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: 1914928
• 负责人: Roland Kays
• 金额: $ 8.96万
• 依托单位: Friends of the North Carolina State Museum of Natural Sciences
• 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=1914928&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的出现地点对于保护和维持生物多样性非常重要。具体来说，这些知识可用于描绘物种的生态位，提供衡量变化的基准，并帮助优先考虑保护区域。鉴于这种重要性，生态学家开发了许多统计工具来识别环境因素和物种发生模式之间的联系。根据是否使用传统调查数据或动物跟踪数据，大多数工具可以分为两类。 在任一类别中，可用数据的数量和质量常常受到限制。该项目旨在将这两种方法统一在可以同时使用两种类型数据的单一方法下。这很重要，因为它可以帮助克服每个数据源的限制，并且因为这些不同的数据类型具有互补的优势，因此组合起来可以提供更多信息。项目工作将重点关注美洲虎和低地貘等濒危物种，这两种数据类型均可用，以展示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问题。高级项目人员将参加 AniMove.org 动物运动分析课程，教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) 举办数据集成研讨会。利用 NCMNS 每年接待的 100 万访客，该项目的外展工作将侧重于创建和展示沉浸式视频，使整个科学过程栩栩如生，从研究设计和现场工作，到分析和预测，再到知情保护决策。识别环境驱动因素和物种发生模式之间联系的工具在生态学中经常使用，其中物种分布模型（SDM）和资源选择函数（RSF）是特别突出的例子。尽管这些方法密切相关，但 SDM 倾向于大规模使用调查数据，而 RSF 通常用于当地人群并应用于动物跟踪数据。各个跟踪数据集内普遍存在的自相关以及相互之间频繁的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，将开发一种新颖的加权对数似然函数来解释跟踪数据集内部和之间的非独立性，以及不同的采样计划和研究持续时间。该加权对数似然将在用于分布建模的非常一般的齐次泊松点过程框架中与仅存在和存在-不存在调查数据集成。这种方法有两个主要优点。首先，它将允许积累跟踪数据库存，以便有效地为广泛的分布分析提供信息，从本地规模的 RSF 到地理范围规模的 SDM。其次，它将利用被跟踪的动物经常去调查员不去的地方这一事实来抵消调查数据中经常明显的空间偏差？ t。与传统的分布模型相比，这种新颖的方法将从当地人口无缝扩展到地理范围，增加总体样本量，并利用不同数据类型的对比特性来减少空间偏差并更准确地估计不确定性。为了促进这种方法的广泛使用，将开发一个免费的软件工具，即分布数据集成模块（DDIM），以构建必要的多源数据集，并用相关的环境协变量注释这些数据。项目结果将在 http://biology.umd.edu/movement.html 上公布。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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

A Novel Framework to Protect Animal Data in a World of Ecosurveillance

• DOI: 10.1093/biosci/biaa035
• 发表时间: 2020-06-01
• 期刊: BIOSCIENCE
• 影响因子: 10.1
• 作者: Lennox, Robert J.;Harcourt, Robert;Cooke, Steven J.
• 通讯作者: Cooke, Steven J.

The Movebank system for studying global animal movement and demography

• DOI: 10.1111/2041-210x.13767
• 发表时间: 2021-12-12
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Kays, Roland;Davidson, Sarah C.;Wikelski, Martin
• 通讯作者: Wikelski, Martin