Quantifying Patterns of Biodiversity in a Changing Climate: Integrating Biological Point and Process Data with Remotely Sensed Environmental Parameters
Preservation of biodiversity of tropical forests is one of the most challenging tasks facing the humankind. Increasing rate of deforestation and forest fragmentation, and changes and variability in climate patterns are altering the characteristics of habits and causing the loss of biodiversity in an unprecedented rate over the past few decades. Currently, considerable effort and resources are directed toward identifying and conserving regions of high species diversity. While defining "biodiversity hotspots" represents an important step toward prioritizing areas for conservation, adhering strictly to the current definition of "hotspot" will result in the loss of regions that are important in generating and maintaining adaptive diversity.
A series of recent studies have shown that the distribution of biodiversity is highly patterned and these patterns are shaped by processes of evolution and ecological interactions operating over spatial template created by geological and hydrological processes. While the natural patterns can potentially be understood and predicted, the actual spatial distribution of patterns are strongly influenced by the rapidly changing environmental variables related to the finite pool of mineral and energy resources and climate system. Certain elements of environment, such as the biotic net primary production, gradients of topography and moisture, vegetation cover, and degree of anthropogenic impacts, when integrated with biological data can predict actual spatial patterns of biodiversity. Quantifying and explaining these spatial patterns is one of the primary goals and challenges of ecology and evolutionary biology, and is an active area of research and competing hypotheses. Development of integrative models that can simulate the interaction of biological and environmental data is the first step to achieving this goal.
Therefore, the overall objective of this investigation is to develop models that can integrate biological point locality and process data with remotely sensed environmental parameters to quantify patterns of diversity in tropical regions. We propose to develop, test, and perform comparative analysis of a model using wide taxonomic representation of vertebrate species (birds, mammals, frogs) over the Amazon basin. Most of our initial model testing and validation will be performed over the Amazonian rain forest and Andean gradient and ecotonal vegetation where there are wider differences in surface parameters defining the habitats of the representative taxa.
The proposed work plan consists of:
- Compiling remotely sensed spatial and temporal data sets on the status of environment and geo-referenced taxonomic data over all three tropical regions.
- Developing and validating a multivariate fusion model to combine remote sensing and biological point and process data to map species distribution and patterns of biodiversity
- Performing comparative studies of three tropical regions to explain relations between the patterns of biodiversity, evolutionary processes and environmental gradients
- Quantifying the impact of human and climate changes on the patterns of diversity in all three regions and predicting potential areas of conservation
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