作者: Jacob W. Malcom
DOI: 10.1371/JOURNAL.PONE.0014799
关键词: Adaptation 、 Genetic architecture 、 Gene regulatory network 、 Artificial intelligence 、 Machine learning 、 Theoretical ecology 、 Population 、 Evolutionary ecology 、 Ecology 、 Trait 、 Biology 、 Competitive advantage
摘要: Ecologists have increasingly come to understand that evolutionary change on short time-scales can alter ecological dynamics (and vice-versa), and this idea is being incorporated into community ecology research programs. Previous has suggested the size topology of gene network underlying a quantitative trait should constrain or facilitate adaptation thereby population dynamics. Here, I consider scenario in which two species with different genetic architectures compete evolve fluctuating environments. An important trade-off emerges between adaptive accuracy speed, driven by ecologically-critical rate environmental change. Smaller, scale-free networks confer competitive advantage rapidly-changing environments, but larger permit increased when sufficiently slow allow time adapt. As differences characteristics increase, time-to-resolution competition decreases. These results augment refine previous conclusions about implications architecture traits, emphasizing role accuracy. Along work, particular considering connectivity, these provide set expectations for what we may observe as field genomics develops.