Spatial autocorrelation analysis in plant population: An overview
Article Main
Abstract
Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given.
Article Details
Article Details
Keywords
Correlogram, Global and Local Autocorrelation, Moran’s I Spatial Autocorrelation, Variogram approaches
References
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Fox, E., Balram, S., Dragicevis, S. and Roberts, A. (2012). Spatial analysis of high resolution aerial photographs to analyse the spread of mountain pine beetle infestation. Journal of Sustainable development, 5 (9): 106-129.
Gangnon, R.E. and Clayton, M.K. (2001). A weighted average likelihood ratio test for spatial clustering of disease. Statistics in Medicine, 20:2977–2987.
Geary, R. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician 5: pp115-45
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Goodchild, M. F. (1986). Spatial Autocorrelation. Catmog 47, Geo Books.
Goslee, S.C. (2006). Behavior of vegetation sampling methods in the presence of spatial autocorrelation. Plant Ecology, 187: 203-2012.
Griffith, D.A. (2003). Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization. Berlin, Germany: Springer-Verlag.
Griffith, D.A. and Peres-Neto, P.R. (2006). Spatial modeling in ecology: the flexibility of eigen function spatial analyses in exploiting relative location information. Ecology, 87: 2603-2613.
Haining, R. (1990). Spatial Data Analysis in Social and Environmental Sciences. Cambridge University Press: New York.
Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Guegan, J.F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorff, T., O’Brien, E.M., Porter, E.E. and Turner, J.R.G. (2003). Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84: 3105–3117.
Heikkinen, R.K. (1996). Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland. Vegetatio, 126: 151–165.
Henebry, G.M. (1995). Spatial model error analysis using autocorrelation indices. Ecological Modelling 82: 75–91.
Hjalmars, U., Kulldorff, M., Gustafsson G. and Nagarwalla, N. (1996). Childhood leukemia in Sweden: Using GIS and a spatial scan statistic for cluster detection. Statistics in Medicine, 15:707–715.
Hubert, L., Golledge, R. and Costanzo, C.M. (1981). Generalized procedures for evaluating spatial autocorrelation. Grographical Analysis, 13: 224-233
Huo, X., N., Li, H., Sun, D.F., Zhou, L.D. and Li, B.G. (2012). Combining geostatistics with Moran’s I analysis for mappings soil heavy metals in Beijing, China. International Journal of Environmental Research and Public Health, 9: 995-1017.
Isaaks, E.H. and Shrivastava, R.M. (1989). An introduction to applied geo-statistics. - Oxford University Press.
Jackson, M.C. and Waller, L.A. (2005). Exploring Goodness-of-fit and spatial correlation using components of Tango's Index of spatial clustering. Geographical Analysis, 37(4):371-382.
Jackson, M.C., Huang, L., Xie, Q and Tiwari, R. (2010). A modified version of Moran’s I. International Journal of Health Geographic, 9: 33-43.
Koenig, W.D. (1998). Spatial autocorrelation in California land birds. Conservation Biology, 12:612–620.
Kühn, I. (2007). Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distribution, 13 (1): 66-69.
Kulldorff, M. (1997). A spatial scan statistic. Community Statics Theory and Methods, 26:1481–1496.
Laffan, S.W. (2006). Assessing regional scale weed distributions, with an Australian example using Nassella trichotoma. Weed Research, 46(3): 194-206.
Lee, J. and Wong, D.W.S. (2001). Statistical analysis with ArcView GIS. Wiley, New York. 192 p.
Legendre, L. and Legendre, P. (1984). l~cologie numrrique. 2irme ed. Tome 2: La structure des donnres 6cologiques. Masson, Paris et les Presses de l'Universit6 du Quebec.
Legendre, P. (1993). Spatial autocorrelation: trouble or new paradigm? Ecology, 74: 1659-1673.
Legendre, P. and Fortin, M.J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80: 107-38.
Legendre, P. and Legendre, L. (1998). Numerical Ecology. Elsevier. USA.
Legendre, P., Dale, M.T.R., Fortin, M.J., Gurvitch, J., Hohn, M. and Myers, D. (2002). The consequences of spatial structure for the design and analysis of ecological field surveys. – Ecography, 25: 601-615.
Lennon, J.J. (2000). Red-shifts and red herrings in geographical ecology. Ecography, 23: 101-113.
Mathur, M. (2014). Attributes of Plant Spatial Analysis. Today and Tomorrow Printer and Publishers, New Delhi, India.
Miller, J. Franklin, J. and Aspinall, R. (2007). Incorporating spatial dependence in predictive vegetation models. Ecological Modeling, 202: 225-242.
Moran, P.A.P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, Series B, 10: 243-251.
Mueller-Warrant, G.W., Whittaker, G.W. and Young, W.C. (2008). GIS analysis of spatial clustering and temporal change in weeds of grass seed crops. Weed Science, 56 (5): 647-669.
Oden N. (1995). Adjusting Moran’s I for population density. Statatistical Medicine, 14(1):17-26.
Oden N.L. (1984). Assessing the significance of a spatial correlogram. Geographical Analysis, 16: 1-16.
Odland, J. (1988). Spatial autocorrelation. In: G.I. Thrall (Ed.), Sage University Scientific Geography Series no. 9. Sage Publications, Beverly Hills. pp 87.
Ord, J.K and Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27: 286-306.
Overmars, K.P., Koning, G.H.I. and Velkamp, A. (2003). Spatial autocorrelation in multi-scale land use models. Ecological Modeling, 164: 257-270.
Palma, L. Beja, P. and Rodrigues, M. (1999). The use of sighting data to analyze Iberian lynx habitat and distribution. Journal of Applied Ecology, 36: 812- 824.
Perry, J.N., Liebhod, A.M., Rosenbery, M.S., Dungan, I., Miriti, M., Jakomulska, A. and Citron-Pousty, S (2002). Illustrations and guidelines for selecting statistical methods for quantifying spatial paterrns in ecological data. Ecography, 25: 578-600.
Pugh, S. and Congalton, R. (2001). Applying spatial autocorrelation analysis to evaluate error in New England forest cover type maps derived from Landsat Thematic Mapper Data. Photogrammetric Engineering and Remote Sensing, 67 (5): 613-620.
Radeloff, V.C., Miller, T.F., He, H. S. and D..J. Mladenoff. (2000). Periodicity in landscape pattern and geostatistical models: autocorrelation between patches. Ecography, 23: 81–91.
Robertson G.P. (2008). GS+ Geostatistics for the environmental sciences. Gamma Design Software, Plainwell, Michigan USA. P. 165.
Roe, C.M., Parker, G.C., Korsten, C.A., Lister, C.J., Weatherall, S.B., Lawrence Lodge, R.H, E. and Wilson, B.J. (2012). Small-scale spatial autocorrelation in plant communities: the effects of spatial grain and measure of abundance, with an improved sampling scheme. Journal of Vegetation Science, 23: 471-482.
Rogerson P. (1999). The detection of clusters using a spatial version of the Chi- Square Goodness-of-Fit Statistic. Geographical Analysis, 31(1):128-147.
Shi, H., and Zhang, L. (2003). Local analysis of tree competition and growth. Forest Science, 49 (6): 938-946.
Sokal, R.R. and Oden, N.L. (1978). Spatial autocorrelation in biology. 1. Methodology. Biological Journal of Linnaeus Society, 10: 199-228.
Sokal, R.R. (1986). Spatial S data analysis and historical processes. In: Diday, E. (eds), Data analysis and informatics, IV. Proceedings of the Fourth International Symposium on Data Analysis and Informatics, pp. 29-43. Versailles, France, North-Holland, Amsterdam.
Sokal, R.R., Oden, N.L. and Thomson, B.A. (1998a). Local spatial autocorrelation in a biological model. Geographical Analysis, 30:331–354.
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Spatial autocorrelation analysis in plant population: An overview. (2015). Journal of Applied and Natural Science, 7(1), 501-513. https://doi.org/10.31018/jans.v7i1.639
