banner literature about projects tools meetings search links BBS GAP home GAP home
home

 

<- Previous | First | Next ->

FINAL REPORT SUMMARIES

Colorado Gap Analysis Project

DONALD SCHRUPP, LEE O’BRIEN, AND SADIE RUSSO

Colorado Division of Wildlife, Denver

The Colorado Gap Analysis Project (CO-GAP) was initiated in 1991 as a cooperative effort between the Colorado Division of Wildlife, the Natural Resource Ecology Center (NERC/USFWS), and state, federal, and private natural resources groups in Colorado.

The objectives of the project were to: 1. produce databases to describe current land cover, predicted distributions of native species of terrestrial (i.e., non-fish, nonmarine) vertebrate species, stewardship responsibilities for conservation and selected public lands, and land management status for use in geographic information systems (GIS) at a scale of 1:100,000; 2. identify land cover types and vertebrate species that currently are not represented or are poorly represented in areas managed or potentially managed for long-term maintenance of biodiversity (i.e., identify conservation gaps); and 3. facilitate cooperative development and use of information so that institutions, agencies, and private land owners may be more effective stewards of the biological resources of the state.

The first GIS layer developed for CO-GAP was the land ownership base layer; digitized at the NERC/USFWS in Fort Collins by TGS, an in-house contractor. The base layer was modified for the purpose of modeling land stewardship as new land management plans for national forests and BLM lands in Colorado became available.

With funding from National GAP, 12 scenes of Landsat imagery were acquired to augment four Division of Wildlife scenes, providing in-house, statewide coverage from which a baseline map of vegetation/land cover was developed at sufficient detail to model vertebrate wildlife distributions based upon habitat relationships. Following methodology developed for Wyoming GAP (WY-GAP), attributes were assigned to each polygon describing primary, secondary, and other land cover; crown closure for forested primary types; and the types of wetlands and/or disturbance found in the polygon, if any. Polygon attributes were assigned using image interpretation, existing maps, field reconnaissance, digital reference layers from federal land management agencies, and literature sources. A formal statewide validation of the land cover map was funded by National GAP and conducted by the University of Wyoming. The results of this accuracy assessment are summarized in this report, and the assessment report is included in our appendices.

Individual distributions for 597 vertebrate species were predicted using habitat associations linked to the vegetation/land cover base layer, constrained by data on elevation ranges and confinement to the east or west side of the Continental Divide from known occurrences of individual species in Colorado. Point localities and thematic distribution maps were used to evaluate preliminary distribution maps, and later as a guide in developing the county-level distribution masks used to constrain occupancy to likely areas of predicted habitat. County-level masks were developed by cross-reference to locational databases providing 101,068 field/museum records of species localities. After synthesizing this information for modeling efforts, the modeled maps predicting species range based on habitats were reviewed by 16 local experts. Comparisons of species predicted to occur at nine field sites to species lists generated from the CO-GAP predictive habitat/distribution maps indicated an overall accuracy of 64%. A special submodel was developed to account for riparian species distribution, given the importance of this habitat and the minor extent to which it was observable based solely on the Landsat imagery.

GAP uses a scale of 1 through 4 to denote the relative degree of management for biodiversity maintenance for a particular tract of land, where “1" represents the highest, most permanent and comprehensive level of maintenance, and “4" represents the lowest or unknown status. Status codes were assigned to public lands with state and federal agency input based on legal and intended management, using a key developed by the New Mexico Gap Analysis Project (NM- GAP). Most private lands were assigned Status 3 or 4 depending on the availability of information on their intended long-term management. After land stewardship was modeled using the land status base layer, the derived layer was overlaid on the vertebrate species habitat/distributions to provide the tabular output essential to a gap analysis of Colorado biodiversity. These tables show the distribution of species habitats across the major land ownership categories and are further partitioned by hectares (and percent) of the species’ habitat distributed by the land stewardship categories. Land cover types and vertebrate species were generally considered underrepresented in areas managed for biological diversity if less than 1%, or less than 50,000 hectares of available cover type or occupied habitat was found within Status 1 and 2 lands.

Barely over 10% of Colorado is classified as Status 1 and 2 lands, concentrated in four national parks or monuments (Rocky Mountain National Park, Mesa Verde National Park, Dinosaur National Monument and Great Sand Dunes National Monument), with the rest distributed mostly in Forest Service wilderness areas, scattered high in the Rockies along the Continental Divide and other major Colorado mountain ranges. Following WY-GAP’s lead, we considered land cover types and vertebrate species as underrepresented (i.e., “gaps”) in management areas if <1% or <50,000 ha of the land they occupied or their habitat in Colorado fell within Status 1 and 2 lands.

Twenty-two (52%) of the 42 natural (non-anthropogenic) land cover types have <1%, or <50,000 ha, in Status 1 and 2 lands. In fact, 11 of those classes each had less than 10,000 ha in Status 1 and 2 lands. The highest priorities for further protection are wetland/riparian types (grass/forb-dominated wetlands, shrub-dominated wetlands, and forested wetlands), a number of grassland types (sand dune complex-grasslands, tallgrass prairie, midgrass prairie, and shortgrass prairie), Rocky Mountain bristlecone pine, and sandy areas. Of additional highest priority but restricted in management opportunity to predominantly public lands are xeric upland shrubs. Bitterbrush shrub, if considered a high priority, presents management opportunities on BLM lands as well as some limited opportunity on private lands. All but the grassland types are restricted in both overall occurrence in the state and representation within Status 1 and 2 lands, yet offer management opportunities on existing, predominantly state-owned, public lands. Grasslands under private management present other possibilities. While grassland types cover many hectares in Colorado, they are truly underrepresented on Status 1 and 2 lands. Other types with little reported area in Colorado and in Status 1 and 2 lands were Wyoming big sage, white fir, blue spruce, and limber pine. Their consideration for having less than 50,000 ha in Status 1 and 2 lands may be more a function of their detectability due to our land cover mapping methodologies, but the question of their consideration as biodiversity gaps should be further investigated.

Mesic upland shrubs, greasewood fans and flats, desert shrub, saltbrush fans and flats, and foothill/mountain grasslands were identified second in priority. Mesic upland shrub management opportunities are limited with less than 100,000 ha of combined public/private lands not in Status 1 and 2. Management opportunities for desert shrubs are available, with land area not in Status 1 and 2 about equal in public/private stewardship. Management opportunities for greasewood fans and flats and foothill/mountain grasslands are largely on private lands, while management opportunities for saltbrush fans and flats appear more abundant on combined BLM, tribal, and state lands than on private lands. Third priority for further protection are mountain big sage, mixed conifer, and deciduous oak vegetation types. Mountain big sage and mixed conifer’s seeming scarcity in Colorado may be an artifact of difficulties in delineating them with our land cover classification methodologies. Management opportunities for these two types on current Status 3 and 4 lands are limited but about equally associated with public (BLM/USFS/state) and private lands. Management opportunities for deciduous oak appear abundant on both public (BLM/USFS) and private lands.

On average, reptiles had the smallest percentage of their potential habitat (3.13%) in Status 1 and 2 lands, followed by birds (6.24%), amphibians (7.25%) and mammals (10.95%). Habitats of 5 (28%) amphibians, 19 (40%) reptiles, 70 (17%) birds, and 16 (13%) mammals were identified as gaps of the highest priority based on both criteria (<1% in Status 1 or 2 lands, and <50,000 ha of habitat under Status 1 and 2 lands) for identifying underrepresentation in management areas. An additional 2 (11%) amphibians, 8 (17%) reptiles, 40 (10%) birds, and 6 (5%) mammals were identified as a secondary level of priority because they met the one “gap” identification criteria of having < 50,000 ha of their habitats in Status 1 and 2 lands, though they have >1% (but <10%) of their habitats protected in Status 1 or 2 lands. A third level of habitat prioritization would be for species with <50,000 ha of their habitat in Status 1 or 2 lands, but greater than 10% of their habitats protected. Species in this third level of priority are all avian species tending to be associated with open water habitats, which are of limited availability statewide.

Three important efforts have already benefited from the completion of Colorado’s initial Gap Analysis efforts. Habitat/distribution information on 190 key species derived from CO-GAP has been provided for presentation in Colorado’s Natural Diversity Information Source (NDIS; http://ndis.nrel.colostate.edu). NDIS provides information for use by the general public, educators, and decision-makers. The information is used in land use and conservation planning as well as in public education. Secondly, habitat/distribution information for all 597 terrestrial vertebrate wildlife species modeled by CO-GAP (as well as the geographic information system coverages underlying the analysis) have been made available to all interested citizen scientists on the Web (http://ndis.nrel.colostate.edu/cogap). Lastly, the Colorado Division of Wildlife has begun efforts to develop baseline profile information on Colorado habitats on a biome basis (grassland/shrubland/forestland) for use in Species Conservation Program planning. Additionally, CO-GAP information has already been applied to CDOW’s Land and Water Acquisition Plan (LWAP) development, which will help identify fee-title and easement acquisition priorities for Division funding and possible cost-sharing with Great Outdoors Colorado (GOCO) and other potential partners.

Florida Gap Analysis Project

LEONARD PEARLSTINE1, SCOT E. SMITH2,AND WILEY M. KITCHENS3

1Dept.of Wildlife Ecology and Conservation, University of Florida, Gainesville

2Dept. of Civil and Coastal Engineering, University of Florida, Gainesville

3USGS Florida Cooperative Fish & Wildlife Research Unit, Gainesville

The Florida Gap Analysis Project (FL-GAP), initiated in 1993, is one of the state or regional projects designed to provide overviews of the distribution and status of biodiversity across the Nation. These projects, conducted under the auspices of the National Gap Analysis Program (GAP) of the U.S. Geological Survey, have the objective of providing critical information to land and resource managers regarding location and spatial distribution of key elements of statewide biodiversity. In addition to biotic distribution, GAP projects map the extent and location of lands in various states of conservation ownership. Contrasting these distributions provides a method of identifying the degree to which native animal species and natural communities are or are not represented in existing conservation lands. The intent is to proactively identify areas of potentially high biodiversity occurring outside protected boundaries as “gaps" in the existing network of conservation lands. Application of this process provides a necessary step toward informing agencies and landowners of potential opportunities to conserve biodiversity and avoid environmental crises.

FL-GAP was conducted as a cooperative effort among several national, state, and nongovernmental organizations (NGOs), with the Florida Cooperative Fish and Wildlife Research Unit of the U.S. Geological Survey and the University of Florida principally responsible for assemblage and assessment of the information. The objective was to provide broad geographic information on the distribution of terrestrial plant communities, vertebrates, butterflies, skippers, and ants and their respective habitats in order to address the status of biological diversity conservation in Florida. The inclusion of the approximately 400 species of invertebrates was a departure from the protocol previously reported by Scott et al. (1993). It was deemed necessary due to the high fragmentation of natural habitats in Florida resulting in a landscape mosaic of small islands of natural habitat in a matrix of partially to completely modified land cover types. The rationale was that while many of these areas may be too small to support a large variety of terrestrial vertebrates, they would nonetheless be species-rich, containing high numbers of plants and invertebrates.

The first step in the process was the development of a statewide spatially explicit land cover database of vegetation types based on the hierarchical National Vegetation Classification Scheme (NVCS) developed by The Nature Conservancy. The scheme was adapted to constraints posed by the uniqueness of Florida and other states in the Southeast, resulting in the addition of compositional groups and ecological complexes to augment the alliance level structure of the NVC. Landsat Thematic Mapper imagery (1992-1994) was used as the base for mapping vegetation and classifying land cover types to the level of dominant or co-dominant species. The image processing scheme was custom-designed to maximize accuracy by incorporating ancillary data at critical steps in the process. For example, ancillary data such as wetland distributions (National Wetlands Inventory), soil maps, other aerial imagery, existing land use/land cover maps, and extensive on-the-ground survey data were employed to provide data masks to facilitate classification. The final product was a seamless composite mosaic of 14 Landsat TM scenes with a 30-meter by 30-meter minimum mapping unit representing 71 categories of vegetative land cover varying from open water to mixed evergreen cold deciduous hardwood forests. Assigned cover types were compared to low altitude aerial imagery for ground-truthing. In summary, the Florida landscape is very heterogeneous. Approximately 53% of the state was classified as forest (mesic-hydric pine forests, 18%; forested swamps, 14%), and shrub (xeric shrub, sandhill, and sand pine, 4%), with no other category occupying more than 23% of the land area. Other major categories included agriculture (pasture and crop, 23%), marsh (8%), developed (8%) and surface water (3%). Overall accuracy was 73%. Most of the error could be explained by the fact that the satellite imagery was taken at a different time and different hydroperiod than the aerial videography and digital camera imagery. A second factor was the high degree of heterogeneity within the 30-meter by 30-meter minimum mapping unit.

The next step in the process was to construct spatially explicit databases of potential distributions of native species of terrestrial vertebrates, skippers, butterflies, and ants. This effort was accomplished by first mapping species ranges to the county level, utilizing museum records for mammals, the Florida Breeding Bird Atlas for birds, and statewide databases for herpetofauna. Butterfly distributions were determined from published county dot maps and ants from published and unpublished data from the U.S. Department of Agriculture. Habitat association data were used to build a species by habitat matrix for each group mapped. Resulting county-level maps were reviewed by experts. These data were joined with EMAP hexagons. Maps for individual species were based upon suitable habitat being available within a given hexagon. The final product represents the potential distributions of 56 mammals, 76 species of herpetofauna, and 234 species of birds, as well as over 400 species of invertebrates (butterflies, skippers, and ants). Highest species richness is associated with swamp forest and sandhill land cover types. In southern Florida, pine communities provide habitat for the largest number of species. Overall species richness follows the pattern of most of the individual groups (mammal, birds, herpetofauna, and selected insects) with swamps, mixed pine-oak, sandhill scrub, longleaf pine, and pine flatwoods all contributing to the species richness of the area. For many of the taxa, this region is an area of overlap of species with northern affinities and species with southern affinities. In south Florida, forested classes also appear to support the highest diversity with the exception of the insect taxa. Xeric scrub is an important component in central Florida and unfortunately is underrepresented in the cover classifications.

The conservation lands of the state were mapped by the Florida Natural Areas Inventory (FNAI) from ARC/INFO GIS coverages maintained in their files. Approximately 26.4% of the state’s lands are maintained in some legal status of conservation. The remaining 73.5% is held in private ownership. The following summarizes the relationship between taxonomic group diversity or richness and conservation stewardship:

Mammals – Lands in Eglin Air Force Base, the Ocala National Forest, and the Withlacoochee State Forest have the highest mammalian species richness in the state. Unprotected areas of high species richness include both coasts of North Florida and the Panhandle, the lands between Eglin Air Force Base and the Blackwater River State Forest as well as the lands between Osceola National Forest, Camp Blanding Military Reservation and the Ocala State Forest.

Birds – State and federal lands including Eglin Air Force Base, Withlacoochee State Forest, Osceola National Forest, Apalachicola National Forest, Fakahatchee Strand State Park, and the Big Cypress National Preserve protect a high diversity of avian species. Unprotected areas of avian diversity, in order of species richness, exist along both coasts of North Florida, east-central Florida, and the pasture lands of the Immokolee Rise (southwest Florida) and north of the Caloosahatchee River.

Herpetofauna – Eglin Air Force Base, Apalachicola National Forest, and the Big Bend Wildlife Management Area are examples of some of the highest herpetofauna diversity in protected areas. The bottomland and wet forests areas of the Gulf Coast appear to be best opportunities for additional protection of species richness.

Ants – The highest species richness of ants is within xeric central Florida sandhill and sand pine communities protected by the Ocala National Forest and Withlacoochee State Forest. The highest unprotected diversity is in forested land cover of North Florida.

Butterflies and skippers – Across the state, areas of high butterfly diversity appear to be within the confines of conservation lands. The highest areal extent of diversity is in South Florida.

In summary, lands with pine flatwoods, xeric pine, and xeric scrub communities in Florida are major contributors to the biodiversity of the state. These lands also are some of the most threatened because of changing fire regimes and their suitability for development or pine plantation agriculture. The unique pine rocklands of South Florida are threatened by exotic invasive vegetation. In North Florida, the sandhill scrub and longleaf pine habitats of Eglin Air Force Base are protected by an aggressive resource management plan that includes restoration.

Finally, GAP tools–while useful in identifying potential areas for conservation–are but one of many tools that need to be used in concert with other supporting information to ensure adequate assessment of biodiversity distribution and conservation.

Literature Cited

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 R.G. Wright. 1993. Gap Analysis: A geographic approach to protection of biological diversity. Wildlife Monographs 123.

Louisiana Gap Analysis Project

JAMES JOHNSTON and STEVEN HARTLEY

National Wetlands Research Center, Lafayette

The Louisiana Gap Analysis Project (LA-GAP) was initiated in 1994 as a cooperative effort between the Biological Resources Division of the U.S. Geological Survey and state, federal, and private natural resources groups in Louisiana. The major objectives of the project were to (1) produce GIS databases describing actual land cover type, terrestrial vertebrate species distributions, land stewardship, and land management status at a scale of 1:100,000; (2) identify land cover types and terrestrial vertebrate species that currently are not represented or are under- represented in areas managed for long-term maintenance of biodiversity, i.e., “gaps”; and (3) facilitate cooperative development and use of information so that institutions, agencies, and private landowners may be more effective stewards of Louisiana’s natural resources. LA-GAP is a preliminary step toward the more detailed efforts and studies needed for long-term planning for biodiversity conservation in Louisiana.

The map of actual land cover was the first GIS layer completed for LA-GAP. Ten Landsat 5 Thematic Mapper (TM) scenes, mostly from the winter of 1992, and the 1988 National Wetlands Inventory (NWI) data constituted the base data layers for the land cover map. The land cover map includes the distribution of 23 land use/land cover types. The original 25-meter-resolution TM imagery was subset into smaller manageable files to perform an unsupervised clustering of the spectral signatures. These clusters were then assigned the appropriate cover type by using on-screen visual interpretation of the TM imagery, along with 1995 aerial photography. Data were resampled from the original 25-meter pixel resolution to meet the new national standard of 30-meter pixel resolution. Aggregation of the classified minimum mapping unit (MMU) 30- meter pixel data to eight continuous pixels or approximately 2.5 ha, was done through the usage of the GigaMerge software.

Formal statewide validation of the land cover map was conducted in conjunction with the mapping effort. Field checks of 10,206 sites by different agency personnel and volunteers indicated a 84.11% accuracy of primary cover mapping.

Individual distributions of 333 vertebrate species were predicted by using both point locality records and habitat associations. Range limits of each vertebrate species were delineated for 248 U.S. Environmental Protection Agency (USEPA) hexagons (~160,200 acres/hexagon) within the state. Species distributions were modeled based on habitat affinity associations, within each hexagon, to produce a predicted distribution map based on habitat affinities of an animal for the various categories of land cover, water, and buffered water areas.

Maps of predicted distributions of terrestrial vertebrates in Louisiana should be considered as coarse depictions of probable range and relative abundance of known suitable habitats within that range. This first attempt at predicting areas of high species richness has identified more gaps in our knowledge of vertebrate distribution and habitat affinities than gaps in protection of biodiversity. No formal accuracy assessment of the predicted vertebrate distribution maps of LA-GAP was undertaken.

The Gap >Analysis Program (GAP) uses a ranking system of 1 through 4 to denote the relative degree of management for biodiversity maintenance for each parcel of land, with “1” being the highest level of maintenance, and “4” being the lowest, or unknown, status. Status codes were assigned to public lands by The Nature Conservancy. Land stewardship and land management status layers were overlaid with land cover and vertebrate species distributions to conduct a gap analysis of Louisiana.

Less than 10% of the state of Louisiana is classified as public land, and less than 0.3% of that is in management status categories 1 and 2. More than 50% of status 1 and 2 lands occur in the U.S. Department of Agriculture Forest Service (USDAFS) stewardship category. With the completion of LA-GAP, it can be recommended that 1) more existing public lands should be managed for better conservation (i.e., higher management status); 2) more government programs such as Wetland Reserve Program (WRP) and Conservation Reserve Program (CRP) should be initiated to encourage the private landowner to manage for biodiversity; or 3) public land holdings should be increased to target areas of high biodiversity without management practices.

Maine Gap Analysis Project

WILLIAM B. KROHN1, RANDALL B. BOONE1, STEVEN A. SADER2, JEFFREY A. HEPINSTALL,2 SANDRA M. SCHAEFER1, AND STEPHANIE L. PAINTON1

1Maine Cooperative Fish and Wildlife Research Unit, University of Maine, Orono

2Maine Image Analysis Laboratory, Dept. of Forest Management, University of Maine, Orono

The Maine Gap Analysis Project (ME-GAP) was initiated in 1992 as a cooperative effort between the Biological Resources Division of the US Geological Survey (USGS) and state, federal, and private natural resources groups in Maine. The objectives of ME-GAP were to: (1) produce databases for use in Geographic Information Systems (GIS) at a scale of 1:100,000 to describe current land cover, distributions of native species of terrestrial (i.e., nonfish, nonmarine) vertebrate species, ownership of conservation and public lands, and land management status; (2) identify land cover types and vertebrate species that currently are not represented or are underrepresented in areas managed for long-term maintenance of biodiversity (i.e., identify conservation gaps); and (3) facilitate cooperative development and use of information so that institutions, agencies, and private landowners may be more effective stewards of Maine’s biological resources. ME-GAP is a preliminary step toward the more detailed studies and efforts needed for the long-term conservation of biodiversity in Maine.

The system used to classify the land cover consisted of 37 types (19 upland types, 16 wetlands, 2 water). This classification was a compromise between the habitats needed to predict vertebrate distributions and those classes that could be discerned from satellite imagery and ancillary GIS databases. Landsat Thematic Mapper (TM) imagery from 1991 and 1993, in conjunction with aerial videography, was used to identify and map the water and upland types. Wetland polygons came primarily from the US Fish and Wildlife Service’s National Wetlands Inventory (NWI). NWI maps of Maine were done at 1:24,000 and based on aerial photographs mostly from the mid- and late-1980s. To facilitate the predicting of vertebrate species’ distributions, NWI wetland types, defined largely in terms of physiographic locations on the landscape, were relabeled so types related to the occurrences of vertebrates in terms of vegetative and structural characteristics. A comparison of vegetation and land cover types mapped from TM data to aerial videography had an overall accuracy of 88.1% at the level of superclasses. For groups of forestland classes, accuracy levels range from 45% to 80%; accuracy by types also varied geographically across the state as different TM scenes were used in various parts of the state.

A GIS database of private and public conservation lands was assembled in cooperation with the Maine State Planning Office. Conservation lands comprise less than 6% of Maine with public lands consisting of approximately 5.3%. Conservation lands are well distributed throughout the state except for the northwestern portion, which is largely without public conservation lands. In southern Maine, conservation lands are highly scattered and generally smaller than in the rest of the state. Private commercial forestlands (i.e., large blocks in corporate ownership) and Native American lands managed for forestry encompass approximately 50% of Maine. Lands were denoted as to the degree to which they are managed for maintenance of biodiversity and long- term ecological processes. The Gap Analysis Program requires use of a 1 through 4 scale to denote high to low management for biodiversity maintenance based on legal and management status. While not all lands could be unequivocally classified as to management category, less than 3% of the state occurs in management Categories 1 and 2, with almost no Category 1 lands in southern Maine (lands owned by the Maine Chapter of The Nature Conservancy are the exception). Category 3 lands made up almost 53% of the state and consist primarily of privately owned or public multiple-use forestlands. Category 4 lands occur mostly in southern Maine, along the coast, and in the northeastern corner of the state. The land ownership map should not be interpreted as a legal document, but as a representation of general ownership patterns.

The number of species (i.e., richness) of native terrestrial vertebrates that regularly breed in Maine (n = 270) is highest in coastal and southern Maine. This pattern is similar to the richness patterns of terrestrial threatened and endangered species and woody plants. In the long term, human occupation of the natural landscape is the driving force underlying habitat loss. The density of Maine’s human populations in 1990 was highest in the coastal and southern portions of the state. The distribution of Maine’s human population is changing (like elsewhere in the nation) with people moving out of population centers into adjacent rural areas; the redistribution of people into rural areas is most extensive in southern Maine. When looking at the distribution of conservation lands by management categories, note few Category 1 areas occur statewide. Southern Maine is clearly the area of highest richness of terrestrial vertebrates, threatened and endangered species, and woody plants, but contains only small and scattered Category 2 and 3 conservation lands. In addition to coastal and southern Maine, the northwestern part of Maine also merits special consideration in conservation planning because this region contains few reserves and provides habitat for northern species at the southern limits of their distributions.

To demonstrate the flexibility of ME-GAP data, two sets of species-specific conservation analyses of terrestrial vertebrates are presented. In set one, data related to the management of a rare forest bird (i.e., Bicknell’s Thrush) and a common aquatic mammal (American Beaver) were analyzed using predicted distributions from ME-GAP. In set two, analyses were done on actual habitat data collected by Maine’s Department of Inland Fisheries and Wildlife (MDIFW) for an uncommon wetland species (Bald Eagle) versus a widespread upland mammal (White-tailed Deer). The range of issues covered by these examples clearly shows that this report has barely touched the potential of the data assembled herein to address conservation and management, as well as research, questions.

With the completion of ME-GAP, the long-term maintenance, revision, and application of the GIS databases is a concern. In addition to these data becoming part of the National Biological Information Infrastructure of the USGS Biological Resources Division, these databases will be housed and used by various state agencies. The MDIFW will continue to use the vertebrate data (i.e., range limits and habitat associations) and the vegetation and land cover map; the Maine Image Analysis Laboratory, University of Maine, will store and use the TM and aerial videography data; and the Maine Office of GIS will maintain and distribute the conservation and public lands database created by the State Planning Office and ME-GAP. In the end, the relative success of this project should be judged on how long these databases are revised and reused in the decision-making processes affecting Maine’s biological resources.

Montana Gap Analysis Project

ROLAND L. REDMOND, MELISSA M. HART, J. CHRIS WINNE, WENDY A. WILLIAMS, POLLY C. THORNTON, ZHENKUI MA, CLAUDINE M. TOBALSKE, MICHELE M. THORNTON, K. POODY, MCLAUGHLIN, TROY P. TADY, FOSTER B. FISHER, AND STEVEN W. RUNNING

Montana Cooperative Wildlife Research Unit, The University of Montana, Missoula

Introduction

The Montana Gap Analysis Project (MT-GAP) began in 1991 for the purpose of identifying vegetation types and areas of high vertebrate species richness in the state that may lack adequate protection under existing land ownership and management regimes. Montana is the fourth- largest state in the union and one of the least populated. When the project began, there were few statewide data sets available. Consequently, much effort was devoted to building key data layers at sufficiently fine scale and resolution for subsequent analysis. These data layers included (1) land cover and existing vegetation at a 2 ha minimum map unit (MMU), (2) ownership and management of public lands (1:100,000 scale), and (3) predicted distributions of 425 terrestrial vertebrates that occur in the state. At the completion of the project, these data became freely available with the intent that they be widely used, not only by those directly responsible for managing the state’s valuable natural resources, but also by the public at large, so that everyone can be better informed. With this in mind, we emphasize that these data are dynamic and, in some places, already out-of-date. Nonetheless, the data and analyses which constitute MT-GAP represent an important first step toward planning for the conservation of biodiversity in Montana.

Database Development

Land Cover - The land cover of Montana was mapped by a two-stage, digital classification procedure that was applied independently to 33 Landsat Thematic Mapper (TM) images covering the state. All TM images were obtained during the growing season (mid-June to early September) between 1991 and 1994. In the first stage, data from TM channels 4, 5, and 3 were combined in an unsupervised classification, and then pixels were merged into raster polygons conforming to designated MMUs on the basis of their spectral similarity. Digital elevation models (7.5 minute wherever available), hydrography, and ground-reference data then were used in supervised classifications to label each mapping unit (raster polygon) according to its land cover type. A total of 50 different land cover types were mapped across the state. The single most abundant type was Low/Moderate Cover Grasslands, which comprised 24.7% of the state; as a group, grasslands covered more than 37% of the state. Twenty-four percent of the state was forested; 19 different forest types were mapped, the most common of which were Mixed Subalpine Forest, Douglas-fir, and Lodgepole Pine. Shrublands comprised another 14%, and riparian types were limited to 3.9% of the state’s land area. Urban or Developed Lands occupied less than 1% of this land area, but agricultural lands comprised nearly 15%. Barren types, including rock, snow, or ice, covered 4.3% of the state, and slightly more than 30,000 ha (0.08%) could not be mapped because of cloud cover in the TM imagery.

Thematic accuracy of the land cover map was assessed using a bootstrap method that did not require the collection of an independent set of reference data. Cover type classification accuracies were estimated for 45 types; these averaged 61.4% and ranged from 4.4% for Western Hemlock to 93.2% for Missouri Breaks. Interpolation of the mean error estimates at each ground reference point allowed us to map the land cover accuracy across the state. Estimated mean accuracy exceeded 80% in the southwest corner (Beaverhead and Madison Counties) and in the western portion of the Highline in Glacier, Toole, and Pondera Counties; lower estimated accuracies were associated with some of the insular mountain ranges in central Montana from Gallatin County north through Cascade and Judith Basin Counties.

redicted Vertebrate Distributions - Distributions of 425 terrestrial vertebrate species were predicted, including 16 amphibians, 17 reptiles, 290 birds, and 102 mammals. The modeling process involved several steps. First, range limits for each species were delineated on the basis of existing information about the species’ presence or absence within either a latilong grid system for birds, or the Environmental Protection Agency’s (EPA) hexagon grid system for amphibians, reptiles, and mammals. Next, associations between species and habitat features such as land cover, elevation, and distance to water were researched and summarized in a Wildlife-Habitat Relationships (WHR) database. After preparing the necessary GIS layers to represent these habitat features, a raster-based modeling approach was used to combine the distributional limits and WHR databases into predicted distributions for each species at a resolution of 90 m grid cells. The actual modeling rules and preliminary maps of the predicted distributions were reviewed by nearly 50 biologists from around the state. After review, any necessary changes were made to the range limits and model rules. Once all predicted distributions were complete, species checklists from 14 wildlife refuges and other management units around the state were used to evaluate their accuracy. This involved a comparison between predicted and observed species’ presence, not absence. As such, it cannot be considered a complete accuracy assessment, in part because potential sampling errors in the validation data limited our ability to distinguish between commission errors and correct predictions of absence.

Geographic patterns of vertebrate species richness indicated generally higher diversity in the mountainous regions of western Montana, and lower values in eastern Montana. Not surprisingly, the high diversity was observed along ecotones and in riparian areas, where habitat diversity was correspondingly high. Comparisons between predicted and observed species presence at 14 areas around the state indicated relatively low omission error rates (< 10%), but considerably higher rates of commission errors (24-41%). This means that the models were more likely to overpredict species distributions than to underpredict them. In the context of most management decisions, this is desirable for the same reason that Type I statistical errors are more serious than Type II errors. Failure to predict a species’ presence in an area where it actually occurs may cause inadvertent harm if land-use decisions are made without that species in mind. If, however, a species is predicted to occur where it has never been recorded, it is more likely that the species will be targeted in future surveys and also considered in subsequent land-use decisions.

and Stewardship and Management - The term “stewardship” is used in place of “ownership” because legal ownership, especially in the case of public lands, does not necessarily identify the entity responsible for management of the land resource. At the same time, it is necessary to distinguish between stewardship and management status because a single land steward, such as a national forest, may manage portions of its lands differently.

The digital land stewardship layer was created by incorporating various administrative boundaries into a base layer of land ownership obtained from the BLM, Montana State Office. The BLM produced a base layer by scanning the plates from their 1:100,000 scale Surface Management Series maps. We added some additional information to this base layer, but only for lands managed to protect some elements of biodiversity (i.e., Status 1 and 2). Each map unit in the stewardship layer was assigned a management status code. Management plans for public lands were consulted when available; otherwise agency personnel were consulted.

Lands were assigned to one of four management classes based on the relative degree to which land stewards were responsible for maintaining biodiversity values. Status 1 lands reflected the highest, most permanent level of restrictive management; such lands included National Parks, designated Wilderness Areas, state Wildlife Preserves, Nature Conservancy Preserves, and National Wildlife Refuges where grazing was not permitted. Management could be changed more easily on Status 2 lands, such as Wilderness Study Areas, Wildlife Management Areas, and National Wildlife Refuges where grazing was permitted, but it was still more restrictive than the remaining multiple use public lands, which were assigned to Status 3. Finally, Status 4 included all private lands with no irrevocable easement or mandate to preserve biodiversity values.

Public lands administered by federal and state agencies comprise approximately 35% of Montana. Most federal lands in the western half of the state are managed by the U.S. Forest Service and the National Park Service, whereas most federal lands in the eastern portion of the state are managed by the BLM. Status 1 and 2 lands occupy less than 10% of the state and are generally found at higher elevations. Status 3 and 4 lands occupy more than 90% of the state, and well over half of these are in private ownership.

Analyses

Once the requisite statewide data were assembled, the actual gap analysis involved intersecting the GIS layers of land cover and predicted vertebrate distributions with land stewardship. Generally, the results indicated that high elevation cover types and associated vertebrate species should be relatively well protected under Status 1 and 2 management regimes. But even in these areas, biodiversity elements could be threatened by disease (e.g., white pine blister rust) and the introduction of exotic weeds. Two areas in the state appear to be rich in vertebrate diversity and perhaps in need of a finer-filter analysis–the East Front of the Rocky Mountains and the Bighorn/Powder River basins in southeast Montana. The former is a very scenic area, which is rich in birds and mammals. Much of the nonforest portion of this area is privately owned, and although relatively large areas have been protected by various conservation measures during the past 20 years, more efforts likely will be required to maintain the ecological integrity of the East Front. The second area, the Bighorn and Powder River basins, are rich in mammals and reptiles. Underlying these lands, however, are massive coal deposits, which threaten the long-term viability of this area for wildlife habitat. We also note with some surprise that the longest free- flowing river in the lower 48 states, the Yellowstone, has no formal protection anywhere along its banks.

>Conclusions

The land cover and vertebrate distribution data developed for Montana GAP are the most detailed ever produced for the entire state. These data are based on a 90 m 2 statewide grid, which contains more than 4.5 million grid cells. One of 50 different land cover types was assigned to each cell, and information pertaining to 425 terrestrial vertebrates was synthesized into rules for predicting species’ presence and absence in each cell. The resulting data sets are large and complex, which may complicate their use by state and regional managers as well as by policy makers. Moreover, we found through the review process that the 90 m resolution was often still too coarse for many wildlife biologists whose day-to-day concerns operate at even finer-scale project levels. This may make product acceptance and use even more difficult.

In spite of these challenges, we point out that the relatively fine scale at which we mapped the state’s land cover should make the data useful for considerably more than predicting wildlife distributions. For example, we have already extended this work to the dasymetric mapping of human population density (Holloway et al. 1998), median income, and median age of housing unit across 34 counties in Montana. These results, in turn, could become inputs for improving vertebrate distribution models or predicting where future conflicts are more likely to occur. With the methodologies and reference data in place, remapping or updating land cover would be a relatively straightforward process. Although 23,351 ground-truth plots sound adequate, we believe that higher accuracies would result from additional data, especially from certain areas in central and eastern Montana. We do not advocate expensive field surveys, however, but rather consideration by a consortium of state and federal agencies to fund airborne video sampling, at least across areas like the Bighorn and Powder River basins where it may be important to improve land cover mapping and to monitor changes in land use.

Validation of predicted vertebrate distributions also could be expanded by using more extensive data sets, such as those from the Forest Service’s Northern Region Landbird Monitoring Program (Hutto 1995). Additional sites in eastern Montana might have to be targeted for future field surveys as well.

The vertebrate distribution models themselves also could be improved in many ways. For example, incorporating interspecific relationships into the models could yield important insights. Although competitors, predators, and brood parasites may not actually limit the distribution of other species, they certainly affect habitat quality. Greene et al. (in press) examined thepredicted breeding distribution of Lazuli Buntings in relation to that of Brown-headed Cowbirds in the state; their results indicated that more than 90% of nesting buntings in Montana may be vulnerable to cowbird parasitism. Similar analyses could be carried out for many other host or prey species.

Finally, at the risk of pointing out the obvious, managers of public lands in Montana have more ready opportunities to manage for biodiversity in some landscapes than they do in others. For example, more than 90% of several cover types, including Missouri Breaks and Mixed Whitebark Pine Forest, is managed by federal agencies. Consequently, these types and any associated wildlife species ought to be easier to manage than several of the riparian cover types, the vast majority of which occur on privately owned lands. Thus, it should come as no surprise that conservation of riparian areas, and their associated species, will depend on participation from the private sector. Elected officials at all levels of government can certainly help encourage this participation through enactment of laws that make conservation more appealing than development.

Literature Cited

Greene, E., J. Jolivette, and R.L. Redmond. In press. Lazuli buntings and brown-headed cowbirds in Montana: a state-wide landscape analysis of potential sources and sinks. In: M. Morrison and J. Rotenberry, eds. Research and Management of Cowbirds in Western Landscapes. Studies in Avian Biology, Allen Press, Lawrence, Kansas.

Holloway, S.R., J.V. Schumacher, and R.L. Redmond. 1998. Dasymetric mapping of human population density using ARC/INFO. Pp. 283-291 in S. Morain, ed. GIS Solutions in Natural Resources Management: Balancing the Technical-Political Equation. High Mountain Press, Santa Fe, New Mexico.

Hutto, R.L. 1995. USFS Northern Region Songbird Monitoring Program - Distribution and

Habitat Relationships. Second Rept., USFS Region 1 Contract #57-0343-5-00012. Missoula, Montana. 120 pp.

New York Gap Analysis Project

CHARLES R. SMITH

Department of Natural Resources, Cornell University, Ithaca, New York

The New York Gap Analysis Project (NY-GAP) was begun in 1993 as a cooperative effort among the Biological Resources Division of the U.S. Geological Survey (U.S. Fish and Wildlife Service at that time), Cornell University, and the NY State Department of Environmental Conservation (NYSDEC). For a list of objectives see the final report summary for Colorado on page . NY-GAP is a preliminary step toward the more detailed studies and efforts needed for the long-term conservation of biodiversity in New York.

New York ranks twenty-seventh in total area among the fifty states. However, with 20 million inhabitants, it is among the three most populous states in the United States, with a population density of 1.7 persons per hectare (0.7 persons per acre). Approximately 200 years of intensive

land use have dramatically affected the natural history of New York, making a complete characterization and mapping of land cover types and other elements of biodiversity a challenging undertaking.

Land Cover Classification and Mapping

A statewide map of 29 land cover types was produced to support habitat modeling for the New York Gap Analysis Project (NY-GAP). The map was produced using 14 Landsat-5 Thematic Mapper multispectral digital images acquired during spring and summer of 1991 to 1993, inclusive. A statistical clustering program was used to produce 240 spectrally homogeneous clusters to which a land cover type label was applied based on spectral properties, ancillary data, and local knowledge. The nomenclature and procedures of the National Vegetation Classification System (NVCS) were adopted for labeling land cover types.

When all clusters of all images were labeled into a land cover type, each scene was digitally mosaicked with its adjacent image and spatially aggregated to a four-hectare minimum mapping unit. Several alternative strategies were investigated to improve the spatial and taxonomic detail of the statewide map. Ultimately, time and resource constraints limited map production techniques to trained image analysts labeling clusters derived from single-date, multispectral Landsat data.

A stratified random sample of 113 field plots was used to assess the accuracy of the NY-GAP map using both conventional and “fuzzy” accuracy assessment methods, as described in the full report. Through the summer and fall of 1998, field crews visited 9,745 map polygons and assigned a land cover type label based on visual inspection and careful assessment of the dominant cover types in the polygon. Each observed polygon was considered a point sample, which was compared to the predicted land cover type for each of the sampled polygons. If polygons were inaccessible, field crews relied on aerial photo interpretation and local knowledge to determine the appropriate land cover type of the polygon. The degree to which the reliance on aerial photo interpretation for assigning land cover type labels to sampled polygons affected the accuracy of the field data was not investigated. Contingency tables at three taxonomic levels were constructed using the field observations. Overall map accuracy at the Superalliance (NVCS), Subclass (NVCS), and modified USGS Anderson Level I classification levels were 42%, 57%, and 74%, respectively.

Application of fuzzy accuracy assessment methods using a subsample of the 9,745 polygons resulted in 19% and 23% improvement in overall map accuracy at the Subclass and Superalliance classification levels, respectively. Fuzzy functions are calculated using linguistic scores that permit greater flexibility in assigning an acceptable answer to an observed land cover type in comparison to what is predicted. There should be further investigation of the extent to which fuzzy accuracy assessment provides additional information useful for improving the quality of land cover maps developed for conservation applications, when compared to conventional accuracy assessment.

The low map accuracies obtained at NVCS Superalliance and Subclass levels have to be balanced with the spatial distribution and area summaries for each land cover type at both levels of the classification scheme. The impact of sampling field plots in close proximity to transportation routes with limited observations for less heterogeneous areas may have resulted in lower map accuracies than would be expected. Additional studies are required to evaluate the impact of the sampling design used in this project. We believe that the accuracy assessment method used in this case possibly undervalues the quality of the statewide map that was produced.

The NY-GAP map compares relatively well with other regional-scale land cover mapping efforts with regard to area and accuracy of major land cover types. Consistently low producer accuracies for agricultural land cover types are indicative of the difficulties of mapping these particular types. The spatial and spectral variation due to diverse cropping systems, rotations of row crops with hay and pasture lands, and short- and long-term abandonment of cropland contributes to the spectral confusion encountered. Improvements in mapping accuracy of these particular land cover types, which are highly dynamic in their spectral response, can be expected by developing a temporal sequence of spectral indices throughout the growing season or sequence of growing seasons to capture both intra- and interannual cropping system spectral variations.

Predicted Animal Species Distributions and Species Richness

NY-GAP created predicted distribution maps for 366 terrestrial vertebrate species, including some common, introduced and commensal species (e.g., ring-necked pheasant and house mouse). All mapped species, except for 8 non-native, introduced species, were included in the gap analysis process, for a total of 358 native vertebrate species evaluated using gap analysis procedures. Accuracy assessment was conducted for as many mapped species as possible, including introduced species. Because marine vertebrates are not included in the gap analysis process, five species of marine turtles were excluded from all analyses. Accuracies for three very rare breeding bird species (Merlin, Black Vulture, Wilson's Phalarope) could not be calculated because of a lack of species occurrence records.

Species richness for native, terrestrial vertebrates was computed for each of 249 EPA hexagons within the state. Total vertebrate richness ranged from 119 to 285 species per hexagon with a mean of 242.6 27.6). The distribution of species richness values varies among species groups. Birds and reptiles follow a normal distribution, while distributions of amphibians and mammals are skewed to the left, indicating that most hexagons contain a relatively high number of amphibians and reptiles.

The state of New York exhibits some noticeable patterns in predicted species richness. Amphibians and reptiles exhibit greatest species richness values in the Hudson River Valley, with fewer species in the Adirondack Mountains, which is higher in elevation and likely too cold for these species in the winter. The opposite is true for birds, where richness is high in the Adirondack Region, especially in its outer portion (the Adirondack Transition), with somewhat lower relative richness in the Hudson River Valley area. Birds also show another diverse area in western New York in the transition area from the edge of the Appalachian Plateau to the Great Lakes Plain, where there is an abundance of open fields and grassland areas. Mammal diversity is highest the Catskills and the outer portion of the Adirondacks, both heavily forested regions. Diversity for mammals is lowest on Long Island, which is about 50% urban, as well as in the Great Lakes Plain, which is about 50% agriculture and open field.

Species richness for predicted distributions also was calculated by 90-meter grid cell, which follows the same general trends as richness by hexagon. The overall accuracy at the town level averaged for all species was 59.3%. Accuracy was highest for birds at 84.8% at the town level, reflecting the detailed coverage of the Breeding Bird Atlas data. Accuracy for mammals was approximately 31% because there were fewer observations for mammals at the town level. Amphibian and reptile accuracy fell between the other two groups, at 67% and 65%, respectively. The same methods were used to calculate accuracy at the ecozone level, with an overall average accuracy of 84.5%.

Accuracy for 33 species of amphibians was lowest for Jefferson Salamander and the Jefferson Salamander complex at 7.0%, with 93% commission error, and highest for the Eastern Tiger Salamander, with 99.2% accuracy and 0.6% commission error. Omission errors were low for nearly all species, except for the Blue-spotted Salamander at 4.6%. Accuracy at the ecozone level and 7.5' quadrangle level ranged from 9.1 - 100% and 10.3 - 96.8%, respectively, for these amphibian species.

Accuracy for 32 species of reptiles was lowest at 14.1% for the Smooth Green Snake with 85% commission error and highest for the Eastern Massasauga, Eastern Mud Turtle and Northern Diamondback Terrapin with 97 - 99.9% and approximately 1% commission error. Omission errors were low overall, ranging from 0 - 2.14%. Accuracy at the ecozone level and 7.5' quadrangle level ranged from 36.4 - 100% and 18.8 - 99.7%, respectively. Accuracy for some of the common species, such as the Snapping Turtle or Eastern Box Turtle fell in the middle of the range at about 50%.

Accuracy for 231 species of birds ranged from 35.2% for Barn Owl and Pied-billed Grebe (with 63% commission error) to 100% for several different species. In fact, more than half of the breeding birds had accuracies above 90%. Omission errors were generally low, from 0 - 1 %, and a maximum of 6.4% for the Northern Bobwhite. Accuracy at the ecozone level ranged from 9.1 - 100%.

Accuracy for 59 mammal species ranged from 4.2% for the Indiana Bat, with a 95% commission error, to 93% accuracy for White-tailed Deer and Rock Vole, with 31% and 62% commission errors, respectively. Omission errors were low, ranging from 0 - 3.6%. Accuracy at the ecozone level ranged from 27.3 - 100%. Besides the high accuracy for White-tailed Deer (93%) and American Beaver (89%), accuracy values for many of the common mammalian species were quite low. For example, the Eastern Gray Squirrel and Virginia Opossum both had an accuracy of about 8.5%. We believe that the low overall accuracy and high commission errors for mammals are a function of limitations of the species occurrence database. Our experience with NY-GAP emphasizes the need for substantial research on the distributions of smaller, nongame mammals in New York State. Overall, distributions and status for small mammals are among the poorest known of terrestrial vertebrates in the state.

Land Stewardship

A digital map of land stewardship was created for the state using data contributed from federal, state, and private organizations. GAP uses an ordinal scale of 1 to 4 to identify the relative degree of management for biodiversity protection for each land unit, with Status 1 being the most permanent, comprehensive level of protection, and Status 4 being the lowest level, or unknown.

Nearly 10% of New York State (NYS) was classified as Status 1 or 2 lands. Most of these lands (86%) are located in the Adirondack Forest Preserve and are managed by NYSDEC. Status 1 and 2 lands are disproportionately located at higher elevations, with 70% of lands above 700 m (2170 ft) elevation and 17% of lands below 700 m elevation classified as Status 1 or 2. Approximately 10% of the state was classified as Status 3 and 81% was classified as Status 4. In contrast to the Status 1 and 2 lands, Status 3 lands are well distributed across NY. State and federal government lands comprise about 14% of NY, a modest proportion compared to many western states.

Analysis Based on Stewardship and Management Status

Our analysis was based on results from intersecting the GIS coverages of land cover types and predicted vertebrate distributions with the stewardship and management status coverages. Generally, the results revealed that forested cover types are well represented on protected lands (those classified as Status 1 or 2), but nearly all shrub and grasslands are under private management and are poorly protected.

Predicted distributions for a total of 358 native, terrestrial vertebrate species were mapped for NY. Of these species, only five were not predicted to occur at all in Status 1 or 2 lands (Eastern Hellbender, Shorthead Garter Snake, Ring-billed Gull, Clay-colored Sparrow, and Yellow- throated Warbler). A large proportion (72%) of vertebrate species in the state have less than 10% of their predicted distribution on Status 1 or 2 lands. Nearly all reptile species (97%) and a majority of amphibian (75%), bird (70%), and mammal species (64%) are in this group.

There are 66 species of vertebrates currently listed as being of special concern in NYS. Among these species, nearly all (92%) of the amphibian and reptile species, 76% of the bird species, and 66% of the mammal species have less than 10% of their predicted statewide distributions within Status 1 or 2 lands. Many species that appear to be least protected are associated with grassland habitats or habitats associated with water bodies. Cover types that represent these habitats are not only uncommon, but also among the least protected areas in the state. There are a few listed species, such as Bicknell’s Thrush, that prefer higher elevation areas in coniferous forest cover. Cover types representing these habitats have some of the highest proportions of occurrence in Status 1 and 2 lands, and it logically follows that nearly 90% of the expected distribution of Bicknell's Thrush is on protected lands.

Conclusions and Management Implications

More than 200 years of intensive land use by humans have created a complex landscape mosaic across NY. Widely dispersed land cover types of small size and substantial within-type diversity present an ongoing technical challenge for landscape characterization. The vegetation map of NY-GAP represents the first time in history that a digital land cover map, including an accuracy assessment, for the entire state has been created and analyzed, based largely upon satellite imagery. This map, however, represents a beginning, not an end. Only if subsequent maps are produced in the future will we be able to assess change in land cover types and extent across the state. Such land cover change detection is essential if we are going to be able to estimate the >spatial nature and temporal trajectories of land cover change and evaluate conservation options in that dynamic landscape context.

The procedures of gap analysis, developed by professionals, widely tested, and thoroughly peer- reviewed, offer a protocol and template for continued collection and use of spatially referenced information about the occurrences of elements of biological diversity at the species level and above. As more management practitioners develop a better understanding of new technologies and become more familiar with applications of remotely sensed imagery, digital image processing, and geographic information systems, greater acceptance and application of these technologies can be expected. Potential applications of GAP data still can be limited by user or institutional inertia, an absence of creativity and imagination on the part of users, and an absence of adequate funding for exploratory studies and refinement of methods. For example, issues related to the propagation of errors in all phases of the gap analysis process still need to be investigated more thoroughly. Errors contained in the land cover map, habitat matrices, vertebrate models, validation data, and land stewardship maps may all contribute to the uncertainty of predictions, relationships of animals to habitats, and map products resulting from the gap analysis process.

New York is a densely populated state, with only approximately 15% of its land area represented by public lands, actually or potentially managed for biodiversity conservation. This proportion of public lands is in marked contrast to most of our large western states. We have learned that substantial observed terrestrial vertebrate biodiversity is represented within the corridor along the Hudson River, between Albany and New York City. The Hudson Valley Region, with 13.5% of the land area of the state and only 12% of the state's public lands, has 83% of the state's terrestrial vertebrate diversity, 86% of the land-cover diversity, and 54% of the state's human population. These statistics capture the essence of the challenges that face biodiversity conservation for our eastern United States in the twenty-first century.

Information from the NY-GAP database about land cover types and predicted occurrences of terrestrial vertebrates could be useful in guiding state inventory and planning efforts. Among such efforts could be open space designation, planning, and management, along with development of long-range management plans for state wildlife management areas and state forests. GAP data can be used to map land cover types and estimate land cover type areas for state lands and also offer a first approximation of expected (i.e., predicted) terrestrial vertebrate species diversity to aid in the planning and inventory process for land management units.

With completion of NY-GAP, we are poised to address a number of questions relevant to the regional content and context for elements of coarse-filter and fine-filter biodiversity within NY. It is hoped that there will be sufficient interest from agency leaders and others for these kinds of questions to be addressed in the future for a variety of regions of NY, both ecological and political. The gap analysis process and NY-GAP database provide the foundation and basic information essential for these kinds of analyses and associated planning efforts at landscape scales.

Oregon Gap Analysis Project

JAMES S. KAGAN1, JOHN C. HAK1, BLAIR CSUTI 2, CHRIS W. KIILSGAARD3, AND ELEANOR P.GAINES1

1Oregon Natural Heritage Program, Portland

2Metro Washington Park Zoo, Portland

3Northwest Habitat Institute, Corvallis

The Oregon Gap Analysis Project (OR-GAP) began work in 1988 as the second GAP project started, following only the original pilot project initiated in Idaho by Mike Scott of the Idaho Cooperative Fish and Wildlife Research Unit of the University of Idaho. OR-GAP was managed by Blair Csuti of the Idaho Cooperative Fish and Wildlife Research Unit until 1997 and was completed by the Oregon Natural Heritage Program (ORNHP). It has been a cooperative venture, with the initial vegetation map completed by contract staff and the ORNHP, and the initial wildlife distributions developed cooperatively by Oregon State University, the Biodiversity Research Consortium, ORNHP, and the Oregon Department of Fish and Wildlife (ODFW). Oregon was also fortunate enough to have a separate statewide biodiversity assessment managed by the Northwest Office of the Defenders of Wildlife. This was the Oregon Biodiversity Project, which started as an effort to implement the results of the first Oregon Gap Analysis work, but wound up as an independent analysis. A second-generation land cover map was developed by ODFW, and ORNHP developed updated species distribution maps based on the second-generation land cover. Because we had access to a historical vegetation cover, we were able to model vertebrate species distributions prior to European settlement. This report outlines all of the work completed to date.

The major objectives of the project were to (1) produce GIS databases describing actual land cover type, historical land cover type, terrestrial vertebrate species distributions, land stewardship, and land management status at a scale of 1:100,000, (2) identify land cover types and terrestrial vertebrate species that currently are not represented or are underrepresented in areas managed for long-term maintenance of biodiversity, i.e., “gaps,” and (3) facilitate cooperative development and use of information so that institutions, agencies, and private land owners may be more effective stewards of Oregon’s natural resources. The OR-GAP project is a step toward the more detailed efforts and studies needed for long-term planning for biodiversity conservation in Oregon. The final (Version 2) map of actual land cover used in this analysis included the distribution of 65 land cover types, mapped as polygons with a minimum mapping unit (MMU) of 100 hectares.

Individual distributions of 457 vertebrate species were predicted using habitat associations. Range limits of each species were delineated within a grid of 441 hexagons (635 km 2 based on published range maps, locality records, and review by local experts. This hexagon database has been in existence at the Oregon Natural Heritage Program for the last 10 years and has been updated based on continual reviews and inclusion of new data. It includes information both on species distributions, the source of information for each hexagon, and the confidence in the record. Within hexagons, species distributions were modeled based on species/land cover associations and the presence of riparian areas. Comparisons of predicted species to species lists maintained for eight wildlife refuges produced variable results since the tested areas often had incomplete lists.

The Gap Analysis Program (GAP) uses a scale of 1 through 4 to denote how well each tract of land is managed for biodiversity maintenance, with “1” being the highest, most permanent, and best managed, and “4” being the lowest, or unknown, status. Status codes were assigned by ORNHP staff following GAP guidelines with review by The Nature Conservancy’s Public Lands Coordinator. A total of 17.2% of the state of Oregon is classified as status 1 and 2 lands; 95% of these are found on federal ownership. Oregon has large portions of its public lands in the western portion of the state as Late Successional Reserves (LSR), and large portions of the eastern part in Bureau of Land Management Wilderness Study Areas (BLM WSA). The designation of these LSRs and BLM WSAs as status 2 lands results in high proportion of Oregon found in protected areas (status 1 and status 2 lands), although these designations do not provide as much protection as do most other status 2 designations. This is a limitation of using only four land management classes to represent a complex array of management designations when examining protection patterns for individual species as well as species richness. Land management status was overlaid with land cover and vertebrate species distributions to conduct a gap analysis of Oregon. We considered land cover types and vertebrate species as underrepresented (i.e., “gaps”) in management areas if < 1% of the land they occupied or their habitat in Oregon fell within status 1 and 2 lands.

Nine (14%) of 63 natural (non-anthropogenic) land cover types have < 1% of the area they occupy in status 1 and 2 lands. The highest priority for further protection is recommended for coastal strand and hawthorn-willow shrublands, because their current protection is low and they are the most vulnerable to ongoing land management practices. Palustrine forest and south coast mixed conifer forest are also high priorities for protection if WSAs and LSRs are excluded from the analysis. Wetland and riparian types are not satisfactorily mapped at our current MMU, and further efforts are needed to provide an adequate spatial description of their location before long- term planning for their conservation can be accomplished.

Five (< 2%) of the 263 birds modeled had < 1% of their habitat protected in status 1 and 2 lands. When LSR lands are put in status 3, three additional species are added to this category. When historic distributions were compared to current status 1 and 2 lands, one fifth (57) of the birds modeled had substantial habitat loss or low habitat protection. Of these, nine are of current conservation concern. No native mammals had < 1% of its habitat in status 1 and 2 lands. When LSR lands are excluded from category 2, two mammals of conservation concern (California and Townsend’s voles) fall into this category. Thirty-one (24 %) mammals (including seven conservation targets) have had substantial habitat loss or low habitat protection when compared to historic distribution. There were no amphibians or reptiles with < 1% of their present distribution in status 1 and 2 lands. When LSR lands are dropped from the analysis, one reptile, the common kingsnake, is added to this group. Thirteen (22 %) of the amphibians and reptiles have had substantial habitat losses or low habitat protection compared to historic distributions.

Using some indices developed to look at how much habitat species have lost and how well their current and historic distributions are protected, OR-GAP was able to compare species richness maps for all species, with a subset of those needing conservation action. The major centers of species richness (areas with very high species richness values) are the Klamath Basin in south- central Oregon, the Malheur Basin in east-central Oregon, and the Siskiyou Mountains in southwestern Oregon in both the all-species and the priority-species richness maps. However, areas of high to moderately high diversity change dramatically in the subset of needy species coverage, with priority areas highlighted in the western Columbia River Gorge, the Alvord Basin, and southeastern Wasco County that were not important in the overall species richness map.

In general, OR-GAP is hesitant to use the results of the analysis to make important statements about either diversity patterns in Oregon or sites where highest biodiversity most needs to be protected. However, the development of the stewardship coverage and the species distribution databases has improved the ability of others to do statewide and local assessments. Both versions of the OR-GAP land cover are uneven, with detailed mapping and classification in some areas and coarser mapping and classification in others, and neither have undergone accuracy assessment. The wildlife by habitat relationship models used in OR-GAP are in the process of being improved, but the new models were not available in time for this analysis. We believe that using higher-resolution vegetation coverages with more detailed classifications and the improved wildlife-habitat models would greatly improve the overall accuracy of the analysis. However, the OR-GAP version 2 species distribution maps are clearly the best wildlife distribution maps available and are quite useful for ecoregional, statewide, and multistate analyses.

OR-GAP has been most valuable as a focus to develop and integrate these important data sets. Using the species distribution databases and the managed area coverage with the new wildlife- habitat models and ecoregional or local vegetation maps that are currently being produced throughout the state, should provide much more accurate species lists and species distribution maps. OR-GAP intends to continually update the managed area cover and species distribution databases and to provide cross-walks between the new wildlife habitat models and any new vegetation or land cover maps which become available. We intend to try to aggregate a 1:24,000 vegetation coverage for Oregon, based on local mapping efforts, and to compare these to the coarser-scale maps based on satellite imagery. And, we intend to remain a source of biodiversity information for anyone interested in studying or protecting biodiversity in Oregon.

Pennsylvania Gap Analysis Project

WAYNE MYERS1, JOSEPH BISHOP2, ROBERT BROOKS2,TIMOTHY O’CONNELL2, DAVID ARGENT2, GERALD STORM3, JAY STAUFFER3, AND ROBERT CARLINE3

1School of Forest Resources & Environmental Resources Research Institute, The Pennsylvania State University, University Park

2School of Forest Resources, The Pennsylvania State University, University Park

3Pennsylvania Cooperative Fish & Wildlife Research Unit, The Pennsylvania State University, University Park

The Pennsylvania Gap Analysis Project (PA-GAP) was initiated in 1993 with the goal of providing a landscape-level perspective on the conservation status of reproductive habitats for mammals, birds, amphibians, reptiles, and fishes. The intent has been to attain this overall goal of landscape perspective within the general framework of the national Gap Analysis Program (GAP), but with some accommodation for Pennsylvania’s special blend of physiography and historical human habitation.

Pennsylvania’s contemporary habitats are largely a legacy of historic human disturbance. Major modes of disturbance have included strip mining, marginal agriculture, and extensive forest clear-cutting, often followed by fire. There has been a physiographic propensity for exposed soils to be degraded by erosion, leading to abandonment of lands and their eventual reversion to the public domain. Regrowth and reforestation, along with restoration of mine spoils, have created habitats that harbor a considerable variety of wildlife.

Thus, geography, physical environment, land cover and disturbance, and wetland occurrence are major determinants of habitat in Pennsylvania. Species composition and density of vegetation are somewhat secondary as habitat factors at landscape scales in this region. For these reasons, the landscape-scale habitat models for Pennsylvania give more emphasis to the former features, whereas GAP would traditionally emphasize vegetation types.

Pennsylvania history is replete with negative human influences on waters and wetlands. These ecosystems have not been as resilient to human impacts as terrestrial systems. Erosion of exposed soils generates sediment that fills in wetlands, aggravating the loss and modification of wetlands due to development. Pollution from industry, mining, agriculture, urbanization, and transportation contributes toxic chemicals to the waters, increases their acidity, and builds up excess nutrients in lakes and ponds, which can ultimately be deadly for fish and other aquatic life. Acid mine drainage and acid rain have been especially problematic for Pennsylvania. Hydrologic engineering for transportation, flood control, cooling, and power generation has disrupted natural hydrologic patterns. The location of major urban centers in the state is strongly associated with large rivers, estuaries, and Lake Erie. Drainage divides between major river basins constitute virtually complete barriers to dispersal and recolonization by aquatic species. This multitude of long-term stresses, coupled with segregation imposed by Pennsylvania’s physical geography, has put several of the state’s aquatic species in jeopardy, and a number of others are apparently already eliminated from entire geographic sectors.

To tracking conservation status, GAP protocols differentiate four levels of land stewardship. On status 1 lands, human disturbance of habitat is legally prohibited and nonhuman disturbance is not controlled unless it threatens human life or property. Status 2 lands are naturalistic areas with a legal mandate prohibiting conversion to humanistic/cultural development. On status 3 lands, any further permanent conversion of lands to humanistic/cultural development is restricted by legal mandate. In Pennsylvania, a distinction was made between status 4 lands having no specific provisions for habitat conservation and lands for which conservation status could not be determined.

Examples of GAP status 1 lands in Pennsylvania are wilderness areas, natural areas, wild areas, and conservancies. Pennsylvania has less than 1% of its approximately 11.6 million hectares in status 1 lands. Status 2 lands in Pennsylvania include state parks, state forests, state gamelands, state scenic rivers, national wildlife refuges, and less restrictive private conservancies. Pennsylvania has 12% of its area in status 2 lands, with the interesting irony that a substantial share of this large area was historically degraded land that reverted to the public domain for rehabilitation. Pennsylvania’s GAP status 3 lands consist mostly of national forest, national parks, national recreation areas, and national scenic and recreational rivers. Status 3 lands account for a little more than 2% of Pennsylvania’s area. Therefore, the Commonwealth has approximately 15% of its land area in stewardship status 3 or better, with the more pristine status 1 lands being quite limited. Importantly, status 2 lands are concentrated in parts of the state that have been demonstrated historically to be unsuitable for intensive human development.

Generalized land cover and disturbance were mapped in several modes from Landsat Thematic Mapper (TM) digital data collected during a period from 1991 through 1994. The image data were compressed for mapping purposes so as to be compatible with geographic information systems (GIS) software. The compressed images have been made available to the public and have received considerable use in Pennsylvania as backdrops for GIS applications. An initial interpretive mapping at 100 ha resolution classified landscapes as being either naturalistic or humanistic.

Naturalistic landscapes included forests, wetlands, and water. Humanistic landscapes included agricultural, suburban, and urban land uses. Nearly 70% of the state has a naturalistic (mainly forested) landscape, with approximately 65% in one large unit encompassing much of the northern third of the Commonwealth and extending through the mountains to the southern border.

Landscapes in several regions of Pennsylvania are heavily influenced by human development. Habitat disturbance due to human development was mapped interpretively in three types with no specific minimum resolution. The disturbance classes were rural, suburban (primarily residential), and urban (commercial/industrial). Pennsylvania is predominantly rural, with 1.5% of its area being intensively urbanized and another 4.1% being suburban. Much of the urbanization is due to a few large metropolitan areas.

By reference to selected digital aerial photos, eight general land cover categories were mapped through computer-assisted classification of spectral groupings in the compressed image data. These land cover categories were (1) water; (2) evergreen forest; (3) mixed evergreen/deciduous forest; (4) deciduous forest; (5) woody transitional such as bushes; (6) perennial herbaceous such as grasslands and forage crops; (7) annual herbaceous such as row crops and grains; and (8) barren/hard-surface/rubble/gravel. Combining the land cover and disturbance mappings yielded 24 classes for habitat modeling.

Habitat models were developed in tabular (matrix) form as spreadsheets, with columns representing habitat factors and rows representing species. A map of suitable habitat was then prepared for each species from the respective model by analytically combining spatial data layers for the habitat factors using computerized geographic information systems (GIS). The modeling for fishes was done on the basis of 9,855 small watersheds.

GAP analysis conventionally takes note whether a species has 10%, 20%, or 50% of its potential habitat on lands with management status 1 or 2. Pennsylvania has approximately 13% of its total land area in GAP status 1 and 2, so common species fall mostly in the 10% to 20% range for this level of conservation. Higher percentages indicate some degree of habitat restriction to conservation areas. Lower percentages indicate relative underrepresentation of habitat within conservation areas but do not necessarily reflect overall degree of statewide habitat scarcity.

There are no mammals having 50% or more of the potential habitat in status 1 and 2. The following species have 20% to 50% of potential habitat in status 1 and 2: northern water shrew, long-tailed shrew, pygmy shrew, Indiana myotis, Appalachian cottontail, snowshoe hare, northern flying squirrel, Allegheny woodrat, woodland jumping mouse, common porcupine, fisher, eastern spotted skunk, bobcat, and elk. Species having less than 10% of potential habitat in status 1 and 2 are: eastern mole, evening bat, Norway rat, house mouse, meadow jumping mouse, and least weasel. The remaining species have 10% to 20% of potential habitat in status 1 and 2.

There are four species of birds with 50% or more of potential habitat in GAP status 1 and 2: American wigeon, black tern, yellow-bellied flycatcher, and blackpoll warbler. Bird species having 20% to 50% of habitat in GAP status 1 and 2 are: northern goshawk, black-necked stilt, northern saw-whet owl, yellow-bellied sapsucker, olive-sided flycatcher, red-breasted nuthatch, winter wren, golden-crowned kinglet, Swainson’s thrush, hermit thrush, blue-headed vireo, yellow-throated vireo, warbling vireo, Nashville warbler, black-throated blue warbler, yellow- rumped warbler, black-throated green warbler, Blackburnian warbler, pine warbler, worm-eating warbler, northern waterthrush, mourning warbler, Canada warbler, rose-breasted grosbeak, white-throated sparrow, dark-eyed junco, and purple finch.

Bird species having less than 10% of potential habitat in GAP status 1 and status 2 are: least bittern, great egret, snowy egret, cattle egret, black-crowned night heron, yellow-crowned night heron, mute swan, Canada goose, mallard, blue-winged teal, northern shoveler, bald eagle, northern harrier, peregrine falcon, ring-necked pheasant, northern bobwhite, king rail, Virginia rail, sora, killdeer, upland sandpiper, common snipe, American woodcock, rock dove, barn owl, short-eared owl, common nighthawk, Chuck Wills’s widow, chimney swift, willow flycatcher, eastern kingbird, horned lark, purple martin, tree swallow, bank swallow, cliff swallow, barn swallow, fish crow, Carolina chickadee, sedge wren, eastern bluebird, loggerhead shrike, European starling, white-eyed vireo, blue-winged warbler, yellow warbler, magnolia warbler, prairie warbler, common yellowthroat, yellow-breasted chat, summer tanager, blue grosbeak, dickcissel, clay-colored sparrow, field sparrow, vesper sparrow, savannah sparrow, grasshopper sparrow, Henslow’s sparrow, song sparrow, bobolink, red-winged blackbird, eastern meadowlark, western meadowlark, common grackle, house finch, house sparrow. The remaining species have 10% to 20% of potential habitat in GAP status 1 and status 2.

The mud salamander is the only amphibian species having 50% or more of the potential habitat in GAP status 1 and 2. The valley and ridge salamander along with Wehrle’s salamander are the only species with 20% to 50% of potential habitat in GAP status 1 and 2. Amphibian species having less than 10% of potential habitat in GAP status 1 and 2 are: hellbender, seal salamander, ravine salamander, mudpuppy salamander, Woodhouse’s toad, northern cricket frog, gray tree frog, mountain chorus frog, western chorus frog, northern leopard frog, and southern leopard frog. The remaining species have from 10% to 20% of potential habitat in status 1 and 2.

There are no turtle species having 20% or more of the potential habitat in GAP status 1 and 2. The wood turtle and bog turtle have 10% to 20% of potential habitat in status 1 and 2. The other 8 turtle species have less than 10% of potential habitat in status 1 and 2.

Among snakes and lizards, there are no species with 50% or more of potential habitat in GAP status 1 and 2. Species having 20% to 50% of potential habitat in status 1 and 2 are: eastern fence lizard, coal skink, five-lined skink, redbelly snake, smooth earth snake, and timber rattlesnake. Species having less than 10% of potential habitat in status 1 and 2 are: broadhead skink, Kirtland’s snake, rough green snake, queen snake, brown snake, copperhead, and massasauga. The remaining species have 10% to 20% of potential habitat in status 1 and 2.

Consistent with the problematic conservation context for fishes in Pennsylvania, the majority of species in this group have less than 10% of potential habitat in GAP status 1 and 2. There are no fish species with 50% or more of habitat in status 1 and 2. Species having 20% to 50% of habitat in status 1 and 2 are: shortnose sturgeon, brook trout, redside dace, bluespotted sunfish, longear sunfish, and slimy sculpin. Species having 10% to 20% of habitat in status 1 and 2 are: Atlantic sturgeon, American eel, rainbow trout, brown trout, chain pickerel, cutlips minnow, bigeye chub, eastern silvery minnow, hornyhead chub, spotted shiner, silver shiner, ironcolor shiner, southern redbelly dace, blacknose dace, fallfish, satinfin shiner, gravel chub, white sucker, creek chubsucker, northern hog sucker, margined madtom, brown bullhead, green sunfish, pumpkinseed, bluegill, mottled sculpin, and Potomac sculpin. The remaining species have less than 10% of the potential habitat in GAP status 1 and 2.

For the Pennsylvania context, it is important to have a relatively objective way of analyzing the model results to determine which species may be particularly problematic with respect to scarcity of suitable habitat and conservation of the habitat that remains. A special mode of analysis was conceived to rank species in this regard and determine where there is notable co- occurrence among such species. A Regional Habitat Insecurity Index (RHII) was formulated which combines overall habitat scarcity with scarcity of habitat in conservation areas and scarcity of conservable habitat. It lends particular emphasis to species that couple overall habitat scarcity with low representation in conservation areas and difficulty of finding habitat outside existing conservation areas by which to enhance the level of stewardship. The RHII results were mapped on a 1 km grid having 118,218 cells in Pennsylvania. A weighted spatial index of landscape importance was determined for each of six (taxonomic) groups of species by summing the RHII values for species having suitable habitat in the cell.

The index of landscape importance was mapped separately for the portion of Pennsylvania not contained in conservation areas having status 3 or better. A threshold was then determined for the composite RHII importance index of each group of species. Cells above this threshold were designated as “leading landscapes” for conservation concern regarding that group of species. Cells occurring as small patches were suppressed in the leading landscapes map to avoid habitat fragments. The mappings of leading landscapes were also cross-compiled among groups of species to show where landscapes are important for multiple groups.

Analysis of turtles in this manner places emphasis on the map turtle, bog turtle, and eastern spiny softshell turtle. Analysis of snakes and lizards emphasizes the broadhead skink, Kirtland’s snake, rough green snake, eastern massasauga, and eastern worm snake. Emphasis for amphibians is on the eastern mud salamander, southern leopard frog, green salamander, eastern spadefoot toad, ravine salamander, northern cricket frog, mudpuppy salamander, mountain chorus frog, and Appalachian seal salamander.

Analysis of mammals lends emphasis to eastern spotted skunk, evening bat, least shrew, rock vole, Indiana myotis, elk, Appalachian cottontail, northern water shrew, fisher, river otter, fox squirrel, least weasel, Allegheny woodrat, and snowshoe hare. Placement of existing conservation stewardship areas generally matches better with the needs for mammals than for other taxonomic groups of vertebrates. of mammals lends emphasis to eastern spotted skunk, evening bat, least shrew, rock vole, Indiana myotis, elk, Appalachian cottontail, northern water shrew, fisher, river otter, fox squirrel, least weasel, Allegheny woodrat, and snowshoe hare. Placement of existing conservation stewardship areas generally matches better with the needs for mammals than for other taxonomic groups of vertebrates.

The RHII approach emphasizes several bird species as given in Table 5.2 of the report, with wetland associated species and grassland species both being prominently represented. The leading landscapes for birds, likewise, show this emphasis. Not surprisingly, the fish list is largest (Table 5.6) and loaded with endangered, threatened, and candidate species. French Creek and the Ohio River are prominent in the leading landscapes for fishes.

Tennessee Gap Analysis Project

JEANETTE JONES and SUSAN MARDEN

Tennessee Wildlife Resources Agency, Nashville

The Gap Analysis Program (GAP) is a nationwide program sponsored by the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS) and is conducted at the state level. National standards ensure edge-matching between adjacent states and will eventually allow for regional and national assessments of biodiversity. Gap analysis utilizes Geographic Information Systems (GIS) technology to map the distribution of plant communities and terrestrial vertebrate animal species, and determine the degree of protection that biological reserves and conservation lands provide to species-rich areas and, hence, identify the “gaps” in representation of biodiversity (Scott et al. 1993). The geographic data layers required to conduct GAP include plant communities or vegetation types, predicted terrestrial vertebrate species distributions, and land ownership/land management status in which lands are categorized as to the current level of management for biodiversity conservation.

The Tennessee Biodiversity and Gap Analysis Program is a federal/state/private joint venture. Coordination of the joint venture was achieved through a steering committee with representatives from the following agencies: Tennessee Conservation League, Tennessee Wildlife Resources Agency, Tennessee Technological University, Tennessee Department of Environment and Conservation, Tennessee Department of Agriculture, U. S. Fish and Wildlife Service, University of Tennessee, Tennessee Valley Authority, Westvaco, and U. S. Forest Service. The Tennessee Gap Analysis Project (TN-GAP) is housed at the Tennessee Wildlife Resources Agency (TWRA).

One of the first GIS layers developed for TN-GAP was the general land cover layer. Landsat Thematic Mapper (TM) satellite imagery provided the basis of land cover mapping in Tennessee. Portions of twelve Landsat TM scenes dated 1990-1993 were used for the spectral classification of open water, pasture/grassland, cultivated crop land, deciduous forest, coniferous forest, urban, barren land, and cloud/cloud shadow. Polygons of forested and nonforested wetlands from the U. S. Fish and Wildlife National Wetland Inventory (NWI) were digitized and added to the classification file. Ancillary data sets were used to add strip mine, rock quarry, and gravel pit locations to the data file. Classes were validated using 1:58,000-scale, color infrared, National High Altitude Photography (NHAP) transparencies.

The forest classes from the Anderson Level II land cover file were further refined to create a detailed vegetation map utilizing aerial videography and other ancillary data sources. Forested lands cover approximately 49% of the state. Approximately 4000 km of video transects were flown over a representative portion of forested lands during mid-April 1995. Geographic position data were recorded with a GPS unit and related to the time encoded on the video. This coding allowed the satellite image to be related to the corresponding video image and allowed an exact location on the ground to be determined during fieldwork. A library of video prints of forest communities created from the field visits was used to interpret the remainder of the videography. Video interpretation and labeling of forest classes were performed in stages by physiographic provinces, in order to take advantage of the variation in the vegetation due to differences in geology and landform. Labeling of forested classes is guided by the National Vegetation Classification: Alliances of the Southeastern United States developed by the regional office of The Nature Conservancy. Classification techniques performed on the forested regions of the satellite imagery were correlated with the interpreted aerial videography. Statistical information (spectral class, slope, elevation, and aspect) surrounding the location of interpreted sites were collected. Inference rules compiled from the summary tables and additional ancillary data were used to create the detailed vegetation map. The final vegetation maps were used with habitat relationship models to predict vertebrate distributions.

Tennessee’s native fauna includes approximately 62 amphibians, 56 reptiles, 71 mammals, and 170 breeding birds. Habitat data for these species resides in the generalized Vertebrate Characterization Abstracts (VCA) and the more specific Tennessee Animal Biographies System (TABS). Updated information has been incorporated into TABS from the Fish and Wildlife Information Exchange Master Species Files from Virginia Polytechnic Institute. The primary source of habitat and distribution data for birds was the Atlas of Breeding Birds of Tennessee and The Land Managers’ Guide to the Birds of the South.

Distribution data from TABS and VCA databases have been incorporated into the GIS to produce range maps for all 359 species based on county, watershed, and physiographic province of occurrence. Ranges for nonbreeding wintering birds in Tennessee were not mapped. The range maps were compared to available data sources such as the Annotated Checklist and Bibliography of Amphibians and Reptiles of Tennessee the Handbook of Mammals of the South- Central States , and the Biodiversity of the Southeastern United States. Comments were solicited from state biologists about the range data. The range maps were then translated into the Environmental Protection Agency’s (EPA) hexagonal grid. Ecological Services of the Tennessee Natural Heritage Program provided data for rare species.

Vertebrate distributions are predicted by mapping the potential range of each species and relating habitat preference to the vegetation map within each species’ range. Data sources for the habitat relationship models are TABS, VCA, the Land Manager’s Guide to Birds of the South and the Land Manager’s Guide to the Amphibians and Reptiles of the South . The vertebrate distribution maps are predictive and may be generalized in some cases, a major factor for the generalization being that the vegetation maps do not contain information on structure or age class. Several animal species are dependent upon this structure and age class. The final distribution maps for all terrestrial vertebrate species were overlaid and analyzed to determine areas of species richness. Analysis of species richness can be generated for each taxon based on range data and habitat preferences. Areas of species richness, when overlaid with locations of publicly managed lands can determine the protection afforded to biodiversity.

The TWRA GIS maintains coverages of major public lands and acquired wetlands in the state. The land ownership coverage includes all federally and state-owned land, as well as local and privately owned lands that are managed for conservation. To obtain an estimate of the protection afforded biodiversity, land ownership was assigned a land management status category. A subcommittee, formed to categorize the lands as to their management status, developed five categories for Tennessee. The land management status categories range from Status 1, for those lands with a management plan for providing the greatest amount of protection to biodiversity, to Status 4, for those lands not identified as functioning to conserve biodiversity. The public lands database, when overlaid with the vertebrate species distributions, can identify the “gaps” in biodiversity protection.

Public lands comprise approximately 8% of the total land area of Tennessee with 4.8% under federal and 2.7% under state jurisdiction. The U.S. Forest Service administers the largest amount of public land in the state, and this land is located in the mountainous regions of East Tennessee. TWRA administers the largest amount of state-owned land. Less than 2% of the state occurs in status 1 and 2 lands.

Approximately 52% of the state falls under the category “natural community.” Of this percentage, only 8% are under federal jurisdiction and 4% are under state jurisdiction. The remainder is privately owned. All 22 natural community types are located on federal lands and all but three plant community types are located on state lands. All natural plant communities, with the exception of one type, are represented to some degree under land management status 1 or 2.

Overall species, breeding birds, and neotropical migrants have a relatively higher area containing higher species richness in status 1 and status 2. Mammals show the largest amount of area; each category predicts a species richness of 31 to 35, except for status 4 with species richness of 26 to 30. The largest area per status type for reptiles was 21 to 25 species for all status types except status 5. Amphibians show the highest area per land status at 12 to 13 species for all land status types except land status 5.

The Tennessee Biodiversity Program (established by the Tennessee Conservation League) and TWRA’s GIS division are continuing to work together to provide planners and community leaders, landowners, natural resource professionals, and educators with information on Tennessee’s natural resources. TWRA provides TN-GAP data and related GIS data layers as Arc View files to county planners and community leaders. Managing Natural Resources - A Planning Guide for the Elk River Watershed of South Central Tennessee and Northern Alabama was published by TWRA, TCL, TVA and National GAP as a planning guide for developing and carrying out natural resource conservation and management programs.

Literature Cited

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 R.G. Wright. 1993. Gap Analysis: A geographic approach to protection of biological diversity. Wildlife Monographs 123.

Virginia Gap Analysis Project

SCOTT D. KLOPFER and JULIE MCCLAFFERTY

Conservation Management Institute, Virginia Tech, Blacksburg

The Virginia Gap Analysis Project (VA-GAP) was initiated as a cooperative effort between the Biological Resource Division of the U.S. Geological Survey, and state, federal, and private natural resources groups in Virginia. The major objectives of the project were to (1) produce GIS databases describing the actual land cover type, predicted distributions of terrestrial vertebrates, land ownership, and land management status at a scale of 1:100,000, (2) identify land cover types and terrestrial vertebrate species that currently are not represented or are underrepresented in areas managed for long-term maintenance of biodiversity (i.e., “gaps”), and (3) facilitate cooperative development and use of information so that institutions, agencies, and private landowners may be more effective stewards of Virginia’s natural resources. The VA- GAP project is a preliminary step toward the more detailed efforts and studies needed for long- term planning for biodiversity conservation in Virginia.

The system used to classify the land cover consisted of 26 classes (14 forest classes, 4 herbaceous/agriculture, 3 wetland, 2 developed, 1 nonvegetated, 1 open water, and 1 mixed/unknown) and 2 forest complexes. Landsat Thematic Mapper (TM) imagery from 1986 to 1994 (mostly 1992-1993), in conjunction with aerial videography and field data collection, was used to identify and map the land cover types. The mapping of wetlands was facilitated by a direct overlay of the US Fish and Wildlife Service’s National Wetlands Inventory (NWI). Topography and phenological index (related to changes in latitude, longitude, and elevation) also were important tools for mapping diverse forest types across the state. A comparison of vegetation and land cover types mapped from TM data to the known points database gave an overall accuracy of 67.5% before spatial error correction and 87.1% after correction of potential spatial errors.

Individual distributions of 566 vertebrate species (67 reptiles, 78 amphibians, 107 mammals and 314 birds) were predicted using both county occurrence records and habitat associations contained within the Biota of Virginia (BOVA) database that was set up and is maintained by the Virginia Department of Game and Inland Fisheries. Range limits of each species were delineated based on county lines and the presence of a either a “known” or “likely” occurrence of a species within each county. Within counties, species distributions were modeled based on species/land cover associations also available in BOVA that were cross-walked with the VA- GAP land cover classification system. Southeast Virginia was found to be the most diverse area of the state. Comparisons of species predicted to occur to species lists maintained for six sites (mammals and herpetofauna on Fort A.P. Hill; birds on five eastern Virginia National Wildlife

Refuges) indicated an overall accuracy ranging from 76.8% to 96.2%. Uncertainties in modeling strategies and final species distribution maps are discussed.

A GIS database of private and public conservation lands was assembled in cooperation with the major federal, state, and private land management agencies managing land for conservation purposes in Virginia. Conservation lands mapped by VA-GAP make up 8.9% of the total state area. By far, the U.S. Forest Service is the largest land-managing entity in Virginia, with 66% of all stewardship lands mapped in VA-GAP. The Virginia Department of Game and Inland Fisheries holds the next largest amount of property with 14.5%. Of all the conservation lands used in VA-GAP, federal agencies control about 82% and state agencies 18%. Lands were denoted as to the degree to which they are managed for maintenance of biodiversity and long- term ecological processes. The Gap Analysis Program requires use of a 1 through 4 scale to denote high to low management for biodiversity maintenance based on legal and management status. Status 1 lands (primarily wilderness areas within National Forests) make up only 3.4% of all conservation lands, with 10.1% in Status 2 and 86.5% in Status 3. Conservation lands are concentrated primarily in the western mountainous portion of the state (98% of status 1 lands and 89% of status 3 lands), especially along the ridge tops, with only small portions of land in the central Piedmont region (1.2% of Status 3 lands, no Status 1 or 2) and eastern Coastal Plain (3% in Status 1-3). The land ownership map should not be interpreted as a legal document but as a representation of general ownership patterns.

Only three land cover types in Virginia have more than 10% of their areas protected under Status 1 or 2. These are the Red Spruce-Fraser Fir type (found exclusively on mountaintops) with 80% of their distribution found in National Forests; Tupelo-Red Maple type (found in extreme southeastern Virginia) with 30% of its distribution found within the Great Dismal Swamp National Wildlife Refuge; and the Coastal Shrub type with over 10% protection afforded by the Assateague and Chincoteague Island National Seashores. Montane Mesic Coniferous, and Riparian Forest, herbaceous, and wetland (especially in central and western Virginia) types are especially underrepresented in conservation lands given their overall proportion of the landscape. When Status 3 lands are included in this analysis, many types appear protected in proportion to their distribution. However, the Riparian Forest and submontane forest (particularly deciduous) types remain poorly protected.

Of the 566 species modeled in VA-GAP, more than one-third (35%) have <1% of their predicted distribution within status 1 and 2 lands. By taxon, this represents 35.9%, 43.3%, 43.0%, and 30.3% of the total number of amphibians, reptiles, mammals, and birds respectively. Sixteen species have almost no protection (<0.1%). No species were predicted to have >50% of their distributions on status 1 and 2 lands. We identified species with moderate or restricted distributions and low representation on status 1 and 2 lands to be at greater risk. Although we did not select a specific threshold for determining significant risk, we considered any species whose protected distribution (as a percentage of all protected lands) is less than its total distribution across the state (as a percentage of the total state area) to be at increased risk. Among birds, four groups of species were identified as being at risk: grassland species (e.g., Bachman’s sparrow, Eastern meadowlark, upland sandpiper); agricultural/forest mix species of the Piedmont region (e.g., pine grosbeak, rough-legged hawk, rough-winged swallow); mountain species that use agricultural, open areas, or wetlands for some component of their habitat (e.g.,   common raven, willow flycatcher, warbling vireo); and agricultural/forest mix species with statewide distributions (e.g., barn swallow, sharp-shinned hawk, orchard oriole). Several reptile species also are underrepresented. The black racer, corn snake, mole kingsnake, and the southeastern crowned snake all utilize agricultural/open areas mixed with forest and, with the exception of the black racer, are all found in the Piedmont region of Virginia and eastward. The bog turtle and wood turtle (found in wetlands types of the mountains), eastern river cooter, and stripe-necked musk turtle (found in waterways and riparian areas of the mountains), and the red- eared slider are turtle species with low representation on conservation lands. Most amphibian species are well represented on conservation lands, with a few widespread species (e.g., gray tree frog and spotted salamander) appearing underrepresented only because of their wide distribution and presence throughout the poorly protected Piedmont region. Mammals identified at increased risk include those that utilize agricultural and open areas (e.g., eastern harvest mouse, common muskrat, common pine vole, star-nosed mole) and those with widespread distributions that utilize agricultural-forest mixed lands (e.g., meadow vole, northern white-footed mouse, Kirtland’s short-tailed shrew). In summary, species that tend to use mountain wetland riparian areas or agricultural/open areas (esp. in the Piedmont) appear to be the least represented in conservation lands and are at greatest risk.

Important implications of this VA-GAP study can be drawn for each of the three physiographic provinces in Virginia. In the western mountain region, riparian and agricultural/open land is predominantly privately owned and poorly represented in conservation lands. Species requiring these lowland habitats typically do not receive concerted protection efforts and may be susceptible to changing land use practices such as urbanization, which typically are restricted to flatter, intermountain terrain. The Piedmont region in central Virginia has very little land in public ownership, regardless of the cover type, and any species or habitat type restricted to this region receives little protection. Habitat loss due to agriculture and timber production, as well as rapid urbanization, continues in this area and threatens to remove existing natural habitats. The public land on the Coastal Plain is predominantly comprised of herbaceous and forested wetlands. Very little upland is included with these properties. As a result, species requiring these habitats are not well represented in the protection network, and these species are susceptible to rapidly encroaching human development. Although this study has provided an important foundation for future land management and acquisition decision-making, it is only that–a foundation. In order to make smaller-scale decisions and fine-tune the species models, more research is needed in the arenas of species/habitat relationships, fine-scale habitat delineations, and subparcel stewardship mapping. More detailed work in defining species ranges in Virginia will also improve prediction models.

<- Previous | First | Next ->