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

考虑到物种之间的竞争，自然界的生物多样性可能会高得令人费解，这可能最终会导致一个赢家。共存机制塑造了群落和生态系统的动态，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的一个长期挑战，因为在现实世界的生态群落中进行的实验将揭示它们在后勤上具有挑战性。例如，树木可以比研究人员活得更久，所以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，生态学家用来揭示是什么“生态位差异”使得竞争物种能够共存的一种方法是观察哪些类型的物种聚集在一起的模式。该项目将通过从数据科学中剪裁新的现代机器学习工具来检测生态群落的模式，从而显着推进生态学中的这种方法。此外，本研究将有助于培养研究生，包括来自代表性不足群体的个人，并扩展到女中学生。具体来说，该项目将开发工具，以解释最近在理论理解方面取得的进展，即生态位分化将在社区中创造什么样的模式。从历史上看，生态学家期望共存的物种在表明其策略的特征上具有独特性。最近，生态学家们意识到，当存在的物种数量超过了通过生态位差异而稳定共存的数量时，相似性会加强额外的共存。因此，他们开始期望性状空间中的物种集群，特别是在高度多样化的群落中。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。该项目将定制聚类检测工具，以检测指示生态位差异的聚类，并在模拟社区中对其进行实地验证。它将包括对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群落的理解方面做出了最近的理论发展，并支持生态学家与数据科学家的初步合作工作，为生态学家建立新的模式检测工具。该奖项反映了美国国家科学基金会的法定使命，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。

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.