National Vegetation Classification System Research Articles

A FLORISTIC INVENTORY OF TWO BOULDER COUNTY OPEN SPACE PARCELS: HEIL VALLEY RANCH AND HALL RANCH, COLORADO, U.S.A

An inventory of the vascular plants of Heil Valley Ranch and Hall Ranch in the north foothills of Boulder County was completed during 2017 and 2018, prior to a catastrophic forest fire that severely impacted this area in the fall of 2020. Heil Valley Ranch and Hall Ranch are keystone units of the Boulder County Parks and Open Space system. These large parcels (∼10,000 acres/4047 ha) represent a significant portion of publicly accessible open space in the county and represent an exemplary legacy of land preservation in the region. The biologically diverse foothill landscapes represent the ecotone between the High Plains and the Southern Rocky Mountains. Vital transition zones in the area harbor globally vulnerable and critically imperiled plant associations, the most detailed categorization described by the US National Vegetation Classification (2021). A total of 607 botanical specimens were collected over the course of 39 separate visits. There are 381 species from 82 families of flowering plants represented at the study area, with the greatest diversity occurring in Poaceae and the Asteraceae. Of these, 319 are native, 62 are non-native, and 7 species are of conservation concern. The present study is the first floristic study of these parcels and provides baseline data for future work in the area, as well as information for comparison to floristic works in the broader Front Range foothills region. Together, these studies inform conservation policy at a time when biodiversity is threatened both globally and locally. As the Front Range megaregion rapidly expands in human population, biological inventories will provide critical data to promote the expansion of conservation areas in parallel with urban growth.

Overview and compatibility of Northern Territory plot-based vegetation data with the National Vegetation Classification System and future vegetation typologies

Plot-based vegetation data have been collected in the Northern Territory since the 1950s. The purpose of data collection broadly includes land resource and vegetation community mapping, flora surveys, habitat assessment for fauna surveys, rare and threatened species surveys, development assessment and monitoring. These datasets were stored in disparate database systems until 2012. Currently over 423 surveys, 69 000 geocoded plots, comprising almost 838 164 flora records, are maintained using a corporate database. The data are of varying degrees of attribute detail including floristics, structure, strata and associated environmental information. The original territory-wide standardised vegetation classification was based on the 1:1 000 000 vegetation mapping project where 112 broad vegetation types were described from 2245 plots using an intuitive appraisal of numerical analytical techniques. This approach continues to be used; however, a standard method would be desirable to classify plot data into meaningful vegetation types in the Northern Territory. The National Vegetation Information System (vegetation hierarchy) was used to qualitatively assess the level of detail of vegetation plot data. Only 9% of the plot data can be assigned to sub-association level, and 28% to the association level. In addition to reviewing the compatibility of plot data to the National Vegetation Information System, we also provide examples of how it can conform with the International Vegetation Classification.

Checklist of vascular plants for Wind River Indian Reservation (USA) high-elevation basins: ecological drivers of community assemblages

Background and aims – Native American reservations in the United States provide biodiversity critical for conservation and ecosystem functions. Unfortunately, botanical inventories are less common for reservations than other land jurisdictions. Such ecological importance and needs are apparent for the Wind River Indian Reservation (WRIR), the 7th largest reservation in the US (>890,000 ha) that is shared by the Eastern Shoshone and Northern Arapaho.Material and methods – A botanical study for two WRIR high-elevation basins (Saint Lawrence Basin (SLB) and Paradise Basin (PB)) to (1) reconcile a 1960 plant list, and (2) quantify plant communities ecologically was conducted. In 2017, 106 monitoring sites were established to quantify species presence. Across basins, 231 total vascular plant taxa (221 to species and 10 to genus) were identified, or > 3× more plant species than noted in the 1960 list. In SLB, 222 plant taxa (213 to species and 9 to genus) were identified and in PB 98 plant taxa (90 to species and 8 to genus) were identified. In 2018, sites were re-sampled to quantify species abundance, soil pH, organic matter, soil nutrients, CEC, salts, and texture. Key results – Slope and elevation explained species distributions in the topography ordination and soil organic matter, pH, texture, P, and K explained species distributions in the soil ordination. Eleven exotic species, and one rare endemic species were documented with implications for empowering tribal management. Using a classification approach followed by an indicator species analysis and fidelity (Phi) assessment, we identified 14 unique plant communities and related these to 6 alliances and 7 associations across 6 macrogroups from the US National Vegetation Classification database. These indicator species of communities included sedges (Carex aquatilus), grasses (Pseudoroegneria spicata, Elymus elymoides, Achnatherum lettermanii, Elymus trachycaulus subsp. trachycaulus, Poa glauca subsp. rupicola), forbs (Polygonum bistortoides, Balsamorhiza incana, Castilleja flava), shrubs (Artemisia tridentata, Betula glandulosa, Dasiphora fruticosa subsp. floribunda) and trees (Pinus contorta).Conclusion – The plant taxa, plant communities, and ecological drivers documented in this study will enhance tribal and federal monitoring of these high-elevation WRIR basins.

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California

The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m).

Bioclimatic classification of US vegetation along a coast-to-coast macrotransect crossing central United States. I: Mediterranean vegetation

This report presents the first of two parts of a bioclimatic classification of the vegetation of the United States. Using a geographical information system, 987 weather stations were located along a longitudinal macrotransect from the shores of the Atlantic to Pacific on four maps: Map of the Physiographic Divisions of the Conterminous US, US Potential Natural Vegetation Map, US Ecoregion Map, and Terrestrial Ecosystems-Isobioclimates Map of the Conterminous United States. Based on these maps, bibliographic resources and field data, we deduced the potential natural vegetation (PNV) of each weather station; then, we assigned the different PNV types to alliance or association levels using the US National Vegetation Classification (USNVC). In a next step, USNVC groups were related with similar level phytosociological syntaxa described in the study area. The bioclimatic distribution of the USNVC units defined was then interpreted using the bioclimatic classification proposed in successive approximations by S. Rivas-Martínez. The distribution of USNVC alliances was mainly linked to the macrobioclimates (Mediterranean, Temperate, and Tropical) of the longitudinal gradient examined, though some edaphic factors induced the appearance of specialized plant groups. Herein, we present our data for the Mediterranean macrobioclimate, in which 53 alliances and 28 isobioclimates were identified.

National vegetation classification of the Czech Republic: a summary of the approach

Aim: To describe the classification approach and protocols that were used in the project Vegetation of the Czech Republic and relate them to the comparative framework for broad-scale plot-based vegetation classification proposed by De Caceres et al. (2015). This description should facilitate comparison of this national vegetation classification with vegetation classifications in other countries. - Location: Czech Republic. - Methods: The project Vegetation of the Czech Republic, realized from 1997 to 2013, was based on the analyses of vegetation-plot records from the Czech National Phytosociological Database. Associations were defined as the basic hierarchical level using formal definitions created with the Cocktail method. These formal definitions were included in a computer expert system for automatic identification of associations. Evaluation and characterization of associations were based on the groups of plot records assigned to the particular associations by the expert system. Higher classification levels were classified by type-based grouping of associations. A novel statistical method for determining diagnostic species of vegetation types was developed within the project. All the analytical procedures used and developed for the project were included in the JUICE program. - Results: The national vegetation classification system was published in a four-volume monograph from 2007 to 2013. It consists of five hierarchical levels with nested vegetation types, including 9 formations, 39 classes, 138 alliances, 496 associations and for some associations also variants. At the same time the project infrastructure, including the expert system and the JUICE program, was published online. - Conclusions: The national vegetation classification of the Czech Republic provided novel classification approach and protocols. Several elements of these protocols have been subsequently applied in other countries. At the national level, the classification has found various applications both in nature conservation and in basic research.

An Irish national vegetation classification system for aquatic river macrophytes

AbstractAimTo construct a classification system for Irish aquatic river vegetation that is directly comparable to European aquatic vegetation classification units.LocationA total of 2,415 river vegetation plots with a wide geographic distribution across both the Republic and Northern Ireland.MethodsThe plots were recorded from a range of river types from upland streams to wadeable lowland rivers. A total of 1,613 plots were classified using supervised K‐means clustering through the programme JUICE. The Phi co‐efficient for presence/absence data was used to measure fidelity of the vegetation communities.ResultsThe vegetation communities could be initially sub‐divided in to four main categories: (1) bryophyte‐dominated aquatic vegetation, (2) bryophyte‐dominated marginal vegetation, (3) vascular plant‐dominated aquatic vegetation and (4) vascular plant‐dominated marginal/emergent vegetation. Within these four categories the vegetation was classified into seven classes, 18 alliances and 30 associations. Descriptions are given for each including variants and sub‐associations if found at association level.ConclusionsThere is a rich diversity of bryophyte communities within Irish rivers and they are an integral part of the river network, whereas there was less diversity among vascular plant communities. The distinction between some communities was not as sharp as expected for a variety of reasons discussed in this paper, nevertheless, a working classification system was constructed.

Summer Habitat Selection of the Lower Colorado River Valley Population of Greater Sandhill Cranes

Abstract Identifying habitat selection and use is important to understand in wildlife management because it informs habitat manipulations, conservation efforts, and species distribution. Habitat selection by sandhill cranes (Antigone canadensis) has been studied primarily on overwintering areas and a few summering locations. Summer habitat selection by the Lower Colorado River Valley Population of greater sandhill cranes (A. c. tabida) in the Intermountain West is not widely known, but has been identified as an information need by many wildlife management agencies. We captured and attached satellite platform transmitter terminals to 21 adult sandhill cranes on Cibola and Sonny Bono Salton Sea National Wildlife Refuges in Arizona and California, and private lands in California and Idaho. Home ranges of all marked cranes (50% core area: x̄ = 525.4 ha, SE = 155.6; 99% isopleth: x̄ = 6,476.5 ha, SE = 1,637.5) were similar to other studies on summering grounds. Resource analysis indicated that marked sandhill cranes used wetland habitats in greater proportion than their availability for both nocturnal and diurnal locations at the population level, by individuals within the entire landscape, and by individuals within their core area. Wetland habitats consist of ∼7% of the available habitat. Within the Wetland category, the Temperate Flooded and Swamp Forest level (a Formation level in the National Vegetation Classification system) was the most important to summering Lower Colorado River Population sandhill cranes. Wetland managers can concentrate their efforts for conservation, enhancement, and restoration on these type of wetlands to ensure the sustainability of this small population of sandhill cranes.

1984–2010 trends in fire burn severity and area for the conterminous US

Burn severity products created by the Monitoring Trends in Burn Severity (MTBS) project were used to analyse historical trends in burn severity. Using a severity metric calculated by modelling the cumulative distribution of differenced Normalized Burn Ratio (dNBR) and Relativized dNBR (RdNBR) data, we examined burn area and burn severity of 4893 historical fires (1984–2010) distributed across the conterminous US (CONUS) and mapped by MTBS. Yearly mean burn severity values (weighted by area), maximum burn severity metric values, mean area of burn, maximum burn area and total burn area were evaluated within 27 US National Vegetation Classification macrogroups. Time series assessments of burned area and severity were performed using Mann–Kendall tests. Burned area and severity varied by vegetation classification, but most vegetation groups showed no detectable change during the 1984–2010 period. Of the 27 analysed vegetation groups, trend analysis revealed burned area increased in eight, and burn severity has increased in seven. This study suggests that burned area and severity, as measured by the severity metric based on dNBR or RdNBR, have not changed substantially for most vegetation groups evaluated within CONUS.

Vegetation-plot database of the Carolina Vegetation Survey

The Carolina Vegetation Survey (CVS) is a multi-institutional research program designed to document the composition of the natural vegetation of the Carolinas and adjacent states. CVS maintains a system of databases (GIVD ID NA-US-006) that contain data from over 8,200 vegetation plots containing records of in excess of 3,500 species and spanning over 600 vegetation types recognized in the US National Vegetation Classification Standard (NVCS). Over 5,300 of these plots were collected using the level-5 CVS protocol, which provides documentation of composition across a range of spatial scales from 0.01 to at least 100 and often 1,000 m2. As such, the CVS database contains the largest set of multi-scale vegetation plot data yet assembled. In addition, the standard CVS protocol includes tallies of woody stems by diameter and detailed documentation of soil and other environmental attributes. The Carolina Vegetation Survey Database system provides a flexible data archive built on the VegBank data model and is designed to store a wide range of vegetation-plot data, from restoration plots tracking the success of planted stems to plots with a complete inventory of both tree stems and plant cover values. Concept-based taxonomy is implemented for both community types and plant taxa, reducing the ambiguity that is inherent in using names only, as the meaning of a name can vary with taxonomic authority. The plots in the CVS archive have been used in numerous publications to document compositional variation in vegetation of the southeastern US and to address a range of broader questions such as patterns in species richness, patterns of species specialization versus generalization, speciesarea relationships, design of targets for ecological restoration, and documentation of long-term trends in vegetation composition.

VegBank – a permanent, open-access archive for vegetation-plot data

Rapid progress is being made in North American vegetation science through recent developments within the U.S. National Vegetation Classification (USNVC). Central to these advances are sharing, archiving, and disseminating field plot data, the fundamental data required for describing and understanding vegetation communities. VegBank (GIVD ID NA-US-002) is the vegetation plot database of the Panel on Vegetation Classification of the Ecological Society of America. VegBank is a stand-alone, Internetaccessible, vegetation plot archive designed to allow users to easily submit, search, view, annotate, cite, and download diverse types of vegetation data. The archive also includes embedded databases that contain classifications of vegetation and individual organisms, designed and implemented to track the many-to-many relationship betweens names and plant or community concepts, as well as alternative party perspectives on accepted taxa. The VegBank data model is also implemented in VegBranch, a desktop tool for data management and for uploading to and downloading from VegBank.

Irish Vegetation Database

The Irish Vegetation Database (GIVD ID EU-IE-001) was established in 2007 by the National Biodiversity Data Centre, in conjunction with the National Parks and Wildlife Service. It aims to collate all of the vegetation data that has been collected over the years in Ireland onto a single TURBOVEG database, and to make the maximum use possible of this valuable resource. In Ireland, the national vegetation database is already being used as an important national baseline. The historical data capture phase will be completed in 2012, when the database is expected to have in the region of 28,000 independent vegetation releves. Ongoing surveys over the coming five years should increase this to between 28,000 and 32,000 releves. Initially, the database will be used to create map layers showing the distribution of habitats and vegetation communities in Ireland. It will assist with reporting on implementation of the Habitats Directive (Article 17), and will support the research community in Ireland. The long term aims are to use the database to create a national vegetation classification system for Ireland; and to inform the development of a national vegetation and ecosystem map.

Classification and description of alluvial plant communities of the Piedmont region, North Carolina, USA

AbstractQuestions: What are the alluvial vegetation types of the North Carolina Piedmont? How is species composition related to site conditions?Location: Catawba, Yadkin‐PeeDee, Cape Fear, Neuse and Tar‐Pamlico River Basins, North Carolina Piedmont region, southeast USA.Methods: We recorded 194 alluvial vegetation plots. Vegetation types were derived using flexible β‐hierarchical cluster analysis and random forests classifiers to reassign misclassified plots. We used canonical correspondence analysis to show the relationship between species composition and key environmental variables.Results: Twelve forested vegetation types and two herbaceous types were distinguished, nested within a hierarchical classification structure ive higher‐level groups. The five mega groups describe broad geomorphic–floristic conditions, whereas the narrower vegetation types characterize finer‐scale floristic variation. Floristic variation is related to stream order and soil chemistry (pH and Ca:Mg ratio), as well as soil texture variables (percentage sand and percentage clay). We present a summary of floristic composition and structure, environmental setting and geographic distribution for each of the 14 vegetation types.Conclusions: We suggest recognition of 14 alluvial vegetation types in the North Carolina Piedmont. In comparing our vegetation types with the community concepts currently recognized in the US National Vegetation Classification, some of our types fit well within recognized NVC associations, whereas others deviate sharply from established types, suggesting the need for reworking the currently recognized NVC alluvial type concepts.

Completing an Ecosystem Classification System for Nova Scotia

Ecosystem classification systems (ECS) are a useful tool for conservation planning, particularly protected areas planning. Nova Scotia Protected Areas Branch has completed a geographical information system based ecosystem classification system for the province. The classification system integrates aspects of existing forestry based classification system and national vegetation classification system. Principles of landscape pattern have been incorporated into the system: (1) use hierarchical system; (2) include both abiotic and biotic factors; (3) use more abiotic factors at coarser spatial scales and more biotic factors at finer spatial scales; and (4) use vegetation only at the finest scale. The ECS builds on previous work, allows for integration of existing landscape classification systems, and establishes methods that can be applied for a variety of landscape planning issues in other regions.

GEOBIA Vegetation Mapping in Great Smoky Mountains National Park with Spectral and Non-spectral Ancillary Information

Vegetation mapping was performed using geographic object-based image analysis (GEOS1A) and very high spatial resolution (VHR) imagery for two study areas in Great Smoky Mountains National Park. This study investigated how accurately GEOB1A with ancillary data emulates manual interpretation in rugged mountain areas for multi-level vegetation classes of the National Vegetation Classification System (NVCS). It was discovered that the incorporation of texture and topographic variables with spectral data from scanned color infrared aerial photographs increased the overall accuracy of GEOBIA vegetation classification by 2.8 percent and 5.0 percent Kappa. In a separate study using multispectral Ikonos imagery, the use of elevation, aspect, slope and proximity to streams produced NVCS macro-group vegetation segmentations that resembled manual interpretation and significantly improved the overall accuracy to 76.6 percent, Kappa 0.57. Ancillary information may thus aid in GEOBIA vegetation mapping for updating vegetation inventories in rugged mountain areas.

Annual Reports To Council Ecological Society of America August 2009

<i>Annual Reports To Council</i> Ecological Society of America August 2009

Mapping Vegetation Communities Using Statistical Data Fusion in the Ozark National Scenic Riverways, Missouri, USA

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers

The study and management of biological communities depends on systems of classification and mapping for the organization and communication of resource information. Recent advances in remote sensing technology may enable the mapping of forest plant associations using image classification techniques. But few areas outside Europe have alliances and associations described in detail sufficient to support remote sensing-based modeling. Northwestern Montana has one of the few completed plant association classifications in the United States compliant with the recently established National Vegetation Classification system. This project examined the feasibility of mapping forest plant associations using Landsat Enhanced Thematic Mapper Plus data and advanced remote sensing technology and image classification techniques. Suitable reference data were selected from an extensive regional database of plot records. Fifteen percent of the plot samples were reserved for validation of map products, the remainder of plots designated as training data for map modeling. Key differentiae for image classification were identified from a suite of spectral and biophysical variables. Fuzzy rules were formulated for partitioning physiognomic classes in the upper levels of our image classification hierarchy. Nearest neighbor classifiers were developed for classification of lower levels (alliances and associations), where spectral and biophysical contrasts are less distinct. Maps were produced to reflect nine forest alliances and 24 associations across the study area. Error matrices were constructed for each map based on stratified random selections of map validation samples. Accuracy for the alliance map was estimated at 60%. Association classifiers provide between 54 and 86% accuracy within their respective alliances. Alternative techniques are proposed for aggregating classes and enhancing decision tree classifiers to model alliances and associations for interior forest types.

Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification

AbstractQuestion:How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring?Location:Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub‐boreal region of North America.Methods:We classified and mapped the natural vegetation using the USNVC, with ‘alliance’and ‘association’as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types.Results and Discussion:Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%.Pinus banksiana ‐ Picea marianaforests were less frequent in areas unburned since the 1930s.Conclusion:The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification.

Standardized Ecological Classification for Mesoscale Mapping in the Southwestern United States

Standardized ecological classification units form the foundation for effective data collection, assessment, and reporting on ecosystems. Attempts at regional land cover mapping often falter on this point or struggle along inefficiently. Over the past decade, NatureServe has worked with the Gap Analysis Program and others to map existing vegetation using the US National Vegetation Classification (US-NVC). US-NVC is a system of hierarchical structure and rules that are designed to provide a national classification of existing vegetation. Experience has demonstrated the need to develop map units at conceptual scales intermediate between the narrowly specific alliance (floristic) and the broadly generalized formation (physiognomic) levels of the US-NVC. NatureServe defined over 630 “mesoscale” vegetation-based units that are described across the lower 48 United States. These mesoscale classification units, which we term “terrestrial ecological systems,” are described using multiple plant communities that tend to co-occur based on recurrent similarities in environmental setting and ecological dynamics. By integrating environmental setting and ecological processes with vegetation into the concept of each unit, this classification system lends itself to biophysical modeling and robust characterization of wildlife habitat. These units apply well to land cover mapping and may be augmented with modifiers for specific variants in composition and structure resulting in robust, standardized maps. Regional-scale mapping of “near-natural” land cover was completed by the Southwest Regional Gap Analysis Project using 109 ecological system units, currently the most detailed regional land cover map of its kind. Terrestrial ecological system units provide a direct, systematic link to the US National Vegetation Classification and may also provide a useful framework for integration with ecological site concepts and descriptions.