IIBR Informatics: A generalized modeling framework for integrating multi-species data sources to estimate biodiversity processes

生物多样性与生态系统的健康和完整性有关，不同物种对生态系统功能的贡献各不相同。至关重要的是要评估整个物种群落的状况和动态，而不仅仅是那些拥有大量数据的物种。该项目开发了“综合社区模型”，这是一个统计建模框架，可以同时使用多物种数据源来估计生物多样性的状态，趋势和动态。其目标是创建一个灵活的基础设施，用于估计物种和社区过程，可以通过对包括鸟类，小型哺乳动物和蝴蝶在内的动物群落的模拟和经验案例研究，将多个物种的多种数据类型结合起来。对物种分布、丰度和人口比例的估计构成了对生物多样性动态和社区对外部威胁的反应的科学理解的基础，为生物保护提供了重要信息。综合群落模型的开发将使研究人员能够在生物多样性加速丧失的时代获得关于跨时空尺度的物种和群落的详细推断。本项目还为研究生和博士后学者提供了层次统计建模的培训，并创建了一个K-12外展模块，教授中学生生物多样性保护知识。（联合收割机结合目标物种的多个数据源）和层次群落模型（估计多物种出现或丰度模式，但仅从单一数据来源）。虽然最近在单物种综合模型和分层群落模型方面取得了进展，但这两种方法都有缺点：前者仅限于单一物种，而后者未能利用合并多个数据来源和数据类型所带来的好处（例如，同时估计丰度和人口统计率，增加时空覆盖面）。通过弥合单物种综合模型和等级群落模型之间的差距，综合群落模型利用了两者的能力，克服了传统上对生物多样性参数的狭隘推断（在空间，时间和信息方面）。建模框架使用不同的可用数据源中的每一个来通过与联合可能性链接在一起的分层观察模型来通知底层生物过程模型的各个组件。群落的生物过程模型可以从简单的（例如物种出现的估计）到复杂的（例如物种生存、繁殖和丰度的估计），并取决于分类组的生物学和现有数据的数量/类型。该项目推进了人口和社区生态学领域，因为它使科学家能够利用多种数据源（尽管采样协议和时空数据结构和数量存在差异），从而提高物种水平动态和生物多样性指标（例如丰富度，组成）的准确性和精确性。该项目的结果将在www.example.com上公布https://ezipkin.github.io.This奖项反映了NSF的法定使命，并被认为值得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来支持。

Biodiversity is linked to the health and integrity of ecosystems with species varying in their contributions to ecosystem functions. It is critical to assess the status and dynamics of whole communities of species and not just those species that have large amounts of data. This project develops ‘integrated community models’, a statistical modeling framework to simultaneously use multi-species data sources to estimate the status, trends, and dynamics of biodiversity. The objective is to create a flexible infrastructure for estimating species and community processes that can incorporate multiple data types on multiple species through simulations and empirical case studies on animal communities including birds, small mammals, and butterflies. Estimates of species distributions, abundances, and demographic rates form the basis of scientific understanding of biodiversity dynamics and community responses to external threats, delivering critical information for biological conservation. The development of integrated community models will enable researchers to obtain detailed inferences on species and communities across spatiotemporal scales during an era of accelerated biodiversity loss. This project also provides training to graduate students and postdoctoral scholars in hierarchical statistical modeling and creates a K-12 outreach module to teach middle school students about biolodiversity conservation.The integrated community modeling framework uses a hierarchical approach merging single-species integrated models (which combine multiple data sources on a target species) and hierarchical community models (which estimate multi-species occurrence or abundance patterns but only from a single data source). Although there have been recent advances in single-species integrated models and hierarchical community models, both approaches have shortcomings: the former is limited to a single species, whereas the latter fails to take advantage of the benefits gained from merging multiple data sources and data types (e.g. estimation of both abundance and demographic rates simultaneously, increased spatiotemporal coverage). By bridging the gap between single-species integrated models and hierarchical community models, integrated community models leverage the capabilities of both and overcome traditionally narrow inferences (in terms of space, time, and information gained) on biodiversity parameters. The modeling framework uses each of the different available data sources to inform various components of the underlying biological process model through hierarchical, observation models linked together with a joint likelihood. The biological process models for communities can range from simple (e.g. estimates of species occurrence) to complex (e.g. estimates of species survival, reproduction, and abundance) and depend on both the biology of the taxonomic group and the quantity/type of available data. This project advances the fields of population and community ecology because it allows scientists to take advantage of multiple data sources (despite differences in sampling protocols and spatiotemporal data structures and quantities), leading to increased accuracy and precision of species-level dynamics and biodiversity metrics (e.g. richness, composition). The results of the project will be made available at https://ezipkin.github.io.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.