Laura Lorraine Hess | Institute Computational Earth System Science - Estados Unidos

• Grant number: 07/07867-9
• Support Opportunities: Research Grants - Visiting Researcher Grant - International
• Duration: May 09, 2008 - May 08, 2009
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Physical Geography
• Principal Investigator: Evlyn Márcia Leão de Moraes Novo
• Grantee: Evlyn Márcia Leão de Moraes Novo
• Visiting researcher: Laura Lorraine Hess
• Visiting researcher institution: Institute for Computational Earth System Science (ICESS), United States
• Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovações (Brasil). São José dos Campos , SP, Brazil

Abstract

Freshwater ecosystems of the Amazon basin, and their associated wetlands, support one of Earth’s great biodiversity resources. However, current knowledge is severely lacking for nearly all taxonomic groups regarding the number of species, the spatial patterns of their distributions, and the life history information needed to formulate sound management plans to ensure their preservation. High degrees of spatial and temporal variability create particular challenges for surveying populations of aquatic and wetland species in the Amazon. Many species migrate locally between whitewater and blackwater systems, or between floodplain and channel; these dynamics occur both seasonally and in relation to particular life stages. Other species such as turtles or large catfish may migrate longitudinally one thousand kilometers or more. Difficulty in accessing remote flooded environments and the persistent cloud cover common to much of Amazônia, further hamper progress toward minimally adequate biotic inventories. Given such constraints, there is an urgent need for appropriate biodiversity surrogates as a basis for understanding the spatial patterns present in existing datasets; for evaluating gaps in current inventories; for designing future sampling strategies; for modeling species distributions based on incomplete data; and for input to decision support software for conservation planning. Such surrogates should be derived on a basin wide level in order to encompass migratory patterns and regional variability, using consistent criteria based on the best current knowledge of environmental factors driving biodiversity patterns for freshwater ecosystems. Based on many published studies and on the consensus of a multi-disciplinary group of experts at a 2004 IBAMA-sponsored workshop, fine-scale floodplain units based on vegetation structure, inundation periodicity, and water chemistry are key biodiversity surrogates for Amazonian freshwater systems and are a required dataset for conservation planning. The proposed research builds on remote sensing and field methodologies developed for wetland biogeochemistry applications as part of the Large Scale Biosphere-Atmosphere Experiment in Amazônia (LBA). We will extend these techniques to new synthetic aperture radar (SAR) datasets from Japans recently launched ALOS (Advanced Land Observing Satellite) and optical datasets from the China-Brazil Earth Resources Satellite (CBERS-2) for new application to biodiversity and conservation studies. Maps of vegetation structure, inundation periodicity, and water type will be created and validated for six focus sites representing a range of wetland types and scales. These biodiversity surrogate maps will be analyzed in collaboration with researchers and students for applications including analysis of species occurrence records in conjunction with a variety of map-derived habitat metrics; mapping numbers and movements of manatees in relation to seasonal changes in water levels, water chemistry, and aquatic macrophyte distribution; assessing regional differences in extent and seasonality of habitat types and degree of protection by existing conservation units; and optimizing location of future wetland reserves using the C-Plan and MARXAN decision support systems. The wetland biodiversity maps and associated studies resulting from this project will provide the first dedicated spatiotemporal framework for quantifying and conserving the biodiversity of Amazonian freshwater communities, as well as maximizing the usefulness of existing biodiversity datasets. (AU)