Gap Analysis Handbook
Literature Cited

The Gap Analysis Concept

from the National Gap Home Page

Much of the following discussion was taken verbatim from Edwards et al. 1995, Scott et al. 1993, and Davis et al. 1995. 

Gap Analysis provides an overview of the distribution and conservation status of several components of biodiversity.  It uses the distribution of actual vegetation and terrestrial vertebrates and, when available, invertebrate taxa.  Digital map overlays in a GIS are used to identify individual species, species-rich areas, and vegetation cover types that are unrepresented or underrepresented in existing management areas.  It functions as a preliminary step to the more detailed studies needed to establish actual boundaries for potential biodiversity management areas.  This data and results are then made available to institutions as well as individual land owners and managers so that they may become more effective stewards through more complete knowledge of the management status of these elements of biodiversity.  Gap Analysis, by focusing on higher levels of biological organization, is likely to be both cheaper and more likely to succeed than conservation programs focused on single species or populations (Scott et al.  1993).

Biodiversity inventories can be visualized as "filters" designed to capture elements of biodiversity at various levels of organization.  The filter concept has been applied by The Nature Conservancy, which has established Natural Heritage Programs in all 50 states, most of which are now operated by state government agencies.  The Nature Conservancy employs a fine filter of rare species inventory and protection and a coarse filter of community inventory and protection (Jenkins 1985, Noss 1987).  It is postulated that 85-90% of species can be protected by the coarse filter, without having to inventory or plan reserves for those species individually.  A fine filter is then applied to the remaining 15-10% of species to ensure their protection.  GAP is a coarse filter method because it can be used to quickly and cheaply assess the other 85-90% of species.

The intuitively appealing idea of conserving most biodiversity by maintaining examples of all natural community types has never been applied, although numerous approaches to the spatial identification of biodiversity have been described (Kirkpatrick 1983, Margules and Nicholls 1988, Pressey and Nicholls 1989, Nicholls and Margules 1993).  Furthermore, the spatial scale at which organisms use the environment differs tremendously among species and depends on body size, food habits, mobility, and other factors.  Hence, no coarse filter will be a complete assessment of biodiversity protection status and needs.  However, species that fall through the pores of the coarse filter, such as narrow endemics and wide-ranging mammals, can be captured by the safety net of the fine filter.  Community-level (coarse-filter) protection is a complement to, not a substitute for, protection of individual rare species.

 Gap Analysis is essentially an expanded coarse-filter approach (Noss 1987) to biodiversity protection.  The cover types mapped in Gap Analysis serve directly as a coarse filter, the goal being to assure adequate representation of all types in biodiversity management areas.  Landscapes with great vegetation diversity often are those with high edaphic variety or topographic relief.  When elevational diversity is very great, a nearly complete spectrum of vegetation types known from a biological region may occur within a relatively small area.  Such areas provide habitat for many species, including those that depend on multiple habitat types to meet life history needs (Diamond 1986, Noss 1987).  By using landscape-sized samples (Forman and Godron 1986) as an expanded coarse filter, Gap Analysis searches for and identifies biological regions where unprotected or underrepresented cover types and vertebrate species occur.

A second filter uses combined species distribution information to identify a set of areas in which all, or nearly all, mapped species are represented.  There is a major difference between identifying the richest areas in a region (many of which are likely to be neighbors and share essentially the same list of species) and identifying areas in which all species are represented.  The latter task is most efficiently accomplished by selecting areas whose species lists are most different or complementary.  Areas with different environments tend to also have the most different species lists for a variety of taxa.  As a result, a set of areas with complementary sets of species for one higher taxon (e.g.  mammals) often will also do a good job representing most species of other higher taxa (e.g.  trees, butterflies).  Species with large home ranges, such as large carnivores, or species with very local distributions may require individual attention.  Additional data layers can be used for a more holistic conservation evaluation.  These include indicators of stress or risk (e.g.  human population growth, road density, rate of habitat fragmentation, distribution of pollutants) and the locations of habitat corridors between wildlands that allow for natural movements of wide-ranging animals and the migration of species in response to climate change.  These more detailed analyses were not part of this project, but are areas of research that GAP as a national program is pursuing.

Literature Cited

Davis, F.W., P.A.  Stine, D.M.  Stoms, M.I.  Borchert, and A.D.  Hollander.  1995.  Gap Analysis of the actual vegetation of California - 1.  The southwestern region.  Madroño 42:40-78.

Edwards, T.C., Jr., C.H.  Homer, S.D.  Bassett, A.  Falconer, R.D.  Ramsey, and D.W.  Wight.  1995.  Utah Gap Analysis: An environmental information system.  Technical Report 95-1, Utah Cooperative Fish and Wildlife Research Unit, Utah State University, Logan, Utah.

Jenkins, R.E.  1985.  Information methods: Why the Heritage Programs work.  The Nature Conservancy News 35(6):21-23.

Kirkpatrick, J.B. 1983.  An iterative method for establishing priorities for the selection of nature reserves: An example from Tasmania. Biological Conservation 25:127-134.

Margules, C.R., A.O. Nicholls, and R.L. Pressey. 1988. Selecting networks of reserves to maximize biological diversity.  Biological Conservation 43:63-76

Nicholls, A.O., and Margules.  1993.  An upgraded reserve selection algorithm.  Biological Conservation 64:165-169.

Noss, R.F.  1987.  From plant communities to landscapes in conservation inventories: A look at The Nature Conservancy (USA).  Biological Conservation
41:11-37

Pressey, R.L.,and A.O. Nicholls. 1989.  Application of a numerical algorithm to the slection of reserves in semi-arid New south Wales.  Biological Conservation 50:263-278.

Scott, J.M., F.  Davis, B.  Csuti, R.  Noss, B.  Butterfield, C.  Groves, H.  Anderson, S.  Caicco, F.  D'Erchia, T.C.  Edwards, Jr., J.  Ulliman, and G.  Wright.  1993.  Gap analysis: A geographic approach to protection of biological diversity.  Wildlife Monographs 123.