Gap Analysis and State or Regional Biodiversity Planning
We view conservation planning as a complex process by which society
develops rational strategies for sustainable use and conservation
of natural resources. We believe that such strategies must engage
all sectors of society and must address multiple levels of social
and biological patterns and processes over local to regional, continental,
and even global extents. One important outcome of such planning
is the prioritization of biota and areas for expanded protection
and/or conservation management. (An equivalent but complementary
view is that conservation planning reveals where consumption and
conversion of natural resources to accommodate population growth
and economic development can proceed with the least threat to biodiversity.)
There is not necessarily a clear distinction between nature reserves
versus areas that are managed for sustainable resource harvest,
and it may be more useful to think of a management gradient which
at one extreme involves intensive management to maximize economic
yield (e.g., creation and sustainable harvest of timber plantations
or improved pastures) and at the other may involve intensive management
to maintain or restore native species or ecosystem processes (e.g.,
tree planting and streamflow manipulation to restore riparian habitats).
While the answers to these questions should be guided by scientific
knowledge, ultimately they reflect the values of those who pose
them (Noss 1996). Gap analysis provides the information necessary
for the process of setting credible conservation goals by identifying
the gaps in the current conservation network.
The second stage of conservation planning, site selection, is generally
undertaken using relatively coarse survey information (e.g., Noss
1987, Scott et al. 1993), whereas the reserve design stage requires
very detailed analyses of the biotic composition, size, shape, connectedness,
and cost of alternative reserve plans (Shafer 1990). Management
practices allowed in reserves must be based upon an understanding
of the impacts of various land uses and natural disturbances on
the viability of the biota and will be highly case-specific (Noss
1996).
Gap analysis serves as a coarse-filter for a preliminary inventory
of plant communities and wildlife species and rates their relative
vulnerability in regard to land management (Scott et al. 1993).
Beyond the initial conservation assessment, these findings can be
applied in at least two additional directions. First they provide
a regional perspective on biodiversity distribution, management,
and conservation priorities that provides a broader context for
assessing the impacts of local land use proposals. GAP data can
quantify how rare a community type is, where else it occurs, and
whether it is well-represented in biodiversity management areas.
Second, the data from GAP can play a significant role in follow-up
conservation planning efforts at a statewide or regional level (Crowe
1996, Vickerman 1996). For instance, GAP data can provide the missing
biodiversity dimension to discussions about alternative wilderness
and national park proposals (Wright et al. 1994, Merrill et al.
1995, Merrill et al. 1996, Wright and Scott 1996). The Nature Conservancy
has already used a small portion of the CA-GAP database to identify
candidate areas to ensure adequate representation of all community
types in the Columbia Plateau ecoregion (Stoms et al. 1997). The
Southern California Association of Governments used CA-GAP data
in conjunction with general plans from a six county area to identify
priorities for conservation of plant communities in the open space
element of their comprehensive regional plan (Crowe 1996). Figure
SW-4 shows the pattern of communities considered vulnerable in gap
analysis for the Southwestern California region overlaid with areas
zoned for development or agriculture in existing general plans of
local jurisdictions.
Gap analysis findings have already been used in research studies
to develop tools to select areas for additional biodiversity management
(Church et al. 1996, Davis et al. 1996, Stoms et al. 1997). Moritz
et al. (1997) demonstrated the use of CA-GAP data to screen potential
sites for Research Natural Areas for the U. S. Forest Service. These
approaches often include additional data layers on stress factors
(e.g., human population density and roadedness) and relative manageability
of sites (e.g., private ownership, fragmentation of ownership) as
well as the biological distribution and status data. Although considerable
progress is being made in developing these tools, our limited understanding
of the requirements of most species and ecosystems hinders our efforts
to create computer models adequate to the task. These site selection
studies were not part of the initial state gap analysis, but are
areas of active research being pursued by the National GAP and others.