University of Maryland Maile C. Neel  
Natural Resource Sciences & Landscape Architecture
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Alyxia

<i>Eriogonum ovalifolium var. vineum</i> habitat Much of my research has emphasized quantifying ecological and spatial patterns of three different types of biological diversity (genetic diversity, vascular plant species diversity, and vegetation community diversity) across one landscape in the San Bernardino Mountains of southern California. This effort has required extensive data collection and analysis using several approaches: vegetation classification and ordination, spatial autocorrelation, population genetic diversity analysis, and landscape pattern analysis.

The ~69,000 hectare study system in which these investigations have been conducted includes the world-wide distributions of five federally-listed plant taxa (Astragalus albens, Erigeron parishii, Eriogonum ovalifolium var. vineum, Lesquerella kingii var. bernardina, and Oxytheca parishii var. goodmaniana). These taxa are endemic to limestone and dolomite soils and rock outcrops in the San Bernardino Mountains; they and their associated ecosystems are threatened by mining and other forest management activities. This research is done collaboratively with the San Bernardino National Forest enabling them to directly apply the results to conservation planning decisions for these species. The work has been funded by the Forest Service, the Environmental Protection Agency´┐Żs Science to Achieve Results (STAR) Program and a Switzer Environmental Fellowship.

Scott in the field Beyond contributing to conservation of these taxa, the results from the San Bernardino Mountains research also provide insights that have broad implications to the conservation biology field. In collaboration with Mike Cummings I have examined the proportion of genetic diversity present in a species that is included when different numbers of populations are conserved (Neel and Cummings 2003). We then evaluated the genetic consequences of applying ecological reserve design guidelines (Neel and Cummings 2003). The results from these two studies indicate that a much larger proportion of populations are required to capture genetic diversity than is typically considered to be sufficient for conservation.

Additionally, I am using geographic information system (GIS) technology to assess consequences of applying basic reserve design principles and computer-based reserve selection algorithms to the existing patterns of diversity. The prevailing conservation goal of encompassing a range of diversity elements (multiple taxa and multiple types, i.e., from within species to community) with a fixed expenditure of funds and extent of land has resulted in establishing reserves in diversity hotspots and in promotion of multi-species planning, and reserve selection based on representing vegetation communities. Such coarse-filter, umbrella conservation approaches will only be successful if different types of diversity are structured similarly.

<i>Erigeron parishii</i> The specific objectives of this research are to use computer-based methods to

1) evaluate how well multiple types of biological diversity are conserved by reserve selection strategies applied to vegetation communities over a range of conservation intensities (i.e., proportion of available area protected).

2) evaluate whether models designed to rank biodiversity value of sites based on landscape characteristics increase the amount of diversity conserved or improve the landscape context of reserves over networks selected using community types alone.

3) evaluate the proportion of the landscape necessary to reliably conserve multiple levels of diversity when representation of vegetation communities is the primary reserve selection criterion. The basic research approach is to apply reserve selection algorithms to GIS maps of vegetation communities and modeled biodiversity value and then evaluate amounts of each type of diversity included in, and landscape characteristics of, the resulting reserve networks.

This work, which is done in collaboration with Kevin McGarigal at the University of Massachusetts, has been funded in part through a David H. Smith Conservation Research Fellowship from The Nature Conservancy. The computer intensive analyses are being run on the University of Maryland computer grid system. The computing grid is managed by the Lattice Project to provide a community-based network of computing resources for life sciences applications. The network includes a wide variety of resources from high-end clusters and multiprocessors to individual desktop computers in offices and classrooms.

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