In this chapter, we turn our attention to the concept of basic study design. We begin by discussing variable classification, focusing on the types of variables: explanatory, disturbing, controlling, and randomized. We then discuss how each of these variable types is integral to wildlife study design. We then detail the necessity of randomization and replication in wildlife study design, and relate these topics to variable selection.
We outline the three major types of designs in decreasing order of rigor (i.e., manipulative experiments, quasi-experiments, and observational studies) with respect to controls, replication, and randomization, which we further elaborate in Chap. 3. We provide a general summary on adaptive management and we briefly touch on survey sampling designs for ecological studies, with a discussion on accounting for detectability, but leave detailed discussion of sampling design until Chap. 4.
We discuss the place of statistical inference in wildlife study design, focusing on parameter estimation, hypothesis testing, and model selection. We do not delve into specific aspects and applications of statistical models (e.g., generalized linear models or correlation analysis) as these are inferential, rather than design techniques. We discuss the relationships between statistical inference and sampling distributions, covering the topics of statistical accuracy, precision, and bias. We provide an outline for evaluating Type I and II errors as well as sample size determination. We end this chapter with a discussion on integrating project goals with study design and those factors influencing the design type used, and conclude with data storage techniques and methods, programs for statistical data analysis, and approaches for presenting results from research studies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amstrup, S. C., T. L. McDonald, and B. F. J. Manly. 2005. Handbook of Capture–Recapture Analysis. Princeton University, New Jersey.
Anderson, D. R. 2001. The need to get the basics right in wildlife field studies. Wildl. Soc. Bull. 29: 1294–1297.
Anderson, D. R. 2003. Response to Engeman: index values rarely constitute reliable information. Wildl. Soc. Bull. 31: 288–291.
Anderson, D. R., K. P. Burnham, and W. L. Thompson. 2000. Null hypothesis testing: problems, prevalence, and an alternative. J. Wildl. Manage. 64: 912–923.
Anderson, D. R., W. A. Link, D. H. Johnson, and K. P. Burnham. 2001. Suggestions for presenting the results of data analyses. J. Wildl. Manage. 65: 373–378.
Anscombe, F. J. 1973. Graphs in statistical analysis. Am. Stat. 27: 17–21.
Arnason, A. N., and C. J. Schwarz. 1999. Using POPAN-5 to analyse banding data. Bird Study 46(Suppl.): 157–168.
Bart, J., and S. Earnst. 2002. Double sampling to estimate density and population trends in birds. Auk 119: 36–45.
Borchers, D. L., S. T. Buckland, and W. Zucchini. 2002. Estimating animal abundance-closed populations. Springer-Verlag, London.
Brownie, C., D. R. Anderson, K. P. Burnham, and D. R. Robson. 1985. Statistical inference from band recovery data–a handbook, 2nd Edition. U.S. Fish and Wildlife Service Resource Publication 156.
Buckland, S. T., I. B. J. Goudie, and D. L. Borchers. 2000. Wildlife population assessment: past developments and future directions. Biometrics 56: 1–12.
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, Oxford.
Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach, 2nd Edition. Springer-Verlag, New York.
Burnham, K. P., D. R. Anderson, G. C. White, C. Brownie, and K. P. Pollock. 1987. Design and analysis of methods for fish survival experiments based on release–recapture. Am. Fish. Soc. Monogr. 5: 1–437.
Burnham, K. P., G. C. White, and D. R. Anderson. 1995. Model selection strategy in the analysis of capture–recapture data. Biometrics 51: 888–898.
Chambers, J. M., W. S. Cleveland, B. Kleinez, and P. A. Turkey. 1983. Graphical methods for data analysis. Wadsworth International Group, Belmont, CA, USA.
Chambers, J. M. 1998. Programming with data. A guide to the S language. Springer-Verlag, New York.
Cherry S. 1998. Statistical tests in publications of The Wildlife Society. Wildl. Soc. Bull. 26: 947–953.
Cleveland, W. S. 1993. Visualizing Data. Hobart, Summit, NJ.
Cleveland, W. S. 1994. The Elements of Graphing Data. Hobart, Summit, NJ.
Cochran W. G. 1977. Sampling Techniques, 3rd Edition. John Wiley and Sons, New York.
Codd, E. F. 1970. A relational model of data for large shared data banks. Commun. ACM 13: 377–387.
Cohen, J. 1988. Statistical power analysis for the behavioral sciences, 2nd Edition. Lawrence Erlbaum Associates, Inc., Mahwah, NJ.
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.
Cook, R. D., and J. O. Jacobsen. 1979. A design for estimating visibility bias in aerial surveys. Biometrics 35: 735–742.
Date, C. J. 2003. An Introduction to Database Systems, 8th Edition. Addison Wesley, Boston, MA.
Dinsmore, S. J., G. C. White, and F. L. Knopf. 2002. Advanced techniques for modeling avian nest survival. Ecology 83: 3476–3488.
Eberhardt, L. L. 2003. What should we do about hypothesis testing? J. Wildl. Manage. 67: 241–247.
Ellison, A. M. 2004. Bayesian inference in ecology. Ecol. Lett. 7: 509–520.
Farfarman, K. R., and C. A. DeYoung. 1986. Evaluation of spotlight counts of deer in south Texas. Wildl. Soc. Bull. 14: 180–185.
Fisher, R. A. 1925. Statistical Methods for Research Workers. Oliver and Boyd, London.
Fisher, R. A. 1929. The statistical method in psychical research. Proc. Soc. Psychical Res. 39: 189–192.
Fisher, R. A. 1935. The Design of Experiments. Reprinted 1971 by Hafner, New York.
Franklin, A. B., T. M. Shenk, D. R. Anderson, and K. P. Burnham. 2001. in T. M. Shenk and A. B. Franklin, Eds. Statistical model selection: the alternative to null hypothesis testing, pp. 75–90. Island, Washington, DC.
Gavin, T. A. 1991. Why ask “Why”: the importance of evolutionary biology in wildlife science. J. Wildl. Manage. 55: 760–766.
Gerard, P. D., D. R. Smith, and G. Weerakkody. 1998. Limits of retrospective power analysis. J. Wildl. Manage. 62: 801–807.
Green, R. H. 1979. Sampling Design and Statistical Methods for Environmental Biologists. Wiley, New York.
Gregory, R., D. Ohlson, and J. Arvai. 2006a. Deconstructing adaptive management: criteria for applications in environmental management. Ecol. Appl. 16: 2411–2425.
Gregory, R., L. Failing, and P. Higgins. 2006b. Adaptive management and environmental decision making: a case study application to water use planning. Ecol. Econ. 58: 434–447.
Guthery, F. S., J. J. Lusk, and M. J. Peterson. 2001. The fall of the null hypothesis: liabilities and opportunities. J. Wildl. Manage. 65: 379–384.
Guthery, F. S., L. A. Brennan, M. J. Peterson, and J. J. Lusk. 2005. Information theory in wildlife science: critique and viewpoint. J. Wildl. Manage. 69: 457–465.
Hayes, J. P., and R. J. Steidl. 1997. Statistical power analysis and amphibian population trends. Conserv. Biol. 11: 273–275.
Holling, C. S. (ed.) 1978. Adaptive Environmental Assessment and Management. Wiley, London.
Hurlbert, S. H. 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54: 187–211.
Johnson, D. H. 1995. Statistical sirens: the allure of nonparametrics. Ecology 76: 1998–2000.
Johnson, D. H. 1999. The insignificance of statistical significance testing. J. Wildl. Manage. 63: 763–772.
Johnson, D. H. 2002a. The role of hypothesis testing in wildlife science. J. Wildl. Manage. 66: 272–276.
Johnson, D. H. 2002b. The importance of replication in wildlife research. J. Wildl. Manage. 66: 919–932.
Johnson, F. A., B. K. Williams, J. D. Nichols, J. E. Hines, W. L. Kendall, G. W. Smith, and D. F. Caithamer. 1993. Developing an adaptive management strategy for harvesting waterfowl in North America. In Transactions of the North American Wildlife and Natural Resources Conference, pp. 565–583. Wildlife Management Institute, Washington, DC.
Kendall, W. L. 1999. Robustness of closed capture–recapture methods to violations of the closure assumption. Ecology 80: 2517–2525.
Kendall, W. L., B. G. Peterjohn, and J. R. Sauer. 1996. First-time observer effects in the North American Breeding Bird Survey. Auk 113: 823–829.
Kish, L. 1987. Statistical Design for Research. Wiley, New York.
Kuehl, R. O. 2000. Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd Edition. Brooks/Cole, Pacific Grove, California.
Laake, J. L. 2007. RMark, version 1.6.1. R package. http://nmml.afsc.noaa.gov/Software/marc/marc.stm.
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.
Lehnen, S. E., and D. G. Krementz. 2005. Turnover rates of fall-migrating pectoral sandpipers in the lower Mississippi Alluvial Valley. J. Wildl. Manage. 69: 671–680.
Link, W. A., and J. R. Sauer. 1998. Estimating population change from count data. Application to the North American Breeding Bird Survey. Ecol. Appl. 8: 258–268.
Link, W. A., E. Cam, J. D. Nichols, and E. G. Cooch. 2002. Of BUGS and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research. J. Wildl. Manage. 66: 227–291.
Lukacs, P. M., W. L. Thompson, W. L. Kendall, W. R. Gould, P. F. Doherty Jr., K. P. Burnham, and D. R. Anderson. 2007. Concerns regarding a call for pluralism of information theory and hypothesis testing. J. Appl. Ecol. 44: 456–460.
MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy Estimation and Modeling. Academic, Burlington, MA.
Maindonald, J H., and J. Braun. 2003. Data Analysis and Graphics Using R. Cambridge University, United Kingdom.
MATLAB. 2005. Learning MATLAB. The MathWorks, Inc., Natick, MA.
Manly, B. F. J. 1991. Randomization and Monte Carlo Methods in Biology. Chapman and Hall, New York.
McCullough, B. D., and B. Wilson. 1999. On the accuracy of statistical procedures in Microsoft Excel 97. Comput. Stat. Data Anal. 31: 27–37.
McCullough, B. D., and B. Wilson. 2002. On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Comput. Stat. Data Anal. 40: 713–721.
McCullough, B. D., and B. Wilson. 2005. On the accuracy of statistical procedures in Microsoft Excel 2003 Comput. Stat. Data Anal. 49: 1224–1252.
Minitab. 2003. MINITAB Statistical Software, Release 14 for Windows. State College, Pennsylvania.
Mitchell, W. A. 1986. Deer spotlight census: Section 6.4.3, U.S. Army Corp of Engineers Wildlife Resources Management Manual. Technical Report EL-86–53, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS.
Mood, A. M., F. A. Graybill, and D. C. Boes. 1974. Introduction to the Theory of Statistics, 3rd Edition, McGraw-Hill, Boston, MA.
Nichols, J. D., J. E. Hines, and K. H. Pollock. 1984. The use of a robust capture–recapture design in small mammal population studies: a field example with Microtus pennsylvanicus. Acta Therilogica 29: 357–365.
Nichols, J. D., J. E. Hines, J. R. Sauer, F. W. Fallon, J. E. Fallon, and P. J. Heglund. 2000. A double observer approach for estimating detection probability and abundance from point counts. Auk 117(2): 393–408.
Norman, G. W., M. M. Conner, J. C. Pack, and G. C. White. 2004. Effects of fall hunting on survival of male wild turkeys in Virginia and West Virginia. J. Wildl. Manage. 68: 393–404.
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: 1–135.
Payne, R. W., Murray, D. A., Harding, S. A., Baird, D. B. & Soutar, D. M. 2006. GenStat for Windows, 9th Edition. Introduction. VSN International, Hemel Hempstead.
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.
Pollock, K. H., J. D. Nichols, C. Brownie, and J. E. Hines. 1990. Statistical inference for capture–recapture experiments. Wildl. Monogr. 107: 1–97.
R Development Core Team. 2006. 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
Ripley, B. D. 1996. Pattern Recognition and Neural Networks. Cambridge University, Cambridge.
Ripley, B. D. 2002. Statistical methods need software: a view of statistical computing. Opening Lecture, RSS Statistical Computing Section.
Robbins, C. S., D. Bystrack, and P. H. Geissler. 1986. The breeding bird survey: the first 15 years, 1965–1979. Resource Publication no. 157, U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC.
Rosenstock, S. S., D. R. Anderson, K. M. Giesen, T. Leukering, and M. F. Carter. 2002. Landbird counting techniques: current practices and an alternative. Auk 119(1): 46–53.
Royle, J. A., and J. D. Nichols. 2003. Estimating abundance from repeated presence-absence data or point counts. Ecology 84: 777–790.
SAS Institute Inc. 2000. SAS language reference: dictionary, version 8. SAS Institute, Inc., North Carolina.
Sauer, J. R., B. G. Peterjohn, and W. A. Link. 1994. Observer differences in the North American Breeding Bird Survey. Auk 111: 50–62.
Schwarz, C. J. and G. A. F. Seber. 1999. Estimating animal abundance: review III. Stat. Sci. 14: 427–456.
Sinclair, A. R. E. 1991. Science and the practice of wildlife management. J. Wildl. Manage. 55: 767–773.
Skalski, J. R., and D. S. Robson. 1992. Techniques for Wildlife Investigations: Design and Analysis of Capture Data. Academic, San Diego, CA.
SPSS Inc. 1999. SPSS Base 10.0 for Windows User’s Guide. SPSS Inc., Illinois.
StataCorp. 2005. Stata Statistical Software: Release 9. Texas.
StatSoft. 2003. STATISTICA data analysis software system, version 6. Oklahoma.
Seber, G. A. F. 1982. The Estimation of Animal Abundance and Related Parameters, 2nd Edition. Griffin, London.
Steidl, R. J. 2006. Model selection, hypothesis testing, and risks of condemning analytical tools. J. Wildl. Manage. 70: 1497–1498.
Steidl R. J., J. P. Hayes, and E. Schauber. 1997. Statistical power in wildlife research. J. Wildl. Manage. 61: 270–279.
Stephens, P. A., S. W. Buskirk, and C. M. del Rio. 2007a. Inference in ecology and evolution. Trends Ecol. Evol. 22: 192–197.
Stephens, P. A., S. W. Buskirk, G. D. Hayward, and C. M. Del Rio. 2007b. A call for statistical pluralism answered. J. Appl. Ecol. 44: 461–463.
Stewart-Oaten, A., W. W. Murdoch, and K. R. Parker. 1986. Environmental impact assessment: “Pseudoreplication” in time? Ecology 67: 929–940.
Sutherland, W. J. 2006. Planning a research programme, in W. J. Sutherland, Ed. Ecological Census Techniques, 2nd Edition, pp. 1–10. Cambridge University, Cambridge.
SYSTAT. 2002. SYSTAT for Windows, version 10.2. SYSTAT software Inc., California.
Thompson, S. K. 2002. Sampling, 2nd Edition. John Wiley and Sons, New York.
Thompson, W. L. 2002. Towards reliable bird surveys: accounting for individuals present but not detected. Auk 119(1): 18–25.
Thompson, S. K., and G. A. F. Seber. 1996. Adaptive Sampling. John Wiley and Sons, New York.
Thompson, W. L., G. C. White, and C. Gowan. 1998. Monitoring vertebrate populations. Academic, New York.
Tufte, E. R. 1983. The visual display of quantitative information. Graphics, Chesire, CT.
Tufte, E. R. 2001. The visual display of quantitative information, 2nd Edition. Graphics, Chesire, CT.
Venables, W. N., and B. D. Ripley. 2002. Modern applied statistics with S, 4th Edition. Springer-Verlag, New York.
Verner, J., and K. A. Milne. 1990. Analyst and observer variability in density estimates from spot mapping. Condor 92: 313–325.
Walters, C. J. 1986. Adaptive Management of Renewable Resources. Macmillan, New York.
Walters, C. J., and C. S. Holling. 1990. Large-scale management experiments and learning by doing. Ecology 71: 2060–2068.
White, G. C. 1996. NOREMARK: population estimation from mark-resighting surveys. Wildl. Soc. Bull. 24: 50–52.
White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46(Suppl.): 120–139.
Williams, B. K. 1996. Adaptive optimization and the harvest of biological populations. Math. Biosci. 136: 1–20.
Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analysis and Management of Animal Populations. Academic, San Diego, CA.
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2008). Concepts for Wildlife Science: Design Application. In: Wildlife Study Design. Springer Series on Environmental Management. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75528-1_2
Download citation
DOI: https://doi.org/10.1007/978-0-387-75528-1_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-75527-4
Online ISBN: 978-0-387-75528-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)