Abstract：
The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.
摘要：
性个体的能力是许多行为和生态调查的重要组成部分，并为保护和物种管理中使用的人口模型提供信息。然而,许多鸟类很难使用形态特征或传统的分子性别方法进行性别鉴定。在这项研究中,我们使用定量PCR（qPCR）数据开发了对鸟类进行性别鉴定的概率模型。首先,我们量化了一组六个性别相关基因的基因拷贝数分布，包括性别决定基因DMRT1，适用于17种物种和7种鸟类（n=150）的个体。利用这些数据，我们建立了用于性别鉴定的预测逻辑模型，并使用来自51个物种和13个订单（n=209）的独立样本测试了它们的性能。使用与性别最高度相关的两个基因座的模型比使用全套性别相关基因座的模型具有更高的准确性，在所有分类层次的分析中。当要分配的个体是模型构建中使用的物种时，性别鉴定非常准确。我们的分析方法广泛适用于跨越数百万年进化差异的各种新鸟谱系。与以前的方法不同，我们的概率框架将qPCR测量的不确定性以及物种内的生物变异纳入决策规则.我们预计这种方法将对鸟类进行性别鉴定有用，包括那些高度关注保护和/或生存价值的人，事实证明，使用传统方法很难做爱。此外,本文提出的一般分析框架也可能适用于其他具有性染色体的生物体。
