Collaborative Research: IIBR Informatics: Data integration to improve population distribution estimation with animal tracking data

合作研究：IIBR 信息学：数据集成，利用动物追踪数据改进人口分布估计

• 批准号: 1914887
• 负责人: Daniel Sheldon
• 金额: $ 8.86万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914887&HistoricalAwards=false
• 关键词: Collaborative; Research; IIBR; Informatics; Data

Identifying how environmental factors affect where particular species occur is important for the preservation and maintenance of biodiversity. Specifically, this knowledge can be used to delineate species' ecological niches, provide benchmarks for measuring change, and help prioritize areas for conservation. Given this importance, ecologists have developed many statistical tools for identifying linkages between environmental factors and species occurrence patterns. Most of these tools can be sorted into two categories, based on whether they use traditional survey data, or animal tracking data. In either category, the amount and quality of available data is frequently limiting. This project aims to unify these two approaches under a single methodology that can simultaneously use both types of data. This is important because it can help overcome limitations in each data source, and because these different data types have complementary strengths, and are thus more informative in combination. Project work will focus on at-risk species including jaguars and lowland tapirs, where both data types are available, to demonstrate how these techniques can inform conservation efforts. By combining the strengths of multiple data sources, these new methods will be able to better resolve priority habitats and areas for these vulnerable species. Senior project personnel will participate in the AniMove.org animal movement analysis courses to teach students to apply these methods to conservation problems and will also host a data-integration workshop at the North Carolina Museum of Natural Science (NCMNS). Leveraging the 1 million yearly visitors that NCMNS receives, this project?s outreach efforts will focus on creating and displaying immersive videos that bring to life the entire scientific process, ranging from study design and field work, through analysis and forecasting, and on to informed conservation decision making.Tools that identify linkages between environmental drivers and species' occurrence patterns are routinely used in ecology, with species distribution models (SDMs) and resource selection functions (RSFs) being especially prominent examples. Though these approaches are closely related, SDMs tend to be employed on large scales with survey data, while RSFs are typically used for local populations and applied to animal tracking data. The ubiquitous auto-correlation within, and frequent cross-correlation among, individual tracking datasets violates the key independence assumption of standard distribution models. To unify these approaches, a novel weighted log-likelihood function will be developed to account for non-independence both within and among tracking datasets, as well as for differing sampling schedules and study duration. This weighted log -likelihood will be integrated with both presence-only and presence-absence survey data in the very general in homogeneous Poisson point process framework for distribution modeling. This approach has two primary advantages. First, it would allow accumulating stockpiles of tracking data to validly inform a broad range of distribution analyses, from RSFs at the local scale, to SDMs at the geographic range scale. Second, it will counteract the often -pronounced spatial biases in survey data by leveraging the fact that tracked animals frequently go where surveyors don? t. Compared to conventional distribution models, this novel methodology will scale seamlessly from local populations to geographic ranges, increase overall sample size, and exploit the contrasting properties of the different data types to reduce spatial bias and more accurately estimate uncertainty. To facilitate broad use of this methodology, a freely available software tool, the Distribution Data Integration Module (DDIM), will be developed to both construct the necessary multi-source datasets, and annotate these data with relevant environmental covariates. Project results will be available at http://biology.umd.edu/movement.html.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.

确定环境因素如何影响特定物种发生的地方对于保护和维持生物多样性很重要。具体而言，这些知识可用于描述物种的生态壁ches，为衡量变化提供基准，并有助于确定保护区域的优先级。鉴于这一重要性，生态学家开发了许多统计工具，用于识别环境因素和物种发生模式之间的联系。这些工具中的大多数可以根据使用传统的调查数据还是动物跟踪数据来分为两类。 在任何一个类别中，可用数据的数量和质量通常都限制。该项目的目的是在可以同时使用两种类型数据的单一方法下统一这两种方法。这很重要，因为它可以帮助克服每个数据源的局限性，并且因为这些不同的数据类型具有互补的优势，因此组合更具信息性。项目工作将集中在可用的两种数据类型的美洲虎和低地tapiirs在内的高风险物种，以证明这些技术如何为保护工作提供信息。通过结合多个数据源的优势，这些新方法将能够更好地解决这些脆弱物种的优先栖息地和区域。高级项目人员将参加Animove.org动物运动分析课程，以教学学生将这些方法应用于保护问题，还将在北卡罗来纳州自然科学博物馆（NCMNS）举办数据整合研讨会。利用NCMN接收的每年100万访客，该项目的外展工作将集中在创建和展示带来的沉浸式视频上，这些视频使整个科学过程栩栩如生，从研究设计和现场工作，通过分析和预测，再到预测，再到知情的保存决策，以确定环境驾驶员和物种的界限，以识别环境驾驶员和物种的界限），以进行环境和物种的界限，以实现环境和物种的态度，以常见的方式进行态度的态度（procive）。功能（RSF）是特别重要的例子。尽管这些方法密切相关，但SDM倾向于在大规模上使用调查数据，而RSF通常用于本地人群，并应用于动物跟踪数据。内部无处不在的自动相关以及各个跟踪数据集之间的频繁互相关违反了标准分布模型的关键独立性假设。为了统一这些方法，将开发出一种新型的加权对数可能性函数，以说明在数据集内部和跟踪数据集中的非独立性，以及不同的采样时间表和研究持续时间。这种加权的对数 - likelihood将与均匀的泊松点过程框架中的一般性和不存在的调查数据集成在一起，用于分配建模。这种方法具有两个主要优势。首先，它将允许累积跟踪数据的库存，从而有效地为从本地规模的RSF到地理范围量表的SDMS有效地为广泛的分配分析提供了信息。其次，它将通过利用追踪动物经常去测量师Don的地方来抵消调查数据中经常发出的空间偏见？ t。与传统的分布模型相比，这种新型方法将从本地人群无缝地扩展到地理范围，增加总体样本量，并利用不同数据类型的对比特性，以减少空间偏见并更准确地估计不确定性。为了促进该方法的广泛使用，将开发一种可自由使用的软件工具，即分发数据集成模块（DDIM），以构建必要的多源数据集，并使用相关的环境协变量来注释这些数据。项目结果将在http://biology.umd.edu/movement.html.html.tml.这一奖项上提供，反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估审查标准，认为值得通过评估来获得支持。

项目成果

Reconstructing bird trajectories from pressure and wind data using a highly optimized hidden Markov model

使用高度优化的隐马尔可夫模型根据压力和风数据重建鸟类轨迹

• DOI: 10.1111/2041-210x.14082
• 发表时间: 2023
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Nussbaumer, Raphaël;Gravey, Mathieu;Briedis, Martins;Liechti, Felix;Sheldon, Daniel
• 通讯作者: Sheldon, Daniel

BirdFlow : Learning seasonal bird movements from eBird data

BirdFlow：从 eBird 数据学习季节性鸟类运动

• DOI: 10.1111/2041-210x.14052
• 发表时间: 2023
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Fuentes, Miguel;Van Doren, Benjamin M.;Fink, Daniel;Sheldon, Daniel
• 通讯作者: Sheldon, Daniel