Metapopulation Modeling and Optimal Habitat Reconstruction for Birds in South AustraliaEPA Grant Number: U915816
Title: Metapopulation Modeling and Optimal Habitat Reconstruction for Birds in South Australia
Investigators: Westphal, Michael I.
Institution: University of California - Berkeley
EPA Project Officer: Lee, Sonja
Project Period: December 1, 2000 through December 1, 2002
Project Amount: $81,980
RFA: STAR Graduate Fellowships (2000) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Terrestrial Ecology and Ecosystems , Ecological Indicators/Assessment/Restoration
The objective of this research project is to: (1) use stochastic dynamic programming (SDP) to elucidate strategies for optimal metapopulation management on a small scale, using the example of the endangered southern emu-wren (Stipiturus malachurus intermedius) in the Mount Lofty Ranges, South Australia; and (2) apply simulated annealing optimization algorithms to develop "rules of thumb" for revegetation priorities for the avifauna across the whole region.
SDP will be applied to one southern emu-wren metapopulation, evaluating management strategies such as: enlarging/creating patches, creating corridors between patches, and translocating individuals into extant patches. For the landscape-scale analysis of habitat reconstruction, it is necessary to determine the environmental factors for species presence across the landscape. Approximately 100 sites will be surveyed, and the logistic regression analyses of these data, plus historical records, will give probabilistic functions for species occurrence across the landscape. Simulated annealing algorithms can be used to determine "rules of thumb" on revegation scenarios that would maximize the number of bird species, including weighting of rarer species and economic costs.
It is important to determine what areas should be prioritized to maximize the bird species diversity. By applying simulated annealing algorithms, a sophisticated, quantitative framework will be developed to determine optimal revegetation.