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TESTS FOR AGGREGATION AND SIZE‐BASED SAMPLE‐UNIT SELECTION WHEN SAMPLE UNITS VARY IN SIZE
Author(s) -
Connor Edward F.,
Hosfield Elizabeth,
Meeter Duane A.,
Niu Xufeng
Publication year - 1997
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(1997)078[1238:tfaasb]2.0.co;2
Subject(s) - sample size determination , statistics , selection (genetic algorithm) , poisson distribution , sample (material) , mathematics , poisson sampling , variance (accounting) , population , population size , null hypothesis , sampling (signal processing) , computer science , monte carlo method , chemistry , importance sampling , artificial intelligence , demography , accounting , slice sampling , chromatography , sociology , business , filter (signal processing) , computer vision
Previous tests for aggregation of organisms among sampling units that vary in size and tests for size‐based sample‐unit selection fail to account for variation in the size of the sample unit. When sample units vary in size, tests that assume equal‐sized sample units overestimate the degree of aggregation and have a tendency to find selection for large‐sized sample units. Previous tests for selection of large or small leaves by phytophagous insects have been biased toward detecting selection for large leaves when no leaf selection or selection for small leaves may actually be present, and toward detecting aggregation even when populations are Poisson random or repulsed. We derive explicitly the population mean and variance of the number of organisms per sample unit when the size of the sample unit is random, and we outline procedures to estimate the degree of aggregation and test for size‐based sample‐unit selection using a generalized linear model based on a Poisson null hypothesis of independent random placement.