MODELING AND DECISION SUPPORT SYSTEM FOR CONSERVATION OF BIOLOGICAL
Davis, David M. Stoms, Allan D. Hollander, Michael J. Bueno, Richard
L. Church, W. J. Okin, Ross A. Gerrard
Computational Earth System Science
of Geography, University of California
to IBM Environmental
September 30, 1997
The earth is
experiencing a mass extinction of species that is unparalleled in
its history. Installation of an effective reserve network to minimize
future loss of biodiversity will require coordinated conservation
assessments at international, national, regional and local levels.
Such assessments already rely heavily on advanced mapping technologies
and computing systems for spatial data analysis and display.
The goal of
this project was to design and test a prototype Spatial Modeling
and Decision Support System for Conservation of Biological Diversity.
The project is closely tied to the USGS-Biological Resources Division's
Gap Analysis Program,
and to related efforts at multi-species conservation planning in
southern California and the Sierra Nevada. Our project objectives
and results are summarized below:
and enable a prototype "regional" computing facility for
storage, analysis and visualization of biodiversity data.
regional computing facility was designed and implemented in the
UCSB Biogeography Lab.
The facility is centered around a 58H workstation file server and
a 39H compute server. These servers support a network of RS6000
workstations and Xterminals, connected by a local fddi network.
The network runs a suite of commercial and public-domain geographic
information system, remote sensing, statistical and analytical software
packages needed for regional conservation assessment and planning.
conceived, the project was designed to develop entirely a new, fully
integrated spatial analysis and decision support system. Emerging
technology progressed so rapidly, however, that it seemed more prudent
to take advantage of widely available new technology such as the
World Wide Web (WWW) and spatial analysis and visualization packages
and to focus our effort on developing specific applications and
linkages between poorly integrated systems. We instituted a WWW
site for sharing data and information via the Internet (http://www.biogeog.ucsb.edu/).
We also took advantage of WWW tools to reconstruct our cataloging
tool interface using HTML and used PERL CGI scripts to access the
database. In addition to offering users a now-familiar WWW browser
interface, the new version provides more display functions and automatic
filling of fields whose values can be obtained from file description
and header information. We now run our own httpd server and are
providing many of our datasets via interactive text and graphical
a set of specific software applications to support national (and
potentially international) gap analysis.
for plant community and habitat type classification, image compositing
for cloud removal, image classification, species distribution modeling,
and visualization were programmed to support the Gap Analysis Program.
For example, a new technique was developed for compositing daily
AVHRR satellite images to obtain cloud-free coverage of large regions.
classification" technique was developed to use these composited
images, or other multispectral data, to generate a thematic land-cover
map from existing maps from either another time period or from a
set of maps of varying resolution. Two programs were developed with
graphical user interfaces to integrate a non-spatial database of
wildlife habitat preferences to a spatial database of the distribution
of habitats to predict the distribution of the wildlife species.
Also, a custom interface was written using the ARCVIEW scripting
language (AVENUE) to improve the accessibility of the complex California
GAP database for visualization and query. This interface and the
database will be published on CD-ROM,
linking the spatial data with the textual and graphical report and
a conservation gap analysis of the Intermountain Semi-Desert Ecoregion
over nine western states.
for the individual states manifested obvious differences in spatial
resolution and pattern caused by use of different mapping techniques
and classification systems. This raised concerns about whether these
databases could be consolidated into a consistent product for regionwide
(i.e., multi-state) gap analysis. We used our image compositing
strategy to develop time-series images for the
Intermountain Semi-Desert Ecoregion (portions of Washington,
Oregon, California, Idaho, Nevada, Utah, Montana, Wyoming, and Colorado)
to provide a data set with consistent spatial, temporal, and spectral
properties over one entire growing season. The map-guided classification
technique was then used to integrate the original statewide GAP
land-cover maps into a more consistent regional map of standardized
cover classes. The nation's first regional gap analysis was conducted
using this new version of the land-cover map.
applications for monitoring wildlife habitats using multi-temporal
in environmental monitoring was directed towards developing an operational
system for retrieval and storage of NOAA-AVHRR data from a local
receiving station, and on the comparisons of different compositing
strategies for cloud-free imagery described above.
software to support reserve siting and reserve design and apply
it to reserve design in southern California and the Sierra Nevada.
that the reserve selection problem described in the conservation
biology literature can be reformulated as a classic maximal covering
location problem (MCLP) described in the operations research and
regional science literature twenty years ago. We solved the MCLP
model for a real application using vertebrate distribution data
prepared for the gap analysis of southwestern California. Using
IBM's Optimization Subroutine
Library on a RS6000 workstation, the MCLP could be solved in
seconds. Previous researchers had used non-optimal heuristics to
solve the problem and found it took hours on a supercomputer to
find optimal solutions using naive exhaustive enumeration methods.
We then reformulated the MCLP approach with three significant refinements,
including weighting species (to emphasize protection for rare species
in a multi-objective problem), weighting sites (based on their suitability
for biodiversity management), and integration of the model into
a commercial geographic information system.
In support of
the USDA Forest Service Sierra Nevada Ecosystem Project, we developed
a related reserve selection tool to explore alternative strategies
for locating core biodiversity management areas in that region to
represent native biodiversity. The new Biodiversity
Management Area Selection (BMAS) model requires that specific
acreage targets be specified for each biodiversity element (rather
than simple representation or covering in the reserve solution).
Second, suitability of the sites for biodiversity management was
incorporated into the objective function of the model. Third, management
class definitions were refined by incorporating data on grazing
and timber management from existing land use plans. The BMAS model
has also been used to explore alternative strategies for The Nature
Conservancy to conserve plant communities and rare species in the
Plateau portion of the Intermountain Semi-Desert.
At a more local
scale, a GIS-based model was developed for designing core reserves
and linking corridors using a combination of spatial analysis techniques.
This model was used to help design a reserve network in the imperiled
coastal sage scrub habitat in western San Diego County in southern
These and other
accomplishments are described in more detail in the main body of
this report and in the appendices. Our specific objectives and activities
evolved over the course of the four-year project in response to
rapid changes in technology, unexpected opportunities for expanding
the scope of our scientific research and collaboration, and changes
in our approaches to specific data and analytical problems. Nevertheless,
the entire enterprise began with, and continues to be built upon,
high-performance IBM technology. Virtually every significant phase
of the project has depended critically upon IBM hardware and software
to manage, analyze, and visualize very large and complex sets of
geospatial data. In summary, the project has been successful and
gone well beyond our original expectations in developing new tools
and solutions for conserving biological diversity, in integrating
those tools to support conservation planning in several regions
of the western U.S., and in demonstrating the potential of modern
computing technologies in addressing and mitigating human-caused
losses of biodiversity.
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