Use of Population Genetics in Cost/Benefit Analysis of Habitat Restoration and Landscape DevelopmentEPA Grant Number: U915938
Title: Use of Population Genetics in Cost/Benefit Analysis of Habitat Restoration and Landscape Development
Investigators: Bruggeman, Douglas J.
Institution: Michigan State University
EPA Project Officer: Packard, Benjamin H
Project Period: January 1, 2001 through January 1, 2004
Project Amount: $101,915
RFA: STAR Graduate Fellowships (2001) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Environmental Justice , Fellowship - Environmental
The objective of this research project is to derive methods for integrating population genetic endpoints with Habitat Equivalency Analysis (HEA) to develop a multidisciplinary decision tool. HEA is a scaling methodology used to determine the spatial, temporal, and financial requirements to provide an optimal level of "ecological service flows" in restored or enhanced habitats. The integration of population genetics and HEA will provide a construct for planning, prioritizing, and assessing ecological restoration based on the genetic viability of populations in the restored habitats. The objective of habitat restoration is to provide adequate resources to sustain natural populations. Therefore, effective restoration plans must address the interacting processes that affect the genetic structure in the species of interest (i.e., natural selection, genetic drift, mutation, gene flow, and reproduction). Habitat restoration efforts that do not effectively provide niche requirements and migration corridors for viable populations will fail. Incorporating predictions of spatial genetic structure into HEA will facilitate a cost/benefit analysis of various parcels considered for acquisition and restoration. Potential applications of this multidisciplinary decision tool include threatened and endangered species management, germplasm conservation, and green space design for Smart Growth.
To integrate population genetic models with HEA, the initial effort will be placed in theoretical justification and modeling. The initial model may be constructed using a simulated habitat mosaic disrupted by varying levels of human development. Geographical genetic models that describe the correlation of genetic structure based on distance (i.e., the stepping stone approach) will be used to predict the gene frequencies over various parcels considered for restoration. Differential migration and recruitment among parcels will provide kernels for this model. Extensions of the space-time autoregressive moving average (STARMA) genetic drift models may be useful for this application. As the STARMA model progresses through time, shared and unshared stochastic effects of genetic drift on gene frequencies of neutral loci in neighboring subpopulations may be estimated for the different parcels. Furthermore, statistical uses of STARMA models with empirical data for gene frequencies can be used to estimate migration rates, strength of selection, and effective population size. Therefore, STARMA models provide a useful platform for theoretical development and empirical validation of ecological interactions.