2026-04-13T05:58:25Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/97202024-06-18T14:31:37Zcom_2072_452955com_2072_2054col_2072_452959

00925njm 22002777a 4500 dc Quintana Pou, Xavier author Brucet Balmaña, Sandra author Boix Masafret, Dani author López i Flores, Rocío author Gascón Garcia, Stéphanie author Badosa i Salvador, Anna author Sala Genoher, Jordi author Moreno i Amich, Ramon author Egozcue, Juan José author 2008 The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposed as the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation This work was supported by the grants from the Ministerio de Ciencia y Tecnología of the Spanish Government, Programa Nacional de Biodiversidad, Ciencias de la Tierra y Cambio Global (ref. CGL2004- 05433/BOS) and the project MEASURE (MTM2006-03040/) http://hdl.handle.net/10256/9720 Mostreig (Estadística) Sampling (Statistics) Estimació de paràmetres Parameter estimation Funcions de variables complexes Functions of complex variables A nonparametric method for the measurement of size diversity with emphasis on data standardization