Antagonistic coevolution between multiple quantitative traits: Matching dynamics can arise from difference interactions
Antagonistic coevolution between multiple quantitative traits: Matching dynamics can arise from difference interactions
复制标题
多个数量性状之间的对抗性协同进化：不同的相互作用可以产生匹配动态
DOI：
10.1002/1438-390x.12022
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发表时间：
2019
影响因子：
1.7
通讯作者：
Swati Patel
中科院分区：
文献类型：
--
作者：
Masato Yamamichi;Kelsey Lyberger;Swati Patel
协同进化是种群生态学复杂动态的主要驱动因素之一。历史上，受害者-剥削者系统中的对抗性协同进化一直是一个特别感兴趣的话题，涉及具有各种遗传结构（例如，所涉及的基因数量）和相互作用影响的特征。例如，为了成功利用，利用者可能需要具有与受害者“匹配”的特征（即匹配交互），或者需要具有超过受害者特征的特征（即差异交互）。不同类型的特征有不同的模型，包括孟德尔（离散）和数量（连续）特征。对于具有多个孟德尔性状的模型，最近的研究表明，由于具有较大性状值的代价的多重差异相互作用，可能出现以匹配相互作用形式出现的拮抗共同进化模式。在这里，我们将他们的发现概括为数量特征，并类似地表明，与成本的多维差异相互作用有时在质量上与匹配相互作用相同。虽然之前的数量遗传学研究使用匹配和差异框架之间的二分法来探索共同进化动力学，但我们认为探索多维性状空间对于检验从一维性状获得的结果的普遍性很重要。
Coevolution is one of the major drivers of complex dynamics in population ecology. Historically, antagonistic coevolution in victim‐exploiter systems has been a topic of special interest, and involves traits with various genetic architectures (e.g., the number of genes involved) and effects on interactions. For example, exploiters may need to have traits that “match” those of victims for successful exploitation (i.e., a matching interaction), or traits that exceed those of victims (i.e., a difference interaction). Different models exist which are appropriate for different types of traits, including Mendelian (discrete) and quantitative (continuous) traits. For models with multiple Mendelian traits, recent studies have shown that antagonistic coevolutionary patterns that appear as matching interactions can arise due to multiple difference interactions with costs of having large trait values. Here we generalize their findings to quantitative traits and show, analogously, that the multidimensional difference interactions with costs sometimes behave qualitatively the same as matching interactions. While previous studies in quantitative genetics have used the dichotomy between matching and difference frameworks to explore coevolutionary dynamics, we suggest that exploring multidimensional trait space is important to examine the generality of results obtained from one‐dimensional traits.