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

这个博士论文研究项目将调查如何高度移动的脊椎动物物种在不同的空间尺度上对环境条件的反应，并将开发方法来整合跨尺度的物种反应，以及增加物种分布模型的准确性。 了解和预测物种分布是植物地理学的长期目标，准确的分布信息是生物多样性保护的关键。该项目将通过在多个尺度上将观察到的物种的地理位置与这些位置的环境条件联系起来来实现这一目标。 这位博士生假设，一个物种将在与其生理和行为特征相对应的空间尺度子集上对环境条件做出相对一致的反应，并在其他尺度上对相同的环境条件做出更弱的反应。 她将通过在每个物种的个体上贴上GPS标签，并通过分析记录的运动路径，来测量选定的东非鸟类物种的这些尺度。 学生将制作网格环境数据，如土地覆盖，植被特性，景观结构，距离关键资源，从遥感图像。 移动数据，发生数据和环境数据在几个分辨率将结合在分层模型中，以预测跨多个尺度的物种分布。 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，并将其与单尺度模型进行比较。通过测量脊椎动物物种对其环境的空间尺度，并通过评估多个尺度的响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、长期移动数据和多种分辨率的环境数据，促进进一步的科学调查，这些数据来自一个过去未受到重大学术关注的地理区域，可供开放获取。 项目成果将作为方便用户的数据库和分布图分发给地方、区域和国家保护组织，以满足当前的保护需求。 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生物多样性评估和保护的技术能力，并将扩大美国，肯尼亚和国际研究界。作为博士论文研究改进奖，该项目将提供支持，使有前途的学生建立一个独立的研究生涯。

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.