Abstract
SADIE (Spatial Analysis by Distance Indices) is designed specifically to quantify patterns in spatially-referenced count-based data. It was developed for dealing with data that can be considered ‘patchy’. Such distributions are commonly found, for example, in insect populations where discrete patches of individuals are often evident. The distributions of such populations have ‘hard edges’, with patches and gaps occurring spatially. In these cases variance of abundance does not vary smoothly, but discontinuously. In this paper we outline the use of SADIE and provide free access to the SADIE software suite, establishing Rethinking Ecology as its permanent home. Finally, we review the use of SADIE and demonstrate its use in a wide variety of sub-disciplines within the general field of ecology.
Highlights
It is over twenty years since Joe Perry introduced and developed the SADIE (Spatial Analysis by Distance Indices) methodology to study spatial patterns in count-based data where locations are specified (Perry and Hewitt 1991; Perry 1995; Perry 1998)
Other approaches available to ecologists that allow the analysis of spatially-referenced data either assume a smooth surface for abundance with gradual change, so that populations may be viewed as contour maps of abundance, or allow the description of individuals at specific locations through, for example, nearest-neighbour techniques or within area-based entities
The SADIE approach has four key components: (i) an index of aggregation (Ia), quantifying the presence and degree of clustering; (ii) indices that quantify the presence of neighbourhoods of relatively high counts (Vi) or low counts (Vj), termed patches and gaps respectively; (iii) red-blue plots that provide a visual representation of the degree of clustering; and (iv) an association index (X), analogous to a correlation coefficient that represents the association or dissociation between two datasets which may be mapped to create plum-green plots
Summary
It is over twenty years since Joe Perry introduced and developed the SADIE (Spatial Analysis by Distance Indices) methodology to study spatial patterns in count-based data where locations are specified (Perry and Hewitt 1991; Perry 1995; Perry 1998). The papers by Dungan et al (2002) and Perry et al (2002) provide a useful general introduction into analytical techniques that may be used to explore spatial pattern in ecological datasets.
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