Doctoral Dissertation Research: Measuring and Predicting Species Distribution Patterns Across Spatial Scales

这项博士论文研究项目将研究高度移动的脊椎动物物种如何在不同的空间尺度上对环境条件做出反应，并将开发方法来整合不同尺度上的物种响应，以及提高物种分布模型的准确性。了解和预测物种分布是生物地理学的长期目标，而准确的分布信息是生物多样性保护的关键。该项目将通过将观察到物种的地理位置与这些位置的环境条件在多个尺度上联系起来来实现这一目标。博士生假设，一个物种将在与其生理和行为特征相对应的空间尺度的子集上对环境条件做出相对一致的反应，而在其他尺度上对相同的环境条件的反应会更弱。她将通过在每个物种的个体上贴上GPS标签并通过分析记录的移动路径来测量选定的东非鸟类物种的这些规模。学生将从遥感图像中产生网格环境数据，如土地覆盖、植被特性、景观结构和到关键资源的距离。几种分辨率的运动数据、发生数据和环境数据将被组合在分层模型中，以预测物种在多个尺度上的分布。为了评估这些方法是否提高了模型的准确性，她将使用额外的发生数据来测试模型，并将它们与单尺度模型进行比较。通过测量脊椎动物物种对其环境做出反应的空间尺度，并通过评估在多个尺度上的反应如何影响更广泛的分布模式，该项目将增加对基本生态过程的理解。该项目将通过提供物种出现数据、长期迁移数据和多分辨率的环境数据来促进进一步的科学调查，以开放获取过去没有受到重大学术关注的地理区域的数据收集。项目成果将作为用户友好的数据库和分布图分发给地方、地区和国家保护组织，以满足即时的保护需求。通过让肯尼亚学生参与数据收集和分析，该项目将提高东非机构在生物多样性评估和保护方面的技术能力，并将扩大美国、肯尼亚和国际研究界之间的合作关系。作为博士论文研究改进奖，该项目将为有前途的学生建立独立的研究生涯提供支持。

This doctoral dissertation research project will investigate how highly mobile vertebrate species respond to environmental conditions at different spatial scales and will develop methods to integrate species responses across scales as well as to increase the accuracy of species distribution models. Understanding and predicting species distributions are longstanding goals in biogeography, and accurate distribution information is critical for biodiversity conservation. This project will address this objective by relating the geographic locations at which a species is observed to the environmental conditions at those locations across multiple scales. The doctoral student hypothesizes that a species will respond to environmental conditions relatively consistently across a subset of spatial scales that correspond to its physiological and behavioral traits and will respond to the same environmental conditions more weakly at other scales. She will measure these scales for select East African bird species by attaching GPS tags to individuals of each species and by analyzing the recorded movement paths. The student will produce gridded environmental data, such as land cover, vegetation properties, landscape structure, and distance to critical resources, from remotely sensed imagery. Movement data, occurrence data, and environmental data at several resolutions will be combined in hierarchical models to predict species distributions across multiple scales. To evaluate whether these methods increase model accuracy, she will test models using additional occurrence data and compare them to single-scale models.By measuring the spatial scales at which vertebrate species respond to their environment and by assessing how responses at multiple scales affect broader distribution patterns, this project will increase understanding of fundamental ecological processes. This project will facilitate further scientific investigation by contributing species occurrence data; long-term movement data; and environmental data at multiple resolutions to open-access data collections from a geographic region that has not received significant scholarly attention in the past. Project results will be distributed to local, regional, and national conservation organizations as user-friendly databases and distribution maps that serve immediate conservation needs. By engaging Kenyan students in data collection and analysis, this project will enhance technical capabilities for biodiversity assessment and conservation in East African institutions, and it will expand collaborative relationships among U.S., Kenyan, and international research communities. As a Doctoral Dissertation Research Improvement award, this project will provide the support to enable a promising student to establish an independent research career.