Advertisement

Experimental Designs

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

This chapter covers the fundamentals of experimental design as applied to wildlife studies. Milliken and Johnson (1984) defined experimental design as the combination of a design structure, treatment structure, and the method of randomization. We discuss most of the common design and treatment structures currently used in wildlife science from the relatively simple to the more complex. While we touch on sampling (randomization) plans because they are an integral part of experimental design, we delay detailed discussion of sampling until Chap. 4. Data analysis also is integral to study design but we leave this discussion to Chap. 5.

Keywords

Wind Turbine Experimental Unit Reference Area Deer Density Null Hypothesis Significance Testing 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, R. L., J. Tom, N. Neumann, and J. A. Cleckler. 1996. Avian Monitoring and Risk Assessment at Tehachapi Pass Wind Resource Area, California. Staff Report to California Energy Commission, Sacramento, CA, November, 1996.Google Scholar
  2. Anderson, R. L., M. L. Morrison, K. Sinclair, and M. D. Strickland. 1999. Studying Wind Energy/Bird Interactions: A Guidance Document. Avian Subcommittee of the National Wind Coordinating Committee, Washington, DC.Google Scholar
  3. Barrett, M. A., and P. Stiling. 2006. Key deer impacts on hardwood hammocks near urban areas. J. Wildl. Manage. 70(6): 1574–1579.Google Scholar
  4. Bates, J. D., R. F. Miller, and T. Svejcar. 2005. Long-term successional trends following western juniper cutting. Rangeland Ecol. Manage. 58(5): 533–541.Google Scholar
  5. Berenbaum, M. R., and A. R. Zangerl. 2006. Parsnip webworms and host plants at home and abroad: Trophic complexity in a geographic mosaic. Ecology 87(12): 3070–3081.PubMedGoogle Scholar
  6. Borgman, L. E., J. W. Kern, R. Anderson-Sprecher, and G. T. Flatman. 1996. The sampling theory of Pierre Gy: Comparisons, implementation, and applications for environmental sampling, in L. H. Lawrence, Ed. Principles of Environmental Sampling, 2nd Edition, pp. 203–221. ACS Professional, American Chemical Society, Washington, DC.Google Scholar
  7. Box, G. E. P., and B. C. Tiao. 1975. Intervention analysis with applications to economic and environmental problems. J. Am. Stat. Assoc. 70: 70–79.Google Scholar
  8. Box, G. E. P., W. G. Hunter, and J. S. Hunter. 1978. Statistics for Experimenters, an Introduction to Design, Data Analysis, and Model Building. Wiley, New York.Google Scholar
  9. Bystrom, P., L. Persson, and E. Wahlstrom. 1998. Competing predators and prey: Juvenile bottlenecks in whole-lake experiments. Ecology 79(6): 2153–2167.Google Scholar
  10. Cade, T. J. 1994. Industry research: Kenetech wind power. In Proceedings of National Avian-Wind Power Planning Meeting, Denver, CO, 20–21 July 1994, pp. 36–39. Rpt. DE95–004090. Avian Subcommittee of the National Wind Coordinating Committee, % RESOLVE Inc., Washington, DC, and LGL Ltd, King City, Ontario.Google Scholar
  11. Cochran, W. G. 1977. Sampling Techniques, 3rd Edition. Wiley, New York.Google Scholar
  12. Cochran, W. G., and G. Cox. 1957. Experimental Designs, 2nd Edition. Wiley, New York.Google Scholar
  13. Cohen, J. 1973. Statistical power analysis and research results. Am. Educ. Res. J. 10: 225–229.Google Scholar
  14. Cox, D. R. 1958. Planning of Experiments (Wiley Classics Library Edition published 1992). Wiley, New York.Google Scholar
  15. Cox, J. J., D. S. Maehr, and J. L. Larkin. 2006. Florida panther habitat use: New approach to an old problem. J. Wildl. Manage. 70(6): 1778–1785.Google Scholar
  16. Crowder, M. J., and D. J. Hand. 1990. Analysis of Repeated Measures. Chapman and Hall, London.Google Scholar
  17. Dallal, G. E. 1992. The 17/10 rule for sample-size determinations (letter to the editor). Am. Stat. 46: 70.Google Scholar
  18. Dillon, W. R., and M. Goldstein. 1984. Multivariate analysis methods and applications. Wiley, New York.Google Scholar
  19. Eberhardt, L. L., and J. M. Thomas. 1991. Designing environmental field studies. Ecol. Monogr. 61: 53–73.Google Scholar
  20. Egger, M., M. Schneider, and D. Smith. 1998. Meta-analysis spurious precision? Meta-analysis of observations studies. Br. Med. J. 316: 140–144.Google Scholar
  21. Erickson, W. P., and L. L. McDonald. 1995. Tests for bioequivalence of control media and test media in studies of toxicity. Environ. Toxicol. Chem. 14: 1247–1256.Google Scholar
  22. Erickson, W., G. Johnson, D. Young, D. Strickland, R. Good, M. Bourassa, K. Bay, and K. Sernka. 2002. Synthesis and Comparison of Baseline Avian and Bat Use, Raptor Nesting and Mortality Information from Proposed and Existing Wind Developments. Prepared by Western EcoSystems Technology, Inc., Cheyenne, WY, for Bonneville Power Administration, Portland, OR. December 2002 [online]. Available: http://www.bpa.gov/Power/pgc/wind/Avian_and_Bat_Study_12–2002.pdf
  23. Fairweather, P. G. 1991. Statistical power and design requirements for environmental monitoring. Aust. J. Mar. Freshwat. Res. 42: 555–567.Google Scholar
  24. Fisher, R. A. 1966. The Design of Experiments, 8th Edition. Hafner, New York.Google Scholar
  25. Fisher, R. A. 1970. Statistical Methods for Research Workers, 14th Edition. Oliver and Boyd, Edinburgh.Google Scholar
  26. Flemming, R. M., K. Falk, and S. E. Jamieson. 2006. Effect of embedded lead shot on body condition of common eiders. J. Wildl. Manage. 70(6): 1644–1649.Google Scholar
  27. Folks, J. L. 1984. Combination of independent tests, in P. R. Krishnaiah and P. K. Sen, Eds. Handbook of Statistics 4, Nonparametric Methods, pp. 113–121. North-Holland, Amsterdam.Google Scholar
  28. Garton, E. O., J. T. Ratti, and J. H. Giudice. 2005. Research and experimental design, in C. E. Braun, Ed. Techniques for Wildlife Investigation and Management, 6th Edition, pp. 43–71. The Wildlife Society, Bethesda, Maryland, USA.Google Scholar
  29. Gasaway, W. C., S. D. Dubois, and S. J. Harbo. 1985. Biases in aerial transect surveys for moose during May and June. J. Wildl. Manage. 49: 777–784.Google Scholar
  30. Gerard, P. D., D. R. Smith, and G. Weerakkody. 1998. Limits of retrospective power analysis. J. Wildl. Manage. 62: 801–807.Google Scholar
  31. Gilbert, R. O. 1987. Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, New York.Google Scholar
  32. 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
  33. Gitzen, R. A., J. J. Millspaugh, and B. J. Kernohan. 2006. Bandwidth selection for fixed-kernel analysis of animal utilization distributions. J. Wildl. Manage. 70(5): 1334–1344.Google Scholar
  34. Glass, G. V., P. D. Peckham, and J. R. Sanders. 1972. Consequences of failure to meet assumptions underlying the fixed effects analyses of variance and covariance. Rev. Educ. Res. 42: 237–288.Google Scholar
  35. Gordon, A. D. 1981. Classification. Chapman and Hall, London.Google Scholar
  36. Green, R. H. 1979. Sampling Design and Statistical Methods for Environmental Biologists. Wiley, New York.Google Scholar
  37. Green, R. H. 1984. Some guidelines for the design of biological monitoring programs in the marine environment, in H. H. White, Ed. Concepts in Marine Pollution Measurements, pp. 647–655. University of Maryland, College Park. MD.Google Scholar
  38. Harner, E. J., E. S. Gilfillan, and J. E. O’Reilly. 1995. A comparison of the design and analysis strategies used in assessing the ecological consequences of the Exxon Valdez. Paper presented at the International Environmetrics Conference, Kuala Lumpur, December 1995.Google Scholar
  39. Herring, G., and J. A. Collazo. 2006. Lesser scaup winter foraging and nutrient reserve acquisition in east-central Florida. J Wildl. Manage. 70(6): 1682–1689.Google Scholar
  40. Highsmith, R. C., M. S. Stekoll, W. E. Barber, L. Deysher, L. McDonald, D. Strickland, and W. P. Erickson. 1993. Comprehensive assessment of coastal habitat, final status report. Vol. I, Coastal Habitat Study No. 1A. School of Fisheries and Ocean Sciences, University of Fairbanks, AK.Google Scholar
  41. Howell, J. A. 1995. Avian Mortality at Rotor Swept Area Equivalents. Altamont Pass and Montezuma Hills, California. Prepared for Kenetech Windpower (formerly U.S. Windpower, Inc.), San Francisco, CA.Google Scholar
  42. Huitema, B. E. 1980. The Analysis of Covariance and Alternatives. Wiley, New York.Google Scholar
  43. Hunt, G. 1995. A Pilot Golden Eagle population study in the Altamont Pass Wind Resource Area, California. Prepared by Predatory Bird Research Group, University of California, Santa Cruz CA, for National Renewable Energy Laboratory, Golden, CO. Rpt. TP-441–7821.Google Scholar
  44. Hurlbert, S. H. 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54: 187–211.Google Scholar
  45. James, F. C., and C. E. McCulloch. 1990. Multivariate analysis in ecology and systematics: Panacea or Pandora’s box? Annu. Rev. Ecol. Syst. 21: 129–166.Google Scholar
  46. Johnson, D. H. 1995. Statistical sirens: The allure of nonparametrics. Ecology 76: 1998–2000.Google Scholar
  47. Johnson, D. H. 1999. The insignificance of statistical significance testing. J. Wildl. Manage. 63(3): 763–772.Google Scholar
  48. Johnson, B., J. Rogers, A. Chu, P. Flyer, and R. Dorrier. 1989. Methods for Evaluating the Attainment of Cleanup Standards. Vol. 1, Soils and Solid Media. Prepared by WESTAT Research, Inc., Rockville, MD, for U.S. Environmental Protection Agency, Washington, DC. EPA 230/02–89-042.Google Scholar
  49. Kalcounis-Ruppell, M. C., J. M. Psyllakis, and R. M. Brigham. 2005. Tree roost selection by bats: An empirical synthesis using meta-analysis. Wildl. Soc. Bull. 33(3): 1123–1132.Google Scholar
  50. Kempthorne, O. 1966. The Design and Analysis of Experiments. Wiley, New York.Google Scholar
  51. Krebs, C. J. 1989. Ecological Methodology. Harper and Row, New York.Google Scholar
  52. Kristina, J. B., B. B. Warren, M. H. Humphrey, F. Harwell, N. E., Mcintyre, P. R. Krausman, and M. C. Wallace. 2006. Habitat use by sympatric mule and white-tailed deer in Texas. J. Wildl. Manage. 70(5): 1351–1359.Google Scholar
  53. Lanszki, J. M., M. Heltai, and L. Szabo. 2006. Feeding habits and trophic niche overlap between sympatric golden jackal (Canis aureus) and red fox (Vulpes vulpes) in the Pannonian ecoregion (Hungary). Can. J. Zool. 84: 1647–1656.Google Scholar
  54. Ludwig, J. A., and J. F. Reynolds. 1988. Statistical Ecology: A Primer on Methods and Computing. Wiley, New York.Google Scholar
  55. Manly, B. F. J. 1986. Multivariate Statistical Methods: A Primer. Chapman and Hall, London.Google Scholar
  56. Manly, B. F. J. 1991. Randomization and Monte Carlo Methods in Biology. Chapman and Hall, London.Google Scholar
  57. Manly, B. F. J. 1992. The Design and Analysis of Research Studies. Cambridge University Press, Cambridge.Google Scholar
  58. Manly, B. F. J. 1997. Randomization, Bootstrap and Monte Carlo Methods in Biology, 2nd Edition, 300 pp. Chapman and Hall, London (1st edition 1991, 2nd edition 1997).Google Scholar
  59. Manly, B. F. J. 2001. Statistics for environmental science and management. Chapman and Hall/CRC, London.Google Scholar
  60. Martin, L. M., and B. J. Wisley. 2006. Assessing grassland restoration success: Relative roles of seed additions and native ungulate activities. J. Appl. Ecol. 43: 1098–1109.Google Scholar
  61. McDonald, L. L. 2004. Sampling rare populations, in W. L. Thompson, Ed. Sampling Rare or Elusive Species, pp. 11–42. Island Press, Washington, DC.Google Scholar
  62. McDonald, L. L., and W. P. Erickson. 1994. Testing for bioequivalence in field studies: Has a disturbed site been adequately reclaimed?, in D. J. Fletcher and B. F. J. Manly, Eds. Statistics in Ecology and Environmental Monitoring, pp. 183–197. Otago Conference Series 2, Univ. Otago Pr., Dunedin, New Zealand.Google Scholar
  63. 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. Butler, 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
  64. McKinlay, S. M. 1975. The design and analysis of the observational study–A review. J. Am. Stat. Assoc. 70: 503–518.Google Scholar
  65. Mead, R., R. N. Curnow, and A. M. Hasted. 1993. Statistical Methods in Agriculture and Experimental Biology, 2nd Edition. Chapman and Hall, London.Google Scholar
  66. Mieres, M. M., and L. A. Fitzgerald. 2006. Monitoring and managing the harvest of tegu lizards in Paraguay. J. Wildl. Manage. 70(6): 1723–1734.Google Scholar
  67. Miles, A. C., S. B. Castleberry, D. A. Miller, and L. M. Conner. 2006. Multi-scale roost site selection by evening bats on pine-dominated landscapes in southwest Georgia. J. Wildl. Manage. 70(5): 1191–1199.Google Scholar
  68. Miller, D. A., E. B. Arnett, and M. J. Lacki. 2003. Habitat management for forest-roosting bats of North America: A critical review of habitat studies. Wildl. Soc. Bull. 31: 30–44.Google Scholar
  69. Milliken, G. A., and D. E. Johnson. 1984. Analysis of Messy Data. Van Nostrand Reinhold, New York.Google Scholar
  70. Montgomery, D. C. 1991. Design and Analysis of Experiments, 2nd Edition. Wiley, New York.Google Scholar
  71. Morrison, M. L., G. G. Marcot, and R. W. Mannan. 2006. Wildlife–Habitat Relationships: Concepts and Applications, 2nd Edition. University of Wisconsin Press, Madison, WI.Google Scholar
  72. National Research Council. 1985. Oil in the Sea: Inputs, Fates, and Effects. National Academy, Washington, DC.Google Scholar
  73. Nichols, J. D. 1991. Extensive monitoring programs viewed as long-term population studies: The case of North American waterfowl. Ibis 133(Suppl. 1): 89–98.Google Scholar
  74. Orloff, S., and A. Flannery. 1992. Wind Turbine Effects on Avian Activity, Habitat Use, and Mortality in Altamont Pass and Solano County Wind Resource Areas. Prepared by Biosystems Analysis, Inc., Tiburon, CA, for California Energy Commission, Sacramento, CA.Google Scholar
  75. Page, D. S., E. S. Gilfillan, P. D. Boehm, and E. J. Harner. 1993. Shoreline ecology program for Prince William Sound, Alaska, following the Exxon Valdez oil spill: Part 1–Study design and methods [Draft]. Third Symposium on Environmental Toxicology and Risk: Aquatic, Plant, and Terrestrial. American Society for Testing and Materials, Philadelphia, PA.Google Scholar
  76. Peterle, T. J. 1991. Wildlife Toxicology. Van Nostrand Reinhold, New York.Google Scholar
  77. Peterman, R. M. 1989. Application of statistical power analysis on the Oregon coho salmon problem. Can. J. Fish. Aquat. Sci. 46: 1183–1187.Google Scholar
  78. Pielou, E. C. 1984. The Interpretation of Ecological Data: A Primer on Classification and Ordination. Wiley, New York.Google Scholar
  79. Samual, 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.Google Scholar
  80. Sawyer, H., R. M. Nielson, F. Lindzey, and L. L. McDonald. 2006. Winter habitat selection of mule deer before and during development of a natural gas field. J. Wildl. Manage. 70: 396–403.Google Scholar
  81. Scheaffer, R. L., W. Mendenhall, and L. Ott. 1990. Elementary Survey Sampling. PWS-Kent, Boston.Google Scholar
  82. Seber, G. A. F. 1984. Multivariate Observations. Wiley, New York.Google Scholar
  83. Shenk, T. M., A. B. Franklin, and K. R. Wilson. 1996. A model to estimate the annual rate of golden eagle population change at the Altamont Pass Wind Resource Area. In Proceedings of National Avian-Wind Power Planning Meeting II, Palm Springs, California, 20–22 September 1995, pp. 47–54. Proceedings prepared for the Avian Subcommittee of the National Wind Coordinating Committee Washington, DC, by LGL Ltd, King City, Ontario.Google Scholar
  84. Sinclair, A. R. E. 1991. Science and the practice of wildlife management. J. Wildl. Manage. 55: 767–773.Google Scholar
  85. Skalski, J. R., and D. S. Robson. 1992. Techniques for Wildlife Investigations: Design and Analysis of Capture Data. Academic, San Diego, CA.Google Scholar
  86. 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
  87. Steel, R. G. D., and J. H. Torrie. 1980. Principles and Procedures of Statistics: A Biometrical Approach, 2nd Edition. McGraw-Hill, New York.Google Scholar
  88. Steidl, R. J. and L. Thomas. 2001. Power analysis and experimental design, in Scheiner, S. M. and J. Gurevitch, Eds. Design and Analysis of Ecological Experiments, 2nd Edition, pp 14–36. Oxford University Press, New York.Google Scholar
  89. Stekoll, M. S., L. Deysher, R. C. Highsmith, S. M. Saupe, Z. Guo, W. P. Erickson, L. McDonald, and D. Strickland. 1993. Coastal Habitat Injury Assessment: Intertidal communities and the Exxon Valdez oil spill. Presented at the Exxon Valdez Oil Spill Symposium, February 2–5, 1993, Anchorage, AK.Google Scholar
  90. Stewart-Oaten, A. 1986. The Before–After/Control-Impact-Pairs Design-for Environmental Impact. Prepared for Marine Review Committee, Inc., Encinitas, CA.Google Scholar
  91. Stewart-Oaten, A., W. W. Murdoch, and K. R. Parker. 1986. Environmental impact assessment: “Pseudoreplication” in time? Ecology 67: 929–940.Google Scholar
  92. Stoner, D. C., M. L. Wolfe, and D. M. Choate. 2006. Cougar exploitation levels in Utah: Implications for demographic structure, population recovery, and metapopulation dynamics. J. Wildl. Manage. 70(6): 1588–1600.Google Scholar
  93. Strickland, M. D., L. McDonald, J. W. Kern, T. Spraker, and A. Loranger. 1994. Analysis of 1992 Dall’s sheep and mountain goat survey data, Kenai National Wildlife Refuge. Bienn. Symp. Northern Wild Sheep and Mountain Goat Council.Google Scholar
  94. Strickland, M. D., G. D. Johnson, W. P. Erickson, S. A. Sarappo, and R. M. Halet. 1998a. Avian use, flight behavior and mortality on the Buffalo Ridge, Minnesota, Wind Resource Area. In Proceedings of National Avian-Wind Power Planning Meeting III. Avian Subcommittee of the National Wind Coordinating Committee, % RESOLVE, Inc., Washington, DC.Google Scholar
  95. Strickland, M. D., D. P. Young, Jr., G. D. Johnson, W. P. Erickson, and C. E. Derby. 1998b. Wildlife monitoring studies for the SeaWest Windpower Plant, Carbon County, Wyoming. In Proceedings of National Avian-Wind Power Planning Meeting III. Avian Subcommittee of the National Wind Coordinating Committee, % RESOLVE, Inc., Washington, DC.Google Scholar
  96. Underwood, A. J. 1994. On beyond BACI: Sampling designs that might reliably detect environmental disturbances. Ecol. Appl. 4: 3–15.Google Scholar
  97. Underwood, A. J. 1997. Experiments in Ecology. Cambridge University Press, Cambridge.Google Scholar
  98. United States Department of the Interior [USDI]. 1987. Type B Technical Information Document: Guidance on Use of Habitat Evaluation Procedures and Suitability Index Models for CERCLA Application. PB88–100151. U.S. Department of the Interior, CERCLA 301 Project, Washington, DC.Google Scholar
  99. Volesky, J. D., W. H. Schacht, P. E. Reece, and T. J. Vaughn. 2005. Spring growth and use of cool-season graminoids in the Nebraska Sandhills. Rangeland Ecol. Manage. 58(4): 385–392.Google Scholar
  100. Walters, C. 1986. Adaptive Management of Renewable Resources. Macmillan, New York.Google Scholar
  101. Western Ecosystems Technology, Inc. 1995. Draft General Design, Wyoming Windpower Monitoring Proposal. Appendix B in Draft Kenetech/PacifiCorp Windpower Project Environmental Impact Statement. FES-95–29. Prepared by U.S. Department of the Interior, Bureau of Land Management, Great Divide Resource Area, Rawlins, WY, and Mariah Associates, Inc., Laramie, WY.Google Scholar
  102. Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and Management of Animal Populations, Modeling, Estimation, and Decision Making. Academic, New York.Google Scholar
  103. Winer, B. J. 1971. Statistical Principles in Experimental Design, 2nd Edition. McGraw-Hill, New York.Google Scholar
  104. Winer, B. J., D. R. Brown, and K. M. Michels. 1991. Statistical Principles in Experimental Design, 3rd Edition. McGraw-Hill, New York.Google Scholar
  105. Wolfe, L. L., W. R. Lance, and M. W. Miller. 2004. Immobilization of mule deer with Thiafentanil (A-3080) or Thiafentanil plus Xylazine. J. Wildl. Dis. 40(2): 282–287.PubMedGoogle Scholar
  106. Zar, J. H. 1998. Biostatistical analysis, 2nd Edition. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Personalised recommendations