGoogle Scholar
  95. MacKenzie, D. I., J. A. Royle, J. A. Brown, and J. D. Nichols. 2004. Occupancy estimation and modeling for rare and elusive species. In W. L. Thompson, Ed. Sampling Rare or Elusive Species, pp. 149–172. Island Press, Washington.Google Scholar
  96. MacKenzie, D. I., J. D. Nichols, J. Andrew Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy Estimation and Modeling. Academic Press, London.Google Scholar
  97. Manly, B. F. J. 1991. Randomization and Monte Carlo Methods in Biology. Chapman and Hall, London.Google Scholar
  98. Manly, B. F. J., L. McDonald, and D. Thomas. 1993. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Chapman and Hall, London.Google Scholar
  99. Manly, B. F. J., L. McDonald, and G. W. Garner. 1996. Maximum likelihood estimation for the double-count method with independent observers. J. Agric. Biol. Environ. Stat. 1(2): 170–189.CrossRefGoogle Scholar
  100. Marques, F. C. C., and S. T. Buckland. 2004. In S. T. Buckland, D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas, Eds. Advanced Distance Sampling, pp. 31–47. Oxford University Press, Oxford.Google Scholar
  101. Matheron, G. 1962. Traite de Geostatistique Appliquee, Tome I. Memoires du Bureau de Recherches Geologiques et Minieres, No. 14. Editions Technip, Paris.Google Scholar
  102. Matheron, G. 1971. The theory of regionized variables and its applications. Cahiers du Centre de Morphologie Mathematique, No. 5. Fontaine-bleau, France.Google Scholar
  103. McCullagh, P., and J. A. Nelder. 1983. Generalized Linear Models. Chapman and Hall, London.Google Scholar
  104. McDonald, L. L. 1980. Line-intercept sampling for attributes other than cover and density. J. Wildl. Manage. 44: 530–533.CrossRefGoogle Scholar
  105. McDonald, L. L. 1991. Workshop Notes on Statistics for Field Ecology. Western Ecosystems Technology, Inc. Cheyenne, WY.Google Scholar
  106. McDonald, L. L. 2004. Sampling rare populations, in W. L. Thompson, Ed. Sampling Rare or Elusive Species, pp. 11–42. Island Press, Washington.Google Scholar
  107. McDonald, L. L., H. B. Harvey, F. J. Mauer, and A. W. Brackney. 1990. Design of aerial surveys for Dall sheep in the Arctic National Wildlife Refuge, Alaska. Seventh Biennial Northern Wild Sheep and Goat Symposium. May 14–17, 1990, Clarkston, Washington.Google Scholar
  108. McDonald, L. L., W. P. Erickson, and M. D. Strickland. 1995. Survey design, statistical analysis, and basis for statistical inferences in Coastal Habitat Injury Assessment: Exxon Valdez Oil Spill, in P. G. Wells, J. N. Buther, and J. S. Hughes, Eds. Exxon Valdez Oil Spill: Fate and Effects in Alaskan Waters. ASTM STP 1219. American Society for Testing and Materials, Philadelphia, PA.Google Scholar
  109. Menkins Jr., G. E., and S. H. Anderson. 1988. Estimation of small-mammal population size. Ecology 69: 1952–1959.CrossRefGoogle Scholar
  110. Miller, S. D., G. C. White, R. A. Sellers, H. V. Reynolds, J. W. Schoen, K. Titus, V. G. Barnes, R. B. Smith, R. R. Nelson, W. B. Ballard, and C. C. Schwartz. 1997. Brown and black bear density estimation in Alsaka using radiotelemetry and replicated mark-resight techniques. Wildl. Monogr. 133.Google Scholar
  111. Mode, N. A., L. L. Conquest, and D. A. Marker. 2002. Incorporating prior knowledge in environmental sampling: ranked set sampling and other double sampling procedures. Environmetrics 13: 513–521.CrossRefGoogle Scholar
  112. Mood, A. M., F. A. Graybill, and D. C. Boes. 1974. Introduction to the Theory of Statistics, 3rd Edition. McGraw-Hill, Boston.Google Scholar
  113. Morrison, M. L., W. M. Block, M. D. Strickland, and W. L. Kendall. 2001. Wildlife Study Design. Springer.Google Scholar
  114. Munholland, P. L., and J. J. Borkowski. 1996. Simple latin square sampling +1: A spatial design using quadrats. Biometrics 52: 125–136.CrossRefGoogle Scholar
  115. Muttlak, H. A., and L. L.McDonald. 1992. Ranked set sampling and the line intercept method: a more efficient procedure Biometrical J. 34: 329–346.CrossRefGoogle Scholar
  116. Nichols, J. D., and K. H. Pollock. 1983. Estimation methodology in contemporary small mammal capture–recapture studies. J. Mammal. 64: 253–260.CrossRefGoogle Scholar
  117. Nichols, J. D., and K. H. Pollock. 1990. Estimation of recruitment from immigration versus in situ reproduction using Pollocks robust design. Ecology 71: 21–26.CrossRefGoogle Scholar
  118. Noon, B. R., N. M. Ishwar, and K. Vasudevan. 2006. Efficiency of adaptive cluster sampling and random sampling in detecting terrestrial herptofauna in a tropical rainforest. J. Wildl. Manage. 34: 59–68.Google Scholar
  119. Otis, D. L., K. P. Burnham, G. C. White, and D. R. Anderson. 1978. Statistical inference from capture data on closed animal populations. Wildl. Monogr. 62.Google Scholar
  120. Otis, D. L., L. L. McDonald, and M. Evans. 1993. Parameter estimation in encounter sampling surveys. J. Wildl. Manage. 57: 543–548.CrossRefGoogle Scholar
  121. Overton, W. S., D. White, and D. L. Stevens. 1991. Design Report for EMAP: Environmental Monitoring and Assessment Program. Environmental Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR. EPA/600/3–91/053.Google Scholar
  122. Packard, J. M., R. C. Summers, and L. B. Barnes. 1985. Variation of visibility bias during aerial surveys of manatees. J. Wildl. Manage. 49: 347–351.CrossRefGoogle Scholar
  123. Patil, G. P., A. K. Sinha, and C. Taillie. 1994. Ranked set sampling, in G. P. Patil and C. R. Rao, Eds. Handbook of Statistics, Environmental Statistics, Vol. 12. North-Holland, Amsterdam.Google Scholar
  124. Pechanec, J. F., and G. Stewart. 1940. Sage brush-grass range sampling studies: size and structure of sampling unit. Am. Soc. Agron. J. 32: 669–682.Google Scholar
  125. Peterjohn, B. G., J. R. Sauer, and W. A. Link. 1996. The 1994 and 1995 summary of the North American Breeding Bird Survey. Bird Popul. 3: 48–66.Google Scholar
  126. Pollock, K. H. 1974. The Assumption of Equal Catchability of Animals in Tag-Recapture Experiments. Ph.D. Thesis, Cornell University, Ithaca, NY.Google Scholar
  127. Pollock, K. H. 1982. A capture–recapture design robust to unequal probability of capture. J. Wildl. Manage. 46: 752–757.CrossRefGoogle Scholar
  128. Pollock, K. H. 1991. Modeling capture, recapture, and removal statistics for estimation of demographic parameters for fish and wildlife populations: past, present, and future. J. Am. Stat. Assoc. 86: 225–238.CrossRefGoogle Scholar
  129. Pollock, K. H., and M. C. Otto. 1983. Robust estimation of population size in closed animal populations from capture–recapture experiments. Biometrics 39: 1035–1049.PubMedCrossRefGoogle Scholar
  130. Pollock, K. H., and W. L. Kendall. 1987. Visibility bias in aerial surveys: A review of estimation procedures. J. Wildl. Manage. 51: 502–520.CrossRefGoogle Scholar
  131. Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. D. Curtis. 1989. Survival analysis in telemetry studies: The staggered entry design. J. Wildl. Manage. 53: 7–15.CrossRefGoogle Scholar
  132. Pollock, K. H., J. D. Nichols, C. Brownie, and J. E. Hines. 1990. Statistical inferences for capture–recapture experiments. Wildl. Monogr. 107.Google Scholar
  133. Pradel, R. 1996. Utilization of capture–mark–recapture for the study of recruitment and population growth rate. Biometrics 52: 703–709.CrossRefGoogle Scholar
  134. Quang, P. X. 1989. A nonparametric approach to size-biased line transect sampling. Draft Report. Department of Mathematical Sciences, University of Alaska, Fairbanks, AK 99775. Biometrics 47(1): 269–279.CrossRefGoogle Scholar
  135. Quang, P. X., and E. F. Becker. 1996. Line transect sampling under varying conditions with application to aerial surveys. Ecology 77: 1297–1302.CrossRefGoogle Scholar
  136. Quang, P. X., and E. F. Becker. 1997. Combining line transect and double count sampling techniques for aerial surveys. J. Agric. Biol. Environ. Stat. 2: 230–242.CrossRefGoogle Scholar
  137. Ramsey, F. L., and J. M. Scott. 1979. Estimating population densities from variable circular plot surveys, in R. M. Cormack, G. P. Patil and D. S. Robson, Eds. Sampling Biological Populations, pp. 155–181. International Co-operative Publishing House, Fairland, MD.Google Scholar
  138. Reed, D. J., L. L. McDonald, and J. R. Gilbert. 1989. Variance of the product of estimates. Draft report. Alaska Department of Fish and Game, 1300 College Road, Fairbanks, AK 99701.Google Scholar
  139. Rexstad, E., and K. Burnham. 1991. Users Guide for Interactive Program CAPTURE, Abundance Estimation for Closed Populations. Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO.Google Scholar
  140. Reynolds, R. T., J. M. Scott, and R. A. Nussbaum. 1980. A variable circular-plot method for estimating bird numbers. Condor 82(3): 309–313.CrossRefGoogle Scholar
  141. Rotella, J. J., S. J. Dinsmore, and T. L. Shaffer. 2004. Modeling nest-survival data: A comparison of recently developed methods that can be implemented in MARK and SAS. Anim. Biodivers. Conserv. 27: 187–205.Google Scholar
  142. Royle, J. A. 2004a. Generalized estimators of avian abundance from count survey data. Anim. Biodivers. Conserv. 27: 375–386.Google Scholar
  143. Royle, J. A. 2004b. Modeling abundance index data from anuran calling surveys. Conserv. Biol. 18: 1378–1385.CrossRefGoogle Scholar
  144. Royle, J. A., and W. A. Link. 2005. A general class of multinomial mixture models for anuran calling survey data. Ecology 86: 2505–2512.CrossRefGoogle Scholar
  145. Royle, J. A., and J. D. Nichols. 2003. Estimating abundance from repeated presence–absence data or point counts. Ecology 84: 777–790.CrossRefGoogle Scholar
  146. Royle, J. A., J. D. Nichols, and M. Kery. 2005. Modelling occurrence and abundance of species when detection is imperfect. Oikos 110: 353–359.CrossRefGoogle Scholar
  147. Samuel, M. D., E. O. Garton, M. W. Schlegel, and R. G. Carson. 1987. Visibility bias during aerial surveys of elk in northcentral Idaho. J. Wildl. Manage. 51: 622–630.CrossRefGoogle Scholar
  148. Scheaffer, R. L., W. Mendenhall, and L. Ott. 1990. Elementary Survey Sampling. PWS-Kent, Boston, MA.Google Scholar
  149. Schoenly, K. G., I. T. Domingo, and A. T. Barrion. 2003. Determining optimal quadrat sizes for invertebrate communities in agrobiodiversity studies: A case study from tropical irrigated rice. Environ. Entomol. 32: 929–938.CrossRefGoogle Scholar
  150. Schwarz, C. J., and A. N. Arnason. 1996. A general methodology for the analysis of capture–recapture experiments in open populations. Biometrics 52: 860–873.CrossRefGoogle Scholar
  151. Schwarz, C. J., and W. T. Stobo. 1997. Estimating temporary migration using the robust design. Biometrics 53: 178–194.CrossRefGoogle Scholar
  152. Seber, G. A. F. 1982. The Estimation of Animal Abundance and Related Parameters, 2nd Edition. Griffin, London.Google Scholar
  153. Shaffer, T. L. 2004. A unified approach to analyzing nest success. Auk 121: 526–540.CrossRefGoogle Scholar
  154. Skinner, R., W. Erickson, G. Minick, and L. L. McDonald. 1997. Estimating Moose Populations and Trends Using Line Transect Sampling. Technical Report Prepared for the USFWS, Innoko National Wildlife Refuge, McGrath, AK.Google Scholar
  155. Smith, W. 1979. An oil spill sampling strategy, in R. M. Cormack, G. P. Patil, and D. S. Robson, Eds. Sampling Biological Populations, pp. 355–363. International Co-operative Publishing House, Fairland, MD.Google Scholar
  156. Smith, D. R., M. J. Conroy, and D. H. Brakhage. 1995. Efficiency of adaptive cluster sampling for estimating density of wintering waterfowl. Biometrics 51: 777–788.CrossRefGoogle Scholar
  157. Smith, D. R., J. A. Brown, and N. C. H. Lo. 2004. Application of adaptive sampling to biological populations, in W. L. Thompson, Ed. Sampling Rare or Elusive Species, pp. 77–122. Island Press, Washington.Google Scholar
  158. Southwell, C. 1994. Evaluation of walked line transect counts for estimating macropod density. J. Wildl. Manage. 58: 348–356.CrossRefGoogle Scholar
  159. Stanley, T. R. 2000. Modeling and estimation of stage-specific daily survival probabilities of nests. Ecology 81: 2048–2053.CrossRefGoogle Scholar
  160. Steinhorst, R. K., and M. D. Samuel. 1989. Sightability adjustment methods for aerial surveys of wildlife populations. Biometrics 45: 415–425.CrossRefGoogle Scholar
  161. Stevens, D. L., and A. R. Olsen. 1999. Spatially restricted surveys over time for aquatic resources. J. Agric. Biol. Environ. Stat. 4: 415–428.CrossRefGoogle Scholar
  162. Stevens, D. L., and A. R. Olsen. 2004. Spatially balanced sampling of natural resources. J. Am. Stat. Assoc. 99: 262–278.CrossRefGoogle Scholar
  163. Strickland, M. D., L. McDonald, J. W. Kern, T. Spraker, and A. Loranger. 1994. Analysis of 1992 Dall’s sheep and mountain goal survey data, Kenai National Wildlife Refuge. Bienn. Symp. Northern Wild Sheep and Mountain Goat Council.Google Scholar
  164. Strickland, M. D., W. P. Erickson, and L. L. McDonald. 1996. Avian Monitoring Studies: Buffalo Ridge Wind Resource Area, Minnesota. Prepared for Northern States Power, Minneapolis, MN.Google Scholar
  165. Thomas, J. W. Ed. 1979. Wildlife Habitats in Managed Forests: The Blue Mountains of Oregon and Washington. US Forest Service, Agriculture Handbook 553.Google Scholar
  166. Thompson, S. K. 1990. Adaptive cluster sampling. J. Am Stat. Assoc. 85: 1050–1059.CrossRefGoogle Scholar
  167. Thompson, S. K. 1991a. Adaptive cluster sampling: designs with primary and secondary units. Biometrics 47: 1103–1115.CrossRefGoogle Scholar
  168. Thompson, S. K. 1991b. Stratified adaptive cluster sampling. Biometrika 78: 389–397.CrossRefGoogle Scholar
  169. Thompson, S. K. 1992. Sampling. Wiley, New York.Google Scholar
  170. Thompson, W. L. 2002a. Towards reliable bird surveys: Accounting for individuals present but not detected. Auk 119: 18–25.CrossRefGoogle Scholar
  171. Thompson, S. K. 2002b. Sampling, 2nd Edition. Wiley, New York.Google Scholar
  172. Thompson, S. K., and G. A. F. Seber. 1996. Adaptive Sampling. Wiley, New York.Google Scholar
  173. Thomas, D., and E. Taylor. 1990. Study designs and tests for comparing resource use and availability. J. Wildl. Manage. 54: 322–330.CrossRefGoogle Scholar
  174. Thompson, W. L., G. C. White, and C. Gowan. 1998. Monitoring vertebrate populations. Academic, London.Google Scholar
  175. Trenkel, V. M., S. T. Buckland, C. McLean, and D. A. Elston. 1997. Evaluation of aerial line transect methodology for estimating red deer (Cervus elaphus) abundance in Scotland. J. Environ. Manage. 50: 39–50.CrossRefGoogle Scholar
  176. Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with S. Springer, New York.Google Scholar
  177. Vojta, C. D. 2005. Old dog, new tricks: Innovations with presence–absence information. J. Wildl. Manage. 69: 845–848.CrossRefGoogle Scholar
  178. White, G. C., D. R. Anderson, K. P. Burnham, and D. L. Otis, 1982. Capture–Recapture and Removal Methods for Sampling, Closed Populations. Rpt. LA-8787-NERP. Los Alamos National Laboratory, Los Alamos, NM.Google Scholar
  179. Wiegert, R. G. 1962. The selection of an optimum quadrat size for sampling the standing crop of grasses and forbs. Ecology 43: 125–129.CrossRefGoogle Scholar
  180. Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and Management of Animal Population. Academic, London.Google Scholar
  181. Wolter, K. M. 1984. Investigation of some estimators of variance for systematic sampling. J. Am. Stat. Assoc. 79: 781–790.CrossRefGoogle Scholar
  182. Zippen, C. 1958. The removal method of population estimation. J. Wildl. Manage. 22: 82–90.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Personalised recommendations