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Open AccessArticle

A New Approach for Interpreting the Morisita Index of Aggregation through Quadrat Size

1,*,† and 2,†
Department of Political Science, Geography, and Economics, Riverside City College, Riverside, CA 92506, USA
Department of Geography and Geology, University of Nebraska Omaha, Omaha, NE 68182, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2017, 6(10), 296;
Received: 6 July 2017 / Revised: 16 September 2017 / Accepted: 18 September 2017 / Published: 24 September 2017
PDF [11528 KB, uploaded 10 October 2017]


Spatial point pattern analysis is commonly used in ecology to examine the spatial distribution of individual organisms or events, which may shed light on the operation of underlying ecological processes driving the development of a spatial pattern. Commonly used quadrat-based methods of measuring spatial clustering or dispersion tend to be strongly influenced by the choice of quadrat size and population density. Using valley oak (Quercus lobata) stands at multiple sites, we show that values of the Morisita Index are sensitive to the choice of quadrat size, and that the comparative interpretation of the index for multiple sites or populations is problematic due to differences in scale and clustering intensity from site to site, which may call for different quadrat sizes for each site. We present a new method for analyzing the Morisita Index to estimate the appropriate quadrat size for a given site and to aid interpretation of the clustering index across multiple sites with local differences. By plotting the maximum clustering intensity (Imr) found across a range of quadrat sizes, we were able to describe how a spatial pattern changes when quadrat size varies and to identify scales of clustering and quadrat sizes for analysis of spatial patterns under different local conditions. Computing and plotting the instantaneous rate of change (first derivative of rMax), we were able to evaluate clustering across multiple sites on a standardized scale. The magnitude of the rMax first derivative is a useful tool to quantify the degree of crowding, dispersion, or random spatial distribution as a function of quadrat size. View Full-Text
Keywords: Morisita Index; clustering; spatial pattern Morisita Index; clustering; spatial pattern

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Hayes, J.J.; Castillo, O. A New Approach for Interpreting the Morisita Index of Aggregation through Quadrat Size. ISPRS Int. J. Geo-Inf. 2017, 6, 296.

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