ࡱ> n  v w x y z { | } ~  o p !` bjbj\\ >>5 4hTh˲˲˲$ihiDz˲;;:Rh `ȭ[|hDuu;u<hhu(@˲f1$Uټ˲˲˲X˲˲˲DDDqyd2Dy  Northeast Regional Conservation Needs Program Project Title: Creation of Regional Habitat Cover Maps: Application of the NE Terrestrial Habitat Classification System Project Director: Mark G. Anderson Ph.D. Director of Conservation Science The Nature Conservancy, Eastern Region  HYPERLINK "mailto:manderson@tnc.org" manderson@tnc.org 11 Avenue de Lafayette, 5th floor Boston, MA 02111 617-542-1908 x 215 phone 617-482-5866 fax Other Principal Investigators: Lesley Sneddon MS. Senior Regional Ecologist East NatureServe  HYPERLINK "mailto:lesley_sneddon@natureserve.org" lesley_sneddon@natureserve.org 617-542-1908 x 245 phone 617-482-5866 fax Susan C. Gawler Ph.D. Regional Vegetation Ecologist East NatureServe  HYPERLINK "mailto:sue_gawler@natureserve.org" sue_gawler@natureserve.org 207-495-3513 phone (207) 495-3444 fax References: Kim Lutz  HYPERLINK "mailto:klutz@tnc.org" klutz@tnc.org 413-584-1016 Jeff Wagner  HYPERLINK "mailto:jwagner@paconserve.org" jwagner@paconserve.org 412-288-2777 In response to the Northeast Regional Conservation Needs Grant Program Request for Proposals, The Nature Conservancys proposal addresses Priority #1 Regional Conservation Need, Creation of Regional Habitat Cover Maps. Product : A comprehensive wildlife habitat map of the eastern region, including all states from Maine to Virginia, west to New York, Pennsylvania and West Virginia. The map will consist of a spatially comprehensive GIS grid of 30 meter pixels with a legend portraying the Northeastern Terrestrial Habitat Classification System (NETHCS). We envision a series of map legends that range from coarser-scale with higher accuracy (habitats or groups of habitats) to finer-scale with lower accuracy (NVC associations or alliances). This assumes that not every habitat type will be equally amenable to the mapping procedures described here. Background: The NETHCS, a standardized terrestrial wildlife habitat classification system for the Northeast, is currently under development through the 2006 State Comprehensive Wildlife Conservation Support Program grant to NatureServe. The NETHCS is based on NatureServes Ecological Systems Classification (Comer et al. 2003), augmenting the current system with additional information from individual state wildlife classifications and other information specific to wildlife managers. The proposal at hand seeks a collaboration between TNC, NatureServe and state wildlife agencies, and aims to comprehensively map the NETHCS. Because the NETHCS will still be in the refinement stage at the beginning of the mapping project, we hope to capitalize on new information gained through mapping to further strengthen the classification, and the strengthened classification to further inform mapping in a process of iterative feedback. Data sources: Our objective is to use all appropriate existing data to produce the best possible depiction of wildlife habitat. Our mapping procedures will use a variety of already developed and compiled GIS data layers, drawing from TNCs Ecological Land Unit (ELU) classification (Anderson and Olivero 2001), National Land Cover Dataset (NLCD) as well as data developed in the course of ongoing mapping efforts in the east. NatureServe is a partner in two major national mapping efforts, LandFire and GAP, providing expertise in the application of the Terrestrial Ecological Systems Classification. Current data, compiled for the entire region, includes bedrock and surficial geology, landforms, elevation zones, climate zones, land cover and canopy density, roads, forest inventory points and natural heritage community inventory points. Some major field datasets include Forest Inventory and Analysis (FIA) data, state natural heritage program field data, and data collected during the GAP project in the Mid-Atlantic (New Jersey, Delaware, Maryland, and portions of Pennsylvania). In addition, NatureServe and The Nature Conservancy are partners in a project to map the Delaware Estuary Watershed using the Terrestrial Ecological Systems Classification. This project relies on an intensive field effort in which plot data are collected to validate a draft Ecological Systems map of the watershed. This project is currently in progress, and field data collected in this project will be available for use in the NETHCS mapping project. In essence, all of the New Jersey and Delaware portions of the watershed, as well as portions of the Pennsylvania section, will be mapped in conjunction with this project through other funds. Although most of the funding from this project is federal in origin and cannot be included as match, the Delaware Estuary watershed portion of the study area will be completed without cost to NEAFWA. Methods Map Creation Our methods are delineated in the following steps: a) Assemble existing ELU, NLCD, and other ancillary data sets for the region. An ELU is a landscape feature depicting combinations of slope, substrate, topographic position, moisture index, elevation, landform, and other variables, that can be used to predict the range and extent of vegetation types. b) Assemble and evaluate existing field data (confirming habitat points) c) Assign a habitat to each data point. d) Using ArcGIS 9.1 software, intersect the habitat point data to the ELU and NLCD data layers, reserving a portion of the points for accuracy assessment. e) Perform preliminary Classification and Regression Tree (CART) analysis to identify connections between biophysical data and existing field data. f) Develop draft habitat map: using the diagnostic classifiers and known range information of each Ecological system / habitat, as well as the dichotomous key, assign an ecological system type to each mapped combination of variables. The mapping process at this stage will be iterative, with stepwise addition of other ancillary data sets to help further refine the map. RESPONSE TO QUESTION 1 Map products provided as a result of this project will be digital as well as hard copy. Digital products will include raster data with classification by habitat, NLCD, conservation ownership, ELU, and the component classes making up each ELU: elevation class, bedrock class, and landform unit (derived from digital elevation models). For example, Figure 1 (on attached PDF) depicts a draft map of ecological systems for the Central Appalachians. The proposed NETHCS map will resemble this map in scale, with a legend of habitats rather than ecological systems, and further refined as a result of additional compiled field data. Figure 2 depicts a simplified map of ecological land units that currently exist for the eastern region. The southern portions of Virginia not currently mapped will be completed as part of this project. Figure  SEQ Figure \* ARABIC 1 Systems map of Central Appalachians (see attached PDF)  Figure 2 Ecological Land Units RESPONSE TO QUESTION 2 We will build on a method developed by The Nature Conservancy (Ferree et al. 2006, Appendix A) that models ecological features of the landscape referred to as ecological land units (ELU). An ELU is a landscape feature depicting combinations of slope, substrate, topographic position, moisture index, elevation, landform, and other variables, that can be used to predict the range and extent of vegetation types. Examples of ELUs include mid-elevation acidic steep slope; low calcareous moist flat; and deep coarse sediments on dry flats. The addition of land cover data further subdivides ELUs into NETHCS units (habitats) that have been derived from the Ecological Systems classification. The method predicts the general location and extent of habitats by modeling underlying abiotic structure (enduring features) and current land cover. The resulting map will identify where and how much of each habitat is predicted to occur in the study area. Existing field data will be used to calibrate the draft map to actual conditions, and the map will then be revised. When combined with conservation ownership data, the map can be used to assess the protection status of each habitat. The steps are listed in more detail below. Existing Data Compilation Assemble list of predicted habitats for the study area. Assemble existing ELUs for the study area. Combine with NLCD (National Land Cover Dataset): classes include deciduous forest, evergreen forest, mixed forest, developed, shrub/scrub, cultivated crops, forested wetland, etc. Compile ancillary datasets that may be of use in developing and tuning habitat maps, particularly in the flattest part of the study area. These may include such datasets as the National Wetlands Inventory of the USFWS; NOAAs Environmental Sensitivity Index (a characterization of linear shoreline features based on substrate and exposure); and Daymet climatic variables. The utility of LIDAR data, which is available for parts of the study area, could also be explored. LIDAR (Light Detection and Ranging) data can produce a much more finely scaled digital elevation model of areas of very little topographic variability, such as the coastal plain of New Jersey, by measuring the distance and return interval of multiple light beams emanating from aircraft. Elevation changes of 0.5m or less, which can have a substantial influence on vegetation in the coastal plain, can be modeled using LIDAR. Assemble and evaluate existing georeferenced field data from state wildlife agencies and Natural Heritage Programs. Assign a habitat to each acceptable data point (data crosswalk). Integrate field data collected in the Delaware Estuary Watershed. Reserve a portion of the data for accuracy assessment. Although field work is beyond the scope of this project, there is a considerable amount of unprocessed georeferenced field data in heritage program offices that exists as a data management backlog, in paper files, and in otherwise inaccessible forms. We propose letting small subcontracts to each interested heritage program (MA, NY, NJ, VA, CT) to process and deliver these data, with the provision that programs fulfill their own match requirements. Draft Habitat Map Using ArcGIS 9.1 software, intersect the habitat point data to the ELU and NLCD data layers, reserving a portion of the points for accuracy assessment. Perform preliminary Classification and Regression Tree (CART) analysis to identify connections between biophysical data and known habitat occurrences. Develop draft habitat map: using the diagnostic classifiers and known range information of each habitat, assign a habitat to each mapped combination of variables. For example, an ELU classified as coarse sediment, dry flat / gentle slope with NLCD class deciduous forest is likely to be the North Atlantic Coastal Plain Dry Hardwood Forest System. The ELU fine sediment, dry flat, gentle slope and sideslope with NLCD class evergreen forest is likely to be the North Atlantic Coastal Plain Pitch Pine Barrens System. The mapping process at this stage will be iterative, with stepwise addition of other ancillary data sets to help further refine the map. For example, the ELU classified as wet flat with NLCD class herbaceous will yield a number of possible habitats; addition of salinity measures or tidal regime, for example, may help to narrow the possible options. In some cases, it may not be possible to identify an individual habitats from the data at hand. In these cases, map classes that contain more than one habitat may be required. Map refinement Provide the habitat map to each state wildlife agency and state natural heritage program for expert review. Ecologists and wildlife biologists will scrutinize the map for obvious errors based on their previous experience, then will compile a list of errors and submit them to The Nature Conservancy and NatureServe. Revise the map from expert review feedback, e.g. adjusting geographic ranges of habitats, or other errors identified during the review. RESPONSE TO QUESTION 3 Map scale and classification scales are two different issues. The map resolution of 30 meter pixels allows for a high level of detail and can be portrayed at different scales by the user. Scale of the classification (NETHCS) is regional in scope, and has been directed by NEAFWA to be based on NatureServes Ecological Systems Classification. It is comprehensive and covers the northeastern states from Maine to Virginia, west to New York, Pennsylvania, and West Virginia. The classification unites all the individual state wildlife classifications under a common framework,. Ecological systems represent recurring groups of biological communities that are found in similar physical environments and are influenced by similar dynamic ecological processes, such as fire or flooding. They are intended to provide a classification unit that is readily identifiable by conservation and resource managers in the field. The NETHCS adopts ecological systems as habitat units where appropriate, and modifies, splits, or lumps ecological systems to more accurately reflect habitat where necessary. In addition, the NETHCS adopts new habitat units currently undefined in the Ecological Systems Classification, most frequently in cases of natural vegetation that has been modified by human use but is actively used by wildlife. The scale of habitats is in most cases closely aligned with that of Ecological Systems, at intermediate geographic scales of tens to thousands of acres. Most state wildlife agencies have developed or adopted classifications from natural heritage programs that are essentially similar in scale to the Ecological Systems classification (Connecticut, Massachusetts, Maryland, Maine, New Hampshire, New Jersey, Rhode Island, West Virginia) or finer (Vermont, Delaware, Pennsylvania), while others are considerably broader in scale (New York and Virginia). Adoption of the scale currently used by eight wildlife agencies suggests that the resulting classification will be mapped at a scale that is appropriate and useful across the entire region. Accuracy assessment and Field Verification Point data reserved under step 4 above will be overlayed on the map to determine the percent accuracy of the map. We will develop a contingency table and calculate percent accuracy by class, as well as over all accuracy. We expect to achieve 60% accuracy or better over all. Because accuracy assessment will rely on existing data, it is a near certainty that not all map classes will have sufficient, or even any, field data to test accuracy. State review We seek the active collaboration of all state wildlife agencies in this project. We will draw from existing wildlife classifications, as well as their experience. We will communicate frequently, using conference calls as well as more informal communications. We will post interim products on the web and request detailed feedback. At least three states (likely VA, NH and MA) will test the map quantitatively based on their own data resources. Comments will be solicited and adjustments made to the map as appropriate. The final map will reflect a balance between the state needs and consistency across the region. RESPONSE TO QUESTION 5: ENSURING ONGOING COORDINATION WITH STATES We are committed to producing products that will meet the needs of wildlife agencies. We will rely on their expertise and their review in order to meet this goal. In the current NETHCS project, NatureServe is establishing a node on the NBII portal on which all interim products as well as communications are posted. We will continue use of the NBII node for the mapping project. We will also draw on our lengthy experience in seeking collaboration from a large group with heavy and competing workloads: we will pursue individual calls with states as needed. In an individual call, we provide a set of questions that need to be addressed and record responses. In this way, questions can be resolved on the spot, and it obviates the need for a time-crunched program to supply us with written responses. In addition, several agencies have committed matching funds, thus ensuring their ongoing involvement. Others who cannot commit a match have also agreed to provide review and comment. Two state agencies (NH and VA) have agreed to collaborate with us in providing detailed testing of the map in their respective states. RESPONSE TO QUESTION 6: MAKING PRODUCTS AVAILABLE TO EACH STATE IN THE REGION: All wildlife agencies and heritage programs will be provided all final deliverables, and have access to some interim products as well. These include the following digital files: NLCD, conservation lands, ELU, and habitat map. These will be disseminated via the NBII node, since spatial data files will be very large and delivery is most efficient via this site. Methods report and classification can be provided by DVD if desired. Relationship to Other Regional Mapping Projects We will communicate directly with representatives from SE GAP and LANDFIRE mapping projects to ensure that this project is completed in a manner that is consistent with, and builds on, the work of these projects. NatureServe ecologists are providing ecological systems expertise over the entire project area to LANDFIREs mapping efforts on large patch systems. In addition, we will be working with the Gap Analysis Program over the next year to address wetland systems in greater detail, by revising descriptions and producing range maps for systems that were not covered by LANDFIRE. Preliminary communication suggests a high level of correspondence in our approaches. Our intent is to apply refinements or new methods developed by our partner organizations to improve the map accuracy or usefulness, as we recognize there will be areas that need refinement. RESPONSE TO QUESTION 4: SOURCE OF AND COMMITMENT BY STATES FOR NEAFWA AND HERITAGE MATCH Each wildlife agency, Natural Heritage Program, as well as The Nature Conservancy and NatureServe will provide letters by October 1 from Federal Aid Coordinators certifying that the source of matching funds is non-Federal. However, the short turn-around time required to submit the final proposal necessitates a longer period of time to procure these letters. The following wildlife agencies have committed to providing a match, and each has verified that the source of funding is non-federal: West Virginia Division of Natural Resources Wildlife Resources Section$1,785Connecticut Bureau of Natural Resources, Wildlife Division$1,785New Hampshire Fish and Game Department$5,000Delaware Division of Fish & Wildlife$1,785Virginia Division of Game and Inland Fisheries$3,565Maryland Department of Natural Resources$1,785TOTAL$15,705 The following state heritage programs have committed to providing a match, and each has verified that the source of funding is non-federal: Connecticut Natural Diversity Database$2,500New York Natural Heritage Program$2,500New Jersey Natural Heritage Program$2,500Virginia Division of Natural Heritage$2,500TOTAL$10,000 Estimate of Project Costs:RCN RequestTNC MatchTotal CostPersonnel (TNC Staff)1 10,926.07  11,070.00  21,996.07 Fringe Benefits (40%)2 4,370.43  4,428.00  8,798.43 Contractual - NatureServe3 36,703.97  43,061.62  79,765.59 Supplies & Misc. 1,500.00  1,500.00  3,000.00 Subtotal Direct Costs 53,500.47  60,059.62  113,560.09 Indirect (23% of direct)4 12,305.11  5,749.54  18,054.65 Grand Total 65,805.58  65,809.16  131,614.74  1TNC staff time estimate of $21,996.07 represents approximately 120 days (840 hours). A portion of this will serve as match and a portion will be grant-funded. The source of funds paying for the match costs is non-federal (private fundraising); and the match costs will not serve as match on any other federal grant. Personnel providing grant/match hours on this project include the Director of Conservation Science, Landscape Ecologist, Aquatic Ecologist, and other TNC staff. 2Fringe benefits for regular staff at 40% in accordance with our July 24, 2007 NICRA with the U.S. Department of the Interior (copy attached). Fringe benefits typically include paid time off, insurance, FICA, worker's comp, state unemployment taxes, 401(k), etc. 3NatureServe contractual costs include (1) grant-funded $36,704 consisting of (a) $10,000 total for subcontracts to four heritage programs ($2500 per program) for processing and delivering additional data; and providing review and comments on reports; and (b) $26,704 for NatureServe costs associated with the following tasks: Acquire and evaluate existing data from heritage programs, wildlife agencies and other sources Collaborate with TNC to match wildlife habitats to mapping signatures Solicit review of map from heritage programs and wildlife agencies Collaborate with TNC to integrate map revisions Review and contribute to draft report and final report Manage and coordinate Heritage subcontracts. (2) match-funded $43,062 consisting of (a) $15,705 in-kind match from 6 state wildlife agencies to provide review and comment on map products; (b) $10,000 in-kind match from 4 state heritage programs to provide same services as detailed in (1) above; (c) $17,356 in-kind match from NatureServe for indirect costs and to classify field data collected to map Delaware Estuary watershed; advise in mapping NETHCS. 4Indirects costs in accordance with our July 24, 2007 NICRA with the U.S. Department of the Interior. RESPONSE TO QUESTION 7d: See budget detail above RESPONSE TO QUESTION 7e: Contractual costs, hourly rate and work to be accomplished: NatureServe Senior Regional Ecologist, Lesley Sneddon Hourly Rate: $36.35 Tasks: Management and coordination of Heritage subagreements Supervise Regional Vegetation Ecologist Review interim and final reports Regional Vegetation Ecologist, Sue Gawler Hourly Rate: $32.75 Tasks: Acquire and evaluate existing data from heritage programs, wildlife agencies and other sources Collaborate with TNC to match wildlife habitats to mapping signatures Solicit review of map from heritage programs and wildlife agencies Collaborate with TNC to integrate map revisions Contribute to draft report Contribute to final report State Heritage Program Ecologists Hourly Rate: $65.00 (includes fringe and overhead; average rate) Tasks: Clean up, process, and submit existing non-electronic plot data RESPONSE TO QUESTION 7e: travel costs No travel costs have been requested References Comer, P., D. Faber-Langendoen, R. Evans, S. Gawler, C. Josse, G. Kittel, S. Menard, M. Pyne, M. Reid, K. Schulz, K., Snow, and J. Teague. 2003. Ecological Systems of the United States: A Working Classification of U.S. Terrestrial Systems. NatureServe, Arlington, Virginia. Ferree, C., M.G. Anderson, and A.Olivero. 2006 Draft.Supplementary Metadata forSystems Raster Dataset.UnpublishedReport to The Nature Conservancy. Smith, R.K., P.L. Freeman, J.V. Higgins, K.S. Wheaton, T.W. Fitzhugh, K.J. Ernstrom, A.A. Das. 2002. Priority Areas for Freshwater Conservation Action: A Biodiversity Assessment of the Southeastern United States. The Nature Conservancy. Sowa, S.P., D.D. Diamond, R. Abbitt, G. Annis, T. Gordon, M.E. Morey, G. R. Sorensen, and D. True. 2005. A Gap Analysis for Riverine Ecosystems of Missouri. Final Report, submitted to the USGS National Gap Analysis Program. 1675 pp. The Nature Conservancy (TNC) Conservation Science Support, Survey, US Geological , US Environmental Protection Agency, and USFWS, 2003, Ecological Land Units: CBY Ecoregion. The Nature Conservancy (TNC) Conservation Science Support, Survey, US Geological , US Environmental Protection Agency, and USFWS, 2006, Ecological Land Units:NAC Ecoregion. Principal Investigator Qualifications The Nature Conservancy Mark G. Anderson, PhD is the Director of Conservation Science of The Nature Conservancys Eastern Region where he developed and performed ecological and biophysical assessments of large regions. Dr. Anderson developed ELU methodology and led science teams to apply the methodology in ecoregional conservation planning and measuring conservation progress in TNCs eastern region. Dr. Anderson has been with TNC since 1991, where he began as an ecologist. Charles E. Ferree, MS, is a Landscape Ecologist in the Eastern Conservation Science Program of TNC's Eastern Region. He has supported local, ecoregional, and northeastern regionalplanning efforts for the last 7 years, with a focus on ecosystem modeling. Arlene P. Olivero, MS, is the Geographic Information Systems Manager and Aquatic Ecologist in TNCs eastern region. Ms. Olivero is responsible for GIS data preparation, analysis, documentation, map production, and technical support. She has supported ecoregional planning in both aquatic and terrestrial realms since 1998. NatureServe Lesley Sneddon, MS, is the Senior Regional Ecologist for NatureServes eastern region. She has contributed to the development of the National Vegetation Classification since its inception in 1994, and also contributed to the development of NatureServes Terrestrial Ecological Systems classification. Ms. Sneddon was formerly a regional ecologist in TNCs eastern region since 1987, and has served in her current capacity at NatureServe since 2000. Susan C. Gawler, PhD, is NatureServes Eastern Regional Vegetation Ecologist. Dr. Gawer contributed to the development of NatureServes Terrestrial Ecological Systems classification, and is currently working with both the LandFire and GAP programs, providing expertise in mapping application of the classification. Appendix A Ferree, C., M.G. Anderson, and A.Olivero. 2006 Draft.Supplementary Metadata forSystems Raster Dataset.UnpublishedReport to The Nature Conservancy. Supplementary metadata for systems30 raster data Background An understanding of patterns of environmental variation and biological diversity is fundamental to conservation planning at any scaleregional, landscape level, or local. This dataset was developed as a tool for assessing the biophysical character of landscapes, and for mapping the distribution and composition of community assemblages across those landscapes. Informed decisions on where to focus conservation efforts require such tools. Data on biological distributions are very often inadequate to a large-scale analysis of biodiversity. The close relationship of the physical environment to ecological process and biotic distributions underpins the ecological sciences, and in the absence of suitable biological datasets, conservation science has recognized that physical diversity could be an acceptable surrogate for biological diversity. Research has repeatedly demonstrated especially strong links between ecosystem pattern and process and climate, bedrock, soils, and topography. This recognition led to the development of the ecological land unit, or ELU. The ELU is a composite of several layers of abiotic information: elevation, bedrock geology, distribution of deep glacial sediments that mask bedrocks geochemical effects, moisture availability, and landform. An ELU grid of 30 meter cells was developed for the US part of the Northern Appalachians/Boreal Forest (NAP) ecoregion. The ELU dataset describes the ecological potential of the landscape, but carries no information about actual landuse or landcover in a region where human alterations to the landscape have everywhere affected the natural vegetation. The systems dataset informs ELUs with landcover data, bringing them to earth by telling us what is actually on the ground. We may use this dataset to map ecological systems, which are dynamic assemblages of communities that occur in a mosaic on the landscape, and that are linked by shared ecological processes and environmental gradients. A brief discussion of each of the layers of information built into the current dataset follows. ELUs: Content and Development Elevation classes Elevation has been shown to be a powerful predictor of the distribution of forest communities in the Northeast. Temperature, precipitation, and exposure commonly vary with changing altitude. We broke continuous elevation data for the NAP ecoregion (from the National Elevation Dataset of the USGS) into discrete elevation classes with relevance to the distribution of forest types region-wide. Meaningful biotic zones would be defined with quite different elevation cut-offs in the northern and southern parts of the region, so class ranges necessarily approximate critical ecological values. Table 1. Ranges for elevation classes. Elevzone M (ft) Characteristic forest types10000 - 234 (0-800)Oak, pine-oak, pine-hemlock, maritime spruce, floodplain forest2000234 - 533 (800-1700)Hemlock-northern hardwoods, N. hardwoods, lowland spruce-fir 3000533 - 762 (1700-2500)Northern hardwoods, spruce-hardwoods4000762 - 1158 (2500-4000)Spruce-fir, spruce-hardwoods5000> 1158 (>4000)Krummholz, montane spruce-fir, alpine communities Bedrock geology and deep sediments Bedrock geology strongly influences area soil and water chemistry. Even in glaciated landscapes, studies suggest that soil parent material is commonly of local origin, rarely being ice-transported more that a few miles from its source. Bedrock types also differ in how they weather and in the physical characteristics of the residual soil type. Because of this, local lithology is usually the principle determinant of soil chemistry, texture, and nutrient availability. Many ecological community types are closely related to the chemistry and drainage of the soil or are associated with particular bedrock exposures. We grouped bedrock units on the bedrock geology maps of NY, VT, NH, and ME into seven general classes (Table 2). We based our scheme on broad classification schemes developed by other investigators which emphasize chemistry and texture, and on bedrock settings that are important to many ecological communities, particularly to herbaceous associations. Please refer to another file accompanying this metadata, bedgeo_src.doc, for information on bedrock geology source materials. In some settings deep sediments of glacial origin mantle the bedrock. The consolidated bedrock of valleys of pro-glacial lakes, for example, may lie under many meters of fine lacustrine sediments, and deep coarse deltaic or outwash deposits often overlay the bedrock in pine barrens and sand plains in the northeast. In these settings it is the nature of the sedimentstheir texture, compactness, and moisture-holding capacity, their nutrient availability, their ability to anchor overstory trees in a wind disturbance--that is ecologically relevant, and not the nature of the underlying bedrock. We used a USGS dataset of sediments of the glaciated northeast to identify such places. The USGS map was compiled at a coarse scale (1:1,000,000), but we made the data a little smarter by informing it with our landform map (please see the document on landforms that accompanies this metadata). Our landform layer was compiled at a much finer scale (the scale of the digital elevation models from which they were constructed, 1:24,000), and we allowed the deep coarse or fine sediments of the USGS dataset to be mapped only on those landforms on which they would naturally be expected to occur. In the case of sandy, coarse sediments, this would be in broad basin and valley/toe slope settings; in the case of fine clayey lacustrine or marine sediments, in these same settings, plus low hills and lower sideslopes. The seven bedrock classes were numbered 100 through 700 (Table 2), and the coarse and fine sediments were numbered 800 and 900, respectively. Table 2. Bedrock geology classes. Geology classLithotypesMeta-equivalentsCommentsSome characteristic communities100: ACIDIC SEDIMENTARY / METASEDIMENTARY: fine- to coarse-grained, acidic sed/metased rockMudstone, claystone, siltstone, non-fissile shale, sandstone, conglomerate, breccia, greywacke, arenites(Low grade:) slates, phyllites, pelites; (Mod grade:) schists, pelitic schists, granofelsLow to moderately resistant rocks typical of valleys and lowlands with subdued topography; pure sandstone and meta-sediments are more resistant and may form low to moderate hills or ridgesMany: low- and mid-elevation matrix forests, floodplains, oak-pine forest, deciduous swamps and marshes200: ACIDIC SHALE: Fine-grained acidic sedimentary rock with fissile textureFissile shalesLow resistance; produces unstable slopes of fine talusShale cliff and talus, shale barrens300: CALCAREOUS SEDIMENTARY / META-SEDIMENTARY: basic/alkaline, soft sed/metased rock with high calcium contentLimestone, dolomite, dolostone, other carbonate-rich clastic rocksMarbleLowlands and depressions, stream/river channels, ponds/lakes, groundwater discharge areas; soils are thin alkaline clays, high calcium, low potassium; rock is very susceptible to chemical weathering; often underlies prime agricultural areasRich fens and wetlands, rich woodlands, rich cove forests, cedar swamps, alkaline cliffs400: MODERATELY CALCAREOUS SEDIMENTARY / METASED: Neutral to basic, moderately soft sed/metased rock with some calcium but less so than aboveCalc shales, calc pelites and siltstones, calc sandstones Lightly to mod. metamorphosed calc pelites and quartzites, calc schists and phyllites, calc-silicate granofels Variable group depending on lithology but generally susceptible to chemical weathering; soft shales often underlie agricultural areasRich coves, intermediate fens500: ACIDIC GRANITIC: Quartz-rich, resistant acidic igneous and high grade meta-sedimentary rock; weathers to thin coarse soilsGranite, granodiorite, rhyolite, felsite, pegmatiteGranitic gneiss, charnockites, migmatites, quartzose gneiss, quartzite, quartz granofelsResistant, quartz-rich rock, underlies mts and poorly drained depressions; uplands & highlands may have little internal relief and steep slopes along borders; generally sandy nutrient-poor soilsMany: matrix forest, high elevation types, bogs and peatlands600: MAFIC / INTERMEDIATE GRANITIC: quartz-poor alkaline to slightly acidic rock, weathers to clays(Ultrabasic:) anorthosite (Basic:) gabbro, diabase, basalt (Intermediate, quartz-poor:) diorite/ andesite, syenite/ trachyteGreenstone, amphibolites, epidiorite, granulite, bostonite, essexiteModerately resistant; thin, rocky, clay soils, sl acidic to sl basic, high in magnesium, low in potassium; moderate hills or rolling topography, uplands and lowlands, depending on adjacent lithologies; quartz- poor plutonic rocks weather to thin clay soils with topographic expressions more like graniteTraprock ridges, greenstone glades, alpine areas in Adirondacks700: ULTRAMAFIC: magnesium-rich alkaline rockSerpentine, soapstone, pyroxenites, dunites, peridotites, talc schistsThin rocky iron-rich soils may be toxic to many species, high magnesium to calcium ratios often contain endemic flora favoring high magnesium, low potassium, alkaline soils; upland hills, knobs or ridgesSerpentine barrens Landforms Stanley Rowe called landform "the anchor and control of terrestrial ecosystems." It breaks up broad landscapes into local topographic units, and in doing so provides for meso- and microclimatic expression of macroclimatic character. It is largely responsible for local variation in solar radiation, soil development, moisture availability, and susceptibility to wind and other disturbance. As one of the five "genetic influences" in the process of soil formation, it is tightly tied to rates of erosion and deposition, and therefore to soil depth, texture, and nutrient availability. These are, with moisture, the primary edaphic controllers of plant productivity and species distributions. If the other four influences on soil formation (climate, time, parent material, and biota) are constant over a given space, it is variation in landform that drives variation in the distribution and composition of natural communities. Of the environmental variables discussed here, it is landform that most resists quantification. Landform is a compound measure, which can be decomposed into the primary terrain attributes of elevation, slope, aspect, surface curvature, and upslope catchment area. The wide availability and improving quality of digital elevation data has made the quantification of primary terrain attributes a simple matter. Compound topographic indices have been derived from these primary attributes to model various ecological processes. We adopted the Fels and Matson (1997) approach to landform modeling. They described a metric that combines information on slope and landscape position to define topographic units such as ridges, sideslopes, coves, and flats on the landscape. That approach is described here: feel free to skip over the details, to the set of defined landforms that emerges from the process (Figure 1 and Table 3 below). The parent dataset for the two grids used to construct the landforms is the 30 meter National Elevation Dataset digital elevation model (DEM) of the USGS. Step one was to derive a grid of discrete slope classes relevant to the Northern Appalachian landscape. We remapped slopes to create classes of 0-2 (0.0-3.5%), 2-6 (3.5 10.5%), 6-24 (10.5 44.5%), 24-35 (44.5-70.0%), and >35 ( >70.0%) (vertical axes of Figure1). Ground checks have shown that, because the NED dataset averages slopes over 30 meter intervals, raster cells in the 2 steepest elevation classes contain actual terrain slopes of from about 35 to 60 degrees (in the 24-35 class) and 60 to 90 degrees (in the steepest class). The next step was the calculation of a landscape position index (LPI), a unitless measure of the position of a point on the landscape surface in relation to its surroundings. It is calculated, for each elevation model point, as a distance-weighted mean of the elevation differences between that point and all other elevation model points within a user-specified radius: LPIo = [ "1,n (zi - zo) / di ] / n, where zo = elevation of the focal point whose LPI is being calculated, zi = elevation of point i of n model points within the specified search radius of the focal point, di = horizontal distance between the focal point and point i, and n = the total number of model points within the specified search distance. If the point being evaluated is in a valley, surrounding model points will be mostly higher than the focal point and the index will have a positive value. Negative values indicate that the focal point is close to a ridge top or summit, and values approaching zero indicate low relief or a mid-slope position (Fig. 1). The specified search distance, sometimes referred to as the "fractal dimension" of the landscape, is half of the average ridge-to-stream distance. We used two methods to fix this distance for each subsection within the region, one digital and one analog. The "curvature" function of the ArcInfo Grid module uses the DEM to calculate change in slope ("slope of the slope") in the landscape. This grid, when displayed as a stretched grayscale image, highlights valley and ridge structure, the "bones" of the landscape, and ridge-to-stream distances can be sampled on-screen. For our analog approach we used 7.5' USGS topographic quadsheets. In each case, we averaged several measurements of ridge-to-stream distances, in landscapes representative of the subsection, to obtain the fractal dimension. This dimension can vary considerably from one subsection to another. [There is a third approach to fixing the landscape fractal dimension that is intriguing. A semivariogram of a clip of the DEM for a typical portion of the regional landscape can be constructed it quantifies the spatial autocorrelation of the digital elevation points by calculating the squared difference in elevation between each and every pair of points in the landscape, then plotting half that squared difference (the semivariance) against the distance of separation. A model is then fitted to the empirical semiovariogram cloud of points. (This model is used to guide the prediction of unknown points in a kriging interpolation.) The form of the model is typically an asymptotic curve that rises fairly steeply and evenly near the origin (high spatial autocorrelation for points near one another) and flattens out at a semivariance sill value, beyond which distance there is little or no correlation between points. Though the sill distance, in the subsections where we tried this approach, was 2 or 3 times the fractal distance as measured with the first 2 methods, the relationship between the two was fairly consistent. With a little more experimentation, the DEM semivariogram could prove to be a useful landscape analysis tool.] The next step was to divide the grid of continuous LPI values into discrete classes of high, moderately high, moderately low, and low landscape position. Histograms of the landscape position grid values were examined, a first set of break values selected, and the resulting classes visualized and evaluated. We did this for several different types of landscapes (rolling hills, steeply cut mountainsides, kame complexes in a primarily wet landscape, broad valleys), in areas of familiar geomorphology. The process was repeated many times, until we felt that the class breaks accurately caught the structure of the land, in each of the different landscape types. Success was measured by how well the four index classes represented the following landscape features: High landscape position (very convex): sharp ridges, summits, knobs Moderately high landscape position: upper side slopes, rounded summits and ridges, low hills and kamic convexities Moderately low landscape position: lower sideslopes and toe slopes, gentle valleys and draws, broad flats Low landscape position (very concave): steeply cut stream beds and coves, and flats at the foot of steep slopes We assigned values 1-5 to the five slope classes, and 10, 20, 30, and 40 to the four LPI classes. Following Fels and Matson (1997), we summed the grids to produce a matrix of values (Fig. 1), and gave descriptive names to landforms that corresponded to matrix values. We collapsed all units in slope classes 4 and 5 into "steep" and "cliff" units, respectively. The ecological significance of these units, which are generally small and thinly distributed, lies in their very steepness, regardless of where they occur on the landscape. Fig. 1: Formulation of landform models from land position and slope classes.  Recognizing the ecological importance of separating occurrences of  flats (0-2) into primarily dry areas and areas of high moisture availability, we calculated a simple moisture index that maps variation in moisture accumulation and soil residence time. We used National Wetlands Inventory datasets to calibrate the index and set a wet/dry threshold, then applied it to the flats landform to make the split. The formula for the moisture index is: Moist_index = ln [(flow_accumulation + 1) /(slope + 1)] Grids for both flow accumulation and slope were derived from the DEM by ArcInfo Grid functions of the same names. For the ecoregional ELU dataset, upper and lower sideslopes are combined, and a simple ecologically relevant aspect split is embedded in the sideslope and cove slope landforms (Figure 2 and Table 3). Last, waterbodies from the National Hydrography Dataset (NHD), which was compiled at a scale of 1:100,000 and is available for the whole region, were incorporated into the landform layer with codes 51 (broader river reaches represented as polygons) and 52 (lakes, ponds, and reservoirs). Single-line stream and river arcs from the NHD were not burned into the landforms-- only those river reaches that are mapped as polygons. Landform units for an area of varied topography in southeastern New Hampshire are shown in map view in Figure 2. Fig. 2: Landforms in Pawtuckaway State Park, NH  The ELU grid With the elevation, substrate, and landform layers, all the elements for assembling ecological land units, or ELUs, are in place. ELU code values for each cell in the region-wide grid are simply the summed class values for elevation zone, substrate, and landform for that cell. For example, a cell in a wet flat (landform 31) at 1400 feet (elevation class 2000) on granitic bedrock (substrate class 500) would be coded 2531. Table 3: How the 4-digit ELU code is calculated. Elevation class (ft)+Substrate class+Landform1000 (0-800)100 acidic sed/metased 4 steep slope2000 (800-1700)200 acidic shale 5 cliff3000 (1700-2500)300 calc sed/metased 11 flat summit/ridgetop4000 (2500-4000) 400 mod. calc sed/metased 13 slope crest5000 (> 4000) 500 acidic granitic 21 Hilltop (flat)600 mafic/intermed granitic 22 Hill (gentle slope)700 ultramafic 23 NW-facing sideslope800 coarse sediments 24 SE-facing sideslope900 fine sediments 30 Dry flat31 Wet flat32 Valley/toe slope41 Flat at bottom of steep slope43 NW-facing cove/draw44 SE-facing cove/draw51 Polygonal rivers from NHD52 NHD lakes/ponds/reservoirsThe ELU grid for the Northern Appalachians and Boreal Forest Ecoregion comprises 497 unique combinations of elevation zone, substrate type, and landform. We added an ELU_color item to the attribute table, and used it to construct a coding scheme that assigns ELU values to groups of a particular ecological character. Symbolizing on the ELU_color item creates a simplified display of the complex ELU dataset (see Displaying the data below). A fragment of the attribute table for the NAP ecoregional ELU grid is reproduced in Table 4. Table 4. Sample set of three ecological land unit codes (value item) from the ELU value attribute table for the NAP ecoregion. VALUE511334231831COUNT134142296712173319ELEVZONE500030001000ELEVZONE_DESC>4000ft1700-2500ft0-800ftSUBSTRATE100400800SUBSTR_DESCacidic sedimentary/ metasedimentarymoderately calcareous sed/metasedcoarse sedimentsLANDFORM30132331LF30_DESCSlope crestSideslope NW-facingWet flatsELU_COLOR122032ELUCOLOR_DESCSlope crestSideslope NW-facingWet flats on deep coarse sediments From ELUs to systems: Landcover The last step in the assembly of the systems grid is the combination of ELUs with a grid of landcover data. The National Land Cover Dataset (NLCD: web site at  HYPERLINK "http://www.epa.gov/mrlc/nlcd.html" http://www.epa.gov/mrlc/nlcd.html) was derived from Landsat-5 TM images for the conterminous United States, and is the only such dataset that covers the entire US portion of NAP. We used elevation and landform information to clean up some systematic errors in the data (forested wetland pixels often appeared on northwest-facing sideslopes, for example, and developed and agriculture pixels were mapped at high elevation with some regularity), and grouped all human landuses into two classes, developed and agriculture. The 2-digit landcover codes (Table 5) were multiplied by 10,000 and added to ELU codesthe resulting 6-digit number is the systems code, and the grid value in the value attribute table (see a fragment from sys30lnenac.vat in Table 6 below). Table 5. Landcover classes. ClassDescription11Water20Developed31Open bare33Open transitional41Deciduous forest42Coniferous forest43Mixed forest51Shrubland80Agricultural91Forested wetland92Emergent wetland The systems grid comprises over 3000 unique combinations of landcover, elevation zone, substrate type, and landform. We added a sys30code item to the attribute table, and used it to construct a coding scheme that groups systems values into biophysical components. We conceive of these as building blocks for assembling and mapping ecological systems. Referring to the small extract from the value attribute table in Table 6, we see that: The sys30_desc (sys30 description) field shows that all flat summits/slope crests (landforms 11 and 13) on acidic sedimentary/metasedimentary and acidic granitic lithologies (substrate values 100 and 500) and in open land cover classes are assigned sys30code 152. This is so no matter what elevation zone the grid cell occurs in. The table fragment doesnt show it, but there are 47 combinations of these biophysical criteria, covering just 2945 acres in the US part of the ecoregion. All coniferous or mixed forest pixels on northwest-facing sideslopes on moderately calcareous or mafic/intermediate granitic lithologies, at any elevation, are assigned sys30code 221. There are 18 such combinations, this time covering over 460,000 acres ecoregion-wide (again, US part). Forested wetlands on sandy flats at any elevation are assigned sys30code 535. There are just four combinations, corresponding to the lowest four elevation zones, with only a minor amount above 1700 feet. About 170,000 acres of this system type are distributed across the region. In the first case, cells of sys30code 152 occur largely at mid to high elevation, and most likely define thinly-vegetated small patch communities and balds of hilltops, mountaintops, and ridges. Sys30code 221 mostly represents the matrix-forming hemlock-hardwood and spruce-northern hardwood forests that grow in slightly enriched soils on slopes that flank the low- to mid-elevation valleys of the region. And code 535 values in NAP generally signify wooded wetland communities in low-lying pitted outwash basins. It is a simple matter to attach elevation distinctions to these components. For example, a spruce-fir system on granite convexities at 3200 feet can be discriminated from lowland spruce-fir in a similar topographic setting by adding the elevation zone value to the sys30code: a sys30code of 4401 would connote the high elevation system, 2401 the lowland system. Versatility and flexibility in this coding scheme are key, because ecosystems will be defined and mapped differently-- that is, assembled from different combinations of biophysical elements-- in different ecoregions, and even in different parts of the same ecoregion. Data structure: the attribute table VALUE315113433423911831COUNT330743605396420LANDCOV314391LANDCOV_DESCOpen bareMixed forestForested wetlandELEVZONE500030001000ELEVZONE_DESC>4000ft1700-2500ft0-800ftSUBSTRATE100400800SUBSTR_DESCacidic sedimentary/ metasedimentarymoderately calcareous sed/metasedcoarse sedimentsLANDFORM30132331LF30_DESCSlope crestSideslope NW-facingWet flatsELU30511334232831ELU_COLOR122032ELUCOLOR_DESCSlope crestSideslope NW-facingWet flats on deep coarse sedimentsSYS30CODE152226535SYS30_DESCFlat summits/slope crests: acidic sed/acidic granitic: openModerately calcareous/ intermed granitic NW-facing sideslopes: mixed forest Wet flats, deep coarse seds: forested wetlandTable 4. Sample set of three system codes (value item) from systems30 value attribute table. To be maximally useful, this systems grid should be flexible and adaptable. Local conservation planners will have access to spatial datasets of greater precision and finer detail than those that were available for this ecoregional grid. They may want to add such a dataset into the grid, or substitute a layer of their own for one of the current system components. A few possible scenarios: Better state-wide or local landcover may exist for a program area. There is no hydrography information in the current dataset, and planners may want to incorporate stream, rivers, and lakes captured at a scale of 1:100,000, 1:24,000, or finer. More detailed digital bedrock or surficial geology data may be available, or county-wide SSURGO soils of the Natural Resources Conservation Service. Planners may determine that the current elevation classes fail to reflect an important ecological zone on the elevational gradient, and may opt to redefine those zones. System codes (the grid value item) and the grid value attribute table (systems30.vat: Table 3) are designed to give the dataset a modular character, and to easily accommodate changes. Looking at the four cases above: (a) Landcover codes are simply front-loaded onto ELU codes to generate system codes, and can be replaced. Assuming that the new landcover codes are a 2-digit number, the Arc/Info Grid command would be : = ( * 10000) + systems30.elu30 The first step would be to extract the landforms embedded in the systems grid to their own grid: = systems30.landform30 Vector hydrography data can then be gridded to 30 meter cells, and merged into the landforms grid. If streams were given code 50, double-banked rivers 51, and lakes/ponds/reservoirs 52, the attribute table for the resulting new landform grid may look like this: VALUECOUNTLF30_DESC4486214Steep slope526753Cliff11211580Flat summit/ridgetop13611933Slope crest2121679594 Hilltop (flat)2222372011Hill (gentle slope)2313670723Sideslope N-facing2413275793Sideslope S-facing3012583900Dry flats3133743501Wet flats3218082229Valley/toe slope41300892Flat at bottom of steep slope43253736Cove/draw N-facing44288485Cove/draw S-facing501000000Stream511000000River528000000Lake/pond/reservoir The systems dataset, complete with hydrography, could then be reconstructed. The Grid command line statement would be: = systems30 - systems30.landform30 + In the same way, more detailed bedrock geology polygons could be gridded and inserted into a new systems grid. So too could textural or nutrient availability information from data on soils or surficial deposits. The digital elevation model that accompanies this dataset can be reclassed at appropriate cut-offs, and new classes coded to multiples of 1000. Then: = systems30 - systems30.elevzone + We show these manipulations as they would be performed in the Arc/Info Grid module, but they can also be done in the Arcview or Arcmap environments (as long as the Spatial Analyst tools are available). Displaying the data Several Arcview legends are included with the dataset. They may be used to symbolize separate components of systems and ELU: landcover (sys30landcov.avl), elevation zone (sys30elev.avl), substrate (sys30substrate.avl), landform (sys30landform.avl), and ELUs (sys30elu.avl). It should be noted that, because of the complexity of the ELU dataset (445 unique values), sys30elu.avl groups ELUs and simplifies their display. Bedrock classes are not broken out for display on the steeper and small patch landforms, but are displayed in the generally broader areas of flats and low hills. The color tones in these broad areas correspond to bedrock types (grey for granite, yellow for high carbonate rocks, etc.) and can be read as a backdrop, a visual context for smaller ELU occurrences associated with more dramatic topography (cliffs, summits, coves, sideslopes). ELU map reading takes practice. This will be true for maps displayed with the newly developed legend sys30.avl as well, which symbolizes on the sys30code item. With over 150 sys30code values, representing over 3000 unique system values, this legend also generates a mightily simplified map display. The article referenced in the landform section of this document (Fels, J, and K.C. Matson. 1997. 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