These are exciting times for GAP. By the end of the fiscal year, ten states will have completed Gap Analysis projects. They are also times of change. With the creation of the National Biological Survey (now the National Biological Service), GAP was placed in the Division of Inventory and Monitoring, and we are working very closely with John Moeller and John Mosesso of that Division to insure a smooth transition from the Division of Research. The administrative structure of the NBS is divided into Research, Inventory and Monitoring, and Information and Technological Services. With GAP, we have an unprecedented opportunity to demonstrate how a research activity can be fully integrated into all three areas of the Service.

The Research Arena

In the research arena, Bill Krohn from Maine, Curt Griffin from Massachusetts, and Lee Graham's group from Arizona have worked together using the Airborne Videography techniques developed in Arizona to help resolve the difficulties of mapping deciduous cover types in New England. Additionally, inventorying and monitoring efforts using GAP maps will assess changes in cover types back to 1972 through examination of three coregistered sets of multispectral scanner scenes. This change assessment will be led by EPA and USGS in the North American Landscape Characteristics Program, as part of NASA Pathfinder.

The Use of GAP Information

In the information transfer arena, the use of GAP by cooperators and others to help their efforts at land use planning has been exceptional. Specifically, Frank Davis at the University of California, Santa Barbara, collaborated with Ventura, Los Angeles, Imperial, Orange, Riverside, and San Bernadino counties (i.e., the Southern California Association of Governments or SCAG, a six-county coalition), to show the occurrence of communities at risk in areas zoned as open space and those occurring in areas zoned for development. The Utah Department of Fish and Game is using GAP for many research and management activities. The Idaho Department of Fish and Game has placed the data set in its Conservation Data Center, where it has been used to advise the Director on environmental values of a proposed bombing range. Dr. Blair Csuti is involved in an exciting collaborative effort with Dr. Pressey; Dr. Steve Polasky, economist at Oregon State University; Dr. Ross Kiester, U.S. Forest Service; and Ms. Melanie Kershaw, with the Institute of Zoology at the University of London. They are using the results of the Oregon Gap Analysis Project to compare different approaches to special management area selection questions, using various algorithms on the same data set. This should provide us with a better idea of the strengths and weaknesses of the various approaches.
One of the more exciting and, hopefully, long lasting developments for GAP has been the creation of private, state, and federal partnerships in Arizona, Oregon, and Tennessee to apply the findings of GAP. As the recently completed GAP users' survey and implementation strategy report indicated, there are three ways in which GAP information is used:

Situation-specific application which includes the use of data sets or individual data layers to answer questions about single species, sites, or management issues. Easy access to GAP data and some assistance in the interpretation of the information is needed by users for these applications.
Integration into existing land-use planning is another important application strategy. Included primary users are county planners, state and federal resource agencies, and large private industrial landowners like timber and utilities companies. Easy, efficient access to that data and some assistance with its interpretation is needed to facilitate this application.
Use in cross-boundary, ecosystem-oriented, landscape-level planning is another purpose for which GAP data is well suited. Given its meso scale, GAP is most useful for statewide, bioregional, and large watershed planning. It provides a context for making more site- specific decisions. This application is the best opportunity to make decisions that will prevent species from being listed as threatened or endangered.

We are currently seeing major use of GAP data in specific situations and their integration into existing land use planning efforts. As the data sets become more widely available, we hope to see use of GAP in transboundary ecosystem application. To facilitate this, Frank Davis at UC Davis is going to create seamless vegetation maps for the Mojave, Sonoran, and Great Basin ecoregions in the next 12 months.
GAP has used a variety of ways to facilitate the transfer of its information. Brian Biggs, now at Utah State University, developed an on-line encyclopedia of Gap Analysis at the United Nations Environment Programme's Global Resource Information Database (UNEP-GRID), located at the EROS Data Center in Sioux Falls, South Dakota. Brian's work was done in conjunction with the U.S. National Biological Survey and was funded by NASA, through the Remote Sensing Research Unit at the University of California, Santa Barbara. It contains the GAP Monograph, manual, and data sets for Utah, California, and Idaho. Other state data sets will be added as they become available. Complete access instructions are available in this bulletin.
Allan Falconer and Tom Edwards at Utah State, in collaboration with the USGS, developed a hard copy (CD-ROM with ArcView 2 shell). Currently, prototype versions are available and were demonstrated in October by Brian Biggs and Mike Jennings at the First Federal Geographic Technology Conference in Washington, D.C., where the Mosaic home page and poster presentations were extremely well attended. Production copies will be available in March at the ACSM/ASPRS Convention.
A formal Memorandum of Understanding will be signed in November 1995 in recognition of the Multi-Resolution Land Consortium's (MRLC) unprecedented collaborative efforts to more fully integrate land cover mapping efforts by federal agencies. Congratulations to Denice Shaw (EPA), Mike Jennings, Don Lauer, Jim Sturdevant, and Tom Loveland at USGS for all their hard work on this project.
We have been invited by the American Society for Photogrammetry and Remote Sensing to present a TOCSECT of papers on GAP at their annual convention. The conference is normally attended by several thousand individuals. The date of the symposium is 27 February - 1 March at Charlotte, North Carolina. More than 30 papers will be presented and later published as a proceedings. A more detailed program can be found in this bulletin.
Again, these are exciting times for GAP, indeed for all collaborative efforts. This year will see 36 states with Gap Analysis projects, with perhaps ten state projects scheduled for completion by the end of 1995. It is my hope that with more projects completed, we will see GAP data sets used in collaborative private, state, and federal efforts to resolve long-term land use issues. The SCAG's effort and the ongoing interagency Klamath Basin effort in California (including GAP data from California and Oregon) are just two examples of interdisciplinary multi-partnered planning efforts using GAP data sets.
Finally, congratulations to the folks in the Washington Gap Analysis project, especially Karen Dvornich and Chris Grue. Karen will receive Renew America's National Award for Environmental Sustainability in the "Wildlife and Habitat" category. The award activities include a White House visit and will be nationally televised.
I look forward to seeing all of you at this summer's meeting in Fayetteville, Arkansas.

J. Michael Scott, Director

National Gap Analysis Program

FEATURES

A Discussion of the Adoption and Diffusion of Gap Analysis as a Technical Innovation

The purpose of this discussion is to broaden the dialog of how to deliver the concepts, products, and results from Gap Analysis to society. As more and more state GAP projects near completion, the unavoidable question then becomes, "where does all this data go from here?" I briefly discuss the results of a review of some recent uses of GAP data to illuminate early uses of GAP as a technical innovation and, in that light, present some important tenets of the adoption and diffusion of technical innovations. I present these concepts as a framework to help those struggling with the issue of "implementing" GAP, especially at the state level.

GAP is on the verge of either becoming irrelevant to society or becoming an accepted basic tool for managing biological diversity across the broad array of related programs and activities, both private and public. The answer to which one of these will prevail hinges on how we go about the task of providing for its adoption and diffusion into society. Admittedly, we have until now had no choice but to focus on the development of GAP's science and technology, on the development of state projects, and on maintaining support for state projects. It's now time to focus on the long-term issues of how GAP can maximize its potential by bringing new knowledge to policy. The only way to do this is by providing individuals with information based on good science. This discussion is concerned with the "providing" part of this equation, or the delivery of GAP to society. The "good science" part has been and will continue to be dealt with as an integral part of GAP.

At this point, it's important to briefly reiterate the original vision and direction of GAP, because in the early adoption and diffusion of the concept among natural resources professionals, the desire for biologically sound land cover data has often overshadowed its deeper meaning, sometimes resulting in misunderstandings of what GAP is intended to do.

Gap Analysis is a scientific method for identifying the degree to which native animal species and natural communities are or are not represented in our present-day mix of conservation lands. Those species and communities not adequately represented in areas that are being managed for the long-term maintenance of native species constitute conservation "gaps." The purpose of the Gap Analysis Program (GAP) is to provide broad geographic information on the status of ordinary species (those not threatened with extinction or naturally rare) and their habitats in order to prevent future conservation crises. To achieve this, maps of natural land cover, vertebrate species distributions, and land management are required in specific formats. The method was originally intended as a first, coarse-scale step in the process of special management area identification and selection, rather than special management area design. Maximizing the use of GAP products for other uses is also important, and this has been central to the GAP philosophy of partnerships.

As it has turned out, GAP has served as an "information catalyst" around which natural resources professionals and their institutions are coalescing naturally. I hypothesize that this represents a major new phenomenon in resources management, made up of three parts. One part is simply a manifestation of the information age within the natural resources field - our newfound ability to model and visualize the living world using digital technology and telecommunications. A second part stems from advances in science, resulting in a better understanding of how the natural world works. For example, the mechanisms by which the different levels of biotic organization are linked - species, natural communities, and large landscapes - are much better understood; GAP is a management tool evolving from this science. Third, diminishing natural and fiscal resources are causing natural resources professionals, thus their institutions, to work together in a more dynamic fashion. This is greatly facilitated by having a common information base and having the ability to share their data. This emerging phenomenon fits, coincidentally, with the present-day trend of decentralized government. If state- level policy is to be effective, sound multi-state biogeographic information will be critical.

A Profile of Some Uses of GAP Thus Far

Recently, I reviewed 47 cases where GAP data were used for a specific purpose, and I stratified these uses into eight general categories. Of these cases, GAP information was used most often for direct land management purposes, such as siting a ski resort on public land or revising wildlife management plans. In most of the cases where GAP information was used for direct land management purposes, its users were driven by an immediate need for explicit landscape-level maps that provide contextual information on a variety of themes (such as the distribution of species or the distribution of habitat types relative to a proposed action or resource use decision). This underscores the demand and the need for large-area contextual biogeographic information for diverse applications, thus the use of Gap Analysis products for purposes beyond its original intended purpose.

The review of case histories also underscores the imperative for state GAP projects to track the uses of their data. If state projects do not yet have a database for tracking how their data are being used, they should construct one now. This topic should also be the focus for discussion among state project cooperators. Cooperators should agree to report back to the GAP principal investigator on how the data are being used, either broadly (used in everyday operations to maintain certain amounts of habitat types in a shifting mosaic) or specifically (used to evaluate a proposal to enhance bighorn sheep habitat across five townships).
One of the greatest problems, of course, is that data dissemination is not funded under state projects' research work orders. So, when a request comes in to a state GAP project, the data are provided pro bono, usually at the expense of completing the project itself. For many project staff, responding to requests for data is a distraction from the work of producing the data, and it is an unfunded demand. What is the solution?

For now, we ask that the project personnel respond to requests as best they can. Staff should make sure that the person requesting the information realizes and appreciates that the work is in progress and that because of this there are limitations to the degree of response. At the same time, please do not just turn a request down flat. Get those requesting the information to understand the present situation. If the data they are asking for is genuinely not ready for release, explain the details to them so that they understand, while giving them a picture of what is to come.

The question is often asked, "Where does GAP end?" The NBS role is to work in partnership with other organizations to develop, interpret, and disseminate scientific information about the nation's biological resources. How all of this is manifested is still developing. It is safe to say, though, that much will depend on the state cooperators.

Eventually, we will find permanent homes for state- level data as both dissemination and feedback nodes. Although the role that the NBS plays in the long term is still developing, there is promise in its incipient National Biological Information Infrastructure (NBII) effort, as well as its State Partnerships program, both of which are within the Division of Information and Technology. As of now, most of us involved with GAP envision state-level information nodes and continued research and development activities among the state project cooperators. Additionally, the EROS Data Center will serve as the long-term federal-level archive for GAP and MRLC data. Ultimately, exactly how the results from GAP are disseminated at the state level will depend largely on the ongoing cohesion of state agencies, non-government organizations, and universities within the states.

Within the NBS, GAP is one of the few programs that flows through each of the three operational divisions In this sense, GAP is contributing to the functional linkages among the NBS divisions.

There are still unsolved structural issues at the state level, such as where GAP data will live, who will pay, and exactly how updates will be done. These issues should be approached as the problems of adoption and diffusion of technical innovations. Because the degree to which GAP becomes useful to society is at stake, and because many who have been focused on the data development phase of GAP are not familiar with how technical innovations (GAP) spread through society, Below is a review of some of the basic concepts as articulated by Rogers (1983). These principles are the basis for the successful agricultural extension model, and they need to be the basis for any GAP extension work.

A Review Of Principles For The Adoption And Diffusion Of Innovations

Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. It is a special kind of communication because the messages have to do with new ideas. The diffusion of innovations is a complex social process.

The four main elements of the diffusion of innovations are:

The innovation
Communication about the innovation
The time, or rate of diffusion
The social system that adopts or rejects the innovation

The innovation's characteristics that explain the different rates of adoption are:

Perceived relative advantage: Not the proven or objective advantage that the innovation may offer, rather, this is the advantage that the potential user believes the innovation may provide, regardless of the bases for their belief.
Compatibility: The degree that an innovation is perceived as being consistent with existing values, experiences, and social systems of potential adopters. Adoption of an incompatible innovation may require prior adoption of a new value system.
Complexity: Perceived or real complexity will slow the rate of adoption. New ideas that are simpler to understand will be adopted more quickly than those that require new skills or new understandings.
Tryability: Innovations will be adopted more quickly that people can try out first before committing themselves.
Observability: The degree to which the results are visible. Innovations that are preventative in nature, such as health practices, are less immediately observable and slower to be adopted.

Most people do not evaluate an innovation on the basis of scientific studies, but depend on a subjective evaluation of the innovation conveyed to them from other individuals who are like themselves and who have had previous experience with the innovation.

This dependence on subjective peer-group communication strongly suggests that the heart of the diffusion process has more to do with who does the communicating than what or how it is communicated, although the what and how also remain important.

One of the most distinctive problems of diffusion is that those attempting to communicate to potential adopters are often not in the same peer group or do not have much in common with the potential adopters. They may not talk the same "language." This situation often results in the rejection of innovations.

The rate of adoption of an innovation by an individual has to do with the individual's (a) decision process, and (b) degree of innovativeness (there are innovative people and there are people who lag in adopting new innovations). An individual decision process for adopting an innovation has five time-related periods to it:

Knowledge: Gaining an understanding of the innovation's existence; what it is and how and why it works.
Persuasion: Formation of a favorable or unfavorable attitude about innovation; how will it help me solve my problems, etc.
Decision: Engaging in activities that lead to a choice to adopt or reject the innovation.
Implementation: Putting the innovation to work.
Confirmation: Reinforcing a previous decision about the innovation; an individual may reverse a previous decision if exposed to conflicting signals.

Individuals clearly have different degrees of innovativeness which can be characterized as innovators, early adopters, early majority, late majority, and laggards.

Innovators actively seek information about new ideas. They have wide interpersonal networks usually beyond their local system. They are able to cope with a higher level of uncertainty about an innovation. However, innovators are likely to be considered deviant from the target social system and often of low credibility to the system, frequently necessitating the use of change agents for the diffusion of their innovations. Early adopters generally do not depend upon subjective evaluations of an innovation from their peer group and are more likely to take risks.

The social structure of the target adopter group has a major bearing on how an innovation may be adopted. When the social system is oriented toward change, the "opinion leaders" may be innovative; when the social system's norms are opposed to change, opinion leaders are slow to adopt an innovation. Opinion leaders make up an informal leadership that can influence the attitudes of others. Their position is earned and maintained by the individual's technical competence, social accessibility, and conformity to the system's norms. Those who can successfully achieve adoption of an innovation by a social system are referred to as "change agents." Change agents work to influence adoption, or rejection, of an innovation most often by influencing the opinion leaders.

Conclusion

I hope this is the beginning of a dialog on a coordinated effort to deliver the results of GAP to society, whether original data sets or derivatives. The adoption and diffusion of GAP will by necessity be driven from the bottom up, yet to be effective, it will require a consistent and cohesive effort. Perhaps in the next GAP bulletin, we can print your thoughts on this.

Literature Cited

Rogers, Everett. 1983. The diffusion of innovations. Free Press, New York, 453 pp.

Portions of this article were excerpted from a presentation made at the fourth annual GAP workshop, July, 1994. Thanks to Gary Machlis and Sara Vickerman for their important contributions and ongoing efforts to foster the adoption and diffusion of GAP.

Michael D. Jennings, National Coordinator

Gap Analysis Program

The Aquatic Component of Gap Analysis

Since the very beginning of Gap Analysis, there has been discussion on the need to apply the method to aquatic environments. The effort was officially launched in early September of 1994 with the formation of an advisory group. The group established the goal for the application of GAP methodology to aquatic environments as:

To characterize aquatic biodiversity in the U.S. on a landscape scale for the effective management of land and water resources in ways that will preserve this biodiversity."

Dr. Patricia Heglund has been appointed as the coordinator for the aquatic GAP TOCSECT. She recently moved to Moscow, Idaho from Alaska, where she had spent the past seven years as a research biologist (wetlands and waterfowl) for the U.S. Fish and Wildlife Service - Alaska Fish and Wildlife Research Center (now the National Biological Service - Alaska Science Center). Dr. Heglund currently holds affiliate faculty status in both the Department of Fish and Wildlife Resources and the Department of Biological Sciences at the University of Idaho.

Three prototype projects have been funded for 1995. These projects will be conducted in New York, Washington, and California. These pilot projects are predicated on the same fundamental tenets as the terrestrial component of GAP: 1) to identify places offering the best opportunities to conserve species while they are still common, through the identification of species and their habitats currently under-represented within our conservation network; 2) to provide a baseline for later biogeographic comparison; and 3) to provide landscape level spatial data useful for holistic resource management. These pilot projects will include lacustrine, palustrine, and riverine environments.

Our objectives for these prototype studies include:

Acquiring EPA River Reach File III data for use as base maps and catalogs of river basins at a scale of 1:100,000. Base maps will be registered with corresponding terrestrial GAP base maps and corrected for errors in River Reach data sets.
Mapping known distributions of fish, macroinvertebrates, amphibians, and reptiles (hereafter referred to as "elements") from museum collection records, agency records, published literature, and other sources.
Mapping general predicted ranges of each element from the published literature (e.g. Freshwater Fishes of Canada).
Mapping general habitat types, for example, aggregated from National Wetlands Inventory database.
Identifying habitat relations models for each element from existing literature.
Combining the steps listed above to generate maps for each water body or river reach of known or predicted occurrences of each element.
Reviewing predicted occurrences with experts and revising data layers as appropriate.
Developing attributes for each river reach, identifying its management status such as:

county or state shoreline, or riparian management regulations,
state fisheries management practices (fishing regulations, stocking, pesticide use, motorized/non-motorized boating regulations, etc.),
state area-specific management designations (e.g., water quality, recreation, water withdrawals, aquatic vegetation management),
federal designation and regulations (e.g., Wild and Scenic Act, Clean Water Act, navigation considerations, other licenses and permits such as NPDES or FERC, federal structures).

9. Showing relations between, a) species distributions and in-stream management, and b) species distributions and terrestrial land cover between successive watershed sizes (fourth to second order watershed).

10. Determining where the best opportunities are to achieve long-term avoidance of threatened or endangered species status by both in-stream and watershed management.

Analyses will be conducted a second time when adjacent river basins are completed and their information is integrated, allowing for comparisons across larger biogeographic regions.

One of the most exciting aspects in developing the aquatic component of GAP is the construction of data sets compatible with the terrestrial data. Through the GAP process, we will integrate aquatic and terrestrial environments for a variety of analytical applications. For example, the data will show land cover for all second-order watersheds upstream of any given river reach. Although we expect others to find many uses for the data, our current goals are to: 1) conduct an initial screening of large areas from which more specific planning and management options can be developed within a bioregional context, and 2) provide a logical starting point at the landscape scale for conservation problem-solving.

In discussions about both the terrestrial and aquatic components of GAP, the question frequently arises, "What about riparian areas?" Our current position is that although riparian areas are of enormous importance, they cannot be adequately treated by our current level of funding. Adequate treatment of riparian areas requires a level of effort similar to the National Wetlands Inventory program, in that they should be mapped at a scale of at least 1:24,000. Given our funding constraints, we believe it is more productive to focus on landscape elements that can be adequately treated and continue to articulate the needs of those elements that are currently beyond our means.

As with terrestrial GAP, the aquatic component is starting with no generally accepted community- based habitat classification system. As with the land cover mapping effort, we hope the aquatic projects will spur a consensus about the structure and substance for a national classification system and how the system can be maintained over time.

Michael D. Jennings

National Coordinator, Gap Analysis Program

Patricia J. Heglund

Aquatic GAP Coordinator, Gap Analysis Program

Steps in Strategies to Manage Biodiversity: Identification, Selection, and Design of Special Management Areas

Gap Analysis provides a regional perspective on the distribution of several elements of biodiversity, notably, plant communities and vertebrate species. The maintenance of much biodiversity will depend on balanced management of multiple-use wildlands. Special management areas however, are a necessary component of an overall biodiversity management strategy, since they serve as a haven for those species and communities incompatible with multiple use management and provide control areas to assess the success of various management prescriptions outside of special management areas.

In their 1994 book, Saving Nature's Legacy, Reed Noss and Allen Cooperrider conclude, "The United States has no national strategy to conserve biodiversity." Aside from the opportunistic protection of scenic wilderness, habitat protection in the USA largely has been focused on areas inhabited by game species or endangered species. Although the recovery needs of species on the brink of extinction are legitimate components of an overall strategy to maintain biological diversity, they must be complemented by a proactive approach to land use planning that ensures that the bulk of biodiversity never becomes endangered in the first place. In an ideal world, an objective consideration of the distribution of biodiversity would lead to the identification of priority areas which would then be managed for their natural values in order to minimize future anthropogenic extinctions. This, of course, has never been the case. In reality, most natural areas have been set aside because they have little economic value, because of their scenic appeal, and because the opportunity to designate them presented itself. The primary danger of opportunistic development of a special management area network is that options to establish new special management areas could be exhausted before all elements of biodiversity are represented in the special management area system.

Developing a natural area network is a multiple step process. First, the distribution of the known elements of biodiversity must be assessed. Next, a set of areas is identified in which all elements of biodiversity are represented. This is an exercise in applied biogeography. Then, potential natural areas are more intensively studied to determine their condition and the feasibility of special management area designation. Sites meeting criteria for natural areas are then chosen. This process is commonly referred to as special management area selection. Following special management area selection, the principles of conservation biology are applied to delineate natural area boundaries sufficient to maintain viable populations and ecosystem processes. This step is commonly referred to as special management area design and draws on the disciplines of ecology, population biology, hydrology, and natural areas management. The spatial questions involved in identifying natural area networks in which biodiversity will be completely represented should not be confused with the practical and biological questions that need to be addressed when designing individual natural areas for long term viability of their constituent biodiversity elements and processes.

This entire process is complicated because of our incomplete knowledge of the occurrence and abundance of the elements of biodiversity, as well as an incomplete understanding of ecological processes. Our lack of knowledge is basic. We do not even have names for all species. Although estimates vary, perhaps 90 % of the world's species are unnamed. It is only for some of the higher vertebrates (large mammals, birds) that we have reasonably complete record. For others, especially invertebrates, we have a much less complete list of species. When it comes to more detailed ecological studies, such as distribution, abundance, demographics, and habitat association, we are far more ignorant. The same is true for process. Thus, while ideally identification, selection, and design of special management area areas should be based on complete knowledge, we are hindered by our ignorance of taxonomy and ecology of the species and the ecological processes occurring in the systems in which they live. However, we must not use lack of complete information as an excuse not to act on what biologically defensible information we do have. If we fail to do so, we will lose much of what we have.

Special management area Identification

Rather than focusing on locations of rare species or difficult-to-classify landscapes, biodiversity can be most efficiently represented if maps of several biodiversity elements are examined in hierarchical manner. First, areas in which all plant communities are represented are identified, corresponding to the "coarse filter" approach of The Nature Conservancy. Then, species-rich areas that are most complementary to one another are identified. Finally, areas containing species still unrepresented are located, a "fine filter" that catches species not represented in areas identified by the "coarse filter" approach.

A subset of areas from a state or region in which all biodiversity elements are represented can be identified using one of a variety of stepwise algorithms. This approach to conservation planning has been most fully developed in Australia. One algorithm, called the "greedy heuristic," proceeds as follows: The presence of plant communities or species becomes an attribute of an area; areas with the largest number of attributes are identified, then areas with the largest number of attributes not already present in the previous choice are identified, and so on. This stepwise approach maximizes complementarity in each successive selection and results in the efficient selection of a special management area network. Since many areas will share biodiversity attributes, alternative choices usually exist at each step, leading to the identification of different configurations of special management area networks, any one of which would be completely representative. Of course, areas containing unique attributes must be included in all potential special management area networks. These areas are irreplaceable (i.e., they must be included in all networks).

Designing and managing natural areas for the long term persistence of species and communities are important but fundamentally different issues than selecting potential special management area networks. No amount of management will maintain species or ecosystems not present in a natural area network in the first place. However, the presence of a species or natural community in an area implies nothing about the potential of the area to maintain that species or community.

Special management area Selection

Once potential areas containing target species or communities have been identified, further information about the quality of each area needs to be gathered and compared with the biological, physical, and spatial requirements for long term persistence of the target species or communities. There are many established protocols for sampling plant and animal populations, and the intensity of sampling necessary to select the best natural area has not been systematically investigated and is likely to differ between ecosystem types. In some cases, a rapid assessment by trained biologists will suffice, in others, multi-year sampling of a number of populations will be necessary.

Social and economic factors are often more critical than biological factors when selecting among a set of potential special management areas. Cost, community attitudes, and projected changes in human land use in surrounding areas all contribute to the selection process. Possible ways to integrate these factors into special management area selection are being explored by Gap Analysis Programs.

Special management area Design

Population, community, ecosystem, and landscape processes are all important factors in special management area design. Furthermore, beyond the physical and biological components of special management area design, the size and shape of a natural area have considerable relevance to practical details of special management area management. Four areas of special management area design become relevant after potential natural areas are selected: 1) minimum area requirements for viable populations; 2) community-level interactions; 3) patch dynamics and other ecosystem processes; and 4) interactions between special management area design and management.

Many initial discussions of nature special management area design centered on the viability requirements for populations of target species, including population dynamics, the effect of environmental variation, genetics, metapopulation structure, and the effects of habitat fragmentation. In simple terms, natural areas must be large enough and have a shape that will support viable populations of most animal and plant species for a relatively long period of time, usually at least 100 years. Population viability analysis (PVA) represents an effort to formalize estimates of population persistence, but rarely are sufficient data available for robust conclusions.

Habitat quality varies spatially for most species, resulting in source and sink populations that interact as a metapopulation which experiences local extinction and colonization events. Habitat heterogeneity tends to increase with area, suggesting that larger natural areas offer more patches of high quality habitat which can carry a species through periods of adverse environmental conditions. Edge effects may result in negative population growth rates near natural area boundaries. Many species will occur in natural areas only when sufficient interior habitat is present. Edge is minimized and interior maximized as special management area shape becomes more compact.
The maintenance of essential community-level interactions and processes is the second major special management area design consideration. At the most basic level, natural areas need to support trophic interactions between producers and consumers. Some exchange of energy and matter will occur between special management areas and surrounding areas, so boundary delineation should always consider the context of natural areas. Carnivores typically occur at lower densities than herbivores of equal body size and often play essential roles regulating herbivore density and diversity. Special management areas must therefore meet the spatial requirements of the most area-sensitive community member. Mutualistic relationships exist between many plants and their animal pollinators, including insects, birds, and bats. Insuring the continuation of community interactions, especially those involving keystone species, becomes a primary special management area design challenge.
The concept that natural areas represent eternal and unchanging examples of particular ecosystems is a widely held fallacy (Botkin 1992). Many ecosystems experience regular disturbances whose frequency and patch size is an integral part of ecosystem function. Disturbance events include fire, windstorms, floods, landslides, and volcanism. While some catastrophic events affect large areas, most disturbances are local and scattered throughout a landscape. Special management areas ideally include the "minimum dynamic area, the smallest area with a natural disturbance regime." Disturbances would then occur in a shifting mosaic pattern within a natural area, with various patches in different stages of succession. This arrangement would ensure that propagules for recolonization of disturbed areas are present on undisturbed portions of the special management area. In practice, ecosystem management activities (such as controlled burning) can be used to recreate a natural mixture of seral stages on a smaller scale where natural disturbance events are larger than the natural area.
The final guidelines for special management area design come not from conservation biology but from the more practical world of park management. The location of special management area boundaries influences essential management activities such as transportation, visitor control, fencing, and controlled burning. Special management area staff, visitors, and researchers all need to move about a special management area without damaging natural communities. Engineering constraints limit the placement and cost of roads and trails. Boundaries should be adjusted to avoid difficult obstacles (canyons, mountains, rivers) between portions of the special management area. Fire burns upslope; when controlled burning is an anticipated management practice, special management area boundaries should follow ridge lines and other natural firebreaks. Many natural areas require fencing to exclude people, livestock, or exotic animals. The cost and ease of fence building is related to topography and soils. Adjusting boundaries to lower the cost of fencing, even if special management area size must be increased, may be cheaper than drilling post holes in lava or granite. Finally, visitor facilities and housing for managers need to be placed on less sensitive parts of nature special management areas. Additional land may be needed within special management area boundaries for buildings, parking lots, etc.

Natural areas are expected to maintain biodiversity for centuries. The long term expenses of management can easily outweigh the costs of special management area establishment. Making boundary adjustments to minimize management costs is as important to special management area viability as those necessary to maintain population, community, and ecosystem processes.

Conclusions

A clear understanding of the differentiation between identifying a representative natural area network and designing individual viable natural areas will assist development of a national strategy to conserve biodiversity. Regional biodiversity distribution data bases are not intended to convey information about population or ecosystem processes. By definition, these processes are dynamic and can be accurately described only for small areas and short time periods. Special management area designers use detailed information about these local processes to make determinations about the special management area size and shape they hope will endow long term viability on particular natural areas. Recognizing the distinction between biogeographic analyses for natural area network identification and the biological, ecological, and practical analyses that constitute special management area design is the first step toward a consensus for developing a national biodiversity conservation strategy.

Blair Csuti

Idaho Cooperative Fish and Wildlife Research Unit, University of Idaho

SPECTRUM - Satellite Image Interpretation with Automated Delineation: A Workshop-Based Assessment of SPECTRUM Software

Abstract

A workshop was conducted June 28-30, 1994 at the USGS National Center in Reston, VA by representatives of the MRLC (Multi-Resolution Land Characteristics) consortium for the purpose of learning and evaluating SPECTRUM image analysis software relative to joint goals of consortium programs. The software is reasonably user-friendly, and permits satellite image data (notably Thematic Mapper) to be approached in an interpretive mode for land-use/land-cover mapping without the necessity of painstaking feature delineations. Suggestions were developed for mapping strategy, a few inconveniences were noted, and recommendations made for possible future enhancements.

Introduction

SPECTRUM implements an unsupervised classification approach to multi-spectral image data. Unsupervised classification involves first "clustering" the image data to capture the major image information and then assigning clusters to categories of interest for mapping. The SPECTRUM version of the unsupervised approach was developed by Patrick M. Kelly and James M. White in the Los Alamos National Laboratory, Computer Research Group. The orginal context of development was defense intelligence. The clustering mechanism uses a nearest-neighbor algorithm giving results similar to the k-means program in the SAS statistical package, but utilizes several innovative strategies to improve speed and accommodate large data sets. A simple user's perspective for MRLC is that SPECTRUM provides a computer-assisted mode of "photointerpreting" satellite image data that is rapid, highly interactive, and does not require extensive prior experience in remote sensing. As is typical of more conventional photointerpretation, however, the quality of the final map improves with the analyst's knowledge of the landscape being mapped and with amount of ancillary information available.
A particular advantage of the system relative to clustering is that many more clusters are generated than typical for other versions of unsupervised analysis, thus capturing more of the scene information. This multiplicity of clusters is called "hyper-clustering," and enables reasonable reproduction of the scene from just the cluster information alone. Therefore, hyper-clustering also constitutes a method of image data compression. Another substantial advantage for MRLC users is that EROS Data Center will precluster the scene and provide this information in the manner of an additional image band. Thus, MRLC users need not be bothered with the clustering phase at all and can get right to the business of assigning clusters to desired map categories with the SPECTRUM software.

Mapping Scenario

One begins by loading the cluster image and associated cluster information into memory of a UNIX workstation computer. The next order of business is to select three "image bands" for display on the screen. In fact, the resulting display is an approximation of the original image as rendered through the spectral band means for the several clusters. Analysts with photointerpretation experience will probably choose either a band combination that gives a "color-infrared" view or a "conventional color" view. Each has advantages for interpreting particular types of landscape features. Various "indexes" such as greenness, brightness, wetness, and so on can also be displayed if the analyst is familiar their formulation as ratios or linear combinations of spectral bands.

The desired map legend is next entered as a set of category labels for landscape features of interest (e.g., land-cover classes). Along with specifying a category label, one chooses a color to appear on the screen for "pixels" which will be placed in that category. The actual process of assigning clusters to map categories then begins. A "zoom" window is opened, and a representative sector of the image is moved into the zoom window with the mouse- driven cursor. As the cursor is moved around in the zoom window, the number of the cluster in that pixel location is displayed. One chooses a pixel location for which the map category is known from ancillary information, "ground truth," or general "lay of the land" as seen in the image display. Double clicking the location brings up a window for assigning the particular cluster number to a map category. All other pixels belonging to the same cluster then appear in the designated category color thoughout the rest of the image. Clusters can be transferred from one map category to another if desired. For those with digital image analysis experience, this latter process is very much like "training set" selection in supervised analysis.
If one is interested only in a very general categorization (perhaps water, forest, agriculture, and other), the assignment can probably be accomplished without recourse to ancillary information according to the appearance of the landscape in the image. If one is interested in a more detailed categorization (perhaps vegetation community types), it becomes necessary to adopt the traditional photointerpreter's approach to convergence of evidence using ancillary information (topo maps, soils maps, airphotos, etc.). This involves a special "highlight" category in which each cluster is temporarily placed by itself so that the distribution of its member pixels over the landscape can be viewed readily. The cluster can then be examined in terms of elevation, aspect, soils, and so on, in order to determine its characteristics relative to criteria for map categories. Although more time-consuming, it may be appropriate to run a text editor as a separate process in a window so that the characterization for each cluster can be documented in the course of interpretation. A bit of counsel based on photointerpretation experience is that careful assignment is generally more than repaid by avoidance of frustration in correcting errors later.
We would advise that you carry a typical quarter- scene (TM) through the entire process, including verification, before proceeding with the rest of your imagery. This will alert you to the likely pitfalls for the remainder of work, give you a good sense of expected accuracy, and perhaps reveal some category confusion that simply cannot be resolved in this particular mode of mapping. In the latter case, you should plan on refining your draft map by subsequent exploitation of other sources of information.

Multi-Temporal Mapping

Phenology is very important in separating land- use/land-cover and vegetation classes on the basis of spectral information. The scene with which we experimented in the workshop was clustered as a composite of two images, one from early summer (June) and the other from fall (late in October). This is a particularly advantageous combination relative to phenology, and the composite clustering is much better than having the same two scenes clustered separately.

The composite gives rise to a large number of clusters, several of which are likely to represent the same map category. It is much easier, however, to assign several clusters to the same map category than to face the prospect of lack of separability between categories. A given forest type may be in different stages of fall color change as a result of elevation differences, giving several clusters for the same category. However, such changes also permit detecting conifers in mixture with hardwoods and induce crop differences associated with senescence or harvest. More ancillary information may be needed to account for phenological distinctions between clusters, but the distinctions at least become possible. Dual dates also allow working under clouds as long as the clouds do not coincide in both images.

Working with a multi-date composite will require the interpreter to alternate views of the image. It will be necessary to switch back and forth between early-season infrared and late-season infrared, perhaps along with conventional color for one or both dates. Multiple dates also increase the importance of learning expected spectral signatures, which are levels of differing reflectance between bands and dates for particular types of features. SPECTRUM makes available a signature profile (plot of band means) when an instance of a cluster number is pending category assignment.

Multi-date composites will complicate the prospect of preclustering at the EROS data center. EROS may find it logistically impractical to precluster in different combinations of years and dates. This will serve as motivation for user sites to undertake their own clustering.

Provision for Refinement

It would be unrealistic to expect that the foregoing SPECTRUM scenarios will adequately address all map categories for all thematic contexts. Thus, it is only prudent to anticipate possible need for further refinement after you have done your best in SPECTRUM. SPECTRUM itself does not currently embody substantial capabilities for on- screen map editing outside the cluster environment. There are several paths by which the results of SPECTRUM work can be carried into other software systems that are better geared to editing operations. Unfortunately, the transport utilities are also not currently part of SPECTRUM per se. You are referred to remote sensing personnel at EROS Data Center for determining the most expedient import/export capability relative to your favorite GIS.

Making Spectrum More Commodious for Interpreters

SPECTRUM developers have apparently done little in the way of multi-temporal interpretation themselves, else they would have made it unnecessary to keep repeating some of the interpretive operations. The most obvious instance involves switching of image views. It is presently necessary to associate a spectral band with each color plane of the computer display each time you want a different view. When you have once set up a view in this manner, it should be possible to "save" the view under some name so that it can be reselected easily when it is needed again. We strongly urge that such a capability be added to SPECTRUM in its next version.

Equally annoying is the need to specify a numeric level of color for each plane in assigning a color to a category. Susan Benjamin currently has a sheet of paper that associates color levels with color names. We wholeheartedly encourage the incorporation of name-based color selection as an option in SPECTRUM. However, the capability to specify colors by numeric level should also be retained.

We also view as practical necessity the ability to "quick save" and retrieve the status of category assignments along with cluster means by cluster and band number to/from an ASCII file. This would not only allow interruption/resumption of worksessions and going-back to prior stages, but also local programming of bridgework to statistical packages.

Procurement and Platforms

SPECTRUM was developed to run in the Khoros software environment on UNIX workstation computers. It is possible to obtain Khoros with SPECTRUM by anonymous FTP through the Internet. If interest lies solely in SPECTRUM, however, one should seek a stand-alone version from EROS Data Center.

It must also be noted that all UNIX workstations are not created equal relative to SPECTRUM. SPECTRUM saw its first intensive use on Data General platforms at the workshop. While individually and collectively instructive, the workshop was not thematically productive due to frequent lock-up of the DGs during SPECTRUM sessions. Such problems have not occurred on Sun workstations. Version 2.0 of SPECTRUM is due for release in September and will have been tested on DGs.

Wish List for Sophisticated Analysts

We would like to:

a) Have current cluster enter scatter plot last so that color/position is not obscured by plotting of other clusters;

b) Have optional scatter plots on principal component axes;

c) Examine the spectral heterogeneity of individual clusters (standard deviations to go with means);

d) Retain the seed for a cluster and examine its relation to the ultimate cluster mean;

e) Examine the spectral heterogeneity of clusters assigned to a thematic category;

f) Explore the prospective addition of clusters to a thematic class on the basis of spectral similarity;

g) Create supercategories of categories for spectral comparison;

h) Explore the intercluster spectral structure though higher-dimensional displays and/or collapsing dendrogram;

i) Create spatial partitions of a spectral cluster for separate labeling by polygonal enclosure with cursor;

j) Have capability for explicit seeding of clusters, including cluster means from other scenes that may not actually exist as a pixel in present scene;

k) Restrict Monte Carlo sampling with an exclusionary binary mask, i.e. cluster for multiple strata;

l) Display multiple spectral reflectance curves, ie. display curves for deciduous forest types to compare �characteristic' spectral signatures;

m) Save a library of spectral reflectance curves;

n) Build a menu of �standard' indices or formulas, i.e. greenness, wetness, brightness, etc. so the user doesn't have to type them in.

Workshop Participants:

Wayne Myers, Penn State University
Gail Thelin, USGS-WRD NAWQA
Susan Benjamin, NMD NASA-AMES Research Center
Ann Raspberry, Maryland, DNR
Joy Hood, EROS Data Center
Paul Etzler, EMSL, Las Vegas, NV
Jim Majure, Iowa State University
John Brakebill, USGS-WRD Potomac NAWQA
Pat Green, EPA-EMAP Forest, RTP, NC
John Findley, USGS-NMD, Reston, VA

Notes

New GAP Handbook

The following is a brief overview of the topics covered in the GAP Handbook. The "Management" TOCSECT points out the necessity of collaboration between many different organizations to conduct Gap Analysis. It explains the structure of the administration of state Gap Analysis projects, the roles of cooperators and principal investigators, and staffing needs. The TOCSECT also covers sources and delivery of funding.

The TOCSECT on "Imagery" examines purchasing, preprocessing, spectral clustering, and copyrights of satellite imagery The two different approaches to land cover pattern delineation are discussed and concerns over differences in resulting maps are addressed. Part of the article deals with the cooperative efforts of the Multi- Resolution Land Characteristic Consortium (MRLC) and the use of SPECTRUM software for spectrally clustered data.

The "Data Layers" TOCSECT contains four articles; the first one covers the actual vegetation layer. It describes the required standards for map products and explains the two different methods available for mapping vegetation - computer-assisted classification (unsupervised or supervised classification) and visual interpretation. Potential sources of existing maps vegetation are listed. Finally, several limitation of Gap Analysis vegetation maps are pointed out.

The article on terrestrial vertebrate distribution maps first deals with the constraints for predicting species distributions from vegetation types. The general method for developing animal distribution maps is detailed step-by- step. The appendix gives specific procedures for mapping the distributions of amphibians, reptiles, birds, and mammals. For each order, a table is included that indicates the data layers that should be used for mapping each species.

In the article on land management categorization, guidance for the development of the land management data layer is provided. In states where digital management maps do not already exist, the land management layer must be synthesized from existing information in digital form or from hard copy maps. Incorporation into a single coverage containing all necessary attributes is stressed. The use of primary and secondary codes for attributing the coverage is explained, as well as assignment of one of the four management status codes.

The last article in the Data Layers TOCSECT describes 33 sources of information that are nationally available, how they may be useful to Gap Analysis projects, and how to this information can be obtained.

The "GAP Standards" TOCSECT provides a summary of standards for Gap Analysis state projects that apply to all products delivered to the national program. The four basic data layers are listed, and 28 general project standards are described in detail.

The TOCSECT "Metadata" outlines metadata standards to be used by Gap Analysis cooperators. The paper explains what metadata are and why they are crucial for increasing the value, accessibility, usefulness, and defensibility of data. Appendix A describes the minimum metadata documentation required for Gap Analysis projects. Appendices B and C illustrate metadata construction with examples from the Utah Gap project. Appendix D lists standardized keywords to be used for queries of data sets.

The first article in the TOCSECT "Validation" serves as a guideline for assessing the reliability of GAP vertebrate distribution maps and derived measures of species richness. After outlining the process of building vertebrate data sets, three methods for accuracy assessment and validation of vertebrate distribution maps (expert review, comparison with existing checklists, and field surveys) are described.

The report on assessing land cover map accuracy presents guidelines to establish the minimum acceptable level of accuracy assessment to be adhered to by all state projects. It describes uses of the GAP land cover maps and purposes of map accuracy assessment and reviews measures of accuracy and constraints on assessment methods. Sampling and measurement strategies developed by participants in the February 1994 workshop in Santa Barbara are presented. The appendix summarizes land cover mapping programs by other agencies and relates them to Gap Analysis.

The "Analysis" TOCSECT of the handbook contains an article on special management area selection. The author reviews selection strategies employed in the past and looks at special management area selection at different levels in the biodiversity hierarchy. His recommendations for a Gap Analysis special management area selection strategy considers the status and protection needs of vegetation types and of individual plant and animal species. Finally, analytical tools available for selecting priority conservation areas are examined.

The last part of the handbook, titled "Literature," contains a list of Gap Analysis publications, samples of a cooperative agreement, a research work order and proposal, and a reprint of Kelly and White's paper on preprocessing remotely-sensed data. A copy of Wildlife Monograph No. 123 is included in the end pocket.

Elisabeth Brackney

Project Assistant, National Gap Analysis Program

Encyclopedia of Gap Analysis

An effort to bring together all aspects of Gap Analysis in one complete package has been undertaken by Brian Biggs at the United Nations Environment Programme's Global Resource Information Database in South Dakota. The goal is to facilitate communication and dissemination of useful information to any persons involved with Gap Analysis. With ideas and contributions from many people, the "Encyclopedia of Gap Analysis" was created and is available via the Internet through NCSA Mosaic. To access the Gap Analysis Home Page, use the following Universal Resource Locator (URL) address:

http://www.gap.uidaho.edu/

The Encyclopedia is a hypertext document containing links to the following TOCSECTs:

"Overview" Here are general Gap Analysis documents. There are three introductions to Gap Analysis. One is the unabridged Wildlife Monographs pamphlet. There is also a TOCSECT on United Nations and Biodiversity, and even an online slideshow!
"How-to Manual" Click here to find out all of the technical aspects of GAP. Here you can find out how to complete each stage of the process. There is also a list of National Gap Analysis Program standards, so you can be sure you're doing the right thing.
"Online Data Available" If you click on this link, you will find a map of the United States, and you can click on a state to find and download completed Gap Analysis data layers. Currently all of Utah, Idaho, Arizona, and parts of California are available.
"Bulletin Board" Click on this link to see what GAP people have to say about anything and everything. There are several bulletin boards ranging from "Notices," and "Meetings," to "Job Opportunities," and "Help and Advice." Click on "Post a message" and create a bulletin of your own!
"Investigators and Collaborators" This is a complete list of individuals who are in some way related to Gap Analysis. It includes their addresses, phone numbers, and e-mail.
"References" Here are two sets of references. You can find almost any article ever written about GAP and biodiversity.

For further information about NCSA Mosaic, or the Encyclopedia, contact Brian Biggs at biggs@nr.usu.edu or (801)797-2792.

Brian Biggs

Geography Department, Utah State University, Logan, Utah

GAP Electronic Bulletin Board

The GAP "Help and Advice" bulletin board is a great opportunity to communicate problems you may have and those you have solved, so that other PIs can take advantage of your experience. Let's only invent the wheel once! To access the GAP home page, you need to be connected to the Internet and have NCSA Mosaic loaded onto your system. The Mosaic executable can be downloaded by anonymous ftp at: ftp.ncsa.uiuc.edu. Then change the directory to Mosaic. Download the executable from the ftp site and have your system administrator load it into your system. Within Mosaic, click on OPEN and type in the URL

http://www.gap.uidaho.edu

On the GAP home page, click on "bulletin" and select "post a message". For information on other GAP topics covered on Mosaic see Brian Biggs' article on the Encyclopedia of GAP in this issue.

Introducing the IDRISI System

It is clear that the data gathered and made available by the GAP Analysis effort will have application far beyond their original intended use. As land managers and land management interest groups become aware of and begin to access these data, they will need to know more about the range of potential applications and available software tools to assist them. One such tool is IDRISI, a powerful geographic analysis system that runs on common MS-DOS machines and is developed, distributed and supported on a nonprofit basis by the Clark Labs for Cartographic Technology and Geographic Analysis, a nonprofit organization within the Graduate School of Geography at Clark University in Worcester, Massachusetts.

IDRISI provides a full suite of GIS and image processing capabilities. However, its decision support routines may be especially interesting to the extended community of GAP data users. Routines are available to facilitate the creation of multi-criteria suitability maps for land use activities, where criteria are weighted for importance by the user. A separate routine aids in the weighting process and provides a mechanism for arriving at group consensus on the weights. The result is a continuous suitability map for the activity, from which the most highly suited areas may be assigned to that activity, or the most unsuitable areas may be restricted from that activity.

When more than one activity is vying for the same area, the multi-objective land allocation procedure in IDRISI may be used to create maps of land allocation under different compromise and prioritized scenarios. One of these maps may then be chosen and implemented or, more likely, examination and discussion of the suite of results will lead the decision makers to further refine their selection of criteria, criteria weighting and compromise schemes. In this fashion, the GIS is used as a surrogate environment to iteratively approach the most desirable solution to the land management problem, before any on-the-ground implementation is initiated. An added advantage of this approach is that decisions leading to the results (selection and weighting of criteria, etc.) are fully documented in the process and may be opened to public comment and revision. In addition, the mechanics of the process are quite intuitive and understandable to those not familiar with GIS, allowing for the demystification of the computer-generated alternatives. For more information about IDRISI, contact the Clark Labs at: phone (508) 793-7526, fax (508) 793-8842, or e-mail idrisi@vax.clarku.edu.

Michele Fulk

The IDRISI Project Clark University

The GLOBE Program

The GLOBE (Global Learning and Observations to Benefit the Environment) Program is an international environmental science and education program. Students will participate in environmental science experiments using personal computers connected to networks like the Internet. Their observational data will be shared with students at other schools through the creation of global environmental pictures of the world based on the student-acquired data.
Under this program, students in grades K through 12 or equivalent grades at schools throughout the world will conduct scientific experiments. The students' environmental observations will be transmitted through the international Internet and direct satellite communications to a central processing site. At the central site, global environmental images will be created and relayed back to the students. The data acquired by the students will also be made available throught the Internet to environmental scientists throughout the world to support their research.

GLOBE will bring school children, educators, and scientist together to monitor the worldwide environment. Its goals are to enhance the collective awareness of individuals throughout the world concerning the environment and the impacts of human activities on it and to increase scientific understanding of the earth.

The GLOBE Program intends to build on environmental education activities and supporting computing and networking infrastructure that is in place or planned to the greatest extent possible. The addition of GLOBE environmental measurements, scientific instruments, global environmental image viewing capability, and educational materials to a program at a school might enable it to broaden its hands-on science program and simultaneously actively support GLOBE program goals.

The program is managed by an interagency team that includes NOAA (the host agency for GLOBE), NASA, the National Science Foundation, EPA, and the Departments of Education and State. GLOBE leadership also includes the White House Office on Environmental Policy and Office of Science and Technology Policy.
GLOBE will begin operation in a number of schools throughout the world on April 22, 1995, the 25th Earth Day. Over the following several years, thousands of schools are expected to participate in GLOBE.

Map products generated by GAP may be useful to the GLOBE Program. Additionally, you may be able to use the GLOBE network for part of your information gathering activities on occurrence of vegetation types and animal species. For further information, contact:
John Schmidt
Tel: (202) 395-7600
Fax: (202) 395-7611
Email: jschmidt@globe.gov
The GLOBE Program
744 Jackson Place jschmidt@globe.gov
Washington, DC 20503

Elisabeth Brackney

Project Assistant, National Gap Analysis Program

MEETING SUMMARIES

1994 National Gap Analysis Workshop

The 1994 National Gap Analysis Workshop was held in Silverdale, Washington, July 19-21. Thirty-two states, as well as British Columbia and Mexico, were represented, with 125 individuals participating. The workshop was separated into a first day overview, which included a national Gap Analysis perspective, as well as individual state status reports, followed by four technical sessions on the remaining two days.

Each of the four technical sessions was moderated by a session chair with committee members presenting short talks followed by open discussions. The first of the technical sessions included discussions pertaining to administrative logistics as well as cooperator networks and public relations. The second session reviewed processes, standards and validation of land cover maps. The final day of the workshop began with a session on Gap Analysis species distribution maps and a talk on modeling habitat relations. The closing session turned to the identification of gaps in the protection of biodiversity and included implementation and data dissemination discussions.

Each of the technical session presentors submitted an extended abstract which was published in the pre- conference packet each participant received. The discussions following the formal presentations have been captured in a meeting summary which will be made available to all meeting participants.

Chris Grue and Jane Cassady

WA Coop. Fish & Wildlife Research Unit

University of Washington, Seattle

Society for Conservation Biology Meetings

I attended the June 1994 meetings of the Society for Conservation Biology and was disappointed not to see GAP better represented. (As far as I could tell, I was the only one there associated with our program).
The meetings themselves were well attended (680 people) and spanned five days and about 450 papers and posters. There were about a dozen papers that dealt with geographic biodiversity management strategies, although most were at continental to global scales.
Two papers were especially germane to Gap Analysis: George Powell presented his "Gap Analysis of Costa Rica" based on Holdridge Life Zones and protected areas. He used the CAMRIS CAM system to develop some very impressive statistics and graphics. His project was carried out on a minimal budget but resulted in some compelling results. He's familiar with our projects up north. His work demonstrates what can be done with GAP. We should take note.
Melanie Kershaw, a doctoral student at the Institute of Zoology, London, carried out a biogeographic Gap Analysis of Natal using 155 1/4 degree grid squares as a sampling unit. She looked at richness of birds, mammals, reptiles, amphibians, and plants. She found taxa with high local endemism were not well represented in hot spots for other taxa. Her paper showed a sophisticated understanding of analysis of biogeographic patterns for conservation purposes, and again demonstrates the power GAP can bring to conservation planning.
In 1995, the meetings will be at Colorado State University. I hope GAP and NBS will not be embarrassed by our conspicuous absence.

Blair Csuti

ID Coop. Fish and Wildlife Research Unit

University of Idaho, Moscow, Idaho

Gap Analysis Symposium in Charlotte, NC

In conjunction with the ACSM/ASPRS Annual Convention and Exposition, a Gap Analysis Symposium will be held at the Charlotte Convention Center from February 27 through March 1, 1995. Following is an outline of topics to be addressed:

Monday, February 27

I. INTRODUCTION

A. The need for a hierarchical approach to conservation

J. Michael Scott, University of Idaho

B. Recent developments in ecological science theory: Hierarchy and scale

Robert V. O'Neill, Oak Ridge National Laboratory

C. The conceptual (not technical) development of Gap Analysis:

1. The application of hierarchical and spatially explicit concepts in Gap Analysis

Gerald Wright, University of Idaho

2. The philosophy of Gap Analysis, the utility of its databases, and the development of partnerships

Mike Jennings, University of Idaho

3. Relevant spatial, temporal, and taxonomic scales for Gap Analysis

Frank Davis, University of California - Santa Barbara

4. Extending Gap Analysis to include socioeconomic factors

Gary Machlis, University of Idaho

5. The analysis part of Gap Analysis:

a. Hierarchical Gap Analysis for identifying priority areas for biodiversity

Blair Csuti. University of Idaho

b. Iterative uses and queries of reserve location

Ross Kiester, U.S. Forest Service

D. Summary

Mark Shaffer, The Nature Conservancy

E. The National Biological Service perspective

Ron Pulliam, National Biological Service

II. TECHNOLOGICAL ISSUES OF GAP ANALYSIS

A. Issues of GIS: "database thinking" and database structure; Allan Falconer, Utah State University; B. Data access - an NBS overview; Phil Wondra, National Biological Service; C. Compiling a Gap Analysis Electronic Encyclopedia; Brian Biggs, Utah State University; D. Emerging technologies: Digital Aerial Photography - an overview; Tuevo Airola, Cook College - Rutgers University
Tuesday, February 28: E. Uses of aerial videography in Gap Analysis for deciduous forests in New England; Curtice Griffin, University of Massachusetts; F. Summary; Tom Lillesand, University of Wisconsin

III. LAND COVER MAPPING

A. An overview and history of the concept

Katherin Lins, U.S. Geological Survey

B. Land cover mapping

1. A protocol for satellite-based land cover classification in the Upper Midwest

Tom Lillesand, University of Wisconsin

2. Nomenclature and mapping units

Mike Jennings, University of Idaho

C. Multi-resolution land characteristics: Landsat thematic mapper processing

Joy Hood, Hughes STX Corporation

D. Today's land cover mapping processes

Len Gaydos, USGS National Mapping Division

E. Accuracy assessment: A critical component of land cover mapping

Russell Congalton, University of New Hampshire

F. Multi-resolution land characteristics monitoring system: Building collaborative partnerships

Tom Loveland, USGS EROS Data Center

1. Land cover mapping with SPECTRUM

Susan Benjamin, USGS EROS Data Center

2. MRLC: Comprehensive land characteristics database-building through collaborative partnerships

Denice Shaw, U.S. EPA

G. Summary

Don Lauer, USGS EROS Data Center

IV. PREDICTED DISTRIBUTIONS FOR VERTEBRATE SPECIES

A. An overview to predicted vertebrate distributions

Larry Master, The Nature Conservancy

B. Modeling vertebrate species distributions for Gap Analysis

Blair Csuti, University of Idaho

C. Species richness: Concepts, calculation, and its pragmatic meaning for conservation

J. Michael Scott, University of Idaho

D. Predicted vertebrate distributions from Gap Analysis: Considerations in the design of a statewide accuracy assessment

William Krohn, University of Maine

Wednesday, March 1

E. Summary

Kim Smith, University of Arkansas

V. USES OF GAP ANALYSIS DATA

A. Analyses of biodiversity conservation status:

1. A description of products and their formats, with examples of Gap results by state

Mike Jennings, University of Idaho

2. Applied Gap data for planning of land use and biological resources: Case studies

Frank Davis, University of California - Santa Barbara

a. Applications of Gap Analysis data in the Mojave Desert of California

Frank Davis, University of California - Santa Barbara

b. Arkansas Gap Analysis

Robert Dzur, University of Arkansas

c. State biodiversity plans

Sara Vickerman, Defenders of Wildlife

d. Examples of use by county governments and state and federal agencies

Kent Smith, McCollum Associates

e. Use of Gap Analysis in regional planning in Southern California

Richard Crowe, Bureau of Land Management

f. Applications for planning and expanding national parks

Gerald Wright, University of Idaho

g. Applied Gap Analysis for conservation planning in British Columbia

Peter Murtha, University of British Columbia

B. Summary

Jim Quinn, University of California - Davis

VI. CONCLUSION

A. A perspective on current trends in conservation and a vision for the future of biodiversity management areas

Jack Estes, University of California - Santa Barbara

B. Closing remarks

J. Michael Scott, University of Idaho

ANNOUNCEMENTS

1995 National Gap Analysis Workshop

Mark your calendars for the fifth annual National Gap Analysis Workshop, to take place from Monday, August 7 through Thursday, August 10, 1995. This year's meeting is hosted by the Center for Advanced Spatial Technologies at the University of Arkansas in Fayetteville, AR. The sessions will be held at the Fayetteville Hilton Hotel. A preliminary announcement will be mailed to each GAP P.I. shortly. The formal announcement will come out later in the spring. We will be contacting you about presentations at a later date. Hope to see you all in the Ozarks this summer!

Biodiversity Gap Analysis: Critical Challenges and Solutions

This report presents the findings of an Advanced Research Workshop held at Semiahmoo, WA in early 1994. Participants included experts in gap analysis, as well as human ecology, cartography, and GIS. The goal of the workshop was to stimulate further development of the gap analysis technique, particularly the integration of socioeconomic factors. The report describes some of the critical challenges facing gap analysis and their potential solutions. Three thematic areas are covered: theory, methods, and application. Copies of the report are available for $5.00 from Dr. Gary E. Machlis, Cooperative Park Studies Unit/Sociology, College of Forestry, Wildlife, and Range Sciences, University of Idaho, Moscow, ID 83844- 1133.

1994/1995 GAP Start-ups

Kansas
Nebraska
Kentucky
North Carolina
New Jersey
Iowa

GAP T-Shirts Available

The official logo for the national Gap Analysis Program, as featured on the cover of the GAP Handbook, is printed in full color on the front of these heavy-weight 100% cotton T-shirts. They are available in adult sizes S, M, L, XL, and XXL in either white or ash (a light grey). Get your entire staff outfitted! These shirts also make great gifts for friends and family. The shirts can be purchased for $12.00 (includes shipping and handling) through the Idaho Cooperative Fish and Wildlife Research Unit, University of Idaho, Moscow, ID 83844-141, phone (208) 885-6336, fax (208) 885-9080.

The Gap Analysis Bulletin is published by the Idaho Cooperative Fish and Wildlife Unit, J. Michael Scott, Unit Leader. The editor is Michael D. Jennings. To receive the bulletin, write to: Gap Analysis Bulletin,
Idaho Cooperative Fish and Wildlife Research Unit, College of Forestry, Wildlife and Range Science, University of Idaho, Moscow, ID 83844-1141, fax: (208) 885-9080.

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