MCA: New approaches to the detection of niche differentiation patterns in ecological communities

MCA：检测生态群落生态位分化模式的新方法

• 批准号: 2122309
• 负责人: Annette Ostling
• 金额: $ 40.43万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122309&HistoricalAwards=false
• 关键词: MCA; New; approaches; detection; niche

Biodiversity in nature can be puzzlingly high in the light of competition between species, which arguably should eventually result in a single winner. Coexistence mechanisms shape the dynamics of communities and ecosystems, and the services ecosystems provide. Uncovering what these mechanisms are has been a long-standing challenge in ecology, as the experiments that would reveal them are logistically challenging to carry out in real-world ecological communities. For example, trees can live longer than researchers, so carrying out experiments long enough to assess competition among tree species is quite a challenge. Hence, one approach ecologists use to reveal what “niche differences” are allowing competing species to coexist is to look at patterns of which types of species are present together. This project will significantly advance this approach in ecology, by tailoring new modern machine learning tools from data science to detect the patterns in ecological communities. Additionally, this study would result in the training of graduate students, including individuals from under-represented groups, and outreach to female middle school students.Specifically, this project will develop tools that account for recent advances in the theoretical understanding of what patterns niche differentiation will create in communities. Historically, ecologists expected coexisting species to be distinctive in traits indicative of their strategies. Recently ecologists have realized that when more species are present than can coexist stably through their niche differences, additional coexistence is enhanced by similarity. Hence, they have come to expect clusters of species in trait space, especially in highly diverse communities. Modern data science offers a host of potential approaches for detecting species clusters. However, it does not provide a one-size-fits-all approach. This project will tailor cluster detection tools to the detection of clustering indicative of niche differences, and ground-truth them on simulated communities. It will involve training in data science of an ecologist who has contributed to recent theoretical developments in our understanding of competitive communities, and support initial collaborative work by the ecologist with a data scientist to build new pattern detection tools for ecologists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自然界中的生物多样性可能高得令人困惑，因为物种之间的竞争最终会产生一个赢家。共存机制塑造了社区和生态系统的动态以及生态系统提供的服务。揭示这些机制是什么一直是生态学中的一个长期挑战，因为揭示它们的实验在现实世界的生态社区中进行具有逻辑挑战性。例如，树木可以比研究人员活得更长，因此进行足够长的实验来评估树种之间的竞争是一个相当大的挑战。因此，生态学家用来揭示什么样的“生态位差异”允许竞争物种共存的一种方法是观察哪些类型的物种一起存在的模式。该项目将通过从数据科学中定制新的现代机器学习工具来检测生态社区中的模式，从而显着推进生态学中的这种方法。此外，本研究还将培养包括代表性不足的群体在内的研究生，并向女中学生推广。具体而言，本项目将开发工具，说明在理论上理解生态位分化将在社区中产生什么样的模式方面取得的最新进展。从历史上看，生态学家认为共存的物种在指示它们的策略的特征上是独特的。最近生态学家已经认识到，当更多的物种存在，而不是通过它们的生态位差异稳定共存时，相似性会增强额外的共存。因此，他们开始期待性状空间中的物种集群，特别是在高度多样化的社区中。现代数据科学为检测物种集群提供了许多潜在的方法。然而，它并没有提供一种放之四海而皆准的方法。该项目将定制集群检测工具，以检测指示生态位差异的集群，并在模拟社区上进行实地考察。该奖项将包括对一位生态学家进行数据科学方面的培训，该生态学家在我们对竞争性社区的理解的最新理论发展方面做出了贡献，并支持生态学家与一位数据科学家的初步合作，为生态学家构建新的模式检测工具。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。