Status of GAP Components This section reviews the status of the constituent parts, or components needed, to conduct Gap Analysis. Gap Analysis was begun with a focus on the terrestrial environment, however, the development of information and analyses must logically be extended to the aquatic environment; the aquatic component of Gap Analysis is also treated in this section. The status of analyses of the GAP data layers is discussed in the "Products" section of this report. Edge-Matching State Project Data
Rendering bioregional data from individual state projects is the key to seamless biogeographic data. It is usually the first opportunity to work intensively with the entire land cover data layer from individual state projects. The earliest attempts at this are reported below. Since these states were the earliest projects, the amplitude of variability among and between them is expected to be greater than for state projects that have followed.
In late 1993, David Stoms, Frank Davis, and other GAP staff at the University of California, Santa Barbara's Biogeography Laboratory worked with Blair Csuti of the Oregon Gap Analysis Project to test the edge-matching between the Oregon map and the California map for the Modoc Plateau region. Both states had used a similar mapping process, and the results showed relatively good agreement. Some changes were made to adjust polygon boundaries in the California map. The technical report, "Prototype GAP State Edgematching Evaluation" (Odion et al. 1993) was prepared as a result of this effort and is available from the University of California at Santa Barbara's Biogeography Laboratory's home page <http://horton.geog.ucsb.edu/top.html>. The results of the California and Oregon edge-matching exercise are shown in Figure 5.
The University of California, Santa Barbara's Biogeography Laboratory provided data for the Mojave Desert region to Tom Edwards, the Utah GAP Principal Investigator, as part of an effort to compile all state maps covering this region. Edwards visited Santa Barbara in August 1995 to present his initial cross-walking of classifications and the spatial detail present in the maps. Progress was made on the cross-walking to a common UNESCO formation level plus cover types. Some of the differences between state maps involved classification decisions of land cover types that occur across broad gradients where the land cover type boundary was not discrete. Edwards and the California GAP staff also explored using Advanced Very High Resolution Radiometer (AVHRR, the weather satellite) multi-temporal data to discriminate cover types. The AVHRR data show promise for smoothing the state maps into a more consistent region-wide map of similar spatial and taxonomic detail, and spectral patterns frequently corresponded to polygon boundaries. Over the next six months, Edwards, Davis, Stoms, and others will be working to develop a classification of AVHRR data to produce this regional product. Similar work, which shows even more promise for discriminating cover types, is just beginning in the Great Basin regions.
The land cover maps from each of these states were matched together into a single GIS data set in July 1995. The process was begun by co-registering the thematic categories (the natural land cover classifications). Since the Idaho and Oregon data were prototypes, produced at coarser scales than the Washington data, and the standard classification system was not developed at that time, some spatial and thematic variation was expected. However, the Oregon and Idaho data included such spatial and thematic detail that, by relying on the attributes of each land cover type, most categories were able to be interpreted as specific standardized Natural Community Alliance types per Bourgeron and Engelking (1994). The regional map of the Pacific Northwest is still being tested and has not been released.
One of the up-and-coming challenges for GAP is the matching of vertebrate range maps across the state project boundaries. Since one of the promises of GAP is that seamless biogeographic data can provide the ecological context now missing from the management of biological diversity, the completion of this task is a high priority. The first step in this process is to match the land cover maps, as described in the sections above. The second step is to review and compare the wildlife habitat relationship models for each species from each of the states. Differences in the models need to be identified and the reasons for these differences documented. If the reason for a difference in a model is related to actual behavioral differences among populations of a species (genuine differences), the edge-matched maps (and associated databases) will reflect that. If differences are related to the way the models were constructed, data limitations, or similar factors (artificial differences), then the vertebrate species data will be corrected. Early in 1996, GAP will convene a workshop on edge-matching vertebrate species distribution data. The adoption of EMAP hexagons for general range delineation will aid edge-matching. |
