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Spatial Analysis of Ecological Data

  • Daniel Borcard
  • François Gillet
  • Pierre Legendre
Chapter
Part of the Use R! book series (USE R)

Abstract

The present chapter deals with methods developed for the analysis of scale-dependent structures of ecological data. These methods are based on sets of variables describing spatial structures derived from the coordinates of the sites or from the neighbourhood relationships among sites. These variables are used to model the spatial structures of ecological data by means of multiple regression or canonical ordination, and to identify significant spatial structures at all spatial scales that can be perceived by the sampling design. Practically, you will learn how to compute spatial correlation measures and draw spatial correlograms; learn how to construct spatial descriptors derived from site coordinates and from links between sites; identify, test and interpret scale-dependent spatial structures; combine spatial analysis and variation partitioning; and assess spatial structures in canonical ordinations by computing variograms of explained and residual ordination scores.

Bibliography

  1. Bivand, R.S., Pebesma, E.J., Gomez-Rubio, V.: Applied spatial data analysis with R. In: Use R Series, 2nd edn. Springer, New York (2013)Google Scholar
  2. Blanchet, F.G., Legendre, P., Borcard, D.: Forward selection of explanatory variables. Ecology. 89, 2623–2632 (2008a)CrossRefGoogle Scholar
  3. Blanchet, F.G., Legendre, P., Borcard, D.: Modelling directional spatial processes in ecological data. Ecol. Model. 215, 325–336 (2008b)CrossRefGoogle Scholar
  4. Borcard, D., Legendre, P.: Environmental control and spatial structure in ecological communities: an example using Oribatid mites (Acari, Oribatei). Environ. Ecol. Stat. 1, 37–61 (1994)CrossRefGoogle Scholar
  5. Borcard, D., Legendre, P.: All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51–68 (2002)CrossRefGoogle Scholar
  6. Borcard, D., Legendre, P.: Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology. 93, 1473–1481 (2012)CrossRefGoogle Scholar
  7. Borcard, D., Legendre, P., Drapeau, P.: Partialling out the spatial component of ecological variation. Ecology. 73, 1045–1055 (1992)CrossRefGoogle Scholar
  8. Borcard, D., Legendre, P., Avois-Jacquet, C., Tuomisto, H.: Dissecting the spatial structure of ecological data at multiple scales. Ecology. 85, 1826–1832 (2004)CrossRefGoogle Scholar
  9. Declerck, S.A.J., Coronel, J.S., Legendre, P., Brendonck, L.: Scale dependency of processes structuring metacommunities of cladocerans in temporary pools of High-Andes wetlands. Ecography. 34, 296–305 (2011)CrossRefGoogle Scholar
  10. Dray, S., Legendre, P., Peres-Neto, P.R.: Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006)CrossRefGoogle Scholar
  11. Dungan, J.L., Perry, J.N., Dale, M.R.T., Legendre, P., Citron-Pousty, S., Fortin, M.-J., Jakomulska, A., Miriti, M., Rosenberg, M.S.: A balanced view of scaling in spatial statistical analysis. Ecography. 25, 626–640 (2002)CrossRefGoogle Scholar
  12. Geary, R.C.: The contiguity ratio and statistical mapping. Inc Stat. 5, 115–145 (1954)Google Scholar
  13. Gray, D.K., Arnott, S.E.: Does dispersal limitation impact the recovery of zooplankton communities damaged by a regional stressor? Ecol. Appl. 21, 1241–1256 (2011)CrossRefGoogle Scholar
  14. Guénard, G., Legendre, P., Boisclair, D., Bilodeau, M.: Assessment of scale-dependent correlations between variables. Ecology. 91, 2952–2964 (2010)CrossRefGoogle Scholar
  15. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)MathSciNetzbMATHGoogle Scholar
  16. Isaaks, E.H., Srivastava, R.M.: An Introduction to Applied Geostatistics. Oxford University Press, New York (1989)Google Scholar
  17. Legendre, P.: Quantitative methods and biogeographic analysis. In: Garbary, D.J., South, R.R. (eds.) Evolutionary Biology of the Marine Algae of the North Atlantic. NATO ASI Series, vol. G22, pp. 9–34. Springer, Berlin (1990)Google Scholar
  18. Legendre, P.: Spatial autocorrelation: trouble or new paradigm? Ecology. 74, 1659–1673 (1993)CrossRefGoogle Scholar
  19. Legendre, P., Legendre, L.: Numerical Ecology, 3rd English edn. Elsevier, Amsterdam (2012)Google Scholar
  20. Legendre, P., Dale, M.R.T., Fortin, M.-J., Gurevitch, J., Hohn, M., Myers, D.: The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography. 25, 601–615 (2002)CrossRefGoogle Scholar
  21. Legendre, P., De Cáceres, M., Borcard, D.: Community surveys through space and time: testing the space-time interaction in the absence of replication. Ecology. 91, 262–272 (2010)CrossRefGoogle Scholar
  22. Méot, A., Legendre, P., Borcard, D.: Partialling out the spatial component of ecological variation: questions and propositions in the linear modelling framework. Environ. Ecol. Stat. 5, 1–27 (1998)CrossRefGoogle Scholar
  23. Moran, P.A.P.: Notes on continuous stochastic phenomena. Biometrika. 37, 17–23 (1950)MathSciNetCrossRefzbMATHGoogle Scholar
  24. Oden, N.L., Sokal, R.R.: Directional autocorrelation: an extension of spatial correlograms to two dimensions. Syst. Zool. 35, 608–617 (1986)CrossRefGoogle Scholar
  25. Peres-Neto, P.R., Legendre, P.: Estimating and controlling for spatial structure in the study of ecological communities. Glob. Ecol. Biogeogr. 19, 174–184 (2010)CrossRefGoogle Scholar
  26. Sharma, S., Legendre, P., De Cáceres, M., Boisclair, D.: The role of environmental and spatial processes in structuring native and non-native fish communities across thousands of lakes. Ecography. 34, 762–771 (2011)CrossRefGoogle Scholar
  27. Sidák, Z.: Rectangular confidence regions for the means of multivariate normal distributions. J. Am. Stat. Assoc. 62, 626–633 (1967)MathSciNetzbMATHGoogle Scholar
  28. Sokal, R.R.: Spatial data analysis and historical processes. In: Diday, E., et al. (eds.) Data Analysis and Informatics IV, pp. 29–43. North-Holland, Amsterdam (1986)Google Scholar
  29. ter Braak, C.J.F.: The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio. 69, 69–77 (1987)CrossRefGoogle Scholar
  30. Wackernagel, H.: Multivariate Geostatistics, 3rd edn. Springer, Berlin (2003)CrossRefzbMATHGoogle Scholar
  31. Wagner, H.H.: Spatial covariance in plant communities: integrating ordination, variogram modeling, and variance testing. Ecology. 84, 1045–1057 (2003)CrossRefGoogle Scholar
  32. Wagner, H.H.: Direct multi-scale ordination with canonical correspondence analysis. Ecology. 85, 342–351 (2004)CrossRefGoogle Scholar
  33. Wiens, J.A.: Spatial scaling in ecology. Funct. Ecol. 3, 385–397 (1989)CrossRefGoogle Scholar
  34. Wright, S.P.: Adjusted P-values for simultaneous inference. Biometrics. 48, 1005–1013 (1992)CrossRefGoogle Scholar
  35. Blanchet, F. G., Legendre, P, Maranger, R., Monti, D. and Pepin, P.: Modelling the effect of directional spatial ecological processes at different scales. Oecologia 166, 357–368 (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Daniel Borcard
    • 1
  • François Gillet
    • 2
  • Pierre Legendre
    • 1
  1. 1.Département de sciences biologiquesUniversité de MontréalMontréalCanada
  2. 2.UMR Chrono-environnementUniversité Bourgogne Franche-ComtéBesançonFrance

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