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A comparison of sexual selection versus random selection with respect to extinction and speciation rates using individual based modeling and machine learning

机译:使用基于个体的建模和机器学习对灭绝和物种形成率进行性选择和随机选择的比较

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It is not clear from empirical and simulation studies that populations with females who employ sexual selection have any evolutionary advantages over populations where mates are randomly selected. There is an ongoing debate regarding whether speciation rates and extinction rates differ significantly between sexual selection and random selection. Although there is evidence that sexual selection drives speciation in some animal species,the biological community remains divided regarding this relationship. Similarly, multiple studies point to a possible connection between sexual selection and extinction rates, although there is no clear consensus regarding this connection: Some studies suggest that sexual selection increases the extinction rate whereas others suggest that sexual selection actually shields populations from extinction. Using individual based computer simulations, we found a significant difference between sexual selection and random selection, with respect to speciation rates, extinction rates and species turnover rates: It turned out that speciation rates were significantly higher for random selection, possibly to help offset the higher extinction and turnover rates. Moreover, we used machine learning to generate rules to help predict rates of speciation and extinction both for sexual selection and random selection. Not only were our rules corroborated by empirical studies but they also help to resolve some disputes regarding the role of sexual selection with respect to speciation rates and extinction rates.
机译:从经验和模拟研究中尚不清楚,采用性别选择的女性人口是否比随机选择伴侣的人口具有任何进化优势。关于性别选择和随机选择之间物种形成率和灭绝率是否存在显着差异的争论一直在进行。尽管有证据表明,性选择会驱动某些动物的物种形成,但生物群落对此关系仍存在分歧。同样,尽管没有明确的共识,但多项研究指出性别选择与灭绝率之间可能存在联系:一些研究表明,性选择会增加灭绝率,而另一些研究则表明,性别选择实际上可以使人们免于灭绝。使用基于个体的计算机模拟,我们发现性别选择和随机选择之间在物种形成率,灭绝率和物种更新率方面存在显着差异:事实证明,随机选择的物种形成率明显更高,可能有助于抵消更高的物种选择率。灭绝率和流失率。此外,我们使用机器学习来生成规则,以帮助预测性选择和随机选择的物种形成和灭绝速度。经验研究不仅证实了我们的规则,而且还有助于解决有关物种选择在物种形成率和灭绝率方面的作用的一些争议。

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