MaxTemp:一种使遗传监测程序中估算Ne的时间方法的精度最大化的方法。

IF 5.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Robin S Waples, Michele M Masuda, Melanie E F LaCava, Amanda J Finger
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引用次数: 0

摘要

我们介绍了一个新的软件程序MaxTemp,它提高了遗传监测程序中估计有效种群大小(Ne)的时间方法的精度,这些方法越来越多地用于系统地跟踪全球生物多样性的变化。科学家和管理人员通常对每代人的Ne最感兴趣,要么是为了与单代人口普查规模(N)的估计相匹配,要么是为了评估特定管理行动或环境事件的后果。系统地采样每一代产生的单代估计时间F (F²)$$ \hat{F}\Big) $$的时间序列,然后可以用来估计Ne;然而,这些估计的精度相对较低,因为每个估计都只反映了遗传漂变的单一事件。系统抽样还产生了一系列多代时间估计,这些估计总体上包含了大量关于遗传漂变的信息,然而,这些信息很难解释。在这里,我们展示了如何利用多代时间估计中包含的附加信息来提高单个代的F´$$ \hat{F} $$的精度。在目标生成之前和之后使用来自额外一代的信息可以将F´$$ \hat{F} $$ (σ F´$$ {\sigma}_{\hat{F}} $$)的标准差降低高达50%, which not only tightens confidence intervals around N ̂ e $$ {\hat{N}}_e $$ but also reduces the incidence of extreme estimates, including infinite estimates of Ne. Practical application of MaxTemp is illustrated with data for a long-term genetic monitoring program for California delta smelt. A second feature of MaxTemp, which allows one to estimate Ne in an unsampled generation using a combination of temporal and single-sample estimates of Ne from sampled generations, is also described and evaluated.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MaxTemp: A Method to Maximise Precision of the Temporal Method for Estimating Ne in Genetic Monitoring Programs.

We introduce a new software program, MaxTemp, that increases precision of the temporal method for estimating effective population size (Ne) in genetic monitoring programs, which are increasingly used to systematically track changes in global biodiversity. Scientists and managers are typically most interested in Ne for individual generations, either to match with single-generation estimates of census size (N) or to evaluate consequences of specific management actions or environmental events. Systematically sampling every generation produces a time series of single-generation estimates of temporal F ( F ̂ ) $$ \hat{F}\Big) $$ , which can then be used to estimate Ne; however, these estimates have relatively low precision because each reflects just a single episode of genetic drift. Systematic sampling also produces an array of multigenerational temporal estimates that collectively contain a great deal of information about genetic drift that, however, can be difficult to interpret. Here, we show how additional information contained in multigenerational temporal estimates can be leveraged to increase precision of F ̂ $$ \hat{F} $$ for individual generations. Using information from one additional generation before and after a target generation can reduce the standard deviation of F ̂ $$ \hat{F} $$ ( σ F ̂ $$ {\sigma}_{\hat{F}} $$ ) by up to 50%, which not only tightens confidence intervals around N ̂ e $$ {\hat{N}}_e $$ but also reduces the incidence of extreme estimates, including infinite estimates of Ne. Practical application of MaxTemp is illustrated with data for a long-term genetic monitoring program for California delta smelt. A second feature of MaxTemp, which allows one to estimate Ne in an unsampled generation using a combination of temporal and single-sample estimates of Ne from sampled generations, is also described and evaluated.

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来源期刊
Molecular Ecology Resources
Molecular Ecology Resources 生物-进化生物学
CiteScore
15.60
自引率
5.20%
发文量
170
审稿时长
3 months
期刊介绍: Molecular Ecology Resources promotes the creation of comprehensive resources for the scientific community, encompassing computer programs, statistical and molecular advancements, and a diverse array of molecular tools. Serving as a conduit for disseminating these resources, the journal targets a broad audience of researchers in the fields of evolution, ecology, and conservation. Articles in Molecular Ecology Resources are crafted to support investigations tackling significant questions within these disciplines. In addition to original resource articles, Molecular Ecology Resources features Reviews, Opinions, and Comments relevant to the field. The journal also periodically releases Special Issues focusing on resource development within specific areas.
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