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Artificial neural networks in models of specialization guild evolution and sympatric speciation

机译:专业化行会演变和同胞物种形成模型中的人工神经网络

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摘要

Sympatric speciation can arise as a result of disruptive selection with assortative mating as a pleiotropic by-product. Studies on host choice, employing artificial neural networks as models for the host recognition system in exploiters, illustrate how disruptive selection on host choice coupled with assortative mating can arise as a consequence of selection for specialization. Our studies demonstrate that a generalist exploiter population can evolve into a guild of specialists with an ‘ideal free’ frequency distribution across hosts. The ideal free distribution arises from variability in host suitability and density-dependent exploiter fitness on different host species. Specialists are less subject to inter-phenotypic competition than generalists and to harmful mutations that are common in generalists exploiting multiple hosts.When host signals used as cues by exploiters coevolve with exploiter recognition systems, our studies show that evolutionary changes may be continuous and cyclic. Selection changes back and forth between specialization and generalization in the exploiters, and weak and strong mimicry in the hosts, where non-defended hosts use the host investing in defence as a model. Thus, host signals and exploiter responses are engaged in a red-queen mimicry process that is ultimately cyclic rather then directional. In one phase, evolving signals of exploitable hosts mimic those of hosts less suitable for exploitation (i.e. the model). Signals in the model hosts also evolve through selection to escape the mimic and its exploiters. Response saturation constraints in the model hosts lead to the mimic hosts finally perfecting its mimicry, after which specialization in the exploiter guild is lost. This loss of exploiter specialization provides an opportunity for the model hosts to escape their mimics. Therefore, this cycle then repeats.We suggest that a species can readily evolve sympatrically when disruptive selection for specialization on hosts is the first step. In a sexual reproduction setting, partial reproductive isolation may first evolve by mate choice being confined to individuals on the same host. Secondly, this disruptive selection will favour assortative mate choice on genotype, thereby leading to increased reproductive isolation.
机译:同卵异形可能是由于选择交配和多效副产物的杂乱交配而引起的。关于宿主选择的研究,利用人工神经网络作为开发人员中宿主识别系统的模型,说明了由于选择进行专业化而导致对宿主选择的破坏性选择以及分类交配是如何产生的。我们的研究表明,通才剥削者群体可以演变成专家行会,并且在主机之间具有“理想的”频率分布。理想的自由分布源于宿主适应性的变化以及不同宿主物种上密度依赖的剥削者适应性的变化。与多才多艺的专家相比,专家们不易受到表型间的竞争,也不会受到利用多名宿主的多才多艺者中常见的有害突变的影响。选择在剥削者的专业化和泛化之间以及在宿主的虚弱模仿之间来回切换,其中无防御的宿主将宿主投资于防御作为模型。因此,主机信号和开发者响应参与了红色女王模仿过程,该过程最终是循环的,而不是定向的。在一个阶段中,可利用主机的不断发展的信号模仿了不太适合利用主机的信号(即模型)。模型宿主中的信号也会通过选择来进化,以逃避模仿者及其开发者的攻击。模型主机中的响应饱和约束导致模拟主机最终完善了其模拟,此后,剥夺了开发者行会的专业性。漏洞利用者专业化的丧失为模型宿主提供了逃脱其模仿的机会。因此,此循环便会重复。我们建议,当针对宿主进行专门化的破坏性选择是第一步时,一个物种可以很容易地同胞进化。在有性生殖环境中,部分生殖隔离可能首先通过将配偶选择限制在同一宿主上的个体而演变出来。其次,这种破坏性选择将有利于基因型的配偶选择,从而导致生殖隔离增加。

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