DISSERTATION RESEARCH: DETECTING SELECTION ACROSS SPATIAL SCALES AND ENVIRONMENTAL GRADIENTS

论文研究：检测跨空间尺度和环境梯度的选择

• 批准号: 1407300
• 负责人: Gordon Luikart
• 金额: $ 1.84万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1407300&HistoricalAwards=false
• 关键词: DISSERTATION; RESEARCH; DETECTING; SELECTION; ACROSS

Species that inhabit Northern latitudes are expected to experience dramatic climate changes in the coming century, and both basic and applied scientists need information about the effect of these changes on living natural resources. This project will produce useful statistical tools and baseline data for long-term population monitoring and management, and deliver important information regarding the ability of wildlife to persist in a changing landscape. One PhD student will be trained through this research, and will communicate results to collaborating public lands agencies for direct application to defining management units, modeling future distribution and abundance of wildlife populations, and planning for potential species range shifts. Results will also be broadly disseminated through public forums including invited speaking events, professional meetings, public radio, and school presentations.When climate and landscape patterns change beyond an organism's physiological threshold, the options for persistence of that population are limited to adaptation or migration. Reliable identification of genes underlying signatures of natural selection is a necessary component of detecting the spatial scale at which adaptation to local conditions occurs, and provides critical information for understanding evolution and population resilience. However, the relative sensitivity of statistical tests to detect selection has not been rigorously examined, even less so for populations in natural landscapes. This project investigates the accuracy and precision of our ability detect local adaptation in naturally occurring populations at both smaller, landscape level scales, and broader, species-level scales. A combination of computer simulations and empirical information from novel genetic techniques will facilitate investigation of the influence of environmental variation, different population sizes, and migration rates on local adaptation of a northern alpine wild sheep throughout its range.

预计居住在北纬地区的物种将在下个世纪经历剧烈的气候变化，基础科学家和应用科学家都需要有关这些变化对生物自然资源影响的信息。 该项目将为长期种群监测和管理提供有用的统计工具和基线数据，并提供有关野生动物在不断变化的景观中生存能力的重要信息。一名博士生将通过这项研究接受培训，并将结果传达给合作的公共土地机构，以便直接应用于定义管理单位、模拟野生动物种群的未来分布和丰度，以及规划潜在的物种范围变化。 结果还将通过公共论坛广泛传播，包括受邀演讲活动、专业会议、公共广播和学校演讲。当气候和景观模式的变化超出生物体的生理阈值时，该种群持续存在的选择仅限于适应或迁移。 可靠地识别自然选择特征背后的基因是检测适应当地条件的空间尺度的必要组成部分，并为理解进化和种群复原力提供关键信息。 然而，检测选择的统计检验的相对敏感性尚未经过严格检验，对于自然景观中的种群来说更是如此。 该项目研究了我们在较小的景观水平尺度和更广泛的物种水平尺度上检测自然发生的种群的局部适应能力的准确性和精确性。 计算机模拟和来自新型遗传技术的经验信息的结合将有助于研究环境变化、不同种群规模和迁徙率对北部高山野羊在其分布范围内的局部适应的影响。