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检测多样性:估计物种多样性的新兴方法。

Detecting diversity: emerging methods to estimate species diversity.

机构信息

Department of Environmental Science, Policy and Management, 130 Mulford Hall, University of California, Berkeley, CA 94720-3110, USA.

Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA.

出版信息

Trends Ecol Evol. 2014 Feb;29(2):97-106. doi: 10.1016/j.tree.2013.10.012. Epub 2013 Dec 5.

Abstract

Estimates of species richness and diversity are central to community and macroecology and are frequently used in conservation planning. Commonly used diversity metrics account for undetected species primarily by controlling for sampling effort. Yet the probability of detecting an individual can vary among species, observers, survey methods, and sites. We review emerging methods to estimate alpha, beta, gamma, and metacommunity diversity through hierarchical multispecies occupancy models (MSOMs) and multispecies abundance models (MSAMs) that explicitly incorporate observation error in the detection process for species or individuals. We examine advantages, limitations, and assumptions of these detection-based hierarchical models for estimating species diversity. Accounting for imperfect detection using these approaches has influenced conclusions of comparative community studies and creates new opportunities for testing theory.

摘要

物种丰富度和多样性的估计是群落和宏观生态学的核心内容,并且经常用于保护规划。常用的多样性指标主要通过控制采样努力来考虑未检测到的物种。然而,物种、观察者、调查方法和地点之间的个体检测概率可能会有所不同。我们回顾了通过分层多物种占有率模型 (MSOM) 和多物种丰度模型 (MSAMs) 来估计α、β、γ和集合多样性的新兴方法,这些模型通过在物种或个体的检测过程中明确纳入观测误差来进行估计。我们考察了这些基于检测的分层模型在估计物种多样性方面的优势、限制和假设。使用这些方法来解释不完全检测已经影响了比较群落研究的结论,并为测试理论创造了新的机会。

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