Skip to main content

Using Statistical Models to Study Temporal Dynamics of Animal—Landscape Relations

  • Chapter
Temporal Dimensions of Landscape Ecology

Abstract

Temporal variation in animal responses to landscape conditions may affect animal distributions, population and community structure, and resource use. Measuring such variation and studying its influence is essential for developing a realistic understanding of animal-landscape relations. Several statistical modeling approaches are appropriate for explicitly incorporating time into analyses of animal-landscape relations, but landscape ecologists have not commonly used them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bowne, D.R., Peles, J.D., and Barrett, G.W. 1999. Effects of landscape spatial structure on movement patterns of the hispid cotton rat (Sigmodon hispidus). Landscape Ecology 14:53–65.

    Article  Google Scholar 

  • Burnham, K.P., and Anderson, D.R. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Second Edition. New York: Springer-Verlag.

    Google Scholar 

  • Evans, K.L., and Gaston, K.J. 2005. People, energy, and avian species richness. Global Ecology and Biogeography 14:187–196.

    Article  Google Scholar 

  • Flack, V.F., and Chang, P.C. 1987. Frequency of selecting noise variables in subset analysis: A simulation study. American Statistician 41:84–86.

    Article  Google Scholar 

  • Gutzwiller, K.J., ed. 2002. Applying Landscape Ecology in Biological Conservation. New York: Springer-Verlag.

    Google Scholar 

  • Gutzwiller, K.J., and Barrow, W.C. Jr. 2001. Bird—landscape relations in the Chihuahuan Desert: Coping with uncertainties about predictive models. Ecological Applications 11:1517–1532.

    Google Scholar 

  • Gutzwiller, K.J., and Barrow, W.C. Jr. 2002. Does bird community structure vary with landscape patchiness? A Chihuahuan Desert perspective. Oikos 98:284–298.

    Article  Google Scholar 

  • Gutzwiller, K.J., Riffell, S.K., and Anderson, S.H. 2002. Repeated human intrusion and the potential for nest predation by gray jays. Journal of Wildlife Management 66:372–380.

    Article  Google Scholar 

  • Keitt, T.H., Bjørnstad, O.N., Dixon, P.M., and Citron-Pousty, S. 2002. Accounting for spatial pattern when modeling organism-environment interactions. Ecography 25:616–625.

    Article  Google Scholar 

  • Kenward, M.G., and Roger, J.H. 1997. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997.

    Article  PubMed  CAS  Google Scholar 

  • Laird, N.M., and Ware, J.H. 1982. Random-effects models of longitudinal data. Biometrics 38:963–974.

    Article  PubMed  CAS  Google Scholar 

  • Littell, R.C., Henry, P.R., and Ammerman, C.B. 1998. Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science 76:1216–1231.

    PubMed  CAS  Google Scholar 

  • Littell, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. 1996. SAS System for Mixed Models. Cary, NC: SAS Institute.

    Google Scholar 

  • McCoy, T.D., Ryan, M.R., Kurzejeski, E.W., and Burger, L.W. Jr. 1999. Conservation Reserve Program: Source or sink habitat for grassland birds in Missouri? Journal of Wildlife Management 63:530–538.

    Article  Google Scholar 

  • McGarigal, K., and McComb, W.C. 1995. Relationships between landscape structure and breeding birds in the Oregon Coast Range. Ecological Monographs 65:235–260.

    Article  Google Scholar 

  • Millspaugh, J.J., and Marzluff, J.M., eds. 2001. Radio Tracking and Animal Populations. San Diego: Academic Press.

    Google Scholar 

  • Mitchell, M.W., and Gumpertz, M.L. 2003. Spatio-temporal prediction inside a free-air CO2 enrichment system. Journal of Agricultural, Biological, and Environmental Statistics. 8:310–327.

    Article  Google Scholar 

  • Morrison, M.L. 2002. Role of temporal and spatial scale. Pages 123–124 Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A., and Samson, F.B., eds. Predicting Species Occurrences: Issues of Accuracy and Scale. Washington, DC: Island Press.

    Google Scholar 

  • Morrison, M.L., Marcot, B.G., and Mannan, R.W. 1998. Wildlife-Habitat Relationships: Concepts and Applications. Madison: University of Wisconsin Press.

    Google Scholar 

  • Naugle, D.E., Higgins, K.F., Nusser, S.M., and Johnson, W.C. 1999. Scale-dependent habitat use in three species of prairie wetland birds. Landscape Ecology 14:267–276.

    Article  Google Scholar 

  • Neter, J., Wasserman, W., and Kutner, M.H. 1989. Applied Linear Regression Models. Second Edition. Burr Ridge, IL: Richard D. Irwin.

    Google Scholar 

  • O'Connor, R.J. 1986. Dynamical aspects of avian habitat use. Pages 235–240 Verner, J., Morrison, M.L., and Ralph, C.J., eds. Wildlife 2000: Modeling Habitat Relationships of Terrestrial Vertebrates. Madison: University of Wisconsin Press.

    Google Scholar 

  • Pearson, S.M. 1993. The spatial extent and relative influence of landscape-level factors on wintering bird populations. Landscape Ecology 8:3–18.

    Article  Google Scholar 

  • Pinheiro, J.C., and Bates, D.M. 2000. Mixed Effects Models in S and S-plus. New York: Springer-Verlag.

    Google Scholar 

  • Riffell, S.K., Keas, B.E., and Burton, T.M. 2003. Birds in Great Lakes coastal wet meadows: Is landscape context important? Landscape Ecology 18:95–111.

    Article  Google Scholar 

  • Robbins, C.S., Bystrack, D., and Geissler, P.H. 1986. The Breeding Bird Survey: Its First Fifteen Years, 1965–1979. Resource Publication 157. Washington, DC: U.S. Fish and Wildlife Service.

    Google Scholar 

  • Saab, V. 1999. Importance of spatial scale to habitat use by breeding birds in riparian forests: A hierarchical analysis. Ecological Applications 9:135–151.

    Article  Google Scholar 

  • SAS Institute. 2002. SAS OnlineDoc 9. Cary, NC: SAS Institute. http://v9doc.sas.com/ sasdoc/ (last accessed 6 October 2006).

    Google Scholar 

  • Sauer, J.R., Hines, J.E., and Fallon, J. 2005. The North American Breeding Bird Survey, Results and Analysis 1966–2004. Version 2005.2. Laurel, MD: USGS Patuxent Wildlife Research Center. http://www.mbr-pwrc.usgs.gov/bbs/ http://www.mbr-pwrc.usgs.gov/bbs/ (last accessed 6 October 2006).

    Google Scholar 

  • Schabenberger, O. 2004. Mixed model influence diagnostics. Proceedings of the 29th Annual SAS Users Group International Conference. Cary, NC: SAS Institute.

    Google Scholar 

  • Schabenberger, O. 2005. Introducing the GLIMMIX procedure for generalized linear mixed models. Proceedings of the 30th Annual SAS Users Group International Conference. Cary, NC: SAS Institute.

    Google Scholar 

  • Schabenberger, O., and Gotway, C.A. 2005. Statistical Methods for Spatial Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.

    Google Scholar 

  • Shochat, E., and Tsurim, I. 2004. Winter bird communities in the northern Negev: Species dispersal patterns, habitat use and implications for habitat conservation. Biodiversity and Conservation 13:1571–1590.

    Article  Google Scholar 

  • Selmi, S., and Boulinier, T. 2001. Ecological biogeography of southern ocean islands: The importance of considering spatial issues. American Naturalist 158:426–437.

    Article  Google Scholar 

  • Stafford, J.D., and Strickland, B.K. 2003. Potential inconsistencies when computing Akaike's information criterion. Bulletin of the Ecological Society of America 84:68–69.

    Google Scholar 

  • Underwood, A.J. 1997. Experiments in Ecology. Cambridge, United Kingdom: Cambridge University Press.

    Google Scholar 

  • White, G.C., and Garrott, R.A. 1990. Analysis of Wildlife Radio-Tracking Data. San Diego: Academic Press.

    Google Scholar 

  • Wolfinger, R.D. 1993. Covariance structure selection in general mixed models. Communications in Statistics, Simulation and Computation 22:1079–1106.

    Article  Google Scholar 

  • Wolfinger, R.D. 1996. Heterogeneous variance-covariance structures for repeated measures. Journal of Agricultural, Biological, and Environmental Statistics 1:205–230.

    Article  Google Scholar 

  • Wolfinger, R.D. 1997. An example of using mixed models and Proc Mixed for longitudinal data. Journal of Biopharmaceutical Statistics 7:481–500.

    Article  PubMed  CAS  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this chapter

Cite this chapter

GUTZWILLER, K.J., RIFFELL, S.K. (2007). Using Statistical Models to Study Temporal Dynamics of Animal—Landscape Relations. In: Bissonette, J.A., Storch, I. (eds) Temporal Dimensions of Landscape Ecology. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-45447-4_7

Download citation

Publish with us

Policies and ethics