论文标题
从深度到当代动力学的生态进化:时间尺度和评分调节器的作用
Eco-evolution from deep time to contemporary dynamics: the role of timescales and rate modulators
论文作者
论文摘要
人们认为生态进化动力学或简短的生态进化涉及快速人口统计学(生态学)和同样快速的表型变化(进化),从而导致新颖,新兴的系统行为。这种对当代动力学的关注可能是由于从古典实验室缩影和自然种群(包括标志性的特立尼达鸟窝)积累了快速进化的证据所致。我们认为,这种观点太狭窄,阻止了生态与进化的成功整合。在保持生态进化涉及出现的同时,我们强调,这对于缓慢的生态学和进化也可能是正确的,这些生态学和进化增长了数千年或数百万年,例如河流地貌和植物进化之间的反馈。因此,我们将地貌学和生物元水平的反馈整合到生态进化中,从而大大扩展了其范围。最重要的是,我们强调的是,生态进化系统不必在状态空间中冻结:我们确定了生态和进化速率的调节剂,例如温度或对突变的敏感性,这可以同步或对同步生态学和进化。我们推测,全球变化可能会增加生态进化和新兴系统行为的发生,这代表了预测的重大挑战。我们的观点代表了一种尝试整合跨学科的生态和进化的尝试,从基因调节网络到地貌,从当代动力学到深度时代。
Eco-evolutionary dynamics, or eco-evolution for short, are thought to involve rapid demography (ecology) and equally rapid phenotypic changes (evolution) leading to novel, emergent system behaviours. This focus on contemporary dynamics is likely due to accumulating evidence for rapid evolution, from classical laboratory microcosms and natural populations, including the iconic Trinidadian guppies. We argue that this view is too narrow, preventing the successful integration of ecology and evolution. While maintaining that eco-evolution involves emergence, we highlight that this may also be true for slow ecology and evolution which unfold over thousands or millions of years, such as the feedbacks between riverine geomorphology and plant evolution. We thereby integrate geomorphology and biome-level feedbacks into eco-evolution, significantly extending its scope. Most importantly, we emphasize that eco-evolutionary systems need not be frozen in state-space: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can synchronize or desynchronize ecology and evolution. We speculate that global change may increase the occurrence of eco-evolution and emergent system behaviours which represents substantial challenges for prediction. Our perspective represents an attempt to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from contemporary dynamics to deep time.