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IDENTIFYING AGGREGATION AND ASSOCIATION IN FULLY MAPPED SPATIAL DATA
Author(s) -
Coomes David A.,
Rees Mark,
Turnbull Lindsay
Publication year - 1999
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/0012-9658(1999)080[0554:iaaaif]2.0.co;2
Subject(s) - randomness , centroid , population , position (finance) , spatial analysis , poisson distribution , association (psychology) , ecology , statistical physics , computer science , mathematics , statistics , artificial intelligence , biology , physics , demography , finance , sociology , economics , philosophy , epistemology
We describe a clump recognition process that may be used to analyze fully mapped spatial data. Any given spatial pattern can be made less aggregated by replacing the closest‐together pair of plants by a single individual at their centroid position. By repeatedly amalgamating pairs of individuals in this way, an initially aggregated pattern can be reduced to one indistinguishable from complete spatial randomness (i.e. a two‐dimensional Poisson pattern). The clump recognition process provides information on the size structure of aggregates within a population. Randomizing the position of clump centers can be used to generate patterns that have similar aggregation characteristic to the original pattern. This property is used to develop Monte Carlo simulations for testing interspecific associations. We also discuss tests of association that are based on measuring segregation between clump centers. We illustrate the methods with a series of patterns from (1) simple, stochastic processes, (2) a spatially explicit population model, and (3) a dune annual community.