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How To Obtain the Data
Appropriate and Inappropriate Use of these Data
Current Uses of CA-GAP Data

How To Obtain the Data

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:


The home page will also provide, over the long term, the status of our state's project, future updates, data availability, and contacts. At the time of this project's completion, a CD-ROM of the final report and data will be available at a nominal cost.

The California Department of Fish and Game will be the long-term custodian of the CA-GAP database, both for its distribution and its maintenance. Ultimately it is anticipated they will establish a web site for users to download the data. Check the CA-GAP web site at:


for links to the Fish and Game site.

The database will also be distributed on a CD-ROM. The CD-ROM disk will contain this report, GIS coverages (except see note below) and TM image mosaics, all metadata, an interactive atlas version with a customized graphical user interface to run ARCVIEW software to make routine queries of the database that are relevant to gap analysis.

Note: At the time of this report, access to the 1:100,000 scale land stewardship/management layer compiled for CA-GAP is restricted to subscribers of the state's Teale Data Center. Therefore it will not be distributed either through GAP or California Department of Fish and Game. Those interested in acquiring the coverage for a fee are directed to the Teale Data Center GIS Solutions Group, http://www.gislab.teale.ca.gov/ or (916) 263-1767, for details. Check the CA-GAP or the Fish and Game web sites, however, for the current status for this layer. As an alternative, CA-GAP has compiled a 1:2,000,000 scale version from unrestricted public-domain data. This lower resolution version is being distributed without restriction. Check the CA-GAP web site listed above for access.


Although these data have been processed successfully on a computer system at the University of California, Santa Barbara, no warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recommended that these data are directly acquired from a USGS Biological Resources Division server (see above for approved data providers) and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. The Biological Resources Division and the University of California, Santa Barbara shall not be held liable for improper or incorrect use of the data described and/or contained herein.

These data were compiled with regard to the following standards. Please be aware of the limitations of the data. These data are meant to be used at a scale of 1:100,000 or smaller (such as 1:250,000 or 1:500,000) for the purpose of assessing the conservation status of vertebrate species and vegetation cover types over large geographic regions. The data have not been assessed for statistical accuracy. Data evaluation and improvement is ongoing. The Biological Resources Division and the University of California, Santa Barbara make no claim as to the data's suitability for other purposes. This is writable data which may have been altered from the original product if not obtained from a designated data distributor identified above.


Proper documentation of all information sources used to assemble gap analysis data layers is central to the scientific defensibility of the Gap Analysis Program. The information used to describe gap analysis data is called metadata. Metadata are information about data. Metadata contain information about the source(s), lineage, content, structure, and availability of a data set. Metadata also describe intentions, limitations, and potential uses, allowing for the informed and appropriate application of the data. Descriptions of metadata function have recently been published by the Federal Geographic Data Committee (FGDC 1994, 1995).

The GAP metadata standards have been closely matched to the FGDC standards to ensure current and future compatibility. As the FGDC standards evolve beyond the current publication, we anticipate corresponding refinements in GAP documentation. The format of the GAP metadata consists of eight major documentation sections (Table 9-1) containing one or more metadata elements. Each element is named (e.g. Map Projection Name), and the "Type" of entry (text, integer, date, time) and "Domain" of the entry (i.e. x > 0) are also defined.

Table 9-1. Metadata Data Element Categories

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.

For additional information via Internet, an HTML verson of the GAP metadata standards (Cogan and Edwards 1994) is available from the Gap web page. The URL is http://www.gap.uidaho.edu/gap/handbook/meta.htm.

For a version of the current FGDC Metadata Standards (8 June 1994) http://www.fgdc.gov/Metadata/Metadata.html. The standard is also available by anonymous file transfer protocol (FTP) from: www.fgdc.gov ( under /pub/metadata.

Appropriate and Inappropriate Use of These Data

All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. For gap analysis, minimum standards were set (see a Handbook for Gap Analysis, Scott et al. 1993) to meet program objectives. These standards include: scale or resolution (1:100,000 or 100 hectare minimum mapping unit), accuracy (80% accurate at 95% confidence), and format (ARC/INFO coverage tiled to the 30'x60' USGS quadrangle).

Recognizing, however, that GAP would be the first, and for many years likely the only, source of statewide biological GIS maps, the data were created with the expectation that they would be used for other applications. Therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by GAP data. For most uses, it is unlikely that GAP will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end it will be the responsibility of each data user to determine if GAP data can answer the question being asked, and if they are the best tool to answer that question.

Scale: First we must address the issue of appropriate scale to which these data may be applied. These data were produced with an intended application at the ecoregion level, that is geographic areas from several hundred thousand to millions of hectares in size. The data provide a coarse-filter approach to analyses, meaning that not every occurrence of every plant community or animal habitat is mapped, only larger, more generalized distributions. The data are also based on the USGS 1:100,000 scale of mapping in both detail and precision. When determining whether to apply GAP data to a particular use, there are two primary questions: do you want to use the data as a map for the particularly geographic area, or do you wish to use the data to provide context for a particular area? The distinction can be made with the following example: You could use GAP land cover to determine the approximate amount of oak woodland occurring in a county, or you could map oak woodland with aerial photography to determine the exact amount. You then could use GAP data to determine the approximate percentage of all oak woodland in the region or state that occurs in the county, and thus a sense of how important the county's distribution is to maintaining that plant community.

Appropriate Uses: The above example illustrates two appropriate uses of the data; as a coarse map for a large area such as a county, and to provide context for finer-level maps. Specific case-study examples are provided at the end of the chapter, but following is a general list of applications:

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.

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