9. DATA USE AND AVAILABILITY

The CA-GAP database has a wide potential set of applications for conservation planning, biogeographical research, and education. It is the goal of the Gap Analysis Program and the BRD to make the data and associated information as widely available as possible. While a CD-ROM of the data will be the most convenient way to obtain the data, it may also be downloaded via the Internet from the national GAP home page at:

I. Identification Information: What the data set is called, file format description.

II. Data Quality Information: Accuracy, consistency, and data sources.

III. Spatial Data Organization Information: Data structure - raster, vector, point, etc.

IV. Spatial Reference Information: Coordinate units, map projection, spatial resolution.

V. Entity and Attribute Information: Attribute codes and reference citations.

VI. Distribution Information: How to order the data, on-line access, transfer size.

VII. Metadata Reference Information: Date of the metadata, contact for metadata updates.

VIII. Contact Information: General data contact, mail, voice, fax, web, e-mail.

Demands for metadata will increase as electronic networks expand across the national and international scene and more requests are made for distribution of information. As the number of users and the diversity of disciplines and programs sharing the data expand, the information carried by metadata will become increasingly important. One of the goals in defining today's metadata standards is to anticipate these future needs.

Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a "fuzzy line" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include:

• Use of the data to map small areas (less than thousands of hectares) typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys.
• Combining GAP data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries.
• Generating specific areal measurements from the data finer than the nearest thousand hectares (minimum mapping unit size and accuracy affect this precision).
• Establishing exact boundaries for regulation or acquisition.
• Establishing definite occurrence or non-occurrence of any feature for an exact geographic area (for land cover, the percent accuracy will provide a measure of probability).
• Determining abundance, health, or condition of any feature.
• Establishing a measure of accuracy of any other data by comparison with GAP data.
• Altering the data in any way and redistributing them as a GAP data product.
• Using the data without acquiring and reviewing the metadata and this report.

Current Uses of CA-GAP Data

Early versions of the CA-GAP land-cover database have already been used in a number of planning and research applications. We include a sampler of those uses here to stimulate the interest and creativity of potential users.

In 1993, the CA-GAP data were used to assist the Southern California Association of Governments (SCAG) develop the open space element of their comprehensive regional plan (Crowe 1996). The distribution of vulnerable plant community types were compared with the pattern of land use zoning in the combined general plans of the jurisdictions in the 6 county area. This information was used to highlight plant communities that were not only vulnerable because of land management status but also because of permitted land uses. The results for the portion of the SCAG area in the Southwestern California region have been updated in Appendix 6-1. Relatively large proportions of several types are zoned for development. Other organizations, such as The Nature Conservancy, are also using CA-GAP data in their planning studies.

The wildlife habitat types and plant communities layer from CA-GAP has been used in a variety of applications beyond gap analysis. Wildlife management studies have included several species, such as the desert tortoise, bighorn sheep, and mountain lion. Several applications are related to fire (e.g., fire impacts on vegetation, fuels loading and fire risk (Sapsis et al. 1996), correlations with lightning strikes, and mapping of fire regimes. The California Air Resources Board is coordinating a study of natural, or biogenic, emissions by modeling rates for different GAP land-cover types. A common limitation for many of these studies has been the absence of data on canopy size and density, which has required users to make generalizations about ecological responses.

The CA-GAP database has been applied in several studies to identify potential sites for additional biodiversity management areas to fill gaps in the representation of native biodiversity. The purpose of these published studies was development of methods and evaluation of a range of alternatives. They all stop short of making formal recommendations for designation, which would require more political discussion on both the objectives and solutions. Several studies used draft data on predicted wildlife distributions in Southwestern California to develop maximal covering location models for identifying priority sites (Church et al. 1996, Gerrard et al. 1997). This type of model was later modified as part of a procedure proposed to the U. S. Forest Service for systematically using gap analysis data to identify likely sites for new Research Natural Areas (Moritz et al. 1997). This class of models is designed to represent the most species or communities in a given number of sites. Davis et al. (1996) developed the Biodiversity Management Area Selection model to explore a range of alternative sets of sites based on varying assumptions to meet different representation goals for plant communities and vertebrates in the Sierra Nevada region. CA-GAP was the only source of information that exhaustively mapped these measures of biodiversity across all stewardships in the region.

Whereas the "reserve selection" models described above take a rather static perspective in conservation planning, others have taken a dynamic approach by modeling urban growth scenarios and its potential impacts on biodiversity. Various approaches to modeling urbanization have been developed at University of California, Davis, Berkeley, Santa Barbara, and Santa Cruz (Cogan 1997), and Harvard (Steinitz et al. 1996, White et al. 1997). CA-GAP data on land-cover and management were used either as constraints on development (e.g., existing reserves) or as effects.