Adaptation in complex scenarios

复杂场景适配

• 批准号: NE/E013066/1
• 负责人: Sinead Collins
• 金额: $ 36.09万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FE013066%2F1
• 关键词: Adaptation; complex; scenarios

The observation that organisms are adapted to their environment is obvious, yet we can only explain how this occurs in extreme scenarios such as the evolution of antibiotic and pesticide resistance, heavy metal tolerance, and starvation. Typical studies that aim to understand how organisms adapt following an environmental change proceed by placing a population in an environment to which it is poorly adapted. This stressful environment is usually extreme so as to provoke an observable response, and is also usually static. For example, a plant population may be transferred from a nutrient-rich environment to one where a particular nutrient is nearly absent. The population then adapts by the sequential fixation of novel mutations that increase its growth and reproduction in the new environment. Theory and experiments that use this framework have allowed us to describe how fast a population adapts over time, how many mutations are involved in a typical round of adaptation, and how many different outcomes we expect if the same population adapts to the same stressful environment many times. However, very few environmental changes outside of laboratories and natural disasters involve the sudden transition from one relatively stable environment to a second, drastically different, stable environment. Instead, environments tend to change gradually over time, such that most populations exist in an environment that is only slightly different from that of a recent ancestor, even though it may differ substantially from a more distant ancestor. Global change is an example of this, where plant populations are currently exposed to levels of carbon dioxide more than twice as high as those of the last glaciation 10,000 years ago, but only a few percent higher than those of a decade ago. Thus, at any given time, populations are adapting to a subtle shift in environment, but the environment does not hold still while they do it. This suggests that studies of adaptation should incorporate both the magnitude and rate of environmental change. A second consideration is that populations do not adapt in isolation, but must compete with other populations while they are doing so. If one considers two populations in a changing environment, it is possible that one population excludes the other, but it is also possible that the populations adapt during this succession, such that both the community composition (which species are present) as well as the genetic makeup of a given species changes over time. For example, if we wish to guess how much carbon will be taken up by oceans in the future, we need to know which species of phytoplankton will be dominant as well as if the future populations of the dominant species will take up carbon at much the same rate as contemporary populations of that same species. Because of this, it is important to know how and if ecological (competitive) and evolutionary (adaptive) processes interact. My research uses laboratory experiments, computer simulations, and studies of natural populations to examine how large populations of single-celled algae respond to different rates of environmental change, either alone or in communities. Using a microbial model system allows me to do experiments using very large populations and span hundreds of generations, which allows the fixation of novel beneficial mutations by natural selection. One of these environmental changes is elevated CO2. Because laboratory systems are necessarily artificial, I will look for similar patterns of adaptation in algal communities from naturally occurring high CO2 springs. This work provides insight into one of the most fundamental processes in biology, that of adaptation. In addition, this work uses ideas and techniques from many disciplines, namely evolutionary biology, ecology, population genetics and molecular genetics. This sort of interdisciplinary, problem-based approach allows me to examine complex scenarios where the theory to do so may be lack

关于生物被适应其环境的观察是显而易见的，但是我们只能解释这是如何在极端情况下发生的，例如抗生素和农药耐药性，重金属耐受性和饥饿。旨在了解环境变化后生物如何适应环境变化后如何适应人口来适应其适应不良的环境，进行的典型研究。这种压力大的环境通常是极端的，以引起可观察到的反应，并且通常也是静态的。例如，植物种群可以从营养丰富的环境转移到几乎没有特定营养素的环境中。然后，人口通过新型突变的顺序固定来适应，从而增加了其在新环境中的生长和繁殖。使用该框架的理论和实验使我们能够描述一个人群随着时间的推移适应的速度，在典型的适应中涉及多少突变，以及如果相同的人群多次适应相同的压力环境，我们期望有多少不同的结果。但是，在实验室和自然灾害之外的环境变化很少，涉及从一个相对稳定的环境突然过渡到第二个，截然不同，稳定的环境。取而代之的是，环境往往会随着时间的流逝而逐渐变化，因此大多数人群都存在于与最近祖先略有不同的环境中，即使它可能与更遥远的祖先有很大不同。全球变化就是一个例子，目前，植物种群暴露于二氧化碳水平的水平是10，000年前最后一次冰川的两倍以上，但仅比十年前的二氧化碳水平高出几％。因此，在任何给定时间，人口都在适应环境的细微转变，但是环境在这样做的过程中并不保持。这表明适应研究应纳入环境变化的幅度和速度。第二个考虑因素是，人口并非孤立地适应，而必须在其他人群中与其他人群竞争。如果一个人在不断变化的环境中考虑了两个人群，那么一个人群可能不包括另一个人群，但人口在此继承过程中也有可能适应，因此社区组成（存在哪种物种）以及给定物种的遗传构成随着时间的推移而变化。例如，如果我们想猜测将来的海洋将占用多少碳，那么我们需要知道哪种种类的浮游植物将是主导的，以及是否将未来物种的未来种群与同一物种的当代种群相同。因此，重要的是要知道生态（竞争性）和进化（自适应）过程是否相互作用很重要。我的研究使用实验室实验，计算机模拟和天然种群的研究来检查单独或社区中的单细胞藻类人群对不同的环境变化率的反应。使用微生物模型系统，我可以使用非常大的人群进行实验，并跨越数百代，从而可以通过自然选择固定新的有益突变。这些环境变化之一是二氧化碳升高。由于实验室系统必然是人造的，因此我将在天然存在的高二氧化碳弹簧中寻找类似的适应模式。这项工作提供了对生物学中最基本的过程之一，即适应的见解。此外，这项工作使用了许多学科的思想和技术，即进化生物学，生态学，人群遗传学和分子遗传学。这种跨学科的基于问题的方法使我能够检查理论可能缺乏理论的复杂场景

项目成果

Experimental evolution and global change

实验进化和全球变化

• 期刊: Evolutionary applications
• 影响因子: 4.1
• 作者: Sinead Collins (Author)

Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change-A review

• DOI: 10.1111/gcb.14102
• 发表时间: 2018-06-01
• 期刊: GLOBAL CHANGE BIOLOGY
• 影响因子: 11.6
• 作者: Boyd, Philip W.;Collins, Sinead;Poertner, Hans-Otto
• 通讯作者: Poertner, Hans-Otto

Adaptive walks toward a moving optimum

• DOI: 10.1534/genetics.107.072926
• 发表时间: 2007-06-01
• 期刊: GENETICS
• 影响因子: 3.3
• 作者: Collins, Sinead;de Meaux, Juliette;Acquisti, Claudia
• 通讯作者: Acquisti, Claudia

Fold or hold: experimental evolution in vitro.

• DOI: 10.1111/jeb.12233
• 发表时间: 2013-10
• 期刊: Journal of evolutionary biology
• 影响因子: 2.1
• 作者: Collins S;Rambaut A;Bridgett SJ
• 通讯作者: Bridgett SJ