Geographers and other environmental scientists are interested in understanding the spatial patterns associated with changes in the diversity of forest trees, including spatial variations due to differing tree species distributions. Changes in spatial variation often can be associated with different habitats at multiple scales, giving insights on how forested landscapes are organized. The humid forests of the western Amazon are some of the most biodiverse tropical forests in the world but are poorly known because of their inaccessibility. An increased understanding of floristic patterns and their associations with habitat type, especially in remote areas such as these forests, not only contributes to scientific research but also makes it possible to facilitate biodiversity conservation. A traditional time-consuming, labor-intensive field survey of vegetation itself can no longer suffice for the needs of documenting vegetation in large areas in order to conduct regional-scale conservation projects in diverse tropical forests. The objectives of this doctoral dissertation research project are to use data from tree inventories, remote sensing, and conservation assessments to examine the similarities among the spatial distributions of trees and the forest diversity patterns as measured at a fine, local scale with the spatial distributions detectable at a coarser, regional scale, and to take a multi-scale approach in terms of data acquisition and analysis to produce scale-dependent conservation recommendations for indigenous territories in a part of the Ecuadorian Amazon. As such, this project will contribute to several different intellectual disciplines, including biogeography, tropical ecology, and biodiversity conservation. It also will allow for evaluation of the effect of scale on pattern and process. At the local scale, the doctoral candidate will use high-resolution satellite imagery, such as Quickbird, to examine patterns of forest structure and diversity, thus combining spatial analysis of the distribution of tree communities at ground level (with tree inventory plots) and from above (through remote sensing). She will spatially and conceptually combine multiple scales of satellite imagery from sensors like Quickbird and Landsat TM/ETM for predicting forest stand characteristics and tree diversity patterns among different vegetation and habitat types at a regional scale. The study region is located in a biodiverse part of the western Amazon. Standardized 1-ha tree inventory plots will be established to document the forest stand composition.
This project is expected to provide fundamental baseline knowledge about vegetation and tree diversity patterns, facilitating understanding of forest biogeography in remote areas with great biodiversity. It has considerable applicability in implementing regional systematic conservation planning, which is a great need in tropical environments. The project is expected to help initiate integrative conservation implementation in the western Amazon. By using multi-scale remote sensing vegetation analysis to identify areas with great tree and habitat diversity, this study will provide an efficient, cost-effective method to locate the areas that still retain abundant environmental and biological diversity. By integrating other data layers that contain other factors influencing strategies for conservation implementation, such as climate, topography, and social aspects, this study will identify and target conservation prioritization zones using multiple criteria. The study area covers the Achuar, Shiwiar, and Zapara indigenous territories in the southeastern Ecuadorian Amazon where no previous tree inventory, remote sensing analysis, or conservation implementation has been conducted. Research results will be disseminated to the indigenous communities for their feedback and evaluation, thereby enabling the needs and opinions of the indigenous people to help attain balance between sustainable natural resource management and biodiversity conservation in a highly species-rich but fragile ecosystem. 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.
