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: 1915347
• 负责人: Justin Calabrese
• 金额: $ 76.3万
• 依托单位: University of Maryland, College Park
• 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=1915347&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对于保护和维持生物多样性至关重要。具体来说，这些知识可以用来描绘物种的生态位，提供衡量变化的基准，并帮助优先考虑保护领域。鉴于这一重要性，生态学家已经开发了许多统计工具，以确定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分为两类，根据它们是否使用传统的调查数据或动物跟踪数据。 在这两个类别中，现有数据的数量和质量往往有限。该项目旨在将这两种方法统一到一种可以同时使用两种类型数据的方法中。这一点很重要，因为它可以帮助克服每个数据源的局限性，而且这些不同的数据类型具有互补的优势，因此组合起来提供更多信息。项目工作将侧重于包括美洲虎和低地貘在内的风险物种，在这两种数据类型都可用的情况下，展示这些技术如何为保护工作提供信息。通过结合多种数据源的优势，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，教学生将这些方法应用于保护问题，并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100万游客，这个项目？的推广工作将集中于创建和展示沉浸式视频，这些视频将整个科学过程带入生活，从研究设计和实地工作，到分析和预测，再到明智的保护决策。识别环境驱动因素和物种发生模式之间联系的工具通常用于生态学，物种分布模型（SDM）和资源选择函数（RSFs）是特别突出的例子。虽然这些方法密切相关，但SDM往往用于大规模的调查数据，而RSFs通常用于当地人群并应用于动物跟踪数据。个体跟踪数据集内普遍存在的自相关性和个体跟踪数据集之间频繁的互相关性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，将开发一种新的加权对数似然函数，以考虑跟踪数据集内部和之间的非独立性，以及不同的采样时间表和研究持续时间。该加权对数似然将与仅存在和存在-不存在的调查数据在非常一般的齐次泊松点过程框架中集成以用于分布建模。这种方法有两个主要优点。首先，它将允许积累跟踪数据的库存，以有效地为广泛的分布分析提供信息，从地方规模的RSF到地理范围规模的SDM。其次，它将抵消调查数据中经常出现的空间偏差，因为它利用了被跟踪的动物经常去调查员不去的地方这一事实。t.与传统的分布模型相比，这种新的方法将从当地人群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。为了便于广泛使用这一方法，将开发一个免费提供的软件工具，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对这些数据进行注释。项目结果将在www.example.com上公布http://biology.umd.edu/movement.html.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。

项目成果

Scale-insensitive estimation of speed and distance traveled from animal tracking data

根据动物跟踪数据对速度和行驶距离进行不敏感的估计

• DOI: 10.1186/s40462-019-0177-1
• 发表时间: 2019
• 期刊: Movement Ecology
• 影响因子: 4.1
• 作者: Noonan, Michael J.;Fleming, Christen H.;Akre, Thomas S.;Drescher-Lehman, Jonathan;Gurarie, Eliezer;Harrison, Autumn-Lynn;Kays, Roland;Calabrese, Justin M.
• 通讯作者: Calabrese, Justin M.

Autocorrelation‐informed home range estimation: A review and practical guide

• DOI: 10.1111/2041-210x.13786
• 发表时间: 2021-12
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Inês Silva;C. Fleming;M. Noonan;Jesse Alston;Cody Folta;W. Fagan;J. Calabrese

How range residency and long-range perception change encounter rates

• DOI: 10.1016/j.jtbi.2020.110267
• 发表时间: 2020-08-07
• 期刊: JOURNAL OF THEORETICAL BIOLOGY
• 影响因子: 2
• 作者: Martinez-Garcia, Ricardo;Fleming, Christen H.;Calabrese, Justin M.
• 通讯作者: Calabrese, Justin M.

Behavioral responses of terrestrial mammals to COVID-19 lockdowns

陆生哺乳动物对 COVID-19 封锁的行为反应

• DOI: 10.1126/science.abo6499
• 发表时间: 2023
• 期刊: Science
• 影响因子: 56.9
• 作者: Tucker, Marlee A.;Schipper, Aafke M.;Adams, Tempe S.;Attias, Nina;Avgar, Tal;Babic, Natarsha L.;Barker, Kristin J.;Bastille-Rousseau, Guillaume;Behr, Dominik M.;Belant, Jerrold L.
• 通讯作者: Belant, Jerrold L.

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting

通过自相关-知情加权减轻资源选择函数中的伪复制和偏差

• DOI: 10.1111/2041-210x.14025
• 发表时间: 2023
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Alston, Jesse M.;Fleming, Christen H.;Kays, Roland;Streicher, Jarryd P.;Downs, Colleen T.;Ramesh, Tharmalingam;Reineking, Björn;Calabrese, Justin M.
• 通讯作者: Calabrese, Justin M.