Community-Level Selection: Laboratory Experiments and Computer Simulations

社区级选择：实验室实验和计算机模拟

• 批准号: 9707650
• 负责人: David Sloan Wilson
• 金额: $ 22.14万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-01 至 2000-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9707650&HistoricalAwards=false
• 关键词: Community; Level; Selection; Laboratory; Experiments

Wilson, D. S. DEB-9707650 Biological communities have often been described as functionally organized units, similar to single organisms in the harmony and coordination of their parts. The evolution of adaptations at any level of the biological hierarchy, however, requires a corresponding process of natural selection at that level. This grant proposal explores the process of community-level selection with laboratory experiments and computer simulations. In the laboratory, a population of isolated soil communities will be established and scored with respect to a phenotypic character (plant growth). The highest and lowest scoring communities will be used as the "parents" to colonize a new set of "offspring" communities. This experimental design is identical to standard artificial selection experiments except that the selected units are communities rather than individuals. If the experiment is successful, it will produce soil communities that are functionally designed to increase (or decrease) plant growth. The computer simulations will allow the same process to be explored in which the interactions among the species can be specified in detail. Artificial selection at the individual level has been employed for centuries to develop strains of plants and animals that are useful to man. The same procedure can potentially be used to evolve multi-species communities that interact as a coordinated team to solve important practical problems, such as promoting plant growth, excluding a target species from a community, or degrading a toxic compound. Artificially selected communities may provide better solutions than artificially selected individuals for the same reason that coordinated teams of people can outperform single individuals. The advantages of "teamwork" are obvious; the problem is how to develop the teams. The proposed research may provide a simple method that so far has been overlooked.

D·S·DEB-9707650生物群落经常被描述为功能上有组织的单位，在其各部分的和谐和协调方面类似于单个有机体。然而，在生物层级的任何层面上的适应的进化，都需要在该层面上进行相应的自然选择过程。这份赠款提案通过实验室实验和计算机模拟探索了社区层面的选择过程。在实验室中，将建立一个孤立的土壤群落种群，并根据表型特征(植物生长)对其进行评分。得分最高和最低的社区将被用作“父母”来殖民一组新的“后代”社区。这个实验设计与标准的人工选择实验相同，只是选择的单位是群落而不是个人。如果实验成功，它将产生功能上旨在增加(或减少)植物生长的土壤群落。计算机模拟将允许探索相同的过程，其中可以详细说明物种之间的相互作用。几个世纪以来，在个体水平上的人工选择一直被用来培育对人类有用的动植物品系。同样的程序可能被用来进化多物种群落，这些群落作为一个协调的团队相互作用，以解决重要的实际问题，如促进植物生长，将目标物种排除在群落之外，或降解有毒化合物。人工选择的社区可能提供比人工选择的个人更好的解决方案，这与协调的人员团队可以比单个个人表现更好的原因是相同的。“团队合作”的优势是显而易见的，问题是如何发展团队。拟议中的研究可能会提供一种到目前为止被忽视的简单方法。