JOINING THE INCOMPATIBLE: EXPLOITING PURPOSIVE LISTS FOR THE SAMPLE-BASED ESTIMATION OF SPECIES RICHNESS

• 成果类型: Article
• 署名作者: Chiarucci, Alessandro; Di Biase, Rosa Maria; Fattorini, Lorenzo; Marcheselli, Marzia; Pisani, Caterina
• 署名单位: University of Bologna; Tuscia University; University of Siena
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/17-AOAS1126
• 发表日期: 2018
• 页码: 1679-1699
• 关键词: nonparametric-estimation; population-size; performance; rarefaction; diversity; number; Extrapolation; biodiversity; abundance; precision

摘要

The lists of species obtained by purposive sampling by field ecologists can be used to improve the sample-based estimation of species richness. A new estimator is here proposed as a modification of the difference estimator in which the species inclusion probabilities are estimated by means of the species frequencies from incidence data. If the species list used to support the estimation is complete the estimator guesses the true richness without error. In the case of incomplete lists, the estimator provides values invariably greater than the number of species detected by the combination of sample-based and purposive surveys. An asymptotically conservative estimator of the mean squared error is also provided. A simulation study based on two artificial communities is carried out in order to check the obvious increase in accuracy and precision with respect to the widely applied estimators based on the sole sample information. Finally, the proposed estimator is adopted to estimate species richness in the Maremma Regional Park, Italy.