CAREER: Next generation mixed membership models for highly multivariate biodiversity data

了解和预测人类活动如何影响生物多样性是一项挑战，因为在任何特定地点都有大量的物种。重要的是，尽管目前生态学中正在进行的遥感革命产生的数据量越来越多（例如，虽然这些信息是由卫星和照相机捕获的照片提供的，但由于缺乏能够综合多个数据流并能够适当说明这些传感器产生的数据的特点的建模方法，这一信息的使用和与生物多样性实地数据的结合受到限制。该项目的长期目标是通过为生物多样性数据集创建广泛适用的方法和培训下一代定量环境科学家来推进网络基础设施。该项目将侧重于大幅改善混合会员制（MM）模型。这些模型最初是为了文本挖掘而开发的，但已被广泛用于各种生态系统的生物多样性研究。不幸的是，这些模式的目前提法仍然有重要的局限性。该项目将开发改进的MM模型，这些模型可以解释这些传感器生成的数据的特征，可以整合多个数据源，并能够进行生物多样性预测。最终，这些改进的MM模型对于提高我们量化和预测生物多样性影响的能力至关重要。该项目还将提高高中教师和学生对气候变化对生物多样性影响的认识。评估和预测物种组成已经和将如何被人为压力改变是维持生物多样性和生态系统功能的关键，但现有的方法来量化生物多样性的变化有重要的局限性。生物多样性数据是高度多变量的（例如，集合体可以包含热带森林中的数百个物种）但是通常用于解释这些数据的许多降维方法通常产生不容易解释的结果（例如，非度量多维缩放轴分数），依赖于不切实际的假设（例如，硬聚类的网站），并不适合野生动物的研究，因为他们不考虑不完善的检测。重要的是，这些方法中的许多不允许进行正式的推断和/或预测，并且这些方法不利用多个数据流。为了规避这些限制，该项目将制定方法，以产生关于生物多样性时空变化驱动因素的新见解。该项目的总体目标是显著改进生物多样性研究的MM模型。该项目的具体目标包括：a）创建MM模型，可以生成可靠的推断和预测，整合不同的数据流，并考虑检测问题;和B）传播和培训科学家开发的模型;并提高高中学生对气候变化对生物多样性影响的认识，同时解决重要的科学，数学和统计标准。该项目的结果将存储在稳定的URL https://denisvalle.weebly.com/mm-models.htmlThis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。

Understanding and predicting how human activities impact biodiversity is challenging given the often large number of species in any given location. Importantly, despite the increasing amount of data generated by the remote-sensing revolution currently underway in Ecology (e.g., photos from satellites and camera traps), the use and integration of this information with field data on biodiversity is limited by the absence of modeling methods that can integrate multiple data streams and that can properly account for the characteristics of the data generated by these sensors. The long-term goal of this project is to advance cyberinfrastructure by creating broadly applicable methods for biodiversity datasets and by training the next generation of quantitative environmental scientists. This project will focus on substantially improving Mixed Membership (MM) models. These models were originally developed for text-mining purposes but have been widely used for biodiversity research in a wide range of ecosystems. Unfortunately, the current formulation of these models still has important limitations. This project will develop improved MM models that can account for the characteristics of the data generated by these sensors, can integrate multiple sources of data, and enable biodiversity predictions to be made. Ultimately, these improved MM models will be critical to enhance our ability to quantify and predict impacts on biodiversity. This project will also increase the awareness of the impact of climate change on biodiversity among high-school teachers and students. Evaluating and forecasting how species composition has been and will be altered by anthropogenic stressors is key to sustaining biodiversity and ecosystem functioning, but existing methods to quantify biodiversity change have important limitations. Biodiversity data are highly multivariate (e.g., an assemblage can contain hundreds of species in tropical forests) but many of the dimension-reduction methods typically used to interpret these data often generate results that are not easily interpretable (e.g., nonmetric multidimensional scaling axis scores), rely on unrealistic assumptions (e.g., hard clustering of sites), and are ill suited for wildlife studies because they do not account for imperfect detection. Critically, many of these methods do not allow for formal inference and/or predictions to be made and these methods do not leverage multiple data streams. To circumvent these limitations, this project will develop methods to generate new insights on the drivers of spatial and temporal variation of biodiversity. The overall objective of this project is to significantly improve MM models for biodiversity research. The specific objectives of this project consist of a) creating MM models that can generate reliable inference and predictions, integrate disparate data streams, and account for detection issues; and b) disseminate and train scientists on the developed models; and increase awareness of the impact of climate change on biodiversity among high-school students while addressing important science, math, and statistics standards. The results of this project will be stored in the stable URL https://denisvalle.weebly.com/mm-models.htmlThis 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.