OCE-PRF A transferable modeling approach to predict species distributions under changing conditions across multiple life stages

OCE-PRF 一种可转移的建模方法，用于预测多个生命阶段不断变化的条件下的物种分布

• 批准号: 2126583
• 负责人: Joshua Cullen
• 金额: $ 27.36万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126583&HistoricalAwards=false
• 关键词: OCE; PRF; transferable; modeling; approach

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Understanding the distributions of species is fundamental to ecology, especially within the marine environment where biotic and abiotic variables are often dynamic over space and time. Ecological models have been used to make inferences on species-environment relationships in addition to predicting past and future patterns of species distributions. However, these models are typically developed for a single location, time period, spatial scale, or species life stage, resulting in poor predictive accuracy beyond the study domain. This project will provide a new transferable modeling approach that is applicable to a wide range of species, which will be used to test predictions of current and future distributions of green turtles (Chelonia mydas) in the Gulf of Mexico, Brazil, and Qatar. Since there is a lack of information on how marine turtles in general will respond to climate change impacts, these results will provide a crucial evaluation of projected range shifts that could be used by both conservationists and decision makers. This project will foster the integration of research and education through mentoring undergraduate students that identify as underrepresented minorities, teaching marine ecology and conservation at Florida State University, hosting workshops on the analysis of animal movement data, as well as hosting public outreach events. These efforts to disseminate knowledge and results beyond traditional means within the scientific community will be used to engage future scientists and the general public on pressing ecological topics. The overall aim of this project is to develop a transferable modeling approach that can provide accurate and precise estimates of species distributions under changing conditions. This study addresses some of the challenges of model transferability through three hypotheses: 1) a model informed by biological processes and physiological constraints will exhibit greater transferability than those that do not; 2) the analysis of environmental variables at the scale of species perception will result in greater model transferability; 3) a model that accounts for differences among life stages will result in greater model transferability. These hypotheses will be tested using a large dataset of green turtle occurrences at three distant locations (Gulf of Mexico, Brazil, Qatar) with a flexible Bayesian species distribution model. The use of independent datasets (from Brazil and Qatar) to validate the species distribution model (developed on data from the Gulf of Mexico) provides a unique opportunity to critically evaluate the spatial transferability of the model. The findings from this proposed work will provide a roadmap for the further development of transferable ecological models to predict biodiversity, disease outbreaks, and risks of biological invasions across global regions and possibly under future climate change impacts. Elucidating the importance of biological mechanisms, scale, and life stage differences to ecological modeling is transformative because these factors have been largely untested with regard to model transferability. Since predictions remain a major frontier in ecology, products of this work will be of broad interest to a diverse group of scientists, such as marine ecologists, biological oceanographers, fisheries scientists, and conservationists.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。了解物种的分布对生态学至关重要，特别是在海洋环境中，生物和非生物变量通常随时间和空间变化。生态模型除了预测过去和未来的物种分布模式外，还被用来推断物种与环境的关系。然而，这些模型通常是针对单个位置、时间段、空间尺度或物种生命阶段开发的，导致在研究领域之外的预测准确性较差。该项目将提供一个新的可转移的建模方法，适用于广泛的物种，这将被用来测试预测的绿色海龟（Chelonia mydas）在墨西哥湾，巴西和卡塔尔的当前和未来的分布。由于缺乏关于海龟一般将如何应对气候变化影响的信息，这些结果将为保护主义者和决策者提供对预计范围变化的重要评估。该项目将促进研究和教育的整合，通过指导确定为代表性不足的少数民族的本科生，在佛罗里达州立大学教授海洋生态学和保护，举办动物运动数据分析研讨会，以及举办公共宣传活动。这些在科学界内传播知识和成果的努力将超越传统手段，用于使未来的科学家和公众参与紧迫的生态专题。 该项目的总体目标是开发一种可转移的建模方法，可以在不断变化的条件下提供准确和精确的物种分布估计。本研究通过三个假设解决了模型可移植性的一些挑战：1）受生物过程和生理约束的模型将比不受生物过程和生理约束的模型表现出更大的可移植性; 2）在物种感知规模上分析环境变量将导致更大的模型可移植性; 3）考虑到生命阶段差异的模型将导致更大的模型可移植性。将使用三个遥远地点（墨西哥湾、巴西、卡塔尔）绿色海龟出现的大型数据集，采用灵活的贝叶斯物种分布模型，对这些假设进行检验。使用独立的数据集（来自巴西和卡塔尔），以验证物种分布模型（从墨西哥湾的数据开发）提供了一个独特的机会，批判性地评估模型的空间可转移性。这项拟议工作的结果将为进一步开发可转移的生态模型提供路线图，以预测生物多样性，疾病爆发以及全球地区以及可能受到未来气候变化影响的生物入侵风险。阐明生物学机制，规模和生命阶段差异的重要性，生态建模是变革性的，因为这些因素在很大程度上是未经测试的模型的可移植性。由于预测仍然是生态学的一个主要前沿领域，这项工作的成果将引起不同科学家群体的广泛兴趣，如海洋生态学家、生物海洋学家、渔业科学家和自然资源保护主义者。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。