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.
