Open AccessExtrapolating inventory results into biodiversity estimates and the importance of stopping rules
Author(s) -
PETERSON A.,
SLADE NORMAN
Publication year - 1998
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1046/j.1365-2699.1998.00021.x
Subject(s) - raw data , sampling (signal processing) , inference , stopping rule , computer science , biodiversity , diversity (politics) , license , statistics , ecology , econometrics , mathematics , artificial intelligence , biology , mathematical optimization , filter (signal processing) , computer vision , operating system , sociology , anthropology
Seven methods for predicting species diversity from inventory data were tested based on two model data sets. These data sets, derived from state automobile license plates observed in Mexico City and Lawrence, Kansas, had the advantage of providing known ‘communities’ to be sampled, allowing evaluation of different inference methods. Of the seven methods, those of Chao (1984), Clench (Soberón & LLorente, 1993), and model M th of CAPTURE (Otis et al ., 1978) were the most robust. Error inherent in calculations based on raw data was reduced substantially using a series of bootstrap manipulations. We recommend that optimal design of inventories should include stopping rules based on precision of results rather than on effort expended, an approach that offers considerable advantages, in terms of both accuracy of results and efficiency of sampling efforts.