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视野子采样:一种用于绝对丰度的新型“外来标记”方法,经模拟和微化石案例研究验证。

Field-of-view subsampling: A novel 'exotic marker' method for absolute abundances, validated by simulation and microfossil case studies.

作者信息

Mays Chris, Amores Marcos, Mays Anthony

机构信息

Geological-Palaeontological Department, Natural History Museum Vienna, Vienna, Austria.

School of Biological, Earth and Environmental Sciences, Environmental Research Institute, University College Cork, Cork, Ireland.

出版信息

PLoS One. 2025 May 6;20(5):e0320887. doi: 10.1371/journal.pone.0320887. eCollection 2025.

Abstract

Key parameters of biological systems-e.g., productivity, population sizes, biomass-are best expressed as absolute values. Exotic markers (e.g., Lycopodium spores introduced into microfossil populations) have long been used to estimate population sizes from representative samples. However, the traditional approach-the 'linear method' herein-can be extremely time consuming and impractical for routine use. Here, we present a new variant of this technique: the 'field-of-view subsampling method' (FOVS method). This new method requires a few simple, easily obtainable statistical parameters, beyond the standard inputs for the traditional linear method. The FOVS method adds error from sample heterogeneity, but enables the collection of very large sample sizes with low additional effort. We compared the FOVS and linear methods with two case studies: 1, Monte Carlo simulations to validate the methods with idealised datasets; and 2, terrestrial organic microfossils from Permian-Triassic rock strata in southeastern Australia as 'real-world' empirical datasets. Three output parameters were measured: 1, absolute abundance; 2, precision (=error rate); and 3, data collection effort (typically, this translates to data collection time). The linear method showed superior efficiency only for assemblages with very low specimen densities and/or near-equivalent target-to-marker ratios, conditions we predict are rare under real-world conditions. In contrast, the FOVS method provided greater precision and/or reduced effort under almost all conditions, without sacrificing accuracy. Although originally developed for microfossils, the new method may apply to any spatial data collection where markers of known quantity can be introduced to a population. Given its demonstrable increased speed and precision, we recommend the FOVS method as the new standard for such absolute abundance estimates. Guidelines and a user-friendly digital interface for implementing both of these count methods are provided, in addition to simulation codes aimed to assist readers in designing their own experiments.

摘要

生物系统的关键参数,如生产力、种群规模、生物量,最好用绝对值来表示。外来标记物(如引入微化石种群的石松孢子)长期以来一直被用于从代表性样本中估计种群规模。然而,传统方法(本文中的“线性方法”)可能极其耗时,且不适合常规使用。在此,我们提出了该技术的一种新变体:“视野子采样方法”(FOVS方法)。这种新方法除了传统线性方法的标准输入外,还需要一些简单且易于获得的统计参数。FOVS方法增加了样本异质性带来的误差,但能够以较低的额外工作量收集非常大的样本量。我们通过两个案例研究比较了FOVS方法和线性方法:1. 蒙特卡洛模拟,用理想化数据集验证方法;2. 来自澳大利亚东南部二叠纪 - 三叠纪岩层的陆地有机微化石作为“真实世界”的实证数据集。测量了三个输出参数:1. 绝对丰度;2. 精度(=错误率);3. 数据收集工作量(通常,这转化为数据收集时间)。线性方法仅在标本密度非常低和/或目标与标记物比率近乎相等的组合中显示出更高的效率,我们预测这些条件在现实世界中很少见。相比之下,FOVS方法在几乎所有条件下都提供了更高的精度和/或减少了工作量,同时不牺牲准确性。尽管该新方法最初是为微化石开发的,但它可能适用于任何可以将已知数量的标记物引入种群的空间数据收集。鉴于其明显提高的速度和精度,我们推荐FOVS方法作为此类绝对丰度估计的新标准。除了旨在帮助读者设计自己实验的模拟代码外,还提供了实施这两种计数方法的指南和用户友好的数字界面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fcd/12054932/fcbf4454f562/pone.0320887.g001.jpg

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