Skip to main content
SpringerLink
Log in

Elements of Spatial Data Analysis in Ecological Assessments

  • Chapter
Book cover A Guidebook for Integrated Ecological Assessments

Abstract

Virtually any aspect of an ecological assessment (EA) is likely to involve the topic of space, including its striking effect on landscapes and the distribution of human populations. For example, a spatially explicit approach is needed to address two policy questions common to many EAs. What is required for maintaining the long-term productivity of ecosystems? What is the impact of maintaining current management scenarios on, for example, major social issues or the maintenance of rural communities and their economies in a given area? Essential tasks of EAs also involve the explicit consideration of space, such as in combining information from various geographic areas and multiple scales. Most measurements of large-scale phenomena, such as the effect of regional carbon and nitrogen cycles, hydrologic regimes, changes in land-use patterns, and demographics, among many others, carry the imprint of spatial variability and scaling. Therefore, explicit consideration of all aspects of space (e.g., spatial variability and its corollary, spatial scaling) is increasingly a central concern in the design and implementation of EAs, whether in map creation or incorporation in predictive modeling (see Chapters 3 and 18; also, Haining, 1990; Ritchie, 1997).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
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

  • Anselin, L. 1988. Spatial econometrics: methods and models. Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Austin, M. P.; Nicholls, A. O.; Margules, C. R. 1990. Measurement of the realized qualitative niche: environmental niches of five Eucalyptus species. Ecol. Monogr.60:161–177.

    Article  Google Scholar 

  • Bailey, T. C.; Gatrell, A. C. 1995. Interactive spatial data analysis. Essex: Longman Scientific & Technical.

    Google Scholar 

  • Baker, W. L.; Cai, Y. 1992. The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecol.7:291–302.

    Article  Google Scholar 

  • Balling, R. C. 1984. Classification in climatology. In: Gaile, G. L.; Willmott, C. J., eds. Spatial statistics and models. Dordrecht, The Netherlands: Reidel: 81–108.

    Google Scholar 

  • Bell, G.; Lechowicz, M. F.; Appenzeler, A.; Chandler, M.; DeBlois, E.; Jackson, L.; Mackenzie, B.; Preziosi, R.; Schallenberg, M.; Tinker, N. 1993. The spatial structure of the physical environment. Oecologia96: 114–121.

    Article  Google Scholar 

  • Bjørnstad, O. N.; Falck, W. 1997. An extension of the spatial correlogram and the x-intercept for genetic data. In: Bjornstad, O. N. Statistical models for fluctuating populations—patterns and processes in time and space. Ph.D. dissertation, University of Oslo. 9 p.

    Google Scholar 

  • Borcard, D.; Legendre, P. 1994. Environmental control and spatial structure in ecological communities: an example using oribatid mites (Acari, Oribatei). Environ. Ecol. Stat.1:37–61.

    Article  Google Scholar 

  • Borcard, D.; Legendre, P.; Drapeau, P. 1992. Partialling out the spatial component of ecological variation. Ecology73:1045–1055.

    Article  Google Scholar 

  • Bradshaw, G. A. 1998. Defining ecologically relevant change in the process of scaling up: implications for monitoring at the “landscape” level. In: Peterson, D. L.; Parker, V. T., eds. Ecological scale: theory and applications. New York: Columbia University Press: 227–249.

    Google Scholar 

  • Burrough, P. A. 1995. Spatial aspects of ecological data. In: Jongman, R. H. G.; ter Braak, C. J. F.; van Tongeren, O. F. R., eds. Data analysis in community and landscape ecology.Cambridge, UK: Cambridge University Press: 213–251.

    Chapter  Google Scholar 

  • Casetti, E.; Jones, J. P., III. 1987. Spatial aspects of the productivity slow down: an analysis of U.S. manufacturing data. Ann. Assoc. Amer. Geogr.77:76–88.

    Article  Google Scholar 

  • Cliff, A. D.; Ord, J. K. 1981. Spatial processes: models and applications. London: Pion.

    Google Scholar 

  • Cliff, A. D.; Haggett, P.; Ord, J. K.; Bassett, K.; Davies, R. B. 1975. Elements of spatial structure. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Cox, N. J.; Jones, K. 1981. Exploratory data analysis. In: Wrigley, N.; Bennet, R. J., eds. Quantitative geography. London: Routledge & Kegan Paul.

    Google Scholar 

  • Craig, R. G. 1979. Sources of variation in LANDSAT autocorrelation. In: Proceedings of the international symposium on remote sensing of the environment, Ann Arbor, MI: 1517–1524.

    Google Scholar 

  • Cressie, N. A. C. 1991. Statistics for spatial data. New York: John Wiley & Sons.

    Google Scholar 

  • Cressie, N.; Read, T. R. C. 1989. Spatial data analysis of regional counts. Biometrical J.6:699–719.

    Article  Google Scholar 

  • Dale, M. R. T. 1999. Spatial pattern analysis in plant ecology. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • David, M. 1977. Geostatistical ore reserve estimation. In: Developments in geomathematics, 2. Amsterdam: Elsevier Science.

    Google Scholar 

  • Davis, F. W.; Quattrochi, D. A.; Ridd, M. K.; Lam, N. S.-N.; Walsh, S. J.; Michaelsen, J. C.; Franklin, J.; Stow, D. A.; Johannsen, C. J.; Johnston, C. A. 1991. Environmental analysis using integrated GIS and remotely sensed data: some research needs and priorities. Photogramm. Eng. Remote Sensing57:689–697.

    Google Scholar 

  • Diaconis, P. 1985. Theories of data analysis: from magical thinking through classical statistics. In: Hoaglin, D. C.; Mosteller, F. M.; Tukey, J. W., eds. Exploring data tables. New York: John Wiley & Sons: 1–36.

    Google Scholar 

  • Dutilleul, P.; Legendre, P. 1993. Spatial heterogeneity against heteroscedasticity: an ecological paradigm versus a statistical concept. Oikos66:152–171.

    Article  Google Scholar 

  • Fortin, M.-J. 1999a. Spatial statistics in landscape ecology. In: Klopatek, J. M.; Gadne, R. H., eds. Landscape ecological analysis: issues and applications. New York: Springer-Verlag: 253–279.

    Chapter  Google Scholar 

  • Fortin, M.-J. 1999b. Effects of quadrat size and data measurement on the detection of boundaries. J. Veg. Sci. 10:43–50.

    Article  Google Scholar 

  • Fortin, M.-J.; Drapeau, P.; Legendre, P. 1989. Spatial autocorrelation and sampling design in plant ecology. Vegetatio83:209–222.

    Article  Google Scholar 

  • Fortin, J.-J.; Drapeau, P.; Jacquez, G. M. 1996. Statistics to assess spatial relationships between ecological boundaries. Oikos77:51–60.

    Article  Google Scholar 

  • Griffith, D. A. 1988. Estimating spatial autoregressive model parameters with commercial statistical packages. Geogr. Anal.20:176–186.

    Article  Google Scholar 

  • Gustafson, E. J. 1998. Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1:143–156.

    Article  Google Scholar 

  • Haining, R. 1990. Spatial data analysis in the social and environmental sciences. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Hammer, R. D. 1998. Space and time in the soil landscape: the ill-defined ecological universe. In: Peterson, D. L.; Parker, V. T., eds. Ecological scale: theory and applications. New York: Columbia University Press: 105–140.

    Google Scholar 

  • Hampel, F. R.; Ronchetti, E. M.; Rousseeuw, P. J.; Stahel, W. A. 1986. Robust statistics. New York: John Wiley & Sons.

    Google Scholar 

  • Hargis, C. D.; Bissonette, J. A.; David, J. L. 1997. Understanding measures of landscape pattern. In: Bissonette, J. A., ed. Wildlife and landscape ecology. New York: Springer-Verlag: 231–261.

    Chapter  Google Scholar 

  • Hargis, C. D.; Bissonette, J. A.; David, J. L. 1998. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecol.13: 167–168.

    Article  Google Scholar 

  • Heuvelink, G. B. M. 1998. Error propagation in environmental modeling with GIS. London: Taylor and Francis.

    Google Scholar 

  • Hoaglin, D. C.; Mosteller, F.; Tukey, J. W. 1983. Understanding robust and exploratory data analysis. New York: John Wiley & Sons.

    Google Scholar 

  • Hoaglin, D. C.; Mosteller, F.; Tukey, J. W. 1985. Exploring data tables, trends and shapes. New York: John Wiley & Sons.

    Google Scholar 

  • Hunsaker, C. H.; O’Neill, R. V.; Jackson, B. L.; Timmins, S. P.; Levine, D. A.; Norton, D. J. 1994. Sampling to characterize landscape pattern. Landscape Ecol.9:207–226.

    Article  Google Scholar 

  • Journel, A. G.; Huijbregts, C. J. 1978. Mining geostatistics. London: Academic Press.

    Google Scholar 

  • King, A. W. 1997. Hierarchy theory: a guide to system structure for wildlife biologists. In: Bissonette, J. A., ed. Wildlife and landscape ecology. New York: Springer-Verlag: 185–212.

    Chapter  Google Scholar 

  • Kolasa, J.; Rollo, C. D. 1991. Introduction: the heterogeneity of heterogeneity—a glossary. In: Kolasa, J.; Pickett, S. T. A., eds. Ecological heterogeneity. New York: Springer-Verlag: 1–23.

    Chapter  Google Scholar 

  • Labovitz, M. L.; Masuoka, E. J. 1984. The influence of autocorrelation in signature extraction: an example from a geobotanical investigation of Cotter Basin, Montana. Int. J. Remote Sens.5:315–332.

    Article  Google Scholar 

  • Learner, E. E. 1978. Specification searches: ad hoc inference with non experimental data. New York: John Wiley & Sons.

    Google Scholar 

  • Legendre, P. 1993. Spatial autocorrelation: trouble or new paradigm. Ecology74:1659–1673.

    Article  Google Scholar 

  • Legendre, P.; Fortin, M.-J. 1989. Spatial pattern and ecological analysis. Vegetation80:107–138.

    Article  Google Scholar 

  • Legendre, P.; Legendre, L. 1998. Numerical ecology. Amsterdam: Elsevier Science B.V.

    Google Scholar 

  • Legendre, P.; McArdle, B. H. 1997. Comparison of surfaces. Oceanol. Acta20:27–41.

    Google Scholar 

  • Legendre, P.; Troussellier, M. 1988. Aquatic heterotrophic bacteria: modeling in the presence of spatial autocorrelation. Limnol. Oceanogr.33:1055–1067.

    Article  Google Scholar 

  • Li, H.; Reynolds, J. F. 1995. On definition and quantification of heterogeneity. Oikos73(2):280–284.

    Article  Google Scholar 

  • Manly, B. F. 1997. Randomization, bootstrap and Monte Carlo methods in biology, 2nd ed. London: Chapman and Hall.

    Google Scholar 

  • Matheron, G. 1965. Les variables régionalisées et leur estimation—une application de la théorie des fonctions aléatoires aux sciences de la nature. Paris: Masson.

    Google Scholar 

  • McGarigal, K.; Marks, B. 1995. FRAGSTATS: spatial analysis program for quantifying landscape structure. PNW-GTR-351. Portland, OR: U.S. Dept. Agric., For. Serv., Pacific Northw. Res. Sta.

    Google Scholar 

  • Milne, A. 1959. The centric systematic area sample treated as a random sample. Biometrics15:270–297.

    Article  Google Scholar 

  • Oden, N. L. 1984. Assessing the significance of a spatial correlogram. Geogr. Anal.16:1–16.

    Article  Google Scholar 

  • Oden, N. L.; Sokal, R. R.; Fortin, M.-J.; Goebl, H. 1993. Categorical wombling: detecting regions of significant change in spatially located categorical variables. Geogr. Anal.25:315–336.

    Article  Google Scholar 

  • O’Neill, R. V.; Gardner, R. H.; Milne, B. T.; Turner, M. G.; Jackson, B. 1991. Heterogeneity and spatial hierarchies. In: Kolasa, J.; Pickett, S. T. A., eds. Ecological heterogeneity. New York: Springer-Verlag: 85–96.

    Chapter  Google Scholar 

  • Overton, W. S.; White, D.; Stevens, D. L., Jr. 1990. Design report for EMAP environmental monitoring and assessment program. EPA/600/3-91/053. Corvallis, OR: U.S. Environmental Protection Agency, Environmental Research Laboratory.

    Google Scholar 

  • Pahl-Wostl, C. 1998. Ecosystem organization across a continuum of scales: a comparative analysis of lakes and rivers. In: Peterson, D. L.; Parker, V. T., eds. Ecological scale: theory and applications. New York: Columbia University Press: 141–170.

    Google Scholar 

  • Palmer, M. W. 1993. Putting things in even better order: the advantages of canonical correspondence analysis. Ecology74:2215–2230.

    Article  Google Scholar 

  • Pearson, S. M.; Gardner, R. H. 1997. Neutral models: Useful tools for understanding landscape patterns. In: Bissonette, J. A., ed. Wildlife and landscape ecology. New York: Springer-Verlag: 215–230.

    Chapter  Google Scholar 

  • Pielou, E. C. 1977. Mathematical ecology, 2nd ed. New York: John Wiley & Sons.

    Google Scholar 

  • Quattrochi, D. A.; Goodchild, M. F., eds. 1997. Scale in remote sensing and GIS. Boca Raton, FL: Lewis Publishers.

    Google Scholar 

  • Riitters, K. H.; O’Neill, R. V.; Hunsaker, C. T.; Wickham, J. D.; Yankee, D. H.; Timmins, S. P.; Jones, K. B.; Jackson, B. L. 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecol. 10:23–39.

    Article  Google Scholar 

  • Ripley, B. D. 1981. Spatial statistics.New York: John Wiley & Sons.

    Book  Google Scholar 

  • Ripley, B. D. 1984. Spatial statistics: developments 1980-3. Inter. Statistical Rev.52:141–150.

    Article  Google Scholar 

  • Ripley, B. D. 1987. Spatial point pattern analysis in ecology. In: Legendre, P.; Legendre, L., eds. Developments in numerical ecological. Berlin: Springer-Verlag: 407–429.

    Chapter  Google Scholar 

  • Ritchie, M. E. 1997. Population in a landscape context: sources, sinks, and metapopulations. In: Bissonette, J. A., ed. Wildlife and landscape ecology. New York: Springer-Verlag: 160–184.

    Chapter  Google Scholar 

  • Rossi, R. E.; Mulla, D. J.; Journel, A. G.; Franz, E. H. 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol. Monogr. 62:277–314.

    Article  Google Scholar 

  • Schneider, D. C. 1994. Quantitative ecology—spatial and temporal scaling. San Diego, CA: Academic Press.

    Google Scholar 

  • Semple, R. K.; Green, M. B. 1984. Classification in human geography. In: Gaile, G. L.; Willmott, C. J., eds. Spatial statistics and models. Dordrecht, The Netherlands: Reidel: 55–79.

    Google Scholar 

  • Smouse, P. E.; Long, J. C.; Sokal, R. R. 1986. Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst. Zool.35:627–632.

    Article  Google Scholar 

  • Sokal, R. R.; Rohlf, F. J. 1995. Biometry—the principles and practice of statistics in biological research, 3rd ed. New York: W. H. Freeman.

    Google Scholar 

  • ter Braak, C. J. F. 1988. Partial canonical correspondence analysis. In: Bock, H. H., ed. Classification and related methods of data analysis. Amsterdam: Elsevier Science: 551–558.

    Google Scholar 

  • ter Braak, C. J. F. 1990. Update notes: CANOCO version 3.10. Wageningen, The Netherlands: Agricultural Mathematics Group.

    Google Scholar 

  • Tilman, D.; Kareiva, P. 1997. Spatial ecology: the role of space in population dynamics and interspecific interactions. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Turner, S. J.; O’Neill, R. V.; Conley, W.; Conley, M. R.; Humphries, H. C. 1991. Pattern and scale: statistics for landscape ecology. In: Turner, M. G.; Gardner, R. H., eds. Quantitative methods in landscape ecology: the analysis and interpretation of environmental heterogeneity. New York: Springer-Verlag: 17–49.

    Google Scholar 

  • Upton, G. J.; Fingleton, B. 1985. Spatial data analysis by example, volume 1: point pattern and quantitative data. New York: John Wiley & Sons.

    Google Scholar 

  • Webster, R. 1985. Quantitative spatial analysis of soil in the field. Advances Soil Science3:1–70.

    Article  Google Scholar 

  • Wrigley, N. 1983. Quantitative methods: on data and diagnostics. Progress Human Geo.7:567–577.

    Google Scholar 

Download references

Authors
  1. Patrick S. Bourgeron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marie-José Fortin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hope C. Humphries
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. USDA Forest Service, Northern Region, 200 East Broadway, Missoula, MT, 59807, USA

    Mark E. Jensen

  2. Institute of Alpine and Arctic Research, University of Colorado, 1560 30th Street, Boulder, CO, 80309-0450, USA

    Patrick S. Bourgeron

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media New York

About this chapter

Cite this chapter

Bourgeron, P.S., Fortin, MJ., Humphries, H.C. (2001). Elements of Spatial Data Analysis in Ecological Assessments. In: Jensen, M.E., Bourgeron, P.S. (eds) A Guidebook for Integrated Ecological Assessments. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8620-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-8620-7_14

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98583-1

  • Online ISBN: 978-1-4419-8620-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation