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

IIBR 信息学：整合多物种数据源以估计生物多样性过程的通用建模框架

• 批准号: 1954406
• 负责人: Elise Zipkin
• 金额: $ 78.27万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954406&HistoricalAwards=false
• 关键词: IIBR; Informatics; generalized; modeling; framework

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.

生物多样性与生态系统的健康和完整性相关，物种对生态系统功能的贡献各不相同，评估整个物种群落的状况和动态至关重要，而不仅仅是那些拥有大量数据的物种。综合群落模型”，一个统计建模框架，可同时使用多物种数据源来估计生物多样性的状况、趋势和动态，其目标是创建一个灵活的基础设施，用于估计物种和群落过程，可以纳入多种数据类型。通过模拟和实证研究多个物种对包括鸟类、小型哺乳动物和蝴蝶在内的动物群落的案例研究，对物种分布、丰度和人口比率的估计构成了对生物多样性动态和群落对外部威胁的反应的科学理解的基础，为生物保护的发展提供了重要信息。综合群落模型将使研究人员能够在生物多样性加速丧失的时代获得跨时空尺度的物种和群落的详细推论。该项目还为研究生和博士后学者提供分层统计模型方面的培训，并创建一个 K-12 外展模块进行教学。中学综合群落建模框架采用分层方法，合并单物种综合模型（结合了目标物种的多个数据源）和分层群落模型（估计多物种的出现或丰度模式，但仅从单个物种尽管单物种集成模型和分层群落模型最近取得了进展，但这两种方法都有缺点：前者仅限于单个物种，而后者无法利用合并多个数据所带来的好处。来源和数据类型（例如，同时估计丰度和人口比率，增加时空覆盖范围）通过弥合单物种综合模型和分层群落模型之间的差距，综合群落模型利用两者的能力并克服传统的狭隘推论（在空间、建模框架使用每个不同的可用数据源，通过与联合可能性联系在一起的分层观察模型来告知基础生物过程模型的各个组成部分。范围从简单（例如物种出现的估计）到复杂（例如物种生存、繁殖和丰度的估计），并且取决于分类群的生物学和可用数据的数量/类型。该项目推动了人口和群落生态学领域的发展。因为它允许科学家利用多个数据源（尽管采样协议和时空数据结构和数量存在差异），从而提高物种层面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。该项目将在 https://ezipkin.github.io 上提供。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Integrated Population Models: Achieving Their Potential

• DOI: 10.1007/s42519-022-00302-7
• 发表时间: 2022-11
• 期刊: Journal of Statistical Theory and Practice
• 影响因子: 0.6
• 作者: Fay Frost;R. McCrea;Ruth King;O. Gimenez;Elise F. Zipkin

Breeding season management is unlikely to improve population viability of a data-deficient migratory species in decline

繁殖季节管理不太可能改善数据缺乏的迁徙物种的种群生存能力

• DOI: 10.1016/j.biocon.2023.110104
• 发表时间: 2023
• 期刊: Biological Conservation
• 影响因子: 5.9
• 作者: Davis, Kayla L.;Saunders, Sarah P.;Beilke, Stephanie;Ford, Erin Rowan;Fuller, Jennifer;Landgraf, Ava;Zipkin, Elise F.
• 通讯作者: Zipkin, Elise F.

Addressing data integration challenges to link ecological processes across scales

• DOI: 10.1002/fee.2290
• 发表时间: 2021-02-01
• 期刊: FRONTIERS IN ECOLOGY AND THE ENVIRONMENT
• 影响因子: 10.3
• 作者: Zipkin, Elise F.;Zylstra, Erin R.;Tingley, Morgan W.
• 通讯作者: Tingley, Morgan W.

Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance

• DOI: 10.1111/2041-210x.13578
• 发表时间: 2021-03-06
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Doser, Jeffrey W.;Finley, Andrew O.;Zipkin, Elise F.
• 通讯作者: Zipkin, Elise F.

Guidelines for the use of spatially varying coefficients in species distribution models

在物种分布模型中使用空间变化系数的指南

• DOI: 10.1111/geb.13814
• 发表时间: 2024
• 期刊: Global Ecology and Biogeography
• 影响因子: 6.4
• 作者: Doser, Jeffrey W.;Kéry, Marc;Saunders, Sarah P.;Finley, Andrew O.;Bateman, Brooke L.;Grand, Joanna;Reault, Shannon;Weed, Aaron S.;Zipkin, Elise F.
• 通讯作者: Zipkin, Elise F.