RATIONALE
A standardized vegetation classification system is essential for the efficient inventory, assessment, and management of ecosystems across the United States This necessity is becoming increasingly evident as individuals, private organizations, and government entities confront the growing challenges of natural vegetation loss and alteration.
Since 1986, remnants of natural vegetation have become increasingly rare, with some types facing imperilment due to habitat loss and degradation, while others have vanished without documentation This loss of vegetation types diminishes habitat diversity, directly increasing the risk of extinction for various species Factors such as climate change, atmospheric pollution, species invasions, and land use changes are expected to cause rapid alterations in vegetation Additionally, widespread land use changes have intensified social and economic conflicts, highlighting the need for timely information on natural environments To address these environmental challenges, a standardized classification system is essential for contextualizing ecological and biodiversity studies, enabling consistent mapping and comparison This classification will be crucial for guiding research, resource conservation, ecosystem management, and restoration efforts, as well as predicting ecosystem responses to environmental changes.
To meet the need for a credible, broadly-accepted vegetation classification, the
The Ecological Society of America (ESA), in collaboration with organizations like the U.S Geological Survey, U.S Federal Geographic Data Committee, and NatureServe, established a Panel on Vegetation Classification to enhance ecological research and data management.
In July 2000, the science staff of The Nature Conservancy, instrumental in developing the U.S National Vegetation Classification (NVC), transitioned to NatureServe, which now advocates for the Conservancy's interests in the NVC's ongoing development To formalize their collaboration, four organizations signed a Memorandum of Understanding (MOU) in August 1998, outlining their partnership aimed at advancing the National Vegetation Classification.
The ESA Vegetation Classification Panel aims to establish a standardized vegetation classification system for the United States, guiding ecologists in the adoption of sampling and analysis standards while ensuring scientific credibility through peer review Additionally, the Panel promotes international collaboration in developing vegetation classifications and associated standards This document outlines the standards and procedures designed to achieve these key objectives.
2 Forming a partnership to further develop and implement the national vegetation classification standards Memorandum of Understanding among ESA, TNC (NatureServe), USGS, and FGDC 1999
Ecological Society of America, Washington, D.C., USA 6p (http://www.esa.org/vegweb/#MOU).
BACKGROUND AND PRINCIPLES
The ESA Panel on Vegetation Classification endorses the FGDC’s “National Vegetation Classification Standard” (1997) as the foundation for a national vegetation classification system This standard features a physiognomic-floristic hierarchy, encompassing higher-level physiognomic units and lower-level floristic units Rooted in the International Classification of Ecological Communities (ICEC), now known as the International Vegetation Classification (IVC), the FGDC standard established a classification hierarchy, outlined component elements, and set the framework for defining floristic levels From 1995 to 1996, the Panel focused on supporting the FGDC by evaluating proposed standards for various physiognomic categories, including class, subclass, group, subgroup, and formation, as well as the specific physiognomic types within these classifications.
The FGDC's guiding principles for the development of the National Vegetation Classification (NVC) emphasize that vegetation types consist of groups with similar plant composition and physiognomy, defined by specific diagnostic criteria (FGDC 1997, Section 5.3) However, due to complex biophysical factors and chance, vegetation is inherently variable, leading to stochastic species distributions Consequently, floristic vegetation units cannot be strictly defined; some examples clearly fit a particular type, while others exist as intermediates, requiring assessment based on relative affinities to various types.
The 1997 FGDC standard acknowledges two floristic categories within the NVC hierarchy—Alliance and Association—but lacks a list of recognized types, nomenclature details, and methods for defining these categories The Nature Conservancy published the current list of Alliances and Associations for the conterminous United States in 1998, in collaboration with the Natural Heritage Network, and it has since undergone continuous refinement Each entry is detailed in a standardized format that compiles literature and field observations, providing a comprehensive overview of U.S plant communities The Panel expects that this recognized list will be updated based on field data and standard methods, as required by the FGDC, although specific standards and criteria were not initially defined The standards presented here aim to fulfill this requirement.
We have utilized the FGDC "Guiding Principles" along with definitions for associations and alliances to establish standards for defining, naming, and describing floristic units Our objective for future updates to the list of alliances and associations, along with their supporting documentation, is to ensure they adhere to established standards in field observation, type description, peer review, and data management Each of these key activities is summarized below.
To accurately identify and describe vegetation associations and alliances, it is essential to conduct a numerical analysis of field plot data collected from various locations within the range of the vegetation type and its closely related types, regardless of political or jurisdictional boundaries Detailed standards for this plot data can be found in Chapter 5.
Proposals for new or revised floristic units must follow established standards for type circumscription and description, ensuring each type includes detailed information on its unique vegetation characteristics and its relationship to other recognized types When revising existing types, comparisons with related types at the same level are essential to avoid duplication or significant overlap, aiming instead to enhance or add to the existing classifications Detailed standards for type description and circumscription are outlined in Chapter 6.
Peer review Proposals for new and revised types need to be evaluated through a credible, open peer-review process Standards for the peer-review process are outlined in
Effective data management requires that all data used to define and describe associations or alliances be permanently stored in a publicly accessible archive This ensures that revisions to existing type concepts, new descriptions of proposed types, and other relevant uses can be easily accessed and utilized.
All accepted proposals for changes to vegetation types and their supporting documents must be submitted to the NVC digital public archive It is essential that all plant taxa mentioned in plot data or community type descriptions are clearly defined using a recognized public database or authoritative publication Unknown taxa should be accurately classified within the phylogenetic hierarchy of such resources Furthermore, the data archives for plant taxa, field plots, and associations must be genuinely archival, ensuring that original data can be retrieved by future researchers For detailed data management standards, refer to Chapter 8.
The standards to be used for collecting field data, describing types, peer review, and data management are enumerated at the end of each of these chapters.
The NVC categorizes the full spectrum of vegetation types, encompassing natural environments like old-growth forests and seminatural areas, as well as cultivated vegetation such as row crops and orchards It is essential to distinguish between these categories based on their naturalness, as outlined by Grossman et al (1998 Appendix E) This classification aids users in effectively differentiating among natural, seminatural, and planted vegetation types.
Consistent with the FGDC principles, the standards described here for floristic units relate to vegetation classification and are not intended as standards for mapping units
Types defined by established standards can be effectively mapped and utilized as a foundation for mapping various other unit types, although this is subject to limitations related to scale and mapping technology The criteria for aggregating or differentiating vegetation types and forming mapping units are influenced by the specific objectives of the mapping project and the resources allocated to it For instance, when employing the National Vegetation Classification (NVC) Alliance class for vegetation mapping in the Gap Analysis Program, not all alliance types can be distinctly identified, leading to the aggregation of these types into broader map units.
"Compositional groups," also known as "ecological complexes," are important units of vegetation that, while not officially included in the National Vegetation Classification (NVC) standard, effectively support mapping activities and maintain a clear connection to recognized NVC units (Pearlstine et al 1998).
Vegetation classification divides the continuous variation of plant life into distinct units for practical purposes, such as enhancing communication about ecological resources, documenting community diversity, and supporting scientific research on vegetation patterns While traditional methods like the NVC and IVC focus on vegetation physiognomy for classification, alternative approaches that prioritize different aggregation methods may be more suitable for specific applications Hierarchical classifications based solely on floristic criteria, as proposed by Westhoff and van der Maarel, offer another perspective on organizing vegetation.
1973), on ecosystem processes (Bailey 1996), or on potential natural vegetation (Daubenmire
The various approaches to vegetation classification address distinct requirements, allowing for the nesting of NVC associations under different hierarchical types While we provide standards for implementing the floristic levels of the U.S National Vegetation Classification, we do not suggest that this is the sole valid method of classification.
A BRIEF HISTORICAL BACKGROUND
DESCRIBING AND CLASSIFYING VEGETATION
For over a century, vegetation scientists have explored plant communities to understand their composition, distribution, dynamics, and environmental interactions, employing various methods such as ecological knowledge, synthetic tables, and mathematical analyses Shimwell (1971) aptly summarized the diversity of opinions in vegetation classification with the phrase "so many men, so many opinions." This article does not aim to provide a comprehensive review of vegetation classification, as that has been addressed in previous works (e.g., Whittaker 1962, 1973; Shimwell 1971; Mueller-Dombois and Ellenberg 1974) Instead, we will concentrate on the key elements pertinent to the National Vegetation Classification initiative, particularly those related to floristic levels.
Vegetation classification serves as an essential tool for effective communication, data reduction, interpretation, and land management By summarizing our understanding of vegetation patterns, classifications facilitate more efficient analysis and decision-making in ecological studies and planning.
Vegetation patterns are perceived uniquely by different individuals, yet all classifications necessitate the identification of distinct vegetation classes Key concepts essential to the foundation of vegetation classification include the principles outlined by Mueller-Dombois and Ellenberg (1974, p 153).
1 Given similar habitat conditions, similar combinations of species recur from stand to stand, though similarity declines with geographic distance
2 No two stands (or sampling units) are exactly alike, owing to chance events of dispersal, disturbance, extinction, and history
3 Species assemblages change more or less continuously with geographic or environmental distance.
4 Stand composition varies with the spatial and temporal scale of analysis
Understanding fundamental concepts is crucial for recognizing the limitations of classification schemes in vegetation science By keeping these principles in focus, we can effectively examine how vegetation scientists and resource managers define and categorize vegetation patterns to fulfill their specific requirements.
Physiognomy, in its specific sense, describes the overall external appearance of vegetation, focusing on the growth forms of dominant plant species It encompasses the structure, which includes the spacing and height of plants that create the vegetation cover matrix This term is frequently employed to combine both aspects, especially when differentiating various types of vegetation.
Physiognomic classifications differ from floristic classifications, with the formation being the fundamental unit in many physiognomic systems A formation is defined as a community type characterized by the dominance of specific growth forms in the uppermost layer or a combination of these dominant forms, as outlined by Whittaker in 1962 This methodology is applied in the physiognomic section of the National Vegetation Classification (NVC).
Physiognomic patterns are generally consistent across large areas due to their correlation with climatic factors, while floristic similarities tend to be more localized, reflecting species composition influenced by geographic barriers and unique historical events As a result, physiognomic classifications are essential for understanding these broader ecological trends.
Continental and global mapping applications have frequently utilized eight classifications, while regional applications have relied on floristic classifications Prior to the internationally recognized classification established by UNESCO, various physiognomic classifications, such as those proposed by Fosberg in 1961, were developed.
In 1973, Mueller-Dombois and Ellenberg developed a UNESCO classification system aimed at creating vegetation maps with a scale of approximately 1:1 million or coarser This framework facilitates the global comparison of ecological habitats by categorizing plant growth forms into equivalent classifications.
Physiognomic classifications play a crucial role in the inventory, management, and planning of natural resources, as they are based on various vegetation attributes that can change over time due to stand development and disturbances These classifications are essential for understanding wildlife habitat, watershed integrity, and range utilization Key criteria for physiognomic classification include dominant plant growth forms (such as forbs, grasses, shrubs, and trees), plant density or cover, the size of dominant plants, and vertical layering (single stratum versus multistrata) These classifications have been widely utilized in regional wildlife habitat studies and are instrumental in assessing old-growth status when combined with stand age and structure.
Physiognomic classifications offer a broad overview of floristic patterns but often lack the specificity needed for local or regional analysis Consequently, they are frequently combined with higher-resolution classifications that focus on the taxonomic identity of plants However, in certain complex and floristically rich ecosystems, such as tropical rainforests, physiognomic classification remains the predominant method for understanding vegetation.
Floristic characterization involves analyzing the composition of taxa to describe vegetation stands, relying primarily on formal field observations known as "plots." These observations are essential for defining, identifying, and describing various vegetation types The methods employed can vary significantly, from focusing solely on dominant species to documenting the abundance of all species within a stand, which reflects the total floristic composition The choice of characterization method significantly impacts the definition and description of ecological alliances and associations.
Vegetation classification traditionally relies on the dominant plant species in the uppermost layer, known as "dominance types." These classifications are determined by assessing key metrics such as biomass, density, height, or canopy cover (Kimmins 1997) Such categories form the foundational levels in various established classification hierarchies (e.g., Cowardin et al 1979, Brown et al 1980).
Determining plant dominance is straightforward and requires basic floristic knowledge, but significant ecological and floristic variations can exist within dominant species due to their wide geographic ranges This dominance approach is popular in aerial photo interpretation and mapping inventories due to its simplicity Recent advancements in remote sensing technology, both spaceborne and airborne, have greatly enhanced efforts to classify and map dominant vegetation types over extensive areas, as evidenced by studies such as Scott and Jennings (1998) and Lins and Kleckner (1996).
The term "cover type" is often used interchangeably with "dominance type," referring to the dominant species in the uppermost layer of vegetation In forest ecosystems, cover types can be evaluated through tree basal area or canopy cover, while rangeland cover types focus on species that make up the majority of canopy cover Despite their limitations, as noted by Whittaker (1973), dominance types are widely utilized due to their straightforward and effective methods for inventorying, mapping, and modeling vegetation.
Total community floristic composition has been widely used for systematic community classification Two of the major approaches used in the United States are those of Braun-
A NATIONAL VEGETATION CLASSIFICATION FOR THE UNITED STATES
Agency and scientific consensus on classification
Prior to the 1990s, the concept of a unified nationwide vegetation classification lacked support within the U.S academic community, as most ecologists believed it offered minimal contribution to broader conceptual frameworks This perspective was influenced by the diverse methodologies used to interpret vegetation patterns, leading to a general disinterest in classifications that were primarily relevant to local or regional contexts.
(Nicolson and McIntosh 2002) As a consequence, little attention was paid to creating a unified national vegetation classification 3
Before the 1990s, U.S federal and state agencies focused on resource inventory and land management conducted vegetation inventories and public land maps with limited scope and inconsistent methods, leading to incompatible information and duplicated efforts However, the need for a cohesive classification system became evident in the 1970s and 80s, resulting in useful classifications such as the U.S Forest Service's habitat type classification of western forests and the Cowardin classification of U.S wetlands The Society of American Foresters has traditionally employed a dominance-based approach for classifying forest types across North America.
In addition, in the early 1980s, five federal agencies collaborated to develop an ecological land classification framework integrating vegetation, soils, water, and landform (Driscoll et al 1984).
In the late 1970s, The Nature Conservancy (TNC) established a network of state Natural Heritage Programs (NHPs) aimed at inventorying and protecting diverse natural communities and rare species across states To facilitate this inventory, each program developed its own community classification system As TNC sought to create national priorities for community preservation, it identified the necessity of unifying the varying state-level vegetation classifications into a coherent national framework.
In the late 1980s, the U.S Fish and Wildlife Service launched a research initiative aimed at identifying deficiencies in biodiversity conservation This project eventually developed into the U.S Geological Survey’s National Gap Analysis Program (GAP), as detailed by Jennings in 2000.
3 In contrast, classification has been a major activity in Europe throughout the twentieth century, with vegetation scientists largely using the methods of the Braun-Blanquet school Moreover, vegetation classification gained new impetus in many European countries during the 1970s and 1980s (Rodwell et al 1995) program classifies and maps existing natural and semi-natural vegetation types of the United States on a state and regional basis as a means of assessing the conservation status of species and their habitats Because a common, widely used, floristically based classification was critical to this work GAP supported TNC’s effort to develop a nationwide classification (Jennings 1993) Collaboration between GAP and TNC led to a systematic compilation of alliance-level information from state natural heritage programs and from the existing literature on vegetation (e.g., Bourgeron and Engelking 1994, Sneddon et al 1994, Drake and Faber-Langendoen 1997, Weakley et al 1997, Reid et al 1999) With support from TNC and an array of federal programs, Grossman et al (1998) and Anderson et al (1998) produced the first draft of what became the U.S National Vegetation Classification (USNVC, referred to here as the NVC) The NVC was initially populated with a compilation of described natural vegetation types taken from as many credible sources as could be found and drawn from the experience vegetation ecologists with extensive regional expertise Although the majority of the types described were not linked to specific plot data, they were often based upon studies that used plot data or on the knowledge of regional and state ecologists (Weakley et al 1998, Faber-Langendoen 2001)
The Federal Geographic Data Committee and the ESA Vegetation Panel
In the early 1990s, the US federal government acknowledged the necessity for a standardized nationwide vegetation classification In 1990, the revised Office of Management and Budget Circular No A-16 was published, introducing spatial information standards and outlining the creation of a National Spatial Data Infrastructure (NSDI) This initiative aimed to minimize data duplication, reduce the costs associated with developing new geographic data, and enhance data accessibility through better coordination and standardization of federal geographic information Additionally, the circular established the Federal Geographic Data Committee (FGDC) to foster the development of database systems, information standards, exchange formats, and guidelines, while promoting broad public access to geographic data.
Interagency commitment to coordination under Circular A-16 was strengthened and urgency was mandated in 1994 under Executive Order 12906 (Federal Register 1994), which
4 The circular was originally issued in 1953 to insure that surveying and mapping activities be directed toward meeting the needs of federal and state agencies and the general public, and that they be performed expeditiously, without duplication of effort Its 1967 revision included a new section, “Responsibility for
Coordination.” It was revised and expanded again in 1990 to include not just surveying and mapping, but also the related spatial data activities
The Federal Geographic Data Committee (FGDC) is directed to engage state, local, and tribal governments in the development of standards, leveraging the expertise of academia, the private sector, and professional societies for effective implementation In 2002, Circular A-16 was updated to reflect the requirements of Executive Order 12906, leading to the establishment of a Vegetation initiative by the FGDC.
The FGDC Vegetation Subcommittee, comprising representatives from federal agencies and various organizations, is working to establish standards for classifying and describing vegetation After evaluating multiple classification methods, they proposed a modified version of the TNC classification To enhance the review process, ecologists from the National Biological Survey, TNC, and academia emphasized the importance of involving the Ecological Society of America (ESA) for peer review and professional discourse on the evolving National Vegetation Classification (NVC) Consequently, the FGDC invited ESA to collaborate on reviewing physiognomic standards and developing floristic level standards This document results from the partnership between ESA, FGDC, USGS, and NatureServe, aimed at creating formal vegetation classification standards in the United States.
5 Now the U.S Geological Survey’s Biological Resources Division.
STANDARDS FOR ESTABLISHMENT AND REVISION OF
This article outlines formal standards for proposing or modifying associations and alliances within the US National Vegetation Classification (NVC) Our goal is to ensure these standards and procedures promote the ongoing development, broad acceptance, and scientific advancement of the NVC.
THE ASSOCIATION AND ALLIANCE CONCEPTS
ASSOCIATION
The association is the fundamental unit of vegetation identified in the National Vegetation Classification (NVC) According to Flahault and Schrüter (1910), it is defined as "a plant community of definite floristic composition, uniform habitat conditions, and uniform physiognomy." Gabriel and Talbot (1984) expand on this by describing the association as "a recurring plant community of characteristic composition and structure." Curtis (1959) further elaborates on the concept by defining the plant community as a segment along a continuum.
A studyable grouping of organisms that coexist in a specific area and engage in mutual interactions reveals four key concepts: characteristic composition, physiognomy and structure, habitat, and a consistent distribution throughout a landscape or region.
As the various association concepts merged into common use, our conceptualization of vegetation also shifted so as to accept more or less continuous variation As noted in Section 3,
Mueller-Dombois and Ellenberg (1974) emphasized that species assemblages exhibit continuous changes across their geographical range, highlighting the inherent variability within ecological associations Additionally, Gleason's (1926) early acknowledgment of the significant role of chance in local vegetation expression has greatly influenced our understanding of vegetation composition Consequently, many classifications have shifted focus from the rigid definitions of original associations by Flahault and Schrüter (1910) to a more flexible approach that considers characteristic ranges in composition, physiognomy, and habitat.
Three other points should be considered:
Habitat encompasses the environmental conditions and ecological processes, including disturbances, that shape a community It also accounts for temporal variations, such as recurrent fires in temperate grasslands or extreme weather events, as long as these factors do not fundamentally alter the presence of species within the habitat.
2 Characteristic physiognomy and habitat conditions may include fine-scale patterned heterogeneity (e.g., hummock/hollow microtopography in bogs, shrub/herb structure in semidesert steppe)
3 Unlike strictly floristic applications of the association (and alliance) concept, the definition for the NVC standard retains an emphasis on both floristic and physiognomic criteria as implied by membership of floristic types in higher order physiognomic units of the classification.
The establishment of a plant association involves utilizing a standardized approach to describe complex ecological realities; however, an effective classification must also acknowledge the inherent variations within the association Therefore, we define the association as the fundamental unit of vegetation, synthesizing these essential considerations.
A vegetation classification unit consistent with a defined range of species composition, diagnostic species, habitat conditions, and physiognomy
Diagnostic species are defined as species or groups whose relative constancy or abundance helps differentiate between various ecological types Guidelines suggest a minimum number of diagnostic species needed to define an association, with a greater number of taxa enhancing the recognition of that unit According to Moravec (1993), associations can be distinguished by character species that are exclusive to specific types, groups of species with similar behaviors, dominant species, or the absence of certain species that typically characterize similar types.
While diagnostic species are essential in vegetation classification, they cannot definitively delineate boundaries between similar associations or alliances due to the continuous variability of vegetation and the stochastic nature of species distributions Factors such as dispersal, reproduction, and chance events significantly influence species occurrence at specific sites To aid users in understanding the limits of vegetation types, various tools are necessary, including vegetation keys that offer clear criteria and type clusters that focus on core concepts while allowing for probabilistic assessments of intermediate categories Best practices emphasize identifying vegetation types based on overall species composition, diagnostic species, and additional criteria to reduce ambiguity.
There is no universally accepted standard for the acceptable level of variability within an association or alliance According to Mueller-Dombois and Ellenberg (1974), a Jaccard presence/absence index of similarity between 25% and 50% may indicate that different stands belong to the same association, while higher similarity levels could define subassociations The concept of "stopping rules" in classification is complex, with various criteria, including physiognomic and habitat factors, influencing decisions Additionally, the characteristics of the vegetation itself play a crucial role in determining boundaries between types, with key considerations such as species richness, variability, anthropogenic alteration, and vegetation homogeneity being vital Ultimately, no single rule can be applied universally to all cases.
ALLIANCE
A vegetation alliance is a classification unit defined by shared floristic characteristics among its associated plant communities, while also being influenced by the physiognomic traits of higher classification levels This concept encompasses a wider range of floristic and structural variability compared to associations, yet still maintains identifiable and specific floristic traits.
A group of associations with a defined range of species composition, habitat conditions, and physiognomy, and which contains one or more of a set of
22 diagnostic species, typically at least one of which is found in the upper most or dominant stratum of the vegetation.
This definition includes both floristic and physiognomic criteria, in keeping with the integrated physiognomic-floristic hierarchy of the NVC It also builds directly from the association concept
The vegetation alliance concept discussed here varies from the floristically-driven Braun-Blanquet approach, as seen in the inclusion of evergreen tree physiognomy within the Dicrano-Pinion alliance, which also accommodates common juniper shrublands Similarly, the Vaccinio-Piceion alliance, characterized by evergreen traits, can encompass broadleaved deciduous birch species.
Betula pubescens woodlands, as noted by Ellenberg (1988) and Rodwell (1991), exhibit consistent physiognomic and habitat features that align with the Braun-Blanquet system's classification standards Specht et al (1974) applied a comparable methodology to establish alliances in Australia.
Alliances exhibit greater compositional and structural variability compared to associations, covering a wider geographical range and adapting to diverse habitat conditions It is advisable to avoid narrowly defined alliances that focus on specialized local habitats or distinctive species, as well as those that primarily differ in the dominance of major species.
Forest alliances often align with the "cover types" established by the Society of American Foresters (SAF) to categorize North American forests When a cover type is determined by the co-dominance of major species, such as Bald Cypress or Water Tupelo, the alliance may encompass a broader classification or rearrange these types based on ecological and floristic relationships Conversely, in regions where dominant tree species thrive across diverse environmental conditions, alliances can offer a more nuanced classification than SAF cover types These alliances may include various dominant or co-dominant tree and understory species, which collectively illustrate the physiognomic, floristic, and environmental characteristics of the alliance For instance, the widespread Jack Pine forest cover type can represent multiple alliances, including a closed, mesic jack pine forest and a more xeric bedrock woodland.
The alliance concept parallels Daubenmire's "series," which categorizes habitat types sharing dominant species under climax conditions While the series focuses on the tree regeneration layer to assess potential late-seral canopy homogeneity, alliances differ by being based on existing vegetation regardless of successional status For instance, a shrub type that emerges post-fire is classified separately from both the burned forest type and the future forest type that may eventually return to the site.
STANDARDS FOR FLORISTIC UNITS
1 The NVC definitions for the floristic units of vegetation are: a Association: A recurring plant community with a characteristic range in species composition, specific diagnostic species, and a defined range in habitat conditions and physiognomy or structure. b Alliance: a grouping of associations with a characteristic physiognomy and habitat and which shares one or more diagnostic species typically are found in the uppermost or dominant stratum of the vegetation
2 Diagnostic species exhibit patterns of relative fidelity, constancy or abundance that differentiate one type from another
3 Diagnostic criteria used to define the association and alliance should be clearly stated, and the range of variability in composition, habitat, and physiognomy and structure should be clearly described, including similarity with other related types
4 Associations and alliances are categories of existing vegetation (i.e., , the plant species present and the vegetation structure found at a given location at the time of observation).
5 Associations and alliances recognized within the NVC must be defined so as to nest within categories of the recognized physiognomic hierarchy (e.g in FGDC 1997,
Association, Alliance, Formation, Subgroup, Group, Subclass, Class; see Figure 1)
VEGETATION FIELD PLOTS
MAJOR TYPES OF REQUIRED DATA
Field plots serve to document vegetation and its surrounding environmental context, while the subsequent analysis of the collected data necessitates proper metadata For the National Vegetation Classification (NVC), the information gathered in field plots is categorized into three primary types.
1 Vegetation data: Floristic composition and physiognomy that can be used to classify vegetation constitute the key component of plot data Floristic data consist of a list of the taxa observed, often recorded by the vertical strata they occur in, and usually associated with some measure of importance such as the relative amount of ground covered by them Vegetation structure is typically assessed in terms of overall cover by vertical strata and the physiognomic attributes of the taxa associated with those strata
2 Site data: Vegetation is best interpreted in the context of habitat, geographic location, and stand history information This includes a abiotic factors such as soils, parent material, elevation, slope, aspect, topographic position, and climate, b stand history and disturbance regime, and c geographic location
3 Metadata: Data that describe the methods used to obtain vegetation and environmental data, or that are critical for subsequent uses of plot data Examples of required metadata are the method and precision used to determine plot location, field methods, the nomenclatural (taxonomic) source or standard for identifying and naming plant species, the field personnel (including contact information and institutional affiliation) and the sampling date Optional metadata include interpretations and reidentifications of plant taxa and the assignment of the plot to a particular type or types within the NVC.
This chapter emphasizes that studies utilizing vegetation plot data often have diverse objectives beyond classification, such as documenting ecological patterns, assessing vegetation structure, evaluating long-term changes, identifying restoration targets, and validating remote-sensed data It outlines the essential plot information necessary for developing associations and alliances within the National Vegetation Classification (NVC) framework While it does not serve as a comprehensive guide to vegetation recording, it highlights critical considerations and core data that investigators must collect to effectively support the development and refinement of NVC types.
STAND SELECTION AND PLOT DESIGN
Vegetation surveys aim to identify the diversity of vegetation within a region or assess specific vegetation types across a broader area To ensure accurate representation, plot selection is often preceded by reconnaissance, either through ground or aerial methods, to evaluate major vegetation patterns and environmental gradients Utilizing key environmental factors to establish an "abiotic grid" facilitates effective plot selection, as seen in techniques like stratified sampling and the gradsect method The chosen selection method is crucial, as it significantly influences the representativeness of the surveyed area.
The selection of stands, defined as contiguous areas of vegetation that exhibit uniformity in physiognomy, floristic composition, and environmental conditions, can be approached through either preferential (subjective) or representative (objective) methods, or a combination of both, as outlined by Podani.
In preferential methods, stands are chosen based on the investigator's prior experience, often excluding those that are degraded, atypical, or redundant A selected stand is deemed representative of its vegetation type, ensuring that each recorded plot provides a typical description of both floristic composition and physiognomy This principle also applies to representative selection methods.
This approach emphasizes the importance of selecting stands with objectivity to effectively characterize the entire vegetation landscape relevant to the study Representative stands can be chosen using various methods, including simple random, stratified random (like the environmental grid or gradsect approach), systematic, or semi-systematic sampling (Podani 2000) While both preferential and representative methods can produce plots suitable for the National Vegetation Classification (NVC), representative sampling generally results in a less biased collection of plots.
In contrast, the representative method may miss or under sample rare and unusual types
To ensure a comprehensive understanding of natural vegetation in modified landscapes, it is essential to supplement representative sampling with plots from rare or unusual types encountered during fieldwork In such environments, preferential selection may be necessary to accurately observe and compare reasonably natural vegetation A hybrid approach, which involves stratifying the landscape into predefined units and randomly locating plots within them, is often the preferred method for effective vegetation analysis.
Stand selection can often be restricted to certain types of vegetation within a given area for various reasons Many research studies primarily examine natural vegetation, including those that are naturally disturbed or in different successional stages Some studies may concentrate solely on late-successional or mature natural vegetation Nevertheless, the National Vegetation Classification (NVC) is applicable to all existing vegetation, irrespective of its successional status or cultural impact It is essential to thoroughly document the criteria used for stand selection in the accompanying metadata.
After conducting reconnaissance and selecting stands, plots are identified within these stands to ensure they accurately represent the vegetation Each plot should be relatively homogeneous in both vegetation and habitat, large enough to reflect the stand's floristic composition, and comparable in the relative importance of dominant species to the surrounding area While complete homogeneity is unattainable, the main criteria can be satisfied by avoiding clear boundaries and unrepresentative features It is crucial to document decisions regarding plot placement and homogeneity in the plot metadata, as these choices significantly influence data quality.
Vegetation exhibits varying degrees of homogeneity depending on the scale of observation Within forests, small gaps can form due to the death of dominant trees, while bryophytes and herbs may indicate substrate variability, such as the presence of rocks or logs Additionally, when examining forests over larger distances, patches may appear homogeneous but can differ significantly in age or disturbance history For effective fieldwork in the National Vegetation Classification (NVC), it is essential to focus on identifying homogeneous stands for plot placement, rather than seeking uniformity at the scale of individual mosses or across extensive landscapes.
100,000 m 2 (6) reflecting a typical pattern of plants co-occurring under common environmental and historical conditions
The floristic composition and structure of plant communities exhibit significant variations over time and space, with seasonal changes often being pronounced, particularly during the growing season These communities can experience substantial shifts in species composition due to unusual weather events or fire over periods ranging from one year to several years For instance, mixed mesophytic forests may showcase a vibrant but temporary spring flora, while certain deserts may reveal unique annual plant assemblages that emerge only once every few decades To ensure accurate data interpretation, plot records in the National Vegetation Classification (NVC) must indicate any missing flora, allowing future analysts to assess data quality effectively Conducting repeated inventories throughout a season is essential for comprehensive species documentation, with these visits recorded as one observation for the same plot In contrast, multiple visits spanning several years should be logged as distinct plot observations.
Two primary methods are utilized for recording vegetation: (a) the plot method, which gathers data from a single, complete area, and (b) the subplot method, which collects information from multiple smaller areas within the larger stand Both approaches can yield sufficient data for vegetation classification, yet they come with distinct requirements and benefits The subsequent discussion will delve into the specifics of each method.
Data taken from an entire large plot
6 As used here, “m 2 ” denotes the amount in square meters (see Taylor 1995), e.g., 1,000 m 2 represents the area within a 50 x 20 m plot
This method provides an efficient and rapid way to gather floristic and physiognomic data for classification purposes The selected plot size strikes a balance, being small enough to maintain uniformity in habitat and vegetation while large enough to encompass the majority of species present in the stand This approach enables statistical analysis of variation between different stands, although it does not account for variation within a single stand.
The recommended plot size for vegetation sampling varies based on factors such as plant size, spacing, and vertical layers Adequate plot sizes must represent the vegetation effectively, ensuring that increasing the area yields few new species that impact the vegetation's physiognomy For temperate hardwood or coniferous forests, plot sizes between 200 and 1,000 m² are suitable, while tropical forests require larger plots of 1,000 to 10,000 m² Grasslands and shrublands typically need plots ranging from 100 to 200 m², whereas deserts and arid zones often necessitate larger plots of 1,000 to 2,500 m² due to sparse vegetation and scattered species These guidelines help ensure that plot sizes meet minimum area calculations for accurate vegetation classification.
Specialized studies focusing on fine-scale variations, such as zonation patterns near small wetlands or specific bryophyte assemblages, may necessitate the use of very small plots, potentially just a few square meters However, it is advisable to avoid such small plots in community classification studies whenever feasible.
When determining plot shape for research, it is essential to avoid recommending a specific design, as it may need to adapt to the characteristics of the stand, such as the linear nature of riparian areas Consistency in plot size and shape is crucial within a study to ensure reliable results Ultimately, the efficiency of plot layout often influences the shape chosen by the researcher.
Data taken from a set of smaller subplots
Collecting data from multiple subplots within a stand is an effective alternative to observing a single large plot, as it allows for the assessment of internal variability and provides a more accurate estimation of species abundance across the stand This method is particularly useful for measuring treatment responses and evaluating experimental vegetation manipulations Additionally, it aids in characterizing average vegetation composition in gently sloping terrains where stand boundaries may be unclear However, it is not suitable for measuring species number per unit area larger than the subplot, though it effectively assesses relative variation within and among stands.
VEGETATION PLOT DATA
Effective vegetation classification requires three types of data: vegetation data, site data, and metadata Among these, the structure and floristic composition of the vegetation must adhere to stringent criteria While environmental data, including soil attributes, topographic position, and disturbance history, are also important, they have less demanding requirements Ultimately, the quality of the vegetation data is crucial in determining a plot's eligibility for use in the National Vegetation Classification (NVC).
We have established standards for two distinct categories of plot data: classification plots, which are utilized to develop vegetation types for the NVC classification, and occurrence plots, which offer supplemental information related to existing NVC types but lack completeness for primary classification analysis The essential attributes that must be collected for each field plot type, both classification and occurrence, are outlined in detail.
Appendix 1 Additionally, to ensure that as many kinds of classification plot sampling data as possible are available to develop the NVC, Appendix 1 distinguishes between those fields that are minimally required for classification (category 1) from others that are optimal, or consistent with best practices (category 2) For classification plots, the minimal requirements include a select set of records such as location fields, species (taxon) cover assessments, elevation, slope gradient and aspect, plot area, sampling method used, and the persons who collected the plot Nonetheless, plots that meet only these minimal requirements are much less valuable for classification than those that contain the optimal set of fields that are part of the standard Occurrence plots have essentially the same minimum requirements as classification plots, but they do not require a complete species list with cover values, nor do they require slope gradient, aspect, plot area, and there are fewer metadata requirements In what follows we discuss the main features of the plot sampling standards for classification purposes
Vertical structure and physiognomy of vegetation
To effectively relate vegetation associations and alliances to the FGDC (1997) hierarchy, specific data on vegetation structure and physiognomy is essential While physiognomy, as defined by Fosberg (1961), refers to the external appearance of vegetation influenced by biomass structure and compositional characteristics, structure pertains to the arrangement and height of plants within the vegetation cover For the National Vegetation Classification (NVC) to be a reliable classification tool, a standardized description of vegetation structure by strata is necessary to enable consistent comparisons across various data sets.
A stratum refers to a layer of vegetation encompassing all plant growth forms present within it Plants are categorized into strata according to their primary height or position in the ecosystem, rather than their taxonomy or mature growth characteristics This means that a tree species may belong to multiple strata based on its varying heights.
In a plot containing 32 seedlings and saplings, it is essential to summarize the key characteristics of the vegetation's physiognomy instead of detailing every layer The goal is to effectively convey the complex stand conditions while highlighting the most significant features.
In terrestrial ecosystems, it's essential to identify four primary vegetation strata: tree, shrub, herb, and moss, as outlined by Fosberg (1961) In aquatic environments, both floating and submerged strata should be acknowledged Recognizing these six strata is crucial for understanding the vertical distribution of vegetation cover and the dominant growth forms, allowing for accurate placement within the National Vegetation Classification (NVC) hierarchy Furthermore, documenting the abundance of each species within these strata enhances the detail of vegetation composition records.
The six strata are defined as follows:
The tree stratum consists of tall trees, typically single-stemmed woody plants that reach heights of over 5 meters under ideal growth conditions This layer may also encompass very tall shrubs resembling trees, along with other life forms like lianas and epiphytes Their specific contributions to the stratum can be detailed using the “life form” classification.
Shrub stratum: includes shrubs (multiple-stemmed woody plants, generally less than 5 m in height at maturity under optimal growing conditions) and by shorter trees (saplings)
In addition to the tree stratum, other life forms can be included, excluding herbaceous plants due to their annual die-back and inconsistent heights Dwarf-shrubs, defined as shrubs under 0.5 meters, should be recognized as a lower version of the shrub stratum when they form a distinct layer, whether in forests or more open environments like tundra or xeric shrublands In various vegetation types, dwarf-shrubs may also exist as a component within the herb stratum.
The herb stratum, also known as the field stratum, comprises herbaceous plants that lack woody stems and typically die back each year This layer often features low creeping semi-shrubs, dwarf-shrubs, vines, and non-woody brambles, such as raspberries, along with seedlings of trees and shrubs.
The moss stratum, also known as the nonvascular, byroad, or ground stratum, is characterized by the presence of mosses, lichens, liverworts, and algae In this layer, ground-creeping vines, prostrate shrubs, and herbs are categorized under the herb stratum Even in the absence of herbs, the moss stratum can still be identified by the presence of low woody or semi-woody life forms.
7 Other kinds of structural data can be important to assess successional trends, such as size-class structure of the woody species These types of data are not required to classify vegetation and therefore are not included in the minimum NVC standards.
Floating aquatic stratum: includes rooted or drifting plants that float on the water surface (e.g., duckweed, water-lily)
Submerged aquatic strata consist of rooted or drifting plants that primarily remain underwater or on the aquatic bottom, such as pondweed This section emphasizes the arrangement of these aquatic plants within the strata Additionally, it's important to categorize emergent plant life forms in wetlands according to the appropriate strata; for instance, cattails and sedges belong in the herb stratum, while duckweed is classified in the floating aquatic stratum.
Epiphytes, vines, and lianas are categorized within the existing strata rather than as distinct layers However, they can be differentiated from other life forms in these strata through the use of the life form classification.
In ecological studies, substrata can be further categorized for greater specificity; for instance, the tree stratum can be divided into canopy trees and subcanopy trees, while the shrub stratum can be classified into tall shrubs and short shrubs However, it is essential that these divisions remain nested within established standard strata rather than overlapping them.
STANDARDS FOR VEGETATION PLOTS
1 Sampling methods: Methods used to select plot locations, choice of plot sampling technique, and comprehensiveness of vegetation description must be described in metadata.
2 Plot methods: A plot must be large enough to represent the stand in terms of total species composition and abundance.
3 Physiognomy: The following vegetation strata should be recognized whenever they are present: tree, shrub, herb, and moss (moss, lichen, liverwort, alga), and in aquatic habitats, floating, and submerged For each of these strata, total percent cover and predominant height of the top and base of the strata should be recorded The percent cover of at least the three most abundant growth forms in the dominant or uppermost stratum should also be estimated (see Table 1 for a list of growth forms)
4 Species composition: a For vegetation classification plots, sampling should be designed to detect and record the complete species assemblage of the stand Only one field visit at an optimal time of year is required, though additional visits can improve plot quality and are recommended for vegetation types with marked phenological variation b For classification plots, cover is the required measure of species abundance If cover values are in discrete categories rather than continuous, the cover scales should be able to nest within the Braun-Blanquet cover-abundance scale classes of: “r” (solitary individual with small cover), “+” (few individuals with small cover), 0-5%, 5-25%, 25-50%, 50-75%, and 75-100% (Table 2) For occurrence plots, only dominant taxa need be recorded, and cover need not be estimated, though both are highly desirable. c Although not required for classification plots, best practice is for each species listed in a plot to be assigned to each of the strata (tree, shrub, herb, moss, floating, submerged) in which it is found, with a separate cover estimate for its abundance in each of these strata At a minimum, total cover of a species in the plot is required, though this may be calculated based on the stratum cover values Epiphytes and lianas may be treated in the strata in which they occur, or treated as separate “strata.”
5 Site data: a Physical features of the stand should be described, including elevation, slope aspect and gradient, topographic position, landform, and geologic parent material, b Soil and water features, including soil moisture, drainage, hydrology, depth of water, and water salinity (where appropriate), should be measured or estimated, c The soil surface should be characterized in terms of the percent cover of litter, rock, bare ground, coarse woody debris, live stem, surface water, and other, d Site conditions should be described, including landscape context, homogeneity of the vegetation, phenological phase at time of observation, stand maturity, successional status, and evidence of disturbance, e The minimum requirement for environmental information for classification plots is: i elevation ii slope aspect iii slope gradient
6 Geographic Data: a Latitude and longitude in decimal degrees and WGS 84 (NAD83) datum, b Coordinates collected in the field and the datum used, or if a nonstandard projection was used, then the specific projection, spatial units, c Description of the method used to determine the plot location (e.g., estimated from a USGS 7.5 minute quadrangle, GSP, etc.), d An estimate of the accuracy of the plot’s location information in the form of the radius for a 95% certainty, e Narrative information useful for plot relocation,
7 Metadata: All plots should have a project name and description associated with them, the methodology used to select and lay out the plots, effort expended in gathering floristic data, cover scale and strata types used, and the name and contact information of the lead field investigators The minimum requirements are: a Author plot code, b Author observation code (to distinguish multiple observation on a plot over time), c Observation date, d Lead field investigator’s name, role, and address, e Plot area in m 2 , f Taxon observation area (if subplots are used) in terms of size and total area of subplots, g Taxon inference area (the area within which the observations were made), h Cover dispersion (if subplots are used, how are they distributed?),
42 i Stratum methods, if applied, j Description of cover method. k Indicate whether or not voucher specimens for the plot were collected and, if so, where they were deposited.
CLASSIFICATION AND DESCRIPTION OF FLORISTIC UNITS
FROM PLANNING TO DATA INTERPRETATION
An association represents a numerical and conceptual synthesis of floristic patterns (Westhoff and van der Maarel 1973, Mueller-Dombois and Ellenberg 1974, Kent and Coker
Vegetation classification involves defining associations and alliances, which are abstractions that reflect specific ranges of floristic, physiognomic, and environmental variations While associations focus on distinct types of vegetation, alliances represent broader categories This classification process is guided by field observations and data analysis, conceptualized in three key stages: planning and scope of plot observation, data collection and preparation, and finally, data analysis and interpretation.
Scope and planning of plot observation
To achieve effective classification, it is essential to gather plots from a broad geographic area, as widespread records are crucial for adequately characterizing distinct types in relation to similar ones While a limited number of plots may suggest a distinct type, comprehensive data covering the full geographic and environmental range is necessary for a thorough understanding Acknowledging that not all field observations can be extensive, it is important for contributors, even those with limited fieldwork, to adhere to established standards This approach ensures that their data and interpretations can be integrated into a larger classification dataset, enhancing the overall classification effort.
Vegetation data from all available, high-quality data sets should be combined with any new field data and various supplemental environmental data to provide the basic information for
44 comprehensive documentation of any given type Where data are applied that do not meet minimum standards for quality, consistency, and geographic completeness, their limitations must be explicitly described.
Data preparation necessitates clear documentation of plant identification through accurate scientific names and reliable published sources that clarify these names It is advisable to adhere to the plant nomenclature standards set by Kartesz (1999) and USDA PLANTS, unless specific circumstances warrant a different approach.
(http://plants.usda.gov/), or ITIS (http://www.itis.usda.gov/index.html), as is explained in
In response to the need to combine field plot data sets from different sources, the ESA Vegetation Panel supports a public database of vegetation plots, known as VegBank
VegBank serves as a valuable resource for documenting and reanalyzing data, streamlining the data preparation process, and enabling the mining of existing information from various sources It focuses on standardizing plant names and their taxonomic concepts to enhance data accessibility and usability.
To ensure a thorough analysis of vegetation types, two essential criteria must be satisfied Firstly, the plot records used should accurately reflect the anticipated compositional, physiognomic, and site variations of the targeted vegetation type or its closely related groups Secondly, there should be adequate redundancy in the plot composition to facilitate the clear identification of compositional variation patterns.
Researchers can utilize a range of analytical methods to identify environmental and floristic patterns from species occurrence matrices in field plots This diverse selection of techniques enables scientists to choose the most effective approaches for their specific data sets, ensuring robust analyses and meaningful insights into ecological patterns.
Ellenberg 1974, Jongman et al 1995, Ludwig and Reynolds 1988, Gauch 1982, Kent and Coker
1992, McCune and Mefford 1999, McCune et al 2002, Podani 2000)
Common methods for identifying and documenting vegetation patterns include direct gradient analysis, ordination, and clustering Direct gradient analysis illustrates floristic changes along environmental or geographic gradients, while ordination organizes stands based on similarities in floristic composition Both techniques can identify discontinuities in plot compositions or segment continuous variations logically Clustering groups stands into distinct categories based on their floristic makeup Each method utilizes various mathematical tools, which must be clearly documented and explained For instance, the initial species-by-plot matrix should be recorded, along with notes on any taxonomic adjustments for consistency across plots Additionally, if environmental factors are analyzed, the corresponding environmental data should also be documented VegBank serves as an effective platform for such documentation, as do digital appendices in proposals.
Effective data preparation involves identifying potential noise sources and outliers within the dataset A comprehensive type description should document significant assumptions, known limitations, and any inconsistencies present in the data It is crucial to detail the methods employed for rejecting plots based on outlier analyses, with references to examples for gradient analysis from Belsey (1980) and for ordination and clustering from Tabachnik and Fidell (1989) Additionally, the outlier analysis function outlined in McCune and Mefford (1999) should be considered If any novel methods are utilized, they must be thoroughly described to ensure clarity and reproducibility.
Standardizing taxonomic resolution is crucial for ensuring consistency across all plots in ecological analysis Inconsistencies may arise from observers' varying abilities to identify taxa, intergrading groups that are difficult to distinguish morphologically, or inconsistent recognition of infraspecific taxa Due to these challenges, few established standards exist for addressing taxonomic discrepancies However, it is essential that researchers document and explain the rules and procedures they employ in standardizing taxonomic resolution within their data sets to maintain clarity and reliability in their findings.
To ensure a comprehensive understanding of dominant taxa, it is essential to identify them at least to the species level Plots exhibiting genus-level entities with a total cover exceeding 20% are typically considered floristically incomplete Additionally, in certain situations, plots with more than 10% of their entities classified only at the genus level may also lack sufficient detail.
Ecologists must aim for the highest taxonomic resolution, ensuring that any aggregation of subspecies and varieties to the species level is meticulously documented Providing narratives on vegetation types that detail the subspecies and varieties combined for numerical analysis enhances the interpretation of the reported results.
Detailed descriptions of data reduction and analysis methods, along with the rationale for their selection, are essential Documentation must include any data transformations and similarity measures used Employing multiple analytical methods is recommended to present converging lines of evidence effectively Critical evidence should be illustrated through tabular and graphical representations, such as biplots of compositional and environmental variation, dendrograms showing cluster relationships, and synoptic tables summarizing community composition Clearly specifying the criteria for identifying diagnostic species, including levels of constancy and fidelity, is important While tables and graphics alone do not establish associations, they provide a quantitative foundation for their identification.
A comprehensive tabular summary should be included, detailing both diagnostic and constant species Constant species are defined as those that appear in more than 60% of the field plots utilized to characterize a specific type, aligning with the top two constancy classes established by Braun-Blanquet (1932).
It is crucial to ensure that analysis accounts for geographic variation to prevent distortion in classification and summary tables caused by the spatial clumping of plot records Field investigators often focus locally, leading to spatial aggregation of plots that may appear distinctive through conventional numerical methods due to inherent spatial autocorrelation in vegetation data This issue is particularly pronounced in regions where field data is scarce overall but abundant in areas with intensive surveys Therefore, further research is needed to understand the significance of spatial autocorrelation in floristic composition and to develop effective measurement methods.
Insular vegetation can be particularly prone to spatially correlated discontinuities
Insular vegetation in patch-like habitats is often discontinuous due to random events of plant migration and establishment, contrasting with the continuous variation of matrix vegetation across a landscape While it may seem unproductive to identify unique associations for every glade or rock outcrop in areas dominated by deep soils, doing so based solely on compositional discontinuities can lead to misclassification Researchers should focus on distinguishing similarity patterns influenced by spatial proximity and random plant dispersal events Nonetheless, distinct types of insular vegetation can be identified through thorough field sampling.
DOCUMENTATION AND DESCRIPTION OF TYPES
Accurate documentation is essential in the classification process of vegetation types, requiring a standardized description or monograph for each recognized type While various platforms, including traditional scientific literature, publish these classifications, inconsistencies in methodology can hinder accessibility and comprehensiveness Effective descriptions of alliances and associations must include: (a) detailed documentation of defining vegetation characteristics, considering geographic and environmental variations; (b) a summary of the type's relationship to habitat and ecological factors; (c) identification of typical plots used for classification; (d) analysis of field data supporting the type's recognition; (e) an assessment of the type's confidence level; and (f) a synonymy with previously described types to clarify relationships with similar NVC types Further rationale for these criteria and an example of a type description can be found in Appendix 3.
The overview section summarizes the key features of the type, starting with a list of names that adhere to the nomenclatural rules outlined in Section 6.4, which includes both Latin names and their common translations It is essential to include a colloquial name for the type The section also details the association's placement within an alliance, noting if a new alliance is necessary, along with its position within a formation Lastly, the summary encapsulates the type concept, covering aspects such as geographic range, environmental conditions, physiognomy and structure, floristic composition, and distinctive characteristics of the type.
The association and alliance concepts are defined primarily using floristics and physiognomy, supplemented with environmental data to assess ecological relationships among the species and types
1 Floristics: This section should summarize the species composition and average cover in the plots for all species, preferably by strata Issues relating to the floristic variability of the type are highlighted Tables are provided in the following form: a A stand table of floristic composition, preferably for each stratum, showing constancy, mean, and range of percent cover (Table 4) Criteria for inclusion in the table should be specified It is recommended that all species with greater than 20% constancy be included to facilitate comparisons of species patterns with that of other types Where a more abbreviated, representative list is required, prevalent species (sensu Curtis (1959) can be listed as the “n” species with highest constancy, where “n” is the mean number of species per plot). b A summary of diagnostic species, through a tabular arrangement, a synoptic table, or other means of identifying and displaying diagnostic species
2 Physiognomy: This section should describe the physiognomy and dominant species of the types, including physiognomic variability across the range of the plots being used Summary information is provided as applicable for each of the main strata (tree, shrub, herb, nonvascular, floating, and submerged; Table 3), including their height and percent cover Dominant growth forms are also noted
3 Dynamics: This section provides a summary of the successional and disturbance factors that influence the stability and within-stand pattern of the type Where possible, a summary of the important natural disturbance regimes, successional trends, and temporal dynamics should be provided for the type Information on population structure of dominant or characteristic species may be appropriate.
The article should begin with a comprehensive overview of the landscape's characteristics, including its elevation, topographic position, landforms, and geological features Following this, detailed insights into soil types, parent material, and relevant physical or chemical properties that influence the vegetation's composition and structure should be presented Additionally, it is recommended to include summary tables that encapsulate the available categorical and quantitative environmental variables for clarity and ease of understanding.
This section provides a concise overview of the geographic range, both current and historical, of the type in question It identifies specific states and provinces where the type is confirmed to exist, as well as those where it is likely or potentially found Additionally, it highlights jurisdictions where the type is believed to have existed in the past but is now extirpated, offering a comprehensive understanding of its distribution and historical presence.
This section outlines the plots and analytical methods utilized for classification, along with the archival location of the plot data The plots must adhere to the classification criteria specified in Section 5.3 and Appendix 1, and the data should be stored in a publicly accessible archive compliant with Chapter 8 standards It is essential to discuss factors influencing data consistency, such as taxonomic resolution and the completeness of physiognomic-structural or environmental information Additionally, the range-wide completeness and variability in the geographic distribution of plot locations should be addressed, particularly in relation to spatial autocorrelation issues discussed in Section 6.2 Finally, the methods for data preparation, analysis, and interpretation must be detailed, including outlier analyses, distance measures, and various numerical and tabular techniques.
This section outlines the confidence levels assigned to each type—Strong, Moderate, or Weak—based on the criteria established in Chapter 7 These levels indicate the quality and comprehensiveness of the data and methods used in defining each type It is essential to identify any data gaps and recommend areas for further research The confidence level serves as a crucial tool for ensuring consistent standards regarding the quality of types included in the NVC, with formal designations determined through the peer review process as detailed in Chapter 7.
Relationships among types and synonymies
This section outlines synonymous types previously defined by the author, highlighting their relationships with closely related categories.
Possible subassociation or suballiance types or variants, if appropriate, should be discussed in greater detail here along with other narrative information.
This section includes a comprehensive list of citations and references utilized in the descriptive fields above, featuring literature and comparative synoptic tables that highlight similarities with related types.
NOMENCLATURE OF VEGETATION TYPES
The main objective of naming units in a classification system is to establish clear and standard labels that promote effective communication about their types Additionally, it is important for names to carry meaning and remain easy to remember However, these goals can sometimes conflict; for example, while a numerical label like "Association 2546" is unambiguous, it lacks meaning and memorability Conversely, a long descriptive name may be meaningful but cumbersome to use To address these challenges, the proposed standards aim to find a balance by incorporating alternative names for each type.
There are two distinct approaches to naming associations and alliances: the descriptive method, exemplified by the habitat type approach in the western United States and the Canadian Forest Ecosystem Classification, and the formal syntaxonomic code of the Braun-Blanquet method The descriptive approach relies on dominant and characteristic species for naming, without a formal process for name adoption In contrast, the Braun-Blanquet method adheres to a structured code that permits researchers to assign legitimate names, establishing a precedent for future literature, similar to species taxonomic rules, where only two species can be included.
The 52 alliance names adhere to Latin grammatical standards, with hybrid approaches suggested by Rejmanek (1997) and others This article adopts a descriptive approach, utilizing a peer-review process to ensure proper nomenclature, as detailed in Chapter 7 Given the complexities of tracking the evolving names and concepts of organisms that underpin association and alliance names, we also implement a concept-based taxonomy through VegBank, further elaborated in Chapter 8 and referenced by Berendsohn (1995).
Every association is designated a scientific name, which includes a standardized translated name The Latin names of the nominal species within the scientific name are converted into common names based on Kartesz (1994, 1999) for English-speaking regions It is also recommended to provide common names in French and Spanish, where applicable Additionally, each association and alliance is assigned a unique database code.
The names of dominant and diagnostic taxa serve as the basis for naming associations and alliances To create these names, it is essential to include one or more species from the dominant stratum, while taxa from secondary strata should be used sparingly Taxa within the same strata are separated by a hyphen (-), whereas those from different strata are separated by a slash (/) The taxa from the uppermost stratum are listed first, followed by those from lower strata, with the order reflecting their dominance and diagnostic value In cases where a dominant herbaceous stratum coexists with a scattered woody stratum, the naming can focus on the species that best represent the type from either stratum.
Alliance names, such as those categorized by the FGDC (1997), encompass various classes like closed tree canopy, shrubland, and herbaceous vegetation (refer to Figure 1) The inclusion of the term "alliance" in these names is crucial for differentiating them from association units (see Box 4).
In situations where diagnostic species are uncertain, a general term can serve as a "placeholder" for weakly confident types, such as Pinus banksiana - (Quercus ellipsoidalis) or Schizachyrium scoparium - Prairie Forbs Wooded Herbaceous Vegetation To accurately describe the vegetation association, environmental, geographic, or height-related modifiers may be utilized, though this should be minimized for standardization and brevity For example, Quercus alba / Carex pennsylvanica represents a Dwarf Forest, while Thuja occidentalis indicates Carbonate Talus Woodland When naming associations, it is advisable to use the least number of species possible, with up to five species allowed for diverse regions and a maximum of three species for alliances.
A colloquial or regionally common name can be developed to enhance understanding and recognition of a community type for a broader audience, similar to how species are identified by their common names.
The nomenclature for vascular plant species utilized in type names must adhere to the latest version of USDA PLANTS or ITIS It is essential to include the dates when these websites were consulted in the metadata, as they are regularly updated.
The nomenclature rules for natural vegetation, as outlined by Grossman et al (1998), do not formally establish standards for sampling and defining cultivated vegetation types However, the National Vegetation Classification (NVC) aims to encompass all vegetation types, distinguishing cultivated formations from natural and semi-natural ones within the FGDC hierarchy For instance, evergreen treed plantations are categorized separately from natural evergreen treed formations Although the formal concepts of association and alliance may not directly apply to cultivated vegetation, users can still identify, name, and classify these types below the physiognomic levels of the hierarchy.
“planted/cultivated” part of the NVC more fully We recommend that the nomenclature for planted and cultivated types follow the nomenclature rules given above, with the exception that
The term "alliance" should not be part of the name, and the use of the physiognomic class name is optional based on the vegetation type It is essential to include a descriptor for the cultivated vegetation being described Additionally, names at the "alliance" level must be pluralized, while those at the association level should be singular For instance, use "Pinus ponderosa Plantation Forests" at the alliance level and "Pinus ponderosa Rocky Mountain Plantation" at the association level.
Forest (at the association level), , Zea mays Crop Field.
STANDARDS FOR DESCRIPTION OF FLORISTIC UNITS OF VEGETATION
The description of a vegetation type must include the following:
1 Names of natural and seminatural types. a Community nomenclatureshould contain both scientific and common names, e.g.,
Pinus taeda - Quercus (alba, falcata, stellata ) Forest Alliance as well as Loblolly
The Pine - (White Oak, Southern Red Oak, Post Oak) Forest Alliance emphasizes the importance of using common names in English, French, and Spanish, where applicable Naming conventions for alliances and associations should include dominant and diagnostic species selected from tabular summaries, with at least one species from the dominant stratum Taxa from secondary strata should be used sparingly, and names within the same stratum are separated by hyphens (-), while those from different strata are separated by slashes (/) The order of taxa reflects their dominance and diagnostic value, with the FGDC (1997) class included in the names Placeholders may be used when diagnostic taxa are uncertain, but their use should be minimal Additionally, names should consist of the fewest possible taxa, with up to five species for associations and no more than three for alliances Nomenclature must adhere to the latest USDA PLANTS or ITIS guidelines, and for planted types, the term "alliance" is omitted, favoring a pluralized name with a relevant descriptor instead.
Pinus ponderosa Plantation Forests (at the level of alliance), Pinus ponderosa
Rocky Mountain Plantation Forest (at the level of association), Zea mays Crop Field)
2 Floristic unit A description should indicate the level of the unit being described:
“Association” or “Alliance.” For planted or cultivated types indicate
3 Placement in the hierarchy Indicate the full name of the alliance or formation under which the type should be placed The list of accepted alliances and formations is accessible from the NatureServe Explorer web site (www.natureserve.org/explorer).
4 Classification comments Describe any classification issues relating to the definition or concept of the type Any assessment of the proposed type’s natural or seminatural status should be clearly identified
5 Rationale for choosing the nominal taxa (the species by which the type is named) Explain the choice of nominal species; for example, whether or not they are dominant, or if they are indicative of distinctive conditions such as alkaline soils, elevation, geographic region, etc
6 Brief description Provide a brief (1-2 paragraph) summary of the structure, composition, environmental setting, and geographic range of the community The summary should start with a sentence or two that provide an overall concept of the The summary should also include a brief description of: a environmental setting in which the type occurs, b structure/physiognomy c species composition, preferably by strata, and d diagnostic characters.
7 Physiognomy Provide the following summary information for the plots: a The physiognomy, structure, and dominant species, including assessment of variability across the range of the plots taken Possible subassociations or variants can be discussed. b Complete a summary table (Table 3) incorporating each stratum present (tree, shrub, herb, nonvascular, floating, submerged).
8 Floristics Species composition and average cover for all species (preferably by stratum) should be provided in the following summary form: a A stand table of floristic composition (preferably by stratum) showing constancy and mean cover (and preferably the range of species cover values) All species should be listed that have more than 20% constancy (Tables 4, 5). b A summary of diagnostic species, through a tabular arrangement, synoptic table, or other means of identifying and displaying constant and diagnostic species Constant species are those occurring in > 60% (i.e Table 5 constancy classes IV, V) of the field plots used to define a type. c Taxonomic usage in floristic tables must include reference to a taxonomic standard so as to define the meaning associated with a name Reference to and consistency with the current version of USDA PLANTS or ITIS, coupled with the specific date of observation of the site, is sufficient.
9 Dynamics Provide a summary of the successional status of the type and the disturbance factors that influence stability and within plot variation for the type Describe the extent to which this information is known and the limitations and assumptions of the assessment
10 Environmental description Provide a detailed description of important factors such as elevation (in meters), landscape context, slope aspect, slope gradient, geology, soils, hydrology, and any other environmental factors thought to be determinants of the biological composition or structure of the type
11 Description of the range Provide a brief textual description (not a list of places) of the total range (present and historic) of the type List national and subnational (states or provinces) jurisdictions of occurrence in North America Distinguish between those states and provinces where the type definitely occurs and those where the type probably/potentially occurs Also note the states/provinces where the type is believed to have historically occurred, but has been extirpated.
12 Identify field plots Identify plots used to define the type and indicate where the plot data are archived and the associated accession numbers All plot records used must conform to the minimum standards described in Chapter 5 and be deposited in a publicly accessible archive that itself meets the standards described in Chapter 8.
13 Evaluate plot data Describe all factors that affect plot data adequacy and quality, including such factors as incomplete sampling throughout the range or poor floristic quality of plots.
14 The number and size of plots Justify the number of and sizes of plots used in terms of the floristic variability and geographic distribution.
15 Methods used to analyze field data Discuss the analytical methods used to define the types Include software citations.
16 Overall confidence level for the type Recommend a level of confidence of Strong, Moderate, or Weak based on criteria described in Chapter 7 The peer-review process will ultimately establish the formal confidence level (see Chapter 7).
17 Citations Provide complete citations for all references used in the above section.
18 Synonymy List any names already in use to describe this or related types, either in whole or in part Include comments or explanations where possible.
The USVC encourages openness to change, allowing individuals and institutions to propose additions and modifications, ensuring equal rules and opportunities for all contributors While we establish a uniform set of standards for sampling, recognizing, describing, and naming types, these guidelines accommodate diverse methods for defining associations and alliances This flexibility arises from the general nature of the concepts, which represent assemblages of taxa shaped by intricate biophysical interactions and chance, ultimately resulting in recognizable and mappable habitat patterns across landscapes.
There is no universally accepted classification for floristic units; instead, various synthetic solutions can be considered The selection of these alternatives should rely on established best practices and the sound judgment of experienced professionals Therefore, a crucial aspect of this process is implementing a formal, impartial, and scientifically rigorous peer review system to evaluate proposals for recognizing new floristic units or modifying existing ones.
To maintain a standardized set of alliance and association types for the NVC, one effective model is inspired by plant taxonomy In this approach, qualified individuals or teams apply credible scientific methods to define taxa, adhere to established rules for their description and naming, and publish their findings in reputable journals This process allows the scientific community to evaluate the results, accepting or rejecting them based on their own assessments.
Certain authorities, whether individuals or organizations, maintain lists of taxa they recognize as valid In zoological nomenclature, the most recent publication is given precedence in cases of conflicting information Alternatively, professional bodies may implement a peer-review process where researchers submit their findings for publication while also ensuring adherence to consistent standards This method helps keep an accurate and current list of taxa and their descriptions, as exemplified by the American Ornithological Union for North American birds.
9 Members of the American Ornithological Union’s (AOU) Committee on Classification and
CLASSIFICATION CONFIDENCE
To enhance the effectiveness of the NVC, it is essential to ensure a broad representation of various vegetation types Therefore, it may be necessary to acknowledge certain types that, while not fully adhering to all best-practice standards outlined in this document, can be recognized on a temporary basis.
As part of the NVC peer-review process, each type will be assigned a “confidence level” based on the relative rigor of description and analysis used to define it
Level 1 - Strong : Classification is based on quantitative analysis of verifiable, high- quality classification plots that are published in full or are archived in a publicly accessible database Classification plots must meet the minimum requirements specified in Chapter 5 and as shown in Appendix 3 High quality classification plots must represent the known geographic distribution and habitat range of the type In addition, plots that form the basis for closely related types must be compared For an alliance, the majority of component associations must have a Strong to Moderate level of confidence
Level 2 - Moderate: Classification is lacking in either geographic scope or degree of quantitative characterization and subsequent comparison with related types, but otherwise meets the requirements for level 1 For an alliance, many associations within the type may have a Moderate to Weak level of classification confidence
Level 3 - Weak : Classification is based on limited, unpublished, or inaccessible plot data, or qualitative analysis, anecdotal information, or community descriptions that are not accompanied by plot data Local experts have often identified these types Although there is a high level of confidence that they represent significant vegetation entities that should be
Supplement to the AOU Check-list (R Banks pers comm 2000).
The 60 alliances incorporated in the NVC may not align with national standards for floristic types due to potential gaps in data availability and classification methods These alliances are considered weak when they are based on incomplete, unpublished, or inaccessible plot data, which often only reflects species in the dominant canopy layer Additionally, reliance on imagery and information focused primarily on dominant species further contributes to the uncertainty in their classification.
PEER-REVIEW PROCESS
Submitting and evaluating changes to the classification should be a formal, impartial, and scientifically rigorous process that remains simple, clear, and timely To ensure efficient use of resources and prompt reviews, it is recommended to utilize templates that include all necessary components for compliance with the standards outlined in Chapter 6 when proposing changes to the NVC.
To ensure effective peer review, it is essential for reviewers to possess regional expertise to assess the impact of proposed changes to the National Vegetation Classification (NVC) on related associations and alliances We recommend utilizing geographically based review teams responsible for ensuring compliance with classification, nomenclature, and documentation standards, while also maintaining the reliability of floristic data and resolving conflicts with established NVC elements Additionally, internal review methods must align across regional teams, and any changes affecting multiple regions should be collaboratively evaluated by all relevant teams.
There are two types of peer review processes available for investigators A full peer-review is necessary when proposing a type at the Strong or Moderate level Conversely, if an investigator lacks enough information to support a Moderate or Strong classification but believes the type is new to the NVC, they can submit it as a Weak type, which will undergo an expedited peer-review process.
The proposal review process for the NVC is managed by a Review Board appointed by the ESA Vegetation Panel, which includes a Review Coordinator, Regional Coordinators, and additional members deemed suitable by the Panel.
The full peer-review process includes the following:
1 An investigator electronically submits a type description following procedures, templates, and required data fields (outlined in Chapter 6), to the NVC Review
2 The Review Coordinator (or his/her designee) evaluates the submission to determine whether it meets established criteria for full peer-review If rejected, the submission is returned to the investigator with an explanation and a statement as to whether a revised submission would be encouraged
3 If approved for full peer review, the coordinator sends the submission to the Regional Coordinator, who solicits reviews as appropriate and consults with other Regional
Coordinators when a type appears likely to span more than one region
4 Reviewers assess the proposal, including a review of the implications for existing NVC types, recommend if appropriate a confidence level for the proposed type, and return their reviews to the Regional Coordinator.
5 After receiving the reviews and soliciting any additional advice required, the Regional Coordinator makes a decision to: a accept as either a Strong or Moderate type, b return for modification or revision, c reject, but recommend as a Weak type, or d reject altogether.
6 If the submission is accepted, the Regional Coordinator indicates what effect (if any) this submission may have on other types in the NVC not addressed by the submission If an effect to other types is determined to be significant, the Regional Coordinator either proposes other updates to related NVC types or requests additional input from the investigator
7 The Regional Coordinator sends the decision and all supporting reviews and documentation to the Review Coordinator The Review Coordinator informs the investigator of the results of the peer review If a submission is accepted, the Review Coordinator ensures that the NVC list and database are updated and that the proposal is posted on the NVC electronic Proceedings.
Expedited Peer Review (Weak Types)
1 An investigator(s) electronically submits a description following the outlined procedures, templates, and required data fields (outlined in Chapter 6) to the Review Coordinator.
2 The Review Coordinator (or his/her designee) evaluates the submission to determine whether it meets the criteria for expedited peer-review of a Weak type If rejected, the submission is returned to the investigator with an explanation and a statement as to whether a revised submission would be encouraged
3 If approved for expedited peer review, the Review Coordinator sends the submission to a Regional Coordinator The Regional Coordinator consults as appropriate with regional experts to help assess the validity and acceptability of the type
4 The Regional Coordinator sends the decision and all supporting documentation to the Review Coordinator The Review Coordinator informs the investigator of the results of the review If submission is accepted, the Coordinator ensures that the NVC list and database are updated and that the proposal is posted in the NVC electronic Proceedings.
STANDARDS FOR PEER REVIEW
1 The peer-review process is administered by the ESA Vegetation Panel and its appointees. Investigators wishing to participate in the NVC must submit their methods and results to the ESA Vegetation Panel’s Review Board, which is responsible for ensuring that specified and consistent standards are followed.
2 The objectives of the peer review team are to: (a) ensure compliance with classification, nomenclature and documentation standards, (b) maintain reliability of the floristic data and other supporting documentation, and (c) referee conflicts with established and potential NVC elements.
3 Reviewers should have sufficient regional expertise to understand how a given proposed change to the NVC would affect related associations and alliances.
4 Each type will be assigned a confidence level (Strong, Moderate, Weak) based on the relative rigor of the data and the analysis used to identify, define, and describe the type.
5 Investigators participating in NVC will use a defined template for type descriptions that can be readily reviewed and, if accepted, easily uploaded into the database system.
6 Investigators who describe association or alliance types must place their proposed types within the context of the list of existing NVC types so as to determine whether the type under consideration is distinct, or whether their data will instead refine or upgrade the definition of a type or types already on the list.
7 Two kinds of peer review are available If an investigator proposes to describe a type at the Strong or Moderate level, a full peer-review process is required If the investigator does not have sufficient information to support strong or moderate confidence but is convinced that the type is new to the NVC, he or she can submit the type as a Weak type, and an expedited peer-review process will be used
8 Full descriptions of types will constitute the NVC primary literature and will be published in a public digital Proceedings The Proceedings will publish official changes to the list of NVC associations and alliances It will include the required supporting information for all changes made to the list.
DATA ACCESS AND MANAGEMENT
COMMUNITY-TYPE DATABASES
The Vegetation Classification Database must be viewable and searchable over the
The NatureServe Explorer website (http://www.natureserve.org/explorer/) serves as the primary source for accessing updated classifications While other sites may host similar information, the data on NatureServe Explorer is considered the authoritative version A key benefit of websites is their ability to be updated regularly Users referencing an association or alliance from the NVC should include the specific website version and observation date to ensure accurate reconstruction of the community concepts and supporting information.
The NVC management team is responsible for maintaining NVC data files, allowing designated individuals to make necessary modifications Minor updates, such as adjustments to a community's range based on new information, can be made without review However, any changes that involve defining, redefining, or altering the confidence level of a vegetation type must be approved by the peer-review team, as outlined in Chapter 7.
PLOT DATA ARCHIVES AND DATA EXCHANGE
Field plot data and plot databases serve a similar purpose for vegetation types as plant specimens and herbaria do for plant species Vegetation scientists rely on plots for systematic observation and documentation of vegetation in natural settings The vegetation plot is the essential unit of vegetation information, and without these plot data, effective classification would be unattainable.
At a minimum, a plot contains information on location, spatial extent, species presence and
64 cover, select environmental data, and metadata Investigators must include plot data summaries in their descriptions of vegetation types (see Chapter 6)
A plot database system is essential for storing plot data that underpins the documentation and refinement of associations within the floristic levels of the National Vegetation Classification (NVC) To ensure transparency and future research accessibility, vegetation plots utilized in NVC development or revisions must be archived in a publicly accessible database, adhering to the standard data schema outlined in Appendix 4 The ESA Vegetation Panel oversees the VegBank archive (http://vegbank.org), which serves as a repository for accessing and discovering plot data All plot data supporting modifications to the NVC should be stored in VegBank or another permanent, searchable database Furthermore, data linked to the description of a vegetation type must include an accession number and be readily available through direct database queries via a web browser.
The collection of plot data is a collaborative effort involving various organizations and individuals, independent of the NVC itself These entities are invited to contribute their plot data to a public database, either as part of proposals for modifications to the NVC or as standalone submissions of fundamental information It is essential that any utilization of plot data in relation to the NVC acknowledges the original author of the data.
To enhance flexibility and encourage greater participation, plot databases must incorporate user-defined fields for diverse data archiving Additionally, qualified users should have the opportunity to annotate plots, allowing them to contribute interpretations of community membership and identify plant taxa.
BOTANICAL NOMENCLATURE
All stages of the NVC process are linked to specific plant taxa, which must be recorded clearly in plot and classification databases The use of plant names can lead to misunderstandings regarding the taxonomic concepts they represent Accurate records of taxa in vegetation plots are essential, but this task is complicated by varying taxonomic standards among different times, places, and investigators When combining plot data from various sources, it's crucial to reconcile differing taxonomic nomenclatures A common approach is to establish a standard list of plant taxa and align various names to this reference In the U.S., notable standard lists include those from Kartesz (1999), USDA PLANTS, and ITIS.
The USDA PLANTS database aims to comprehensively cover U.S taxa and provides synonyms for recognized taxa; however, it faces challenges in effective data integration First, the online lists are updated periodically without simultaneous archiving, making it impossible for users to reconstruct past versions of the database, which necessitates citing the observation date Second, the use of a single name for multiple taxonomic concepts creates ambiguities, as the lists do not define the taxonomic concepts or their changes over time Lastly, differing perspectives on acceptable names among users can hinder data merging, as reliance on different standards, such as the Kartesz 1999 list versus the USDA PLANTS list, complicates collaboration.
Biological nomenclature often leads to confusion because when a taxon is divided, the original name is retained for the group linked to the type specimen Additionally, interpretations of taxa can vary among different authors.
Plant names can denote various definitions of plant taxa, and conversely, a single plant taxon may have multiple names To eliminate confusion, it is essential that plant taxa linked to the National Vegetation Classification (NVC) are documented by a specific name and its corresponding use, usually found in published works All databases that support the NVC must meticulously track plant types through the documentation of name-reference pairs Following Pyle (2000), we refer to these name-and-reference pairs as "assertions," which is similar to the term "potential taxon" used by Berendsohn.
A name-reference combination serves as a declaration of a taxonomic concept, which may be synonymous with or connected to other assertions To ensure clear and precise identification of an organism—whether in field plots, museum specimens, or scientific literature—it's essential to reference a specific assertion This practice facilitates unambiguous identification of the intended taxonomic concept.
66 assertion to attach to an organism does not immediately dictate what names should be used for that assertion Different parties will have different name usages for a particular accepted assertion.
When reporting unknown or irregular taxa, such as composite morphotypes that encompass multiple similar taxa, it is essential to include the name of the taxon with a confirmed identification as the primary label Additionally, a note field should be incorporated into the database to offer supplementary information, for instance, "Peet, R.K., plot #4-401, third 'unknown grass', aff Festuca, NCU 777777." To adhere to best practices, it is advisable to include a name field that follows the identified taxon in parentheses, such as "Potentilla (simplex + canadensis)" or "Poaceae (aff Festuca)."
PROPOSAL SUBMISSION AND THE NVC PROCEEDINGS
Proposals for revisions in the NVC must be submitted in digital format using standard templates available through links that can be found at Vegbank (http://vegbank.org) or
The NatureServe Explorer website serves as a vital resource where key elements of successful proposals will be published in the Proceedings of the NVC These proceedings will be accessible via VegBank and NatureServe Explorer, providing essential literature that supports classification efforts This digital journal will be permanently available to the public, ensuring easy access to the classification database.
STANDARDS FOR DATA MANAGEMENT
1 The Vegetation Classification Database must be viewable and searchable over the web, and must be regularly updated The primary access point for viewing the classification will be the NatureServe Explorer website (http://www.natureserve.org/explorer/)
Although mirrors of this information may be found at other sites, the NatureServe
Explorer release should be viewed as definitive.
2 Users of the NVC should cite the website and the explicit version observed (or date observed) so as to allow exact reconstruction of the community concepts employed and supporting information observed.
3 Maintenance of NVC data files is the responsibility of the NVC management team Individuals assigned to this function will be able to modify appropriate NVC files Minor changes based on new information, such as an increase in the range of a community, will be inserted without review However, definition, redefinition, or change in the confidence level of a vegetation type would require approval of the peer- review team.
4 Plot data used to support changes in the NVC must be archived in VegBank or in another publicly accessible and searchable database.
5 Plot data used to support description of a vegetation type must be linked by accession number to the description of the type in the Vegetation Classification Database and should be publicly available via a direct database query from a web browser All uses of plot data with respect to the NVC must cite the original author of the plot.
6 If a database other than VegBank is used to archive plot data supporting the NVC, that archive must have assured data permanency and must be able to export plot data in a format consistent with the schema shown in Appendix 4.
7 Proposals for revisions in the NVC must be submitted in digital format using standard templates available through links that can be found at Vegbank (http://vegbank.org) or NatureServe Explorer (http://www.natureserve.org/explorer/).
8 Key components of successful proposals will be posted on the web as the Proceedings of the NVC and will be accessible through VegBank or NatureServe Explorer The
Proceedings will constitute the primary literature underpinning the classification and will be permanently and publicly available as a form of digital journal linked to the classification database.
9 Each taxon must be reported as a name and publication couplet For example, if the plot author based all the taxa on Fernald (1950), then the names would each be linked to Fernald (1950) If USDA PLANTS or ITIS was used, then an observation date must be provided so that the correct version can be determined All databases supporting the NVC must track plant types through documentation of name-reference couplets
10 Unknown or irregular taxa (such as composite morphotypes representing several similar taxa) should be reported with the name of the taxon for the first level with certain identification and must be associated with a note field in the database that provides additional information (e.g., Peet, R.K., plot #4-401, third “unknown grass”, aff Festuca, NCU 777777) For best practice provide a name field to follow the given taxon in parentheses (e.g., Potentilla (simplex + canadensis), Poaceae (aff Festuca))
AMENDMENTS AND REVISIONS
The official position of the Ecological Society of America's Panel on Vegetation Classification emphasizes the importance of maintaining stable standards for effective application, while also acknowledging the necessity for future amendments and revisions to both the standards and supporting text.
A new version of this document will be officially released annually, typically taking effect on January 1 Both the current and previous versions will be accessible on the Panel website.
(http://www.esa.org/vegweb/).
Proposals for revising the document will be discussed during regularly scheduled Panel meetings Written proposals can be submitted to the Chair at any time but must be received at least one month before the next meeting to ensure discussion Panel members can introduce changes during the meeting and suggest modifications to existing proposals Approved proposals will be posted on the Panel website for a minimum of two months prior to a formal vote by the full Panel, conducted via mail or email The Panel Chair or a designated representative will gather and distribute public comments received within two months of the posting A two-thirds majority is required for the approval of any changes to the document.
INTERNATIONAL COLLABORATION, PROSPECTS AND DIRECTIONS
INTERNATIONAL COLLABORATION
Vegetation transcends political borders, making international collaboration essential for effective classification The US National Vegetation Classification serves as a key component of the broader International Vegetation Classification (IVC) initiative This document outlines standards that align with the objectives of the IVC, aiming for a cohesive set of guidelines that all IVC partners will adopt.
The improvement of the International Vegetation Classification (IVC) is an ongoing process that involves five key elements: the collection and integration of new data, the evaluation and adoption of innovative analysis methods, the publication of updated vegetation types, the application of existing knowledge about vegetation, and the unification of national classification efforts into a coherent IVC The ESA Panel advocates for international collaboration to enhance the development and implementation of these standards.
10.2 BUILDING THE CLASSIFICATION CONSORTIUM FOR THE FUTURE
The successful development and implementation of the IVC as a credible scientific endeavor relies heavily on the active support and involvement of scientists and their respective institutions In the United States, a consortium has been established to promote the advancement of the NVC, formalized through a Memorandum of Understanding.
Future activities will focus on revising the standards, ensuring open access to databases for classification support, and maintaining a review process for floristic unit changes The FGDC will represent the needs of US federal agencies, coordinating the testing and evaluation of the classification NatureServe will leverage its extensive experience in this domain to contribute effectively.
The development and management of the National Vegetation Classification is essential for maintaining consistency in classification applications This initiative also aims to represent a network of natural heritage programs and conservation data centers across various provinces, states, and countries.
The ESA serves as a representative body for the professional scientific community in the Americas, leveraging its extensive experience in publication and independent peer review to uphold the credibility of its classifications The ESA Panel offers an impartial platform for all stakeholders to evaluate proposed changes to established standards and recognized classification units.
The international development and implementation of the International Vegetation Classification (IVC) necessitates collaboration among national programs, as exemplified by the Canadian National Vegetation Classification (C-NVC), which aligns with the IVC framework The Canadian Forest Service collaborates with provincial governments, Conservation Data Centers, and various federal organizations to establish forest and woodland types that adhere to the association concept of the IVC standards Provinces have conducted comprehensive surveys using standardized plots, resulting in established vegetation classifications or ongoing development efforts Many provinces have created alliance and association units that utilize the same standards, nomenclature, and codes as those in the U.S., while also introducing new classifications This collaborative approach enhances the potential for integrating U.S and Canadian vegetation associations into a globally recognized IVC framework.
PROSPECTS FOR SCIENTIFIC ADVANCEMENT
The establishment of national standards and the advancement of the Integrated Vegetation Classification (IVC), along with the creation of national plot archives, will significantly enhance the collection of new field data and improve access to existing legacy data By adhering to these standards and processes, the new data will ensure consistency in identifying, describing, and documenting various vegetation types, ultimately advancing our overall understanding of vegetation.
Prospects for new analytic methods
The NVC aims to establish a structured framework for the development and characterization of vegetation alliances and associations By adopting a unified approach and generating consistent field data, the potential for enhanced statistical power and innovative analytical methods in vegetation science is significantly increased Consequently, there are promising opportunities for new technical solutions to address various unresolved issues in the field.
Discovery and description of vegetation types
A comprehensive classification of vegetation, aligned with established standards, will only be achieved as plot databases are expanded and analysis is finalized This effort involves the ongoing reassessment of previously published names and type concepts at the alliance and association levels Careful analysis and documentation will be conducted by the scientific community within various agencies and institutions, with findings published in papers or monographs.
Peer-review teams ensure that proposals for changes in types, nomenclature, and description take place within a systematic, credible and consensual peer-review process
Researchers are encouraged to submit proposals for both new vegetation types and for revisions of types already described
Changes in vegetation units are significantly influenced by invasive species, climate change, fire suppression, and other large-scale biophysical dynamics The long-term impacts of invasive species on vegetation stability, distribution, and functioning remain poorly understood, particularly in light of the rapid global mixing of species Additionally, the implications of climate change on species distributions are just starting to be explored It is essential to comprehend these factors and incorporate their consequences into vegetation classification.
New applications of present knowledge
The primary reason for establishing standards for vegetation classification has been to ensure compatibility of applications across federal agencies, state agencies, universities, and
Seventy-two private organizations are utilizing standardized vegetation classification to create unique map units tailored to specific projects, ensuring comparability across different applications As mapping and inventory techniques continue to improve, the range of these applications is expected to grow significantly.
Effective resource inventory, conservation, and management are essential for both government and private agencies to identify rare or threatened vegetation types and their locations This understanding has spurred the development of innovative vegetation inventory applications Furthermore, the acknowledgment that many rare species inhabit unique vegetation types has driven biodiversity conservation efforts, emphasizing the importance of maintenance and restoration initiatives for these critical ecosystems.
Resource mapping relies on established standards for vegetation classification, which enhances the consistency and reliability of vegetation mapping Major land development initiatives, such as Habitat Conservation Plans mandated by the Endangered Species Act of 1982, will incorporate detailed vegetation classification to inform their development conservation management strategies.
Resource monitoring across North America involves studies aimed at tracking changes in vegetation, with agencies tasked to oversee specific resources like forests and grasslands, as well as assess ecosystem health However, many monitoring efforts produce results that lack the spatial or thematic resolution needed for effective land management Until recently, there has been no standardized method to define species assemblages for monitoring or to assess deviations from typical community occurrences Effective research in this area necessitates a clear definition and documentation of vegetation types as a baseline, followed by consistent measurements and comparisons over extended periods.
Ecological integrity is closely linked to vegetation, which serves as a crucial framework for understanding the complexity of ecosystems Acting as habitat for countless species, vegetation influences ecological dynamics across various environments Its changes over time and space can create widespread effects within ecosystems Furthermore, advancements in remote-sensing technologies allow for the mapping of vegetation, making it an effective tool for tracking and comprehending ecosystem changes.
This document outlines a framework for an international classification of vegetation aimed at promoting sustainable resource conservation, effective environmental management, and advancing fundamental vegetation science.
Undoubtedly, new applications to vegetation classification will emerge and lead to further improvements The standards described here provide a point of departure toward those ends.
Adam, P 1994 Australian rainforests Oxford University Press, New York 308 p.
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Margules and M.P Austin (eds.) Nature conservation: cost effective biological surveys and data analysis CSIRO, Clayton South, Victoria, Australia pp 31-36.
Avers, P.E., Cleland, D., and McNab, W.H (1994) present a National Hierarchical Framework of Ecological Units in their contribution to the General Technical Report SE-88, edited by Louise H Foley This work, published by the U.S Department of Agriculture's Forest Service, Southeastern Forest Experiment Station, spans pages 48-61 and emphasizes the significance of structured ecological classifications for effective silviculture and ecosystem management.
Bailey, R.G 1996 Ecosystem geography Springer-Verlag, New York 204 pp.
Bailey et al (1994) discuss the design and application of ecological mapping units in their contribution to "Ecosystem Management: Principles and Applications." This work, featured in USDA Forest Service General Technical Report PNW-GTR-318, emphasizes the importance of ecological mapping for effective ecosystem management, providing insights into methodologies and practical applications.
Barbour, M 1994 Special committee on vegetation classification Bulletin of the Ecological
Barbour, M.G., R.F Fernau, J M Rey, N Jurjavcic, and E B Royce 1998 Tree regeneration and early succession following clearcuts in red fir forests of the Sierra Nevada,
California Journal of Forest Ecology and Management 104:101-111.
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Heritage Task Force (WHTF) Site and Community Survey Manual Version 91C
Western Heritage Task Force, Boulder, Colorado, USA 24p.
Bourgeron, P.S., and L.D Engelking (eds.) conducted a preliminary vegetation classification of the western United States in 1994 This unpublished report was prepared by the Western Heritage Task Force for the Nature Conservancy in Boulder, Colorado Note that unpublished reports typically do not appear in literature sections.
Braun-Blanquet, J 1928 Pflanzensoziologie Gründzuge der Vegetationskunde Springer-
1932 Plant Sociology: the study of plant communities McGraw-Hill, New York,
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Brown, D.E., Lowe, C.H., and Pase, C.P (1980) developed a digitized systematic classification for ecosystems, complemented by an illustrated summary of North America's natural vegetation This work is documented in the USDA Forest Service General Technical Report RM-73, published by the Rocky Mountain Forest and Range Experiment Station in Fort Collins, Colorado, USA.
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Classification and occurrence plots are essential for accurately defining and developing classified vegetation types, such as associations and alliances Required attributes, marked with a number 1, are the minimum necessary for these plots, while optimal attributes, indicated with a number 2, represent best practices for recording data Occurrence plots must contain sufficient information to correctly assign a plot to an existing association or alliance, ensuring reliable classification and analysis of vegetation.
1 Information that should be included on the form used to record plot data in the field.
1.1 Field form information about the plot record.
1.2 Field form information about the plot location.
1.3 Field form information about the plot vegetation.
1.4 Field form information about the plot environment.
1.5 Field form information about the plot habitat.
2 Information that should be included as metadata.
2.1 Metadata about the original field project for which the plot record was collected. 2.2 Metadata about the plot and the plot observation.
2.3 Metadata about the methods used to collect the field data.
2.4 Metadata about the human sources of the field data.
2.5 Metadata about references for other sources of plot data.
2.5 Metadata about plot record confidentiality and links to publications and sources.
3 Information that should be included about each assignment of a field plot to a vegetation type or types in the NVC.
For access to an ASCII file of each table as well as more detailed information, see http:// www.vegbank.org.
When recording plot data in the field, it is essential to include specific information on the form used The attribute names utilized in this form are based on those found in the VegBank plot archive, with underscores added for enhanced readability This ensures clarity and consistency in data collection, facilitating better analysis and comparison of ecological information.
1.1 Field form information about the plot record.
Author's plot number/code, or the original plot number if taken from literature.
Attribute Name Attribute Definition C O Author Observation Code
Code or name that the author uses to identify this plot observation Where a plot has only one observation, code will often equal Author Plot Code.
The placement method outlines the approach utilized to establish the positioning of a plot Additionally, the observation start date refers to the initial day of observation, particularly when the observation period extends beyond a single day.
Observation Stop Date The last day of the observation if the observation spanned more than one day 2 2 Date Accuracy
Estimated accuracy of the observation date Accuracy is often low for legacy data Consult VegBank for constrained vocabulary.
1.2 Field form information about the plot location (some can be determined after a return to office, for example, with coordinate conversions).
WGS84 Latitude and Longitude of the plot origin in degrees and decimals following any adjustments, conversions and postprocessing.
Field data includes precise coordinates such as latitude and longitude with their respective datum, UTM coordinates with datum, or alternative geographic projections Essential elements of these coordinates are the longitude and latitude of the projection's center, along with the false easting and false northing values.
Estimated accuracy of the location of the plot Plot origin has a 95% or greater probability of being within this many meters of the reported location.
Location Narrative Text description that provides information useful for plot relocation 2 2 Area
Total area of the plot in square meters If many subplots, this area includes the subplots and the interstitial space.
Stand Size Estimated size of the stand of vegetation in which the plot occurs 2 2
USGS Quad U.S Geological Survey 7.5 minute 2 2
Attribute Name Attribute Definition C O quadrangle name.
Place name Country Country of plot location 2 2
Place name State/Prov State, province, or similar subnational jurisdiction 2 2
Place name Canton County, township, parish, or similar local jurisdiction 2 2
1.3 Field form information about the plot vegetation.
Dominant Stratum Identify the dominant stratum (of the six standard strata) 2 2
Growth form 1 The predominant growth form 2 2
Growth form 2 The second-most predominant growth form 2 2
Growth form 3 The third-most predominant growth form 2 2
Growth form1 Cover Total cover of the predominant growth form 2 2
Growth form 2 Cover Total cover of the second-most predominant growth form 2 2
Growth form 3 Cover Total cover of the third-most predominant growth form 2 2
Basal Area Total basal area of woody stems in m 2 /ha 2 2
The following stratum variables are recorded once for each stratum recognized
The first three are required if strata are used
Stratum Index Indices used to represent strata 1* 2
Stratum Name Names of strata 1* 2
Stratum Description Description of strata 1* 2
Stratum Height Average height to the top of the stratum in meters 2 2
Stratum Base Average height of the bottom of the stratum in meters 2 2
Stratum Cover Total cover of vegetation within the given stratum in percent 1* 2
The following apply for recording plant taxa, with at least one record per taxon, and multiple records when taxa are observed in multiple strata
Plant Name Name of the taxon 1 1
Plant Reference Authority followed for taxon (could be 1 1
Attribute Name Attribute Definition C O entered by taxon, or collectively for the whole plot or as a default where not otherwise specified in the metadata).
Taxon Stratum Cover Percent cover of taxon in stratum 2 2
Overall cover of the taxon across all strata For occurrence plots, only dominant taxa are required, whereas for classification plots a nearly complete list of taxon occurrences is required.
This is the area in square meters used to infer the cover of a given taxon
Generally this should be equal to Taxon Observation Area, but at times this area may be larger or smaller for a specific taxon.
1.4 Field form information about the plot environment.
Elevation The elevation of the plot origin in meters above sea level 1 2
Elevation Accuracy The accuracy of the elevation in percentage of the elevation reported 2 2 Slope Aspect
Representative azimuth of slope gradient (0-360 degrees); if too flat to determine
= -1; if too irregular to determine = -2.
Representative inclination of slope in degrees; if too irregular to determine, = - 1.
Position of the plot on land surface (e.g., summit, shoulder, upper slope, middle slope, lower slope, toeslope, no slope, channel bed, dune swale, pond) Consult VegBank for constrained vocabulary.
Landform Landform type Consult VegBank for constrained vocabulary 2 2
Geology Surface geology type Consult VegBank for constrained vocabulary 2 2
Hydrologic regime based on, frequency and duration of flooding) (Cowardin et al 1979) Consult VegBank for constrained vocabulary.
Soil Moisture Regime Soil moisture regime, such as xeric, 2 2
Attribute Name Attribute Definition C O mesic, hygric, hydric Consult VegBank for complete constrained vocabulary
Drainage of the site (generally consistent with USDA classes) Consult VegBank for constrained vocabulary.
How saline is the water, if a flooded community Consult VegBank for constrained vocabulary 2 2
Water Depth For aquatic or marine vegetation, the water depth in m 2 2
Shore Distance For aquatic or marine vegetation, the closest distance to shore in m 2 2
Median depth to bedrock or permafrost in m (usually from averaging multiple probe readings).
Organic Depth Depth of the surficial organic layer, where present, in centimeters 2 2
Percent Bed Rock Percent of surface that is exposed bedrock 2 2
Percent Rock & Gravel Percent of surface that is exposed rock and gravel 2 2
Percent Wood Percent of surface that is wood 2 2
Percent Litter Percent of surface that is litter 2 2
Percent Bare Soil Percent of surface that is bare mineral soil 2 2
Percent Water Percent of surface that is water 2 2
Soil Taxon Name of soil type 2 2
Soil Taxon Source Source of soil type 2 2
1.5 Field form information about the plot habitat.
Observation Narrative Additional unstructured observations useful for understanding the ecological attributes and significance of the plot observations.
The landscape narrative involves unstructured observations regarding the context of the observed plot, focusing on the overall homogeneity of the community This includes assessing whether the plot exhibits a homogeneous composition, notable inclusions, or an irregular mosaic pattern For precise terminology, it is recommended to consult VegBank for a constrained vocabulary.
Attribute Name Attribute Definition C O Phenological Aspect Season expression of the community
(e.g., typical growing season, vernal, aestival, wet, autumnal, winter, dry, irregular ephemerals present) Consult VegBank for constrained vocabulary.
Representativeness How representative was the plot of the stand Consult VegBank for constrained vocabulary.
Stand Maturity Assess maturity of stand (e.g., young, mature but even-aged, old-growth, etc.) Consult VegBank for constrained vocabulary.
Successional Status Description of the assumed successional status of the plot 2 2
The following should be repeated once for each type of disturbance reported
Disturbance Type The type of disturbance being reported
Repeat this field as many times as necessary where there is more than one type of disturbance
Disturbance Intensity Intensity or degree of disturbance
Consult VegBank for constrained vocabulary
Disturbance Age Estimated time in years since the disturbance event 2 2
Disturbance Extent Percent of the plot that experienced the event 2 2
The disturbance comment should include a detailed description of the disturbance and its effects on vegetation If multiple types of disturbances are present, this section should be repeated accordingly to capture the various impacts comprehensively.
2 Information that should be included as metadata.
2.1 Metadata about the original field project for which the plot record was collected.
Project Name Project name as defined by the principal investigator 2 2
Project Description Short description of the project including the original purpose for conducting the project This can be viewed as the project
Attribute Name Attribute Definition C O abstract plus supporting metadata.
Start Date Project start date 2 2
Stop Date Project stop date 2 2
2.2 Metadata about the plot and the plot observation.
Layout Narrative Text description of and the rationale for the layout of the plot 2 2
Additional metadata helpful for understanding how the data were collected during the observation event.
The total surface area, measured in square meters, utilized for cover estimates is detailed, along with a comprehensive species list In cases where subplots were employed, the reported area reflects the total of these subplots, excluding any interstitial spaces.
Were cover values for the total taxon list collected from one contiguous area or dispersed subplots (e.g., contiguous, dispersed-regular, dispersed-random).
Original Data Location where the hard data reside and any access instructions 2 2
Effort spent making the observations as estimated by the party that submitted the data Consult VegBank for constrained vocabulary.
Subjective assessment of floristic quality by the party that submitted the plot
Consult VegBank for constrained vocabulary.
Bryophyte Quality see Floristic Quality Consult VegBank for constrained vocabulary 2 2
Lichen Quality see Floristic Quality Consult VegBank for constrained vocabulary 2 2
Indicate if voucher specimens were collected and, if so, where they were deposited
This section provides metadata regarding the methods employed for collecting field data It is essential to specify if a standard stratum method was utilized Additionally, the vertical strata implemented for documenting taxon cover should be clearly identified.
Cover class scales should be defined by their minimum, maximum, and representative cover percentages, as outlined in Table 1.3 You can either utilize an existing, recognized cover scale, which should be indicated in field 3, or you can create a new scale by providing multiple entries in fields 4-8.
Name of the stratum method (e.g., Braun-Blanquet, NatureServe, , North Carolina Vegetation Survey #1,etc ).
Stratum Method Description This field describes the general methods used for strata 1 2
Name of the cover class method (e.g., Braun-Blanquet, Barkman, Domin, Daubenmire, North Carolina Vegetation Survey, etc.).
Cover Code The name or label used in the cover class scale for this specific cover class 1 2 Upper Limit Upper limit, in percent, associated with the specific cover code 1 2
Lower Limit This is the lower limit, in percent, associated with a specific Cover Code 1 2
The database stores a middle value, typically the mean or geometric mean, for each taxon observation, which serves as a basis for all cover class conversions and interpretations This value is designated by the author of the cover class schema.
Description of the specific cover class
This is particularly helpful in the case that there is no numeric value that can be applied.
2.4 Metadata about the human sources of the field data.
Given Name One's first name 1 1
Middle Name One's middle name or initial, if any 2 2
Name shared in common to identify the members of a family, as distinguished from each member's given name.
Organization Name Name of an organization 2 2
Current Name Recursive foreign key to current name of this party 2 2
Address Start Date The first date on which the 1 1
Attribute Name Attribute Definition C O address/organization information was applied.
Delivery Point Address line for the location (street name, box number, suite) 2 2
City City of the location 2 2
Administrative Area State, province of the location 2 2
Postal Code Zip code or other postal code 2 2
Country Country of the physical address 2 2
The following can be repeated an indefinite number of times per person
Role: Plot submitter Name of the person submitting the analysis 1 1
Name of the person who made the field observation (e.g., PI, technician, volunteer, etc.).
Role: Plot Author Name of the author of the plot record 1 1 Role: Project PI Name of the field plot inventory project’s principal investigator 2 2
Role: Other Report other roles as appropriate See
2.5 Metadata about references for other sources of plot data.
Authors Name of authors if plot record is taken from published work 1 1
Title Title of publication, if plot record is taken from published work 1 1
Publication Date Date of publication, if plot record is taken from published work 1 1
Edition of publication if applicable, and if plot record is taken from published work.
Name of publication series, if applicable, and if plot record is taken from published work.
Page Page number of publication, if plot record is taken from published work 1 1 Table Cited
Table number or code, if applicable and if plot record is taken from published work 1 1
Plot Cited Original plot name, if plot record is taken from published work 1 1
International Standard Book Number (ISBN), if applicable, and if plot record is taken from published book.
ISSN International Standard Serial Number, if applicable 2 2
Provides a concise or abbreviated name that describes the resource that is being documented.
Describes the type of reference this generic type is being used to represent
Examples: book, journal article, webpage.
Title The formal title given to the work by its author or publisher 1 1
A second, higher order title where appropriate, which in the case of a reference to a chapter is the Book title, and in the case of a Conference
Presentation is the Name of the Conference.
Pub Date Represents the date that the reference was published 1 1
The date the reference being referenced was accessed This is useful if the reference is could be changed after formal publication, such as websites or databases.
Conference Date The date the conference was held 1 1
Volume The volume of the journal in which the article appears 1 1
Issue The issue of the journal in which the article appears 1 1
Page Range The beginning and ending pages of the journal article that is being documented 1 1
Total Pages The total number of pages in the book that is being described 1 1
Publisher The organization that physically put 1 1
Attribute Name Attribute Definition C O together the report and publishes it.
The location at which the work was published This is usually the name of the city in which the publishing house produced the work.
The ISBN, or International Standard Book Number assigned to this literature reference.
Edition The edition of the generic reference type that is being described 1 1
Number Of Volumes Number of volumes in a collection 1 1
Chapter Number The chapter number of the chapter of a book that is being described 1 1
The unique identification number that has been issued by the report institution for the report being described.
The type of personal communication
Could be an email, letter, memo, transcript of conversation either hardcopy or online.
Degree The name or degree level for which the thesis was completed 2 2
A URL (Uniform Resource Locator) from which this reference can be downloaded or additional information can be obtained.
A Digital Object Identifier (DOI) is a unique digital identifier used for intellectual property, enabling persistent identification of digital content on networks It ensures that intellectual property is easily locatable and connected to relevant, up-to-date information.
Any information that is not characterized by the other reference metadata fields
Journal The name of the publication in which the article was published Example(s):
Ecology, New York Times, Harper's,
Canadian Journal of Botany/Revue Canadienne de Botanique ,The Journal of the American Medical Association ISSN
The ISSN, or International Standard Serial Number assigned to this literature reference Example(s): ISSN 1234-5679
Standard abbreviation or shorter name of the journal Example(s): Can J
Bot./Rev Can Bot., JAMA
The following can be repeated an indefinite number of times for each alternate identifier used to describe the reference
A data management system requires that each identifier, typically represented as a unique URL, is distinct within its scope This ensures that identifiers used in the same system point to the same object; for instance, if the same identifier appears in two places within identical systems, it refers to the same entity An example of such an identifier is http://metacat.somewhere.org/svc/mc/.
This section allows for the inclusion of secondary identifiers for a reference, which complements the primary identifier found in the reference table These additional identifiers, potentially sourced from various data management systems, can be documented here for clarity and organization For instance, an example of such an identifier is VCR3465.
The following can be repeated an indefinite number of times for each contributor to the reference (e.g author, editor)
Role Type The role the party played with respect to the reference contribution Some potential roles include technician, reviewer, principal investigator, and
Attribute Name Attribute Definition C O many others.
Numerical order in which this contributor's name should be in the order of contributors, if applicable Examples:
1 [for the first author], 2, [for the second author], etc.
Type The type of Party that a given record refers to, usually a person or institution 1 1
This section is designed to specify an individual's role within an organization Including the position name is essential for maintaining consistency, especially in situations where the person occupying the role may change frequently.
The salutation field is used in addressing an individual with a particular title, such as Dr., Ms., Mrs., Mr., etc.
The given name field is used for the all names, except the surname, of the individual Examples: Jo, Jo R., Jo R.W., John Robert Peter
Surname The surname field is used for the last name of the individual 1 1
A suffix or suffix abbreviation that follows a name Examples: Jr., Senior, III, etc.
The organization associated with the referenced contribution is crucial for identifying the institution linked to the described resource This field serves to clarify the overall entity involved with the information provided.
A link to the record of the current name of the party, if different from the name used in this record
2.6 Metadata about plot record confidentiality and links to publications and sources
Recommended constrained vocabularies are essential for accurately recording plot information related to specific conditions These standardized vocabularies are utilized in database "picklists," promoting consistency in data types and enhancing information exchange across platforms.
3 Accuracy of Time of Day
11 Soil Moisture Regime of Plot
Types Descriptions of Plot Observation Types
Entire Cover based on observation of an entire plot consisting of a single contiguous area of land
Subplot-contiguous Cover based on observation of a single contiguous area of land of less spatial extent than the entire plot
Subplot-regular Cover based on observation of multiple subplots arranged in a regular pattern within the overall plot
Subplot-random Cover based on observation of multiple randomly dispersed within the overall plot
Subplot-haphazard Cover based on observation of multiple subplots haphazardly arranged within the overall plot
Descriptions of Time of Day Accuracy
Categories One minute Time of day is accurate to within one minute One hour Time of day is accurate to within one hour
Quarter-day Time of day is accurate to within one quarter-day (e.g., during morning, during afternoon)
Half day Time of day is accurate to within one half- day (e.g., between 00:00 and 11:59, or between 12:00 and 23:59)
Accuracy of Date Descriptions of Date Accuracy Categories
One day Date accurate to within one day
One week Date accurate to within one week
One month Date accurate to within one month
Three months Date accurate to within three months
One year Date accurate to within one year
Three years Date accurate to within three years
Ten years Date accurate to within then years
Greater than ten years Date accurate to within more than ten years
Appendix 4, Table 5 NOTE: Vegetation strata are not to be confused with life forms.
Stratum Types Descriptions of Vegetation Stratum Types
Tree Includes tall trees (single-stemmed woody plants, generally more than 5 m in height or greater at maturity under optimal growing conditions)
Tall shrubs that resemble trees, along with other life forms like lianas and epiphytes, can be categorized in this context, and their roles within the stratum can be detailed using the “life form” classification.
Shrubs are defined as multiple-stemmed woody plants that typically reach a height of less than 5 meters under optimal growing conditions, including shorter trees or saplings This stratum may also encompass other life forms, excluding herbaceous plants, which die back annually and lack the consistent height of woody species In cases where dwarf-shrubs, defined as those under 0.5 meters, create a distinct layer—either as part of a forest's structure or in open vegetation like tundra or xeric shrublands—they should be classified as a low variant of the shrub stratum Additionally, in various vegetation types, dwarf-shrubs may function as a component of the herb stratum.
Herb, often known as field stratum, comprises non-woody plants that typically die back each year, along with low creeping semi-shrubs, dwarf-shrubs, vines, and non-woody brambles like raspberries This layer also includes the seedlings of trees and shrubs, creating a diverse and rich ecosystem.
Moss, often called nonvascular or ground stratum, is primarily composed of mosses, lichens, liverworts, and algae It is important to distinguish that ground-creeping vines, prostrate shrubs, and herbs belong to the herb stratum.
Where herbs are entirely absent, it is still possible to recognize this stratum if other very low woody or semi-woody life forms are present.
Floating Includes rooted or drifting plants that float on the water surface (e.g., duckweed, water-lily)
Submerged aquatic plants, such as pondweed, can be either rooted or drifting and typically remain fully submerged in the water column or on the bottom Understanding the arrangement of these plants within different strata is essential, as emergent plants in wetlands, like cattails and sedges, belong in the herb stratum, while floating species, such as duckweed, are categorized in the floating aquatic stratum.
Vine/Liana (woody climbers or vines)
Soil Moisture Regime of Plot
Salinity Saltwater greater than 30 ppt
This article provides a comprehensive overview of various rock types, including acidic-ash, andesite, basalt, and coal, among others It categorizes igneous rocks into acid, basic, coarse crystal, fine crystal, intermediate, and ultrabasic varieties Additionally, it highlights sedimentary formations such as limestone, sandstone, and shale, along with their specific types, including arenaceous, argillaceous, and phosphatic limestone The inclusion of metamorphic rocks like gneiss and hornfels further enriches the discussion on geological classifications Understanding these diverse rock types is essential for geological studies and applications.
This article provides an overview of various rock types, including limestone, shale, and siltstone, as well as their specific classifications such as calcareous and noncalcareous varieties It also covers metamorphic rocks like marble, schist, and quartzite, alongside igneous types such as rhyolite and obsidian The text highlights mixed rock categories that combine igneous, metamorphic, and sedimentary features, while also detailing pyroclastic materials and unique forms like pumice and scoria Overall, it serves as a comprehensive guide to understanding the diverse classifications and characteristics of rocks in geology.
This article explores various rock types, including tuff, which can be categorized into acidic, basic, and unspecified varieties It also discusses volcanic bombs and volcanic breccia, with similar classifications of acidic, basic, and unspecified Additionally, the article mentions the presence of wood and the absence of visible rock in certain observations.
Stand Size Descriptions of Stand Sizes
Very Extensive greater than 1000x plot size
Extensive greater than 100x plot size
Inclusion less than 1x plot size
Surficial Geologic Material Residual Material: Bedrock
Residual Material: Deeply Weathered Rock
Glacial Deposits: Undifferentiated glacial deposit
Glacial Deposits: Bedrock and till
Glacial Deposits: Glacial-fluvial deposits (outwash)
Marine and Lacustrine Deposits: Unconsolidated Sediments
Marine and Lacustrine Deposits: Coarse sediments
Marine and Lacustrine Deposits: Fine-grained sediments
Slope and Modified Deposits: Talus and scree slopes
Slope and Modified Deposits: Colluvial
Slope and Modified Deposits: Solifluction, landslide
Aeolian Deposits: Aeolian sand flats and cover sands
Chemical Deposits: Evaporites and Precipitates
High slope shoulder slope, upper slope, convex creep slope
High level mesa, high flat
Midslope transportational midslope, middle slope
Step in slope ledge, terracette
Lowslope lower slope, foot slope, colluvial footslope
Low level terrace, low flat
Channel bed narrow valley bottom, gully arroyo
Soil Texture Description of Soil Texture Types
Sands: Coarse Sand Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Sands | Texture Subclass: Coarse Sand
Sands are classified within the sandy soils texture group, characterized by their coarse texture This classification includes two main subclasses: the general sand texture and fine sand texture Both types fall under the broader category of sands, highlighting their distinct properties and applications in various soil management practices.
Sands: Very Fine Sand Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Sands | Texture Subclass: Very Fine Sand
Sands: (unspecified) Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Sands | Texture Subclass:
Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Loamy Sands | Texture Subclass:
Loamy Coarse Sand Loamy Sands: Loamy Sand Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Loamy Sands | Texture Subclass:
Loamy Sand Loamy Sands: Loamy Fine
Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Loamy Sands | Texture Subclass:
Loamy Fine Sand Loamy Sands: Loamy Very
Fine Sand Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Loamy Sands | Texture Subclass:
Soil Texture Description of Soil Texture Types
Loamy Sands: (unspecified) Texture Group: Sandy soils | General Term: Coarse- textured | Texture Class: Loamy Sands | Texture Subclass:
(unspecified) Sandy Loams: Coarse Sandy
Texture Group: Loamy soils | General Term: Moderately coarse-textured | Texture Class: Sandy Loams | Texture Subclass: Coarse Sandy Loam
Sandy Loams: Sandy Loam Texture Group: Loamy soils | General Term: Moderately coarse-textured | Texture Class: Sandy Loams | Texture Subclass: Sandy Loam
Texture Group: Loamy soils | General Term: Moderately coarse-textured | Texture Class: Sandy Loams | Texture Subclass: Fine Sandy Loam
Texture Group: Loamy soils | General Term: Medium- textured | Texture Class: Sandy Loams | Texture Subclass:
Very Fine Sandy Loam Sandy Loams: (unspecified) Texture Group: Loamy soils | General Term: Moderately coarse-textured to Medium-textured | Texture Class:
Sandy Loams | Texture Subclass: (unspecified)
Loamy soils, categorized under the loam texture group, are classified as medium-textured soils This group includes both loam and silt loam texture classes, with silt loam serving as a specific subclass.
Loamy soils are categorized into various texture groups, including the silt texture group, which is characterized as medium-textured silt Another important classification is the sandy clay loam texture group, recognized as moderately fine-textured sandy clay loam These classifications play a crucial role in understanding soil composition and its suitability for different agricultural practices.
Clay Loam Texture Group: Loamy soils | General Term: Moderately fine-textured | Texture Class: Clay Loam | Texture Subclass: Clay Loam
Silty Clay Loam Texture Group: Loamy soils | General Term: Moderately fine-textured | Texture Class: Silty Clay Loam | Texture Subclass: Silty Clay Loam
Sandy Clay Texture Group: Clayey soils | General Term: Fine- textured | Texture Class: Sandy Clay | Texture Subclass:
Sandy Clay Silty Clay Texture Group: Clayey soils | General Term: Fine- textured | Texture Class: Silty Clay | Texture Subclass:
Silty Clay Clay Texture Group: Clayey soils | General Term: Fine- textured | Texture Class: Clay | Texture Subclass: Clay
An example of the description of a floristic association.
Name: Sporobolus heterolepis - Schizachyrium scoparium - (Carex scirpoidea) /
Name.translated: Prairie Dropseed - Little Bluestem - (Scirpus-like Sedge) / (Creeping Juniper) Herbaceous Vegetation
Common Name: Little Bluestem Alvar Grassland
FORMATION: V.A.5.N.c Medium-tall sod temperate or subpolar grassland
ALLIANCE: V.A.5.N.c.41 SPOROBOLUS HETEROLEPIS - (DESCHAMPSIA CAESPITOSA, SCHIZACHYRIUM SCOPARIUM) HERBACEOUS ALLIANCE
Summary: The little bluestem alvar grassland type is found primarily in the upper Great
The Lakes region of the United States and Canada, particularly in northern Michigan and southern Ontario, features unique grasslands characterized by shallow, patchy soils, typically less than 20 cm deep and averaging around 6 cm These soils, predominantly loams with high pH and organic matter content, support a distinctive moisture regime that alternates between wet and dry periods, resulting in saturated soils during spring and fall, alongside summer droughts Often found in large patches exceeding 20 hectares (50 acres), these grasslands create a small-scale matrix with smaller patches of various alvar communities, contributing to a diverse landscape mosaic Commonly associated alvar communities include Juniperus horizontalis - Dasiphora fruticosa ssp floribunda / Schizachyrium scoparium - Carex richardsonii Dwarf-shrubland and Deschampsia.
120 caespitosa - (Sporobolus heterolepis, Schizachyrium scoparium) - Carex crawei - Packera paupercula Herbaceous Vegetation, Tortella tortuosa - Cladonia pocillum - Placynthium spp
Sparse vegetation in the region is characterized by a dominance of grasses and sedges, often covering over 50% of the area Key species include Sporobolus heterolepis, Schizachyrium scoparium, and Juniperus horizontalis, alongside Thuja occidentalis, Pinus banksiana, Dasiphora fruticosa ssp floribunda, and Clinopodium arkansanum, which contribute to the diverse wooded herbaceous vegetation.
Carex scirpoidea, Deschampsia caespitosa, Packera paupercula, and Carex crawei are the dominant plant species in this ecosystem, where shrub cover over 0.5 m tall is typically less than 10% However, dwarf-shrubs, particularly Juniperus horizontalis, can cover up to 50% of the area, often found beneath the taller grass canopy Despite the significant presence of these dwarf-shrubs, the area is classified as grassland due to the dominance of grasses Additionally, less than 50% of the ground surface is exposed bedrock, which may be covered by nonvascular plants such as lichens, mosses, and algae.
Classification Comments: The most commonly associated alvar communities that occur with this community in a landscape mosaic are Juniperus horizontalis - Dasiphora fruticosa ssp floribunda / Schizachyrium scoparium - Carex richardsonii Dwarf-shrubland
(Creeping Juniper - Shrubby-cinquefoil Alvar Pavement Shrubland; CEGL005236),
Deschampsia caespitosa - (Sporobolus heterolepis, Schizachyrium scoparium) - Carex crawei - Packera paupercula Herbaceous Vegetation (Tufted Hairgrass Wet Alvar
Grassland;CEGL005110), Tortella tortuosa - Cladonia pocillum - Placynthium spp Sparse Vegetation (Alvar Nonvascular Pavement;CEGL005192) and, Thuja occidentalis - Pinus banksiana / Dasiphora fruticosa ssp floribunda / Clinopodium arkansanum Wooded
Herbaceous Vegetation (White-cedar - Jack Pine / Shrubby-cinquefoil Alvar Savanna;
Sporobolus heterolepis and Schizachyrium scoparium are the dominant species in this ecosystem, exhibiting over 60% constancy Carex scirpoidea, while less constant, serves as a significant differentiator among various alvar types Although Juniperus horizontalis is also less constant, it can become a dominant species when present.
The vegetation is primarily composed of grasses and sedges, typically covering over 50% of the area In contrast, shrubs taller than 0.5 meters account for less than 10% of the cover, while dwarf shrubs under 0.5 meters can reach up to 50% coverage.
Juniperus horizontalis is a dwarf shrub that typically grows beneath the canopy of taller grasses, contributing to a grassland ecosystem despite its significant presence In this environment, less than 50% of the ground surface is visible bedrock, which may also be covered by nonvascular plants such as lichens, mosses, and algae.
Floristics: Characteristic species of the grassland are Sporobolus heterolepis,
Schizachyrium scoparium, Juniperus horizontalis, Carex scirpoidea, Deschampsia caespitosa, Packera paupercula (= Senecio pauperculus), and Carex crawei Juniperus horizontalis may co- dominate in some stands
Environment: These grasslands occur on very shallow, patchy soils (usually less than
The grassland community, characterized by 20 cm deep soils averaging around 6 cm, thrives on flat limestone and dolostone outcrops, known as pavements These loamy soils are rich in organic matter and experience a unique moisture regime with alternating wet and dry periods, featuring saturated conditions in spring and fall, followed by summer droughts Typically spanning large patches over 20 hectares (50 acres), this little bluestem alvar grassland forms a small-scale matrix that includes smaller patches of other alvar communities, creating a diverse landscape mosaic (Reschke et al 1998).
Range: The little bluestem alvar grassland type is found primarily in the upper Great
Lakes region of the United States and Canada, in northern Michigan, and in Ontario on
Manitoulin Island and vicinity, on the Bruce Peninsula, and at a few sites further east in the Carden Plain and Burnt Lands.
USFS Ecoregions: 212H:CC, 212Pc:CCC
Archived plot data, including spreadsheet files with compiled vegetation information from various plots and structural types, can be accessed through The Nature Conservancy's Great Lakes Program Office or the respective state and provincial Heritage Programs Additionally, original field forms are maintained at these state or provincial Heritage Programs for reference.
Factors affecting data consistency: [See methods below]
The number and size of plots: Vegetation data were collected using 10 x 10 m relevé plots placed haphazardly within subjectively defined stands
Methods used to analyze field data and identify type:
In a collaborative effort, field data from Michigan, Ontario, and New York were compiled by the Heritage program staff and provided to Carol Reschke, the inventory and research coordinator for the Alvar Initiative With the help of contractor Karen Dietz, this data on vegetation, environment, and ecological processes from alvar sites was organized into spreadsheets The data underwent editing to clarify ambiguous taxa, standardize nomenclature, remove duplicates, and exclude species found in only a few samples Additionally, environmental data and evidence of ecological processes were compiled into two separate spreadsheets The resulting plot data set included information from 85 sample plots, encompassing a total of 240 vascular and nonvascular taxa.
The plot data set provided extensive structural details, capturing the presence of tree species across various vegetation strata For instance, Thuja occidentalis was documented as a distinct taxon in each layer it occupied, such as being classified as a tree when over 5 meters tall and as a tall shrub in lower strata.
The study analyzed a dataset of 85 samples across 240 taxa, focusing on vegetation heights ranging from 2 to 5 meters for trees and 0.5 to 2 meters for shrubs Using PC-ORD v 3.0 software, the percent cover data for each sample was relativized and subsequently transformed using an arcsine-square root transformation, a method recommended for handling percentage data.
The analysis of the complete data set involved two classification methods: cluster analysis utilizing the UPGMA group linkage method with Sørensen's distance measure, and TWINSPAN with default settings Additionally, two ordination procedures were employed, including Bray-Curtis ordination.
Sứrenson's distance and variance-regression endpoint selection, and 2) non-metric multidimensional scaling (NMS) using Sứrenson's distance and the coordinates from the Bray- Curtis ordination as a starting configuration.
Environmental data recorded for each plot and data on evidence of ecological processes were used as overlays in ordination graphs to interpret ordination patterns and relationships among samples.
The classification dendrograms and ordination graphs were presented to a core group of ecologists to discuss the results Participants in the data analysis discussions were: Wasyl
In a collaborative effort, ecologists Bakowsky, Faber-Langendoen, Jones, Comer, Cuddy, Gilman, Albert, and Reschke compared two classifications to analyze plot groupings and validated these through ordinations By the conclusion of their initial meeting, they identified eight distinct alvar community types and proposed an additional four or five types observed in field surveys that were not included in the plot data set The team also suggested refinements to enhance the data analysis process.
In accordance with the ecology group's recommendations, the plot data underwent two modifications The initial data set for nonvascular plants encompassed individual species or genera, along with taxa that represent simple growth forms Due to the limited number of collaborators capable of identifying nonvascular plants in the field, it was decided to classify these plants by their growth form and collect specimens only if a species exhibited a minimum of 5% cover in the plot If surveyors were able to identify nonvascular species or from the collected specimens, the species were documented accordingly.
The dataset included 124 samples, which may have introduced bias, as plots examined by knowledgeable investigators showed greater potential diversity compared to those with limited growth forms identified Consequently, nonvascular taxa were categorized into nine growth form groups: foliose algae, rock surface algae, microbial crusts, turf or cushion mosses, weft mosses, thalloid bryophytes, crustose lichens, foliose lichens, and fruticose lichens Additionally, various structural growth forms of woody taxa were consolidated into a single category, exemplified by combining trees, tall shrubs, and short shrubs of Thuja occidentalis into one taxon.
Field Plot Data Exchange Schema.
Table 1 Recommended growth forms to be used when describing vegetation structure.
Table 2 Comparison of commonly used cover-abundance scales in the United States.
Table 3 Summary of layer data from field plots for a given type.
Table 4 A stand table of floristic composition for each layer.
Table 1 Recommended growth forms to be used when describing vegetation structure (see also
Whittaker 1975:359) Not to be confused with vegetation strata.
Tree Trees (larger woody plants, mostly well above 5 m tall)
Needle-leaved tree (mainly conifers – pine, spruce, larch, redwood, etc.) Broad-leaved deciduous tree (leaves shed in the temperate zone winter, or in the tropical dry season)
Broad-leaved evergreen tree (many tropical and subtropical trees, mostly with medium-sized leaves)
Thorn tree (armed with spines, in many cases with compound, deciduous leaves, often reduced in size)
Evergreen sclerophyllous tree (with smaller, tough, evergreen leaves) Succulent tree (primarily cacti and succulent euphorbs)
Palm tree (rosette trees, unbranched with a crown of large leaves) Tree fern (rosette trees, unbranched with a crown of large leaves) Bamboo (arborescent grasses with woody-like stems)
Shrub Shrubs (smaller woody plants, mostly below 5 m tall)
Needle-leaved shrub (mainly conifers – juniper, yew, etc.) Broad-leaved deciduous shrub (leaves shed in the temperate zone winter, or in the tropical dry season)
Broad-leaved evergreen shrub (many tropical and temperate shrubs, mostly with medium to small-sized leaves)
Thorn shrub (armed with spines, in many cases with compound, deciduous leaves, often reduced in size)
Evergreen sclerophyllous shrubs feature tough, small leaves that remain green year-round Palm shrubs are characterized by their rosette formation, consisting of unbranched structures topped with a short crown of leaves Dwarf-shrubs, on the other hand, are low-growing plants that spread close to the ground, typically reaching heights of less than 50 cm.
Semi-shrub (suffrutescent, i.e., with the upper parts of the stems and branches dying back in unfavorable seasons)
Succulent shrub (cacti, certain euphorbias, etc.) Other shrub
Herbaceous Herbs (plants without perennial aboveground woody stems)
Forb (herbs other than ferns and graminoids) Graminoid (grasses, sedges, and other grass like plants) Fern (pteridophytes –ferns, clubmosses, horsetails, etc) Succulent forb
Aquatic herb (floating & submergent) Other herbaceous
Other Epiphyte (plants growing wholly above the ground surface on other plants)
Vine/liana (woody climbers or vines) Other/unknown
Table 2 presents a comparison of commonly used cover-abundance scales in the United States, highlighting various methodologies employed by agencies and authors The abbreviations include BB for Braun-Blanquet (1928), NC for North Carolina Vegetation Survey (Peet et al 1998), K for Domin sensu Krajina (1933), and DA for Daubenmire (1959).
The Forest Service utilizes the modified Daubenmire scale (1959) alongside various classification systems, including the Pfister and Arno method (1980) and New Zealand LandCare approaches (Allen 1992, Hall 1992) Additionally, research by BDSrkman et al (1964) and Domin (1928) contributes to these classifications, while the Western Heritage Task Force and The Nature Conservancy (Bourgeron et al 1991) provide further insights The breakpoints indicated in the Cover-abundance column highlight significant thresholds identified by the Braun-Blanquet method.
The Blanquet scale serves as the minimum standard for cover classes, with the NC and K cover class systems easily integrated into the B-B standard Additionally, the DAUB, FS, PA, and NZ scales can also be effectively merged into the B-B scale without compromising data integrity However, the D, BDS, and WHTF scales are somewhat inconsistent with the B-B standard and should only be used when necessary for incorporating legacy data.
Cover-abundance BB NC K DAUB FS PA NZ BDS D WHTF
Present but not in plot ( ) † +
† Species present in the stand but not in the plot are usually added in parentheses to the species list.
‡ This is a cover/abundance scale; if numerous individuals of a taxon collectively contribute less than 5% cover, then the taxon can be assigned a value of 1 or, if very sparse, a “+.”
Table 3 Summary of layer data from field plots for a given type.
Table 4 A stand table of floristic composition for each layer Strata are defined in Table 3).
Figure 1 Categories and examples of the National Vegetation Classification, showing the levels from class to association.
Figure 2 Flow of information through the process for formal recognition of an association or alliance.
Figure 3 Schematic diagram of the peer review process.
Figure 1 Categories and examples of the National Vegetation Classification, showing the levels from Class to Association The FGDC (1997) standard also includes two higher levels above Class: Division and Order
Subclass Evergreen Open Tree Canopy
Group Temperate or Subpolar Needle-leaved Evergreen Open Tree Canopy Subgroup Natural/Seminatural
Formation Rounded-crowned temperate or subpolar needle-leaved evergreen open tree canopy.
Alliance Juniperus occidentalis Woodland Alliance
Association J uniperus occidentalis /Artemesia tridentata
The process for formally recognizing an association or alliance begins with the collection of field plot data, which is then submitted to the VegBank plots database Following this, the data undergoes analysis, leading to the submission of a proposal that describes the type for review.
If accepted by reviewers, the type description is classified under the NVC, the monograph is published, and the description made available.
Figure 3 Schematic diagram of the peer-review process
C Sufficient geographic and habitat coverage
3 Promotion of a type’s confidence level
Text Box 1 Guiding principles of the FGDC National Vegetation Classification Standard
Text Box 2 Required topical sections for monographic description of alliances and associations.
Text Box 3 Examples of Association and Alliance names.
Text Box 1 Guiding principles of the FGDC Vegetation Classification Standard (FGDC
The classification is applicable over extensive areas.
The vegetation classification standard compatible, wherever possible, with other Earth cover/land cover classification standards
The classification will avoid developing conflicting concepts and methods through cooperative development with the widest possible range of individuals and institutions.
Application of the classification must be repeatable and consistent.
When possible, the classification standard will use common terminology (i.e., terms should be understandable, and jargon should be avoided).
The classification categories for mapping are designed to be mutually exclusive and collectively account for 100% of an area across various hierarchical levels, including Division, Order, Class, Subclass, Subgroup, Formation, Alliance, and Association To address uncertainties in assigning floristic units to a single physiognomic classification category, specific guidelines have been established, with plans for further development as new situations arise.
The classification standard will be dynamic, allowing for refinement as additional information becomes available.
The National Vegetation Classification System (NVCS) focuses on existing vegetation rather than potential types, assessing the condition of plants at their peak during the growing season Vegetation types are categorized based on their inherent attributes, including structural characteristics, growth forms, and coverage.
The NVCS is designed as a hierarchical system, allowing for the aggregation of a few generalized categories at higher levels, while accommodating a growing number of detailed categories at lower levels This structure ensures that the categories are applicable and beneficial across various scales.
(UNEP/FAO 1995, Di Gregorio and Jansen 1996)
The upper levels of the National Vegetation Classification System (NVCS) focus on the physiognomy of vegetation, which includes life forms, cover, structure, and leaf type, rather than individual species The classification of vegetation types is primarily determined by the dominant life forms, such as herbs, shrubs, or trees, found in the uppermost stratum While climate and other environmental factors play a role in the organization of the classification system, physiognomy remains the key factor in determining vegetation types.
The lower levels of the National Vegetation Classification System (NVCS) are grounded in actual vegetation composition, with Alliance and Association types defined through field-collected data using standardized sampling methods These classifications are derived from the gathered floristic data and are organized within the hierarchy under physiognomic classes.
Text Box 2 Required topical sections for monographic description of alliances and associations.
1 Proposed names of the type (Latin, translated, common).
2 Floristic unit (alliance or association).
4 A brief description of the overall type concept.
11 A description of the range/distribution.
12 A list of U.S states and Canadian provinces where the type occurs or may occur.
13 A list of any nations outside the U.S and Canada where the type occurs or may occur.
14 Plots used to define the type
15 Location of archived plot data.
17 The number and size of plots.
18 Methods used to analyze field data and identify the type. a Details of the methods used to analyze field data. b Criteria for defining the type.
19 Overall confidence level for the type (see Chapter 7).
21 Full citations for any sources
23 Possible sub-association or -alliance types or variants, if appropriate, should be discussed here along with other narrative information.