%0 Journal Article %1 legendre_spatial_1993 %A Legendre, Pierre %D 1993 %J Ecology %K I autocorrelation, bias, cited, error highly p-value, spatial type %N 6 %P 1659--1673 %R 10.2307/1939924 %T Spatial Autocorrelation: Trouble or New Paradigm? %U http://www.esajournals.org/doi/abs/10.2307/1939924 %V 74 %X Autocorrelation is a very general statistical property of ecological variables observed across geographic space; it most common forms are patches and gradients. Spatial autocorrelation, which comes either from the physical forcing of environmental variables or from community processes, presents a problem for statistical testing because autocorrelated data violate the assumption of independence of most standard statistical procedures. The paper discusses first how autocorrelation in ecological variables can be described and measured, with emphasis on mapping techniques. Then, proper statistical testing in the presence of autocorrelation is briefly discussed. Finally, ways are presented of explicitly introducing spatial structures into ecological models. Two approaches are proposed; in the raw—data approach, the spatial structure takes the form of a polynomial of the x and y geographic coordinates of the sampling stations; in the matrix approach, the spatial structure is introduced in the form of a geographic distance matrix among locations. These two approaches are compared in the concluding section. A table provides a list of computer programs available for spatial analysis. See full-text article at JSTOR

@article{legendre_spatial_1993, abstract = {Autocorrelation is a very general statistical property of ecological variables observed across geographic space; it most common forms are patches and gradients. Spatial autocorrelation, which comes either from the physical forcing of environmental variables or from community processes, presents a problem for statistical testing because autocorrelated data violate the assumption of independence of most standard statistical procedures. The paper discusses first how autocorrelation in ecological variables can be described and measured, with emphasis on mapping techniques. Then, proper statistical testing in the presence of autocorrelation is briefly discussed. Finally, ways are presented of explicitly introducing spatial structures into ecological models. Two approaches are proposed; in the raw—data approach, the spatial structure takes the form of a polynomial of the x and y geographic coordinates of the sampling stations; in the matrix approach, the spatial structure is introduced in the form of a geographic distance matrix among locations. These two approaches are compared in the concluding section. A table provides a list of computer programs available for spatial analysis. See full-text article at JSTOR}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Legendre, Pierre}, biburl = {https://www.bibsonomy.org/bibtex/2a2d4c2345985c832bb85f8145a5c0d98/yourwelcome}, doi = {10.2307/1939924}, interhash = {29741cf8f181a393a7ce58af54773214}, intrahash = {a2d4c2345985c832bb85f8145a5c0d98}, issn = {0012-9658}, journal = {Ecology}, keywords = {I autocorrelation, bias, cited, error highly p-value, spatial type}, month = sep, number = 6, pages = {1659--1673}, shorttitle = {Spatial {Autocorrelation}}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Spatial {Autocorrelation}: {Trouble} or {New} {Paradigm}?}, url = {http://www.esajournals.org/doi/abs/10.2307/1939924}, urldate = {2013-01-23}, volume = 74, year = 1993 }