Doctoral Dissertation Research: Habitat Mapping and Species-Environment Interactions of Rare Species using Multi-Sensor Remote Sensing: Case Study: Endangered Mangrove Finch

了解物种地理分布的限制和变化是保护生物学、景观生态学和生物地理学研究的一个重要领域。建模分布提供了关于物种分布的地点和原因的有用信息，包括生态系统服务、功能和生物多样性模式，以及物种与环境相互作用的详细理解。鉴定稀有和濒危物种的关键或潜在栖息地对保护生物学尤为重要。用于绘制物种分布图的模型的数据需求仍然是一个重大挑战。虽然物种发生数据是以实地为基础的，但环境数据需要连续呈现，因此，遥感作为一种数据源提供了独特的可能性。利用天基遥感技术绘制物种分布图面临着数据质量、图像处理与信息提取、数据融合与集成、模型选择与变量表征等诸多挑战。虽然有许多研究利用遥感绘制栖息地地图，但很少有研究使用真正适合在适当空间尺度上捕捉物种-环境相互作用的环境数据。本研究的主要目的是以加拉帕戈斯群岛的红树林雀为例，对珍稀濒危物种的潜在适宜栖息地和地理分布进行建模。红树雀是厄瓜多尔加拉帕戈斯群岛伊莎贝拉岛的特有物种。为了模拟包括鸟类在内的许多动物物种的栖息地，与环境数据相关的挑战需要一种多传感器方法，不仅要描述植被覆盖和景观的空间组织，还要描述植被的垂直结构，如冠层高度、分支特征和其他生物物理参数，包括生物量或叶面积。本研究将使用多传感器遥感方法，结合被动光学和主动微波传感器，在现有技术条件下，以尽可能高的分辨率，通过范围、物种组成、生物量和冠层高度来表征红树林。这些数据将与使用物种分布模型MaxEnt对红树林雀的实地观察相结合，以预测和绘制合适的栖息地。这项研究的结果将包括红树林树种、冠层高度、生物量、潜在红树林雀类栖息地的适宜性以及详细的物种-环境关系图。本研究结果将有助于进一步发展物种分布图的方法和技术。该研究将为珍稀濒危物种的空间生境表征和物种分布制图提供潜力。该研究可作为未来保护研究和应用的模型，通过开发数据融合方法。这项研究的结果在加拉帕戈斯群岛有直接应用，加拉帕戈斯群岛是联合国教科文组织的生物圈保护区和世界遗产。这项研究将绘制红树林雀的栖息地适宜性地图，以协助保护生物学家进行正在进行的圈养繁殖计划，以增加和维持红树林雀的数量。同样，将产生的物种-环境关系可以用来帮助保护现有的红树林。描述红树林物种组成和物理结构的地图将提供加拉帕戈斯群岛红树林的第一个全面代表，从而作为跟踪红树林未来动态的基线。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

Understanding the limits and shifts of the geographic distribution of species is an important area of research for conservation biology, landscape ecology, and biogeography. Modeling distributions provides useful information about the where and why of species distributions including ecosystem services, functions, and patterns of biodiversity, as well as detailed understanding of species-environment interactions. Identification of critical or potential habitats of rare and endangered species is particularly important for conservation biology. The data requirements of models used to map species distributions remain a significant challenge. While species occurrence data are field based, environmental data need to be continuously represented, and therefore, remote sensing offers unique possibilities as a data source. There are many challenges to mapping species distributions through space-based remote sensing including data quality, image processing and information extraction, data fusion and integration, and model selection and variable characterization. While numerous studies have used remote sensing to map habitats, very few have done so using environmental data that are truly suitable for capturing the species-environment interactions of interest at the appropriate spatial scales. The primary objective of this research is to model the potential suitable habitat and geographic distribution of rare and endangered species using the mangrove finch in the Galapagos Islands as a case study. The mangrove finch is an endemic species to Isabela Island in the Galapagos Archipelago, Ecuador. To model habitat for many animal species including birds, the challenges related to environmental data require a multi-sensor approach that describes not only vegetation cover and the spatial organization of the landscape, but the vertical structure of vegetation, such as canopy height, branching characteristics, and other biophysical parameters, including biomass or leaf area. This study will use a multi-sensor remote sensing approach to combining passive optical and active microwave sensors to characterize mangrove forests by extent, species composition, biomass, and canopy height at the finest resolution possible given available technologies. These data will be incorporated with field observations of the mangrove finch using the species distribution model, MaxEnt, to predict and map suitable habitat. The results of this research will include maps of mangrove tree species, canopy height, and biomass, and suitability of potential mangrove finch habitat, as well as detailed species-environment relationships.The results of this study will contribute to furthering the methods and techniques of mapping species distributions. This research will demonstrate the potential for space-based habitat characterization and species distribution mapping of rare and endangered species. The research could serve as a model for future conservation research and applications through the data fusion approaches to be developed. The results of this research have a direct application in the Galapagos Islands, a UNESCO Biosphere Reserve and World Heritage Site. This research will produce habitat suitability maps of the mangrove finch to assist conservation biologists with an on-going captive breeding program to increase and maintain the mangrove finch population. Likewise, the species-environment relationships that will be generated can be used to help conserve existing mangrove forests. Maps describing the species composition and physical structure of mangrove forests will provide the first comprehensive representations of mangrove forests in the Galapagos Islands and thus serve as a baseline from which to track future dynamics of mangrove forests. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.