Environmental Monitoring and Assessment

, Volume 64, Issue 3, pp 591–605 | Cite as

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

  • Mohammed A. Kalkhan
  • Thomas J. Stohlgren


Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, IYZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m2) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m2 subplots within each of the 1000 m2 plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.

geographic information systems (GIS) invasive plant species multi-scale sampling patterns plant species richness remotely sensed data spatial auto-correlation spatial statistics 


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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  1. 1.Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsU.S.A.
  2. 2.Midcontinent Ecological Science Center, U.S. Geological Survey, Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsU.S.A.

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