home
To view a poster, please click on the abstract title.
Cantú, César
Facultad de Ciencias Forestales, UANL, Km. 145 carr. Nacional # 85, Linares, N.L., Mexico; ccantu@fcf.uanl.mx
Mexico currently has 149 nature reserves covering approximately 9% of its land area. Most of these reserves were established for a variety of reasons―often unrelated to the protection of biodiversity. In 2000, in response to a growing concern about the lack of organized conservation reserve planning to protect the important threatened biological and physical features of Mexico, the Mexican Commission for Knowledge and Use of Biodiversity (CONABIO) proposed the establishment of 151 new reserves for Mexico, covering 51,429,500 ha. Mexico is considered a developing country with approximately 50% of its inhabitants living in poverty. Social marginalization in Mexico was determined by the National Council of Population (CONAPO) to fall into five categories: very high; high; median; low; and very low, based on nine variables. We compiled a GIS analysis using digital thematic maps of current and proposed nature reserves of Mexico to correlate to marginalization rate of localities. We found that more than 73% of the 105,732 localities of Mexico correspond to high and very high marginalization categories. Nevertheless, these categories comprise only 19% of the total population of the country. The current nature reserves comprise 2,477 localities (2.3%) of Mexico, including around 1 million inhabitants, 31% of them corresponding to very high and high marginalization levels. The inclusion of the 151 proposed priority regions to the current nature reserves would include 19,223 localities (18%) of Mexico, which would be inhabited by approximately 7 million people, 42% of them in very high and high marginalization categories. The areas proposed by CONABIO would increase the proportion of protected lands in the country to over 27%. However, a great proportion of their inhabitants live under extreme conditions of poverty, which is a very important aspect to consider for conservation activities.
Carrero, G.1, W. Gould1, B. Fevold1, and S. Martinuzzi1,2
1U.S. Forest Service, International Institute of Tropical Forestry, Río Piedras, Puerto Rico; gcarrero@fs.fed.us
2Universidad Nacional de La Plata, Argentina Laboratorio de Investigación de Sistemas Ecológicos y Ambientales (LISEA)
We have compiled vegetation descriptions from the plant community level and organized them into a hierarchical structure along gradients of climate, substrate, and topographic position for a land cover map legend for use in the Puerto Rico gap analysis of vertebrate distributions and diversity patterns. Each land cover unit is accompanied by (1) a brief description of the composition, structure, and ecology of the dominant plant communities; (2) a characteristic photograph; (3) a line drawing indicating characteristic vertical layering of species and structural characteristics; and (4) references to more detailed descriptions in the literature. The landscape units link directly to vertebrate habitat descriptions in the Puerto Rico GAP vertebrate database of over 300 species found on the island and will be linked to our land cover map as it progresses.
Cassidy, Kelly M., and Christian E. Grue
Washington Cooperative Fish and Wildlife Research Unit, University of Washington, Seattle, WA 98185; lostriver@completebbs.com
Two counties in Washington State have used WA-GAP data in attempts to incorporate biodiversity protection into long-term planning efforts. We reviewed these projects and concluded: (1) Raw biological data, irrespective of source, are of little use to county planners who do not have time or expertise to interpret them and rarely the resources to hire someone who can; (2) counties already have intricate land use plans that include goals, zoning, easements, and a host of regulations. Those plans are not easily meshed with external recommendations about site-specific conservation priorities that are blind to existing land use planning and regulations; and (3) counties often contain substantial areas, most notably state and federal lands, over which county land planners have no jurisdiction and, consequently, little or no ability to affect the future of species whose ranges lie primarily on those lands. To address these issues, we are producing a report focused specifically on biodiversity priorities for WA-GAP vertebrate species within each of Washington's 39 counties. We will rank vertebrate species based on a combination of the species' vulnerability to human activity and the proportion of their current ranges on private lands. We will rely on the county planners to locate specific sites with priority species and to determine how sites best fit into each county's long-term plans, zoning, and regulations. The goal is to interpret available biological information at the county level such that species most likely to benefit from biodiversity protection outside state and federal lands are prioritized, while allowing county planners to utilize existing land use policies and regulations to locate and protect the most "suitable" sites.
Elliott, Matt
Project Coordinator, Georgia Gap Analysis Program; melliott@uga.edu
This poster details the use of GAP data by the Georgia Department of Natural Resources as part of their Comprehensive Wildlife Conservation Strategy.
Epstein, Jeanne, Kevin Samples, and Elizabeth Kramer
Institute of Ecology, University of Georgia; jepstein@uga.edu
This poster discusses the methods developed to create a 30-meter resolution coverage of percent canopy cover built from 1-meter resolution color digital aerial photos and ETM satellite imagery. This interpreted high-resolution imagery will be the training data for a regression tree algorithm that will produce a canopy cover layer for the Southeast.
Falzarano, Sarah, and Kathryn Thomas
USGS Southwest Biological Science Center, Colorado Plateau Field Station, Northern Arizona University, P.O. Box 5614, Flagstaff, AZ 86011-5614; (928) 556-7466 ext. 240; Sarah_Falzarano@usgs.gov
The USGS Southwest Biological Science Center, Colorado Plateau Field Station in Flagstaff, Arizona, is developing the second generation gap analysis in Arizona. As part of the Southwest Regional Gap Analysis Project, the Arizona team is cooperating with teams from Colorado, Nevada, New Mexico, and Utah to produce consistent and seamless land cover, potential animal habitat, and land status maps. The second-generation project will model land cover using remote sensing, field data, ancillary data, and biophysical modeling using classification trees. Vegetation communities are based on Ecological Systems, maintained and updated by NatureServe.
Landsat 7 Enhanced Thematic Mapper Plus 1999-2001 imagery for spring, summer, and fall, imagery-derived layers (NDVI, SAVI, and tasseled cap indices), elevation, and elevation-derived layers (slope, aspect, and others) are being evaluated as predictor variables in classification trees using extensive field data as the dependent variable. In an iterative process, a model is developed to estimate vegetation communities of the Grand Canyon area. The model establishes rules regarding the predictor variables that can be implemented in ERDAS Imagine to produce a vegetation map. The results are preliminary and are being reviewed. Special classes such as agriculture, urban, and riparian areas require a different mapping approach. The draft land cover map for the Southwest region is expected in May of 2004.
Gould, William1, Sebastian Martinuzzi1,2, and Olga Ramos1
1U.S. Forest Service, International Institute of Tropical Forestry, Río Piedras, Puerto Rico; wgould@fs.fed.us
2Universidad Nacional de La Plata, Argentina Laboratorio de Investigación de Sistemas Ecológicos y Ambientales (LISEA)
Interpretation and classification of recent Landsat TM imagery is a key component of the Gap Analysis process. Accurate vertebrate distribution mapping and analysis is only possible if both species-habitat models and habitat maps reflect real relationships and landscape patterns. The Puerto Rican landscape is complex in terms of climate, substrate, topography, and disturbance patterns with strong gradients over short distances. It is also a region rich in biodiversity. The combination of these factors creates a diverse landscape mosaic of habitat types. Three major problems in using Landsat imagery for mapping vegetation in tropical areas are that (1) cloud-free imagery of even a single scene is often unavailable for any given date; (2) spectral signature is insufficient for distinguishing floristically distinct tropical forest types. Species composition can change dramatically with shifts in substrate, elevation, or topographic position with little change in spectral signal. Additionally, the spectral signature of the same forest type can vary according to the topography and illumination angle. (3) Vegetation can vary across broad climatic ecotones as well as sharp substrate and topographic boundaries. We have developed a methodology of image stratification by biogeoclimatic zone and topographic position combined with image spectral analysis to develop the land cover map for the Puerto Rico GAP project. In order to avoid artificial shifts in land cover class caused by reducing broad ecotones to a sharp boundary, we stratified our landscape along well-defined ecotones with sharp shifts in vegetation visible in Landsat TM imagery and then developed classification methods within these geoclimatically homogeneous landscape units using spectral and topographic data. The methodology involves (1) creation of a cloud-free composite image, (2) initial classification of forest, nonforest, urban/barren, and water classes for the island, (3) stratification by biogeoclimatic zone, classification by one of 9 landscape units using topography and site moisture indices, and (4) classification of land cover using spectral signal and models of vegetation/landscape unit relationships based on literature, expert opinion, and field reconnaissance.
Grunau, Lee
Conservation Planner, Colorado Natural Heritage Program, Colorado State University, 254 General Services Building, Fort Collins, CO 80523; (970) 491-2844; lgrunau@lamar.colostate.edu
The Shortgrass Prairie Initiative was undertaken by the Colorado Department of Transportation (CDOT) for the purpose of achieving three goals: (1) proactive conservation of declining species in Colorado's Central Shortgrass Prairie ecoregion; (2) advance compensation for potential impacts to these species from transportation improvements on the existing highway network; and (3) improved efficiency and effectiveness of environmental assessments associated with CDOT projects. CDOT partnered with the U.S. Fish and Wildlife Service, Colorado Natural Heritage Program, The Nature Conservancy, Colorado Division of Wildlife, Colorado Department of Natural Resources, and the Federal Highway Administration to conduct a GIS-based impact analysis, and to identify lands with appropriate habitat that could be protected and managed for the benefit of the target species, thereby offsetting habitat loss from highway projects. The Colorado Natural Heritage Program conducted the impact analysis using GAP vegetation data. We used the GAP vegetation layer to identify potential habitat for 17 declining prairie species within CDOT right-of-ways, and to estimate acres of potential habitat loss from transportation improvement projects. We also used GAP vegetation data, in conjunction with field surveys, to estimate acres of each habitat type that would be protected within the potential conservation areas.
Our poster consists of a series of maps depicting "presumed presence" distributions for one or two sample species, areas of potential impact, and habitat distribution on a sample conservation site. These are presented along with tables illustrating the process of relating existing habitat, potential impact, and habitat distribution across conservation lands. We also included photographs of some of the species and habitats that will be protected as a result of this effort.
Henebry, Geoffrey M., Brian C. Putz, Weirong Chen, and J.W. Merchant
Center for Advanced Land Management Information Technologies (CALMIT), School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588-0517; ghenebry@calmit.unl.edu
Accurate, consistent land cover maps are clearly critical to the GAP Program. In order to facilitate regionalization and national-level gap analyses and to allow for studies of change over time, individual states must be mapped so as to ensure state-to-state consistency. Despite efforts at standardization, a wide range of methods has been used by the individual states to map land cover. We present here the results of research into the use of AVHRR NDVI biweekly composites (1990-2000) to delineate areas of similar land surface phenology to provide regional context for reconciling the current individual state classifications. What is of interest is both the expectation of the land surface phenology and its interannual variability. Principal components analysis (PCA) on these image time series provides insight into the dominant modes of spatio-temporal variation. For comparison, we examine MODIS NDVI and EVI composites for the 2001 and 2002 growing seasons. Finally, we illustrate a significant challenge to the use of NDVI image time series for land cover mapping; specifically, the loss of sensitivity of NDVI to change in surface vegetation once the leaf area index (LAI) exceeds about 2.
Hutchison, Vivian
U.S. Geological Survey, National Biological Information Infrastructure (NBII), 12201 Sunrise Valley Drive, MS 302, Reston, VA 20192; (703) 648-4311; vhutchison@usgs.gov
In an age of increasing technological abilities and access to information, metadata represents a critical element in information-sharing environments. Metadata repositories serve as valuable tools for researching data that has already been collected, analyzed, and reviewed. Conservation management processes are greatly enhanced by the user's ability to evaluate existing information in making more informed decisions. The US Fish and Wildlife Service and NBII are working together to develop a metadata management system for the National Wildlife Refuge program.
Metadata provides conservation managers with a standardized way to display information about data sets and other research. Documentation of valuable resources in the organization allows institutional knowledge to be preserved. Metadata provides a method for maintaining information for future use, and a standard way to share information with data catalogs and clearinghouses. Repetition of expensive research projects can be averted if metadata is shared. The NBII hosts an extensive clearinghouse site for the purpose of sharing metadata records. Single records can be uploaded into the clearinghouse, or organizations can opt to create their own clearinghouse "node." The USFWS and the NBII are developing a node on the NBII Clearinghouse for National Wildlife Refuge records. More informed conservation management decisions can be facilitated through use of information found in metadata records.
Metadata creation and data sharing are crucial components of conservation management. The National Biological Information Infrastructure supports this effort by providing training workshops, creation services, quality control, and a clearinghouse repository to the science and land management community.
Klopfer, Scott, Ken Convery, and Laura Roghair
The Urbin Biodiversity Information Node (UrBIN) is a part of the National Biological Information Infrastructure (NBII) coordinated by the USGS-BRD. This project was initiated to compile information about biodiversity within the Hunting Creek watershed in Northern Virginia. The UrBIN Gap Analysis Project was funded by the National Gap Analysis Program to synthesize the spatial information on biodiversity protection within the watershed, through gap analysis, for land managers.
Lee, Jason, and Liz Kramer
University of Georgia, Natural Resources Spatial Analysis Laboratory; jwlee@uga.edu
In order to classify impervious surface using a regression tree algorithm, high-quality training data is required. We discuss our methodology for sampling and classifying training sets for mosaicked ETM imagery of large areas. For sampling we used a combination of the 1992 NLCD and a Population Density map. For high-resolution imagery, we employed NAPP CIR DOQQs, since they are easily classified for impervious surface and are widely available for the Southeast. We also discuss our alternatives where CIRs are not available: IKONOS imagery and also NAPP black-and-white DOQQs.
Lowry, John, and Christine Garrard
Remote Sensing and GIS Laboratories, Forest, Range and Wildlife Sciences, Utah State University, Logan, UT 84322-5275; (435) 797-0653; jlowry@gis.usu.edu
Land cover mapping using traditional image classifiers typically require specialized, and sometimes expensive, software such as ERDAS Imagine. Predictive modeling using Classification and Regression Trees (CART) has been used by ecologists for some time, and the use of "decision tree" classifiers is becoming more common within the remote sensing-based land cover mapping community. Decision tree classifiers offer several advantages, one of which is the ability to effectively use categorical and/or continuous non-remotely sensed ancillary data. A difficult technical challenge has been developing an efficient integration of the CART statistical software and the spatial application of the decision tree rules in a GIS. This poster presents an ArcView extension that integrates the CART utilities of S-PLUS with the GIS capabilities of ArcView. A demonstration of how STATMOD was used for land cover mapping in a Utah study area is included.
Maiorano, Luigi1, Alessandra Falcucci1, Alessandro Montemaggiori2, Luigi Boitani3
1University of Idaho, Moscow; maio1323@uidaho.edu
2Institute of Applied Ecology, Rome
3University of Rome "La Sapienza"
The poster presents the results obtained from habitat suitability models for the Italian vertebrates (mammals, breeding birds, reptiles, amphibians, and freshwater fish). The high suitability areas for each species have been considered in an irreplaceability analysis conducted with the software C-Plan. The analyses consider the single taxonomic groups and all the vertebrates and allow us to identify the priority areas for the conservation of vertebrates in Italy. The poster gives a brief outline of the methods and presents the resulting maps giving some brief consideration of the limits and the importance of these analyses in the Italian context.
Martinuzzi, Sebastian 1,2, William Gould1, and Olga Ramos1
1U.S. Forest Service, International Institute of Tropical Forestry, Río Piedras, Puerto Rico; smartinuzzi@fs.fed.us
2Universidad Nacional de La Plata, Argentina Laboratorio de Investigación de Sistemas Ecológicos y Ambientales (LISEA)
Clouds and cloud-shadows are a common feature of optical remotely sensed images collected from many parts of the world, particularly in humid and tropical regions such as Puerto Rico. We have developed a semiautomated method to identify and mask clouds and cloud-shadows in Landsat imagery and have created the most recent cloud and cloud-shadow free composite multitemporal image for Puerto Rico and its adjacent islands, a key component of the Puerto Rico Gap Analysis Project. Our assumption is that clouds and cloud-shadows can be identified in a reference image, and they can be replaced with imagery from other dates. Cloud masks were created using Landsat 7 ETM+ bands 1 and 6. Band 4 and parameters of sun angle, topography, and cloud shadow projection were used for directing and masking cloud-shadows. Methodology was applied to a set of 18 images from 1999 to 2003 (the majority from 2000 and 2001) to develop an islandwide image that is 96.5% cloud-free. We converted the imagery from radiance to reflectance corrected by Rayleigh to address problems in image mosaicking related to variation in atmospheric conditions. We considered the seasonality of the imagery when choosing reference images and building the mosaic in order to minimize variation in reflectance related to dry or wet season. Finally, we have developed a cloud and cloud-shadow cover index that allows us to monitor the suitability of newly acquired imagery for incorporation into composites, and for analyses of the annual and seasonal variability and spatial distribution of cloud cover for the island. The current image can be updated as new imagery becomes available and is being used for current land cover classifications for the GAP program and for analyses of land cover change, particularly land cover conversion from abandoned agricultural to secondary forest and from vegetated to impervious surfaces. The methodology developed is simple, straightforward, can be used with a variety of remotely sensed data, and useful wherever obtaining cloud-free imagery is desirable.
Mattson, Kimberly M.1, Scott D. Klopfer2, and Paul L. Angermeier1
1Department of Fisheries and Wildlife Sciences, Virginia Tech, Blacksburg, VA 24061-0321
2Conservation Management Institute, 203 West Roanoke Street, Blacksburg, VA 24061-0534; sklopfer@vt.edu
One of the main obstacles in applying gap analysis to aquatic systems is that the basic tenet of terrestrial gap analysis, identification of protected areas, is not readily transferable to aquatic systems. Terrestrial GAP employs spatial analysis of large extent using land stewardship, dominant vegetation, and species distributions to assess protection status within terrestrial systems. This process, which uses land ownership as a surrogate for threat assessment, is inadequate for identifying areas of conservation priority within stream networks. Cost-effective conservation of freshwater aquatic systems requires planning at large spatial scales, the use of watershed-level frameworks, and recognition of factors that threaten biodiversity. We propose that gap analysis of aquatic systems should employ risk-based threat assessments for identifying conservation areas. As part of the Upper Tennessee Aquatic Gap Analysis Project, we are using land use and hazard identification as parameters in a watershed-based threat assessment. This approach deviates from the terrestrial GAP approach in two important ways. First, watersheds are affected by actions on the surrounding landscape, not necessarily by land ownership alone. Second, the magnitude and frequency of occurrence of harms within watersheds is paramount in identifying areas potentially available for protection. Our modeling scheme uses a relative ranking approach to identify, categorize, and prioritize hazards and their associated harms. We will provide examples of threats and illustrate the ranking process within one watershed of the Upper Tennessee. Preliminary results suggest that both distance to stream and the location of a hazard within a stream network are important considerations in freshwater conservation. We will discuss the applicability of our approach to gap analysis and offer suggestions to others interested in completing gap analysis in aquatic environments.
McKerrow, Alexa J.
North Carolina State University,Zoology Annex, 1200 Flex Lab Building, 1575 Varsity Drive, Raleigh, NC 27695; alexa_mckerrow@ncsu.edu
In North Carolina, where less than 10% of the land is publicly managed, the National Wildlife Refuge lands often support the largest concentrations of a vegetation type on Status 1 and 2 lands. Wildlife refuge lands are generally restricted to the Outer Coastal Plain on North Carolina, thus they are an important management type for the protection of the coastal plain cover types. Some of the underrepresented cover types for which refuge lands support over 50% of the Status 1 and 2 examples include Seepage and Streamhead Swamps, Peatland Atlantic White-Cedar Forest, Coastal Plain Mixed Oak Bottomland Forest, Tidal Swamp Forest, Maritime Pinelands, and Coastal Plain Nonriverine Wet Floodplain. This analysis shows the contribution of Refuge lands to conservation of North Carolina's vegetation.
dora_reader@usgs.gov
In 2001 the USGS, in cooperation with several state natural resource-management agencies, began a regional Aquatic GAP project in the Great Lakes Region focusing on the distribution of aquatic species in riverine and coastal habitats. The goals and objectives of the Great Lakes Aquatic GAP Project are as follows: (1) Evaluate biological diversity of Great Lakes aquatic habitats and identify gaps in the distribution and protection of these species and their habitats, and (2) use an integrated approach in which common methods and protocols are established, and results are comparable across the Great Lakes landscape. Progress toward the following objectives will be highlighted in this poster: (1) Build and maintain partnerships with GAP stakeholders, (2) develop a central database for Aquatic GAP data, (3) classify aquatic habitats in rivers, streams, and coastal zones of the Great Lakes, (4) model aquatic species and habitat relationships, (5) map actual and predicted distributions of aquatic species, and (6) complete an Aquatic Gap Analysis for the Great Lakes Region.
Colorado State Coordinator, SW Regional Gap Analysis Project, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523; (970) 482-1802; lee@nrel.colostate.edu
Wildlife habitat suitability models based upon wildlife habitat relationships, such as those used in the Gap Analysis Program, have been criticized because they lack quantification of uncertainty in the final predicted habitat distribution maps. The maps are typically binary maps depicting only either suitable or unsuitable habitat without any indication of how strong the evidence is for these predictions across the area. In land cover mapping, the accuracy of extrapolated classifications is derived by withholding field site samples from the classification and comparing them to the final map in an accuracy assessment. This is not feasible with wildlife habitat models, since what is being modeled is wildlife habitat suitability and not wildlife occurrence. Even if wildlife locations could be used as a test of accuracy, comprehensive wildlife distribution data for most species are scarce. This poster will illustrate a method of depicting the uncertainty in the data used to create predictive habitat distribution models, using Bayesian inference methods to combine empirical data and expert opinion on the strength of wildlife habitat relationships.
Wildlife Resource Assessment Specialist, Minnesota Department of Natural Resources; (218) 855-5079; jodie.provost@dnr.state.mn.us
The Minnesota Department of Natural Resources has completed statewide land cover and land stewardship GIS coverages, and drafted range maps for breeding birds and birds of management interest as part of the Minnesota Gap Analysis Project (MN-GAP). Two applications of these products are highlighted in the poster by demonstrating how they can aid landscape-scale bird conservation planning at the Sherburne National Wildlife Refuge (NWR) in east-central Minnesota.
Each NWR within the U.S. Fish and Wildlife Service's (USFWS) NWR system must develop a Comprehensive Conservation Plan (CCP). Each plan outlines a future vision for a refuge and directs all aspects of refuge management for 15 years. This planning process is currently under way at Sherburne NWR. As part of the CCP, each refuge must examine its landscape context in terms of wildlife populations, habitat distributions, and human demographics. Looking beyond refuge boundaries to facilitate larger-scale conservation efforts to positively affect wildlife and habitat sustainability is a strategy being considered in each management scenario at Sherburne NWR.
The first application demonstrated in the poster assesses statewide species richness of birds that are both MN-GAP species and priority species in USFWS Region 3. Distributions of these bird species were used to create statewide hyper-distributions by bird habitat groups (open, water, and forest) and for all birds combined. The second application uses land cover classes to identify potential oak savanna within the private lands work area of Sherburne NWR. Oak savanna is the rarest habitat in the Midwest. Both applications, when combined with land stewardship to identify existing conservation lands and potential conservation partners, will aid landscape-planning efforts for bird conservation.
spencer.120@osu.edu
This poster gives an overview of our process for generating a land cover map at Ohio GAP. Included in the description is information on digital aerial photography, field verification, image interpretation, classification steps, and accuracy assessment.
Biodiversity and Spatial Information Center (BaSIC), Department of Zoology, North Carolina State University, Raleigh, NC 27695-7617; matt_rubino@ncsu.edu
Various Geographic Information Systems (GIS) data layers are being used in support of regional vertebrate distribution modeling and land cover mapping for gap analyses in the Southeastern United States. This poster discusses the issues specific to the development of consistent ancillary data for this region. We focus our discussion on approaches for developing elevation data derivatives from Digital Elevation Models (DEMs) based on the National Elevation Dataset (NED) and the Shuttle Radar Topography Mission (SRTM). We also address hydrography data derived from the National Hydrography Dataset (NHD), roads data derived from a variety of state and federal sources, and wetlands data derived from the National Wetlands Inventory (NWI).
1U.S. Army Corps of Engineers, co-located at U.S. EPA, 944 E. Harmon Ave., Las Vegas NV 89109; (702) 798-2279; Sajwaj.Todd@epa.gov
2U.S. Environmental Protection Agency, 944 E. Harmon Ave., Las Vegas NV 89019
The Southwest Regional Gap Analysis Project (SW ReGAP) improves upon previous GAP projects conducted in Arizona, Colorado, Nevada, New Mexico, and Utah to provide a consistent, seamless vegetation map for this large and ecologically diverse geographic region. Nevada's component of the land cover mapping effort comprises 15 map zones, or 291,700 km2. As of October 2003, preliminary field sampling has been completed via road-based sampling and backpacking surveys in all 15 of Nevada's map zones yielding a data set of 17,000+ sites. Based on plant community data collected in the field, each site is labeled with NVCS alliance and ecological system labels, and a National Land Cover Database (NLCD) label. Site polygons were intersected with 40+ spectral, topographic, climatic, and edaphic data layers. A set of decision rules (or land cover models) was generated by the application of a classification/regression tree (CART) algorithm to the plant community label and its associated dependent variables. Land cover models were implemented in Imagine 8.6 image processing software to create classified vegetation maps. Three maps were constructed for each mapping unit at increasing levels of ecological resolution: an NLCD level map (coarsest), an ecological systems map (intermediate), and an alliance level map (finest). Maps have been constructed for the Mojave, Eastern Great Basin, and Lahontan Basin mapping units. Final vegetation maps were assessed for thematic accuracy at each of the three levels of ecological resolution. The NLCD level maps produced the highest thematic accuracy, while the alliance level map produced the lowest thematic accuracy. The procedures used in field data collection, land cover modeling, accuracy assessment, and edge-matching adjacent mapping units are illustrated with examples from the east Great Basin mapping unit of east central Nevada.
Natural Resource Analysis Center, P.O. Box 6108, West Virginia University, Morgantown, WV 26506-6108; (304) 293-4832 ext. 4455; jmstrager@mail.wvu.edu
Explanation of the format of the NHD and its relevance to GAP projects and related efforts in conservation planning. Examples of NHD data will be included, as well as a status graphic of NHD data availability. The usefulness of NHD data in both terrestrial and aquatic gap analyses will also be covered.
Taylor, Benton D.1, Amy L. Silvano1, Michael S. Mitchell2, and James B. Grand2
1Auburn University, Alabama Cooperative Fish and Wildlife Research Unit, School of Forestry and Wildlife Sciences, Auburn University, AL; silvaal@auburn.edu
2 USGS, Alabama Cooperative Fish and Wildlife Research Unit, School of Forestry and Wildlife Sciences, Auburn University, AL
In an effort to automate the process of species range mapping, AL-GAP has developed an interactive geo-database within Environmental Systems Research Institute's (ESRI) ArcObjects environment. To demonstrate the functionality of this database, the ArcObjects poster includes a detailed schematic, outlining each automated step from inputting data to coding each hexagon with an occurrence status. Textual information describes the iterative fashion in which ArcObjects is used to select occurrence records, create precision buffers or grids, and assign occurrence status to each hexagon for multiple records and multiple species through visual basic coding. The supplemental functionalities of displaying species ranges and performing reviewer updates are also described in detail.
Thoma, Roger F.
Ohio Environmental Protection Agency/Ohio State University; roger.thoma@epa.state.oh.us
Crayfish have been selected as one organism group to be used in Ohio's Aquatic GAP analysis. Considerable effort has been expended in obtaining and compiling suitable databases to be used in the analysis. Preliminary assessment of Ohio crayfish indicates that they provide a unique perspective in evaluating aquatic biological diversity. Due to Ohio's historic partial glaciation, aquatic systems in the state are comprised of both youthful (10,000 yrs. BP) and ancient (>3.5 million yrs. BP) stream systems. While fish, insects, and bivalve mollusk all have shown rapid postglacial dispersal, crayfish display limited or no postglacial dispersal. In addition the primary, secondary, and tertiary burrowing behavior of crayfish presents significant implications for GAP analytical approaches. Preliminary assessments of distribution and ecology will be discussed. Crayfish appear to provide important information related to the geologic evolution of North American stream systems.
1Center for Advanced Land Management Information Technologies (CALMIT); mildav@calmit.unl.edu
2School of Natural Resources, University of Nebraska, Lincoln, NE 68588-0517 USA
Attempts to regionalize species models by mosaicking range distributions produced by neighboring state Gap Analysis projects have proved problematic. Variations in habitat modeling have resulted in significant differences in predicted species distributions within and across state lines. The use of national geospatial data to map surrogates of habitat facilitates regional modeling. Yet, there is a decided knowledge gap between the spatial and temporal scales understood by biogeographers and those routinely employed by wildlife managers. Can the flexibility of statistical decision trees help fill this gap? We generated regional distributions of 20 selected breeding birds in the GAP Great Plains region (IA, KS, MN, ND, NE, SD) using three recursive partitioning algorithms (CART, QUEST, CRUISE). BBS route level summaries over two time periods (last 10 and 30 years) were used for the occurrence data (presence/absence and abundance). Environmental variables included land cover, daily climatic means and variances, soil texture, and terrain. Multiple statistical decision trees were generated for each target species to evaluate the relative strengths and weaknesses of the different algorithms. Principal considerations were speed of tree generation, interpretability of the cross-validated tree, and plausibility of the predicted range distribution resulting from tree inversion. CART's exhaustive search of parameter space took much longer than QUEST or CRUISE. Unbiased variable selection in QUEST and CRUISE appeared to facilitate the identification of parsimonious, robust models and plausible range distributions. QUEST trees were generally preferable to CRUISE trees because the latter algorithm relied only upon presence/absence at the route level, while the former included data on route-level abundance.
Vandegraft, Doug
Chief Cartographer, US Fish and Wildlife Service, 4401 N. Fairfax Dr. Room 622, Arlington, VA 22203; (703) 358-2404; doug_vandegraft@fws.gov
In December 2002, the National Atlas of the USA released a new map of the National Wildlife Refuge System (NWRS). As 2003 marks the centennial of the NWRS, the timing was perfect. The map was a combined effort of Cartographers working for both agencies and is the first digital map of the entire NWRS. The associated digital boundaries and point data will be updated on a yearly basis and made part of the data layers available for users of the National Atlas web site. The digital boundary and digital land status program of the FWS is a vital part of GIS applications for the Service and is essential for conservation and resource planning.
1USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ
2Center of Environmental Science and Education, Northern Arizona University, Flagstaff, AZ
3Current Address: Colorado Plateau Field Station, Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, AZ; james.wynne@nau.edu
Wildlife-habitat relationship models are employed routinely in guiding land management decisions. Understanding and identifying potential sources of error is imperative to providing managers with the highest-quality models. We developed an evaluative criterion to (1) identify potential sources of error in model data sets, and (2) select the best habitat models. Using a competing models framework, we modeled habitat using classification tree and logistic regression models for eight songbird species on the Pinaleños Mountains, southeastern Arizona. A three-year data set (1993-1995) of bird survey points, habitat information derived from literature, and landscape-scale variables were used to develop models. Models were verified using a one-year data set (2002) of bird survey points. GIS information was considered of the highest quality with the best elevation model (for deriving elevation, slope and aspect), vegetation land cover (overall accuracy = 71.2%), and maps of springs and streams used. Sample sizes in the model-building data set were considered small (≤ 30 samples for presence and absence), and verification data were collected during the 2002 drought. Although none of the species' models attained 80% accuracy, most yielded overall accuracy values better than chance and were comparable to other studies using similar habitat variables. Low predictive success of these models was probably due to a combination of inappropriate study design, small sample size, environmental stochasticity in the verification data set, and lack of finer-scale GIS information. Although use of these models in guiding management decisions is considered limited, the criterion developed provides a systematic framework for evaluating data quality for modeling wildlife-habitat relationships. We recommend using a method, which includes elements identified here for evaluating model data sets for the potential errors and to potentially reduce error propagation. Ultimately, this approach will provide land managers with higher quality wildlife-habitat relationship models and enable them to make better management decisions.
Literature | About GAP | Projects and Products | Tools | Meetings | Search | Links | Bulletin Boards | Site Map
USGS ||Gap Analysis Program || NBII
||
Disclaimer || Privacy || Accessibility||
GAP Webmaster ||