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National hierarchical framework of ecological units

The following is an electronic version of
National Hierarchical Framework of Ecological Units1
David T. Cleland, Peter E. Avers, W. Henry McNab, Mark E. Jensen, Robert G. Bailey,
Thomas King, and Walter E. Russell
1 Full citation:
Cleland, D.T.; Avers, P.E.; McNab, W.H.; Jensen, M.E.; Bailey, R.G., King, T.; Russell, W.E. 1997.
National Hierarchical Framework of Ecological Units. Published in, Boyce, M. S.; Haney, A., ed. 1997.
Ecosystem Management Applications for Sustainable Forest and Wildlife Resources. Yale University
Press, New Haven, CT. pp. 181-200.
To implement ecosystem management, we need basic information about the nature and distribution of
ecosystems. To develop this information, we need working definitions of ecosystems and supporting
inventories of the components that comprise ecosystems. We also need to understand ecological patterns
and processes and the interrelationships of social, physical, and biological systems. To meet these needs,
we must obtain better information about the distribution and interaction of organisms and the
environments in which they occur, including the demographics of species, the development and
succession of communities, and the effects of humans activities and land use on species and ecosystems
(Urban et al. 1987). Research has a critical role in obtaining this information.
This chapter presents a brief background of regional land classifications, describes the hierarchical
framework for ecological unit design, examines underlying principles, and shows how the framework can
be used in resource planning and management. The basic objective of the hierarchical framework is to
provide a systematic method for classifying and mapping areas of the earth based on associations of
ecological factors at different geographic scales. The framework is needed to improve our efforts in
national, regional, and forest level planning; to achieve consistency in ecosystem management across
National Forests and regions; to advance our understanding of the nature and distribution of ecosystems;
and to facilitate interagency data sharing and planning. Furthermore, this framework will help us evaluate
inherent capabilities of land and water resources and the effects of management on them.
Ecological units delimit areas of different biological and physical potentials. Ecological unit maps can be
coupled with inventories of existing vegetation, air quality, aquatic systems, wildlife, and human elements
to characterize complexes of life and environment, or ecosystems. This information on ecosystems can be
combined with our knowledge of various processes to facilitate a more ecologically sound approach to
resource planning, management, and research.
Note that ecological classification and mapping systems are devised by humans to meet human needs
and values. Ecosystems and their various components often change gradually, forming continua on the
earth's surface which cross administrative and political boundaries. Based on their understanding of
ecological systems, humans decide on ecosystems boundaries by using physical, biological, and social
We recognize that the exact boundaries for each level envisioned in this process and developed in map
format may not fit every analysis and management need. Developing boundaries of areas for analysis,
however, will not change the boundaries of ecological units. In some cases, an ecological unit may be the
analysis area. In other cases, watersheds, existing conditions, management emphasis, proximity to
special features (for example natural, wilderness, or urban areas), or other conditions may define an
analysis area. In these cases, ecological units can be aggregated or divided if necessary to focus on
relevant issues and concerns.
Hierarchical systems using ecological principles for classifying land have been developed for geographical
scales ranging from global to local. Using a bioclimatic approach at a global scale, several researchers
have developed ecological land classifications: Holdridge (1967), Walter and Box (1976), Udvardy (1975),
and Bailey (1989a,b). Wertz and Arnold (1972) developed land stratification concepts for regional and land
unit scales. Other ecologically based classifications proposed at regional scales include those of Driscoll
et al. (1984), Gallant et al. (1989), and Omernik (1987) in the United States and those of Wiken (1986)
and the Ecoregions Working Group (1989) in Canada. Concepts have also been presented for ecological
classification at subregional to local scales in the United States (Barnes et al. 1982), Canada (Jones et al.
1983, Hills 1952), and Germany (Barnes 1984).
Each of these systems have strong points that contribute to the strength of the national hierarchy. But no
single system has the structure and flexibility necessary for developing ecological units at continental to
local scales. The concepts and terminology of the national system draw upon this work to devise a
consistent framework for application throughout the United States.
The primary purpose for delineating ecological units is to identify land and water areas at different levels of
resolution that have similar capabilities and potentials for management. Depending on scale, ecological
units are designed to exhibit similar patterns in: (1) potential natural communities, (2) soils, (3) hydrologic
function, (4) landform and topography, (5) lithology, (6) climate, and (7) natural processes such as nutrient
cycling, productivity, succession, and natural disturbance regimes associated with flooding, wind, or fire.
It should be noted that climatic regime is an important boundary criterion for ecological units, particularly at
broad scales. In fact, climate, as modified by topography, is the dominant criterion at upper levels. Other
factors, such as geomorphic process, soils, and potential natural communities, take on equal or greater
importance than climate at lower levels.
It follows, then, that ecological units are differentiated and maps designed by multiple components,
including climate, physiography, geology, soils, water, and potential natural communities. These
components may be analyzed individually, and then combined, or multiple factors may be simultaneously
evaluated to classify ecological types, which are then used in ecological unit design. The first option may
be increasingly used as geographic information systems (GIS) become more available. The
interrelationships among independently defined components, however, will need to be carefully evaluated,
and the results of layering component maps may need to be adjusted to identify units that are both
ecologically significant and meaningful to management. When various disciplines cooperate in devising
integrated ecological units, products from existing resource component maps can be modified, and
integrated interpretations can be developed (Avers and Schlatterer, 1991).
Ecological unit inventories are generally designed and conducted in cooperation with the Natural Resource
Conservation Service, Agricultural Experiment Stations of Land Grant Universities, Bureau of Land
Management, and other appropriate state and federal agencies. Mapping conventions and soil
classification meet standards of the National Cooperative Soil Survey.
Table 1. National hierarchy of ecological units
Planning and
analysis scale Ecological
Units Purpose, objectives, and general use
Broad applicability for modeling and sampling.
Strategic planning and assessment. International planning.
Subregion Section
Subsection Strategic, multiforest, statewide, and multiagency analysis and
Landscape Landtype
association Forest or areawide planning, and watershed analysis.
Land unit Landtype
Project and management area planning and analysis.
Hierarchy can be expanded by user
to smaller geographical areas and
more detailed ecological units if
Very detailed project planning.
The National Ecological Unit Hierarchy is presented in Tables 1, 2, and 3. The hierarchy is based on
concepts and terminology developed by numerous scientists and resource managers (Hills 1952, Crowley
1967, Wertz and Arnold 1972, Rowe 1980, Allen and Starr 1982, Barnes et al. 1982, Forman and Godron
1986, Bailey 1987, Meentemeyer and Box 1987, Gallant et al. 1989, Cleland et al. 1992). The following is
an overview of the differentiating criteria used in the development of the ecological units. Table 2
summarizes the principal criteria used at each level in the hierarchy.
Table 2. Principal map unit design criteria of ecological units.
Ecological unit Principal map unit design criteria
Domain Broad climatic zones or groups (e.g., dry, humid, tropical)
Division Regional climatic types (Koppen 1931, Trewatha 1968)
Vegetational affinities (e.g., prairie or forest)
Soil order
Province Dominant potential natural vegetation (Kuchler 1964)
Highlands or mountains with complex vertical climate-vegetation-soil zonation
Section Geomorphic province, geologic age, stratigaphy, lithology
Regional climatic data
Phases of soil orders, suborders, or great groups
Potential natural vegetation
Potential natural communities (PNC) (FSH 2090)
Subsection Geomorphic process, surficial geology, lithology
Phases of soil orders, suborders, or great groups
Subregional climatic data
PNC—formation or series
Landtype association Geomorphic process, geologic formation, surficial geology, and elevation
Phases of soil subgroups, families, or series
Local climate
PNC—series, subseries, plant associations
Landtype Landform and topography (elevation, aspect, slope gradient, and position)
Phases of soil subgroups, families, or series
Rock type, geomorphic process
PNC—plant associations
Landtype phase Phases of soil subfamilies or series
Landform and slope position
PNC—plant associations or phases
Note: The criteria listed are broad categories of environmental and landscape components. The actual
classes of components chosen for designing map units depends on conditions and relative importance of
factors within respective geographic areas.
Table 3. Map scale and polygon size of ecological units.
Ecological unit Map scale range General polygon size
Domain 1:30,000,000 or smaller 1,000,000s of square miles
Division 1:30,000,000 to 1:7,500,000 100,000s of square miles
Province 1:15,000,000 to 1:5,000,000 10,000s of square miles
Section 1:7,500,000 to 1:3,500,000 1,000s of square miles
Subsection 1:3,500,000 to 1:250,000 10s to low 1,000s of square
Landtype association 1:250,000 to 1:60,000 1,000s to 10,000s of acres
Landtype 1:60,000 to 1:24,000 100s to 1,000s of acres
Landtype phase 1:24,000 or larger <100 acres
Ecoregion Scale
At the Ecoregion scale, ecological units are recognized by differences in global, continental, and regional
climatic regimes and gross physiography. The basic assumption is that climate governs energy and
moisture gradients, thereby acting as the primary control over more localized ecosystems. Three levels of
ecoregions, adapted from Bailey (1980), are identified in the hierarchy:
1. Domains, subcontinental divisions of broad climatic similarity, such as lands that have the dry climates
defined by Koppen (1931), which are affected by latitude and global atmospheric conditions. For example,
the climate of the Polar Domain is controlled by arctic air masses, which create cold, dry environments
where summers are short. In contrast, the climate of the Humid Tropical Domain is influenced by
equatorial air masses and there is no winter season. Domains are also characterized by broad differences
in annual precipitation, evapotranspiration, potential natural vegetation, and biologically significant
drainage systems. The four Domains are named according to the principal climatic descriptive features:
Polar, Dry, Humid Temperate, and Humid Tropical.
2. Divisions, subdivisions of domains determined by isolating areas of definite vegetational affinities (for
example, prairie or forest) that fall within the same regional climate, generally at the level of the basic
types of Koppen (1931) as modified by Trewartha (1968). Divisions are delineated according to: (a) the
amount of water deficit (which subdivides the Dry Domain into semi-arid, steppe, or arid desert), and (b)
the winter temperatures, which have an important influence on biological and physical processes and the
duration of any snow cover. This temperature factor is the basis of distinction between temperate and
tropical/subtropical dry regions. Divisions are named for the main climatic regions they delineate, such as
steppe, savannah, desert, Mediterranean, marine, and tundra.
3. Provinces, climatic subzones, controlled primarily by continental weather patterns such as length of dry
season and duration of cold temperatures. Provinces are also characterized by similar soil orders. The
climatic subzones are evident as extensive areas of similar potential natural vegetation such as those
mapped by Kuchler (1964). Provinces are named typically using a binomial system consisting of a
geographic location and vegetative type such as Bering
Tundra, California Dry-Steppe and Eastern Broadleaf Forests (Bailey et al. 1985).
Highland areas that exhibit altitudinal vegetation zonation and that have the climatic regime (seasonality of
energy and moisture) of adjacent lowlands are classified as provinces (Bailey et al. 1985). The climatic
regime of the surrounding lowlands can be used to infer the climate of the highlands. For example, in the
Mediterranean division along the Pacific Coast, the seasonal pattern of precipitation is the same for the
lowlands and highlands except that the mountains receive about twice the quantity. The provinces are
named for the lower-elevation and upper-elevation (subnival) belts, for example, Rocky Mountain forest-
alpine meadows.
Subregional Scale
Subregions are characterized by combinations of climate, geomorphic process, topography, and
stratigraphy that influence moisture availability and exposure to radiant solar energy, which in turn directly
control hydrologic function, soil-forming processes, and potential natural community distributions. Sections
and Subsections are the two ecological units mapped at this scale.
1. Sections, broad areas of similar sub-regional climate, geomorphic process, stratigraphy, geologic origin,
topography, and drainage networks. Such areas are often inferred by relating geologic maps to potential
natural vegetation "series" groupings such as those mapped by Kuchler (1964). In recent years, numerical
analyses of weather station and remotely sensed climatic information have assisted in determining
Section boundaries. Boundaries of some sections approximate geormorphic provinces (for example, Blue
Ridge) as recognized by geologists. Section names generally describe the predominant geomorphic type
or feature upon which the ecological unit delineation is based, such as Flint Hills, Great Lakes Morainal,
Bluegrass Hills, Appalachian Piedmont.
2. Subsections, smaller areas within Sections with similar surficial geology, lithology, geomorphic process,
soil groups, subregional climate, and potential natural communities. Subsection boundaries usually
correspond with discrete changes in geomorphology. Names of Subsections are usually derived from
geologic features, such as Plainfield sand dune, Tipton till plain, and granite hills.
Landscape Scale
At the landscape scale, ecological units are defined by general topography, geomorphic process, surficial
geology, associations of soil families, and potential natural communities, patterns, and local climates
(Forman and Godron 1986). These factors affect biotic distributions, hydrologic function, natural
disturbance regimes, and general land use. Local landform patterns become apparent at this level in the
hierarchy, and differences among units are usually obvious to on-the-ground observers. At this level,
terrestrial features and processes may also have a strong influence on ecological characteristics of
aquatic habitats (Platts 1979, Ebert et al. 1991).
Landtype association ecological units represent this scale in the hierarchy. These are groupings of
landtypes or subdivisions of subsections based on similarities in geomorphic process, geologic rock types,
soil complexes, stream types, lakes, wetlands, subseries or plant association vegetation communities.
Repeatable patterns of soil complexes and plant communities are useful in delineating map units at this
level. Names of Landtype Associations are often derived from geomorphic history and vegetation
Land Unit Scale
At the basic land unit scale, ecological units are designed and mapped in the field based on properties of
local topography, rock types, soils, and potential natural vegetation. These factors influence the structure
and composition of plant communities, hydrologic function, and basic land capability. Landtypes and
landtype phases are the ecological units mapped at this scale.
1. Landtypes, subdivisions of landtype associations or groupings of landtype phases based on similarities
in soils, landform, rock type, geomorphic process, and plant associations. Land surface form that
influences hydrologic function (for example, drainage density, dissection, and relief) is often used to
delineate different landtypes in mountainous terrain. Valley bottom characteristics (for example,
confinement) are commonly used in establishing riparian landtype map units. Names of landtypes include
an abiotic and biotic component (USDA Forest Service Handbook 2090.11).
2. Landtype Phase, subdivisions of Landtypes based on topographic criteria (for example, slope-shape,
steepness, aspect, position), hydrologic characteristics, associations and consociations of soil taxa, and
plant associations and phases that influence or reflect the microclimate and productivity of a site.
Landtype phases are often established based on interrelationships between soil characteristics and
potential natural communities.In riparian mapping, landtype phases may be established to delineate
different stream-type environments (Herrington and Dunham, 1967). Naming is similar to landtypes.
The Landtype Phase is the smallest ecological unit recognized in the hierarchy. However, even smaller
units may need to be delineated for very detailed project planning at large scales (Table 1). Map design
criteria depend on project objectives.
Plot Data
Point or plot sampling units are used to gather ecological data for inventory, monitoring, and quality
control, and for developing classifications of vegetation, soils or ecological types. This plot data feeds into
databases for analysis, description, and interpretation of ecological units (Keane et al. 1990). Plots, while
not mappable, can be shown on maps as point data.
Ecosystem Concept
Ecosystems are places where life forms and environment interact; they are three-dimensional segments
of the Earth (Rowe 1980). Tansley introduced the term ecosystem in 1935, first articulating the explicit
idea of ecological systems composed of multiple abiotic and biotic factors (Major 1969). The ecosystem
concept brings the biological and physical worlds together into a holistic framework within which ecological
systems can be described, evaluated, and managed (Rowe 1992). The structure and function of
ecosystems are largely regulated along energy, moisture, nutrient, and disturbance gradients. These
gradients are affected by numerous environmental and biological factors including climate, geology, soils,
flora, fire, and wind, and these factors vary at different spatial and temporal scales (Barnes at al. 1982,
Jordan 1982, Spies and Barnes 1985). Ecological systems therefore exist at many spatial scales, from the
global ecosphere down to regions of microbial activity. Using multiple biotic and abiotic factors, the
National Hierarchy of Ecological Units organizes the environmental components of ecosystems into an
orderly set of spatial scales based on measurable features and processes. The National Hierarchy thus
takes the infinite variety of ecosystems and places them into a limited number of discrete, practical units
that are mappable, repeatable, and distinguished from one another by differences in various structural or
functional characteristics.
At global, continental, and regional scales, ecosystem patterns correspond with climatic regions, which
change mainly due to latitudinal, orographic, and maritime influences (Bailey 1987, Denton and Barnes
1988). Within climatic regions, landforms modify macroclimate (Rowe 1984, Smalley 1986, Bailey 1987),
and affect the movement of organisms, the flow and orientation of watersheds, and the frequency and
spatial pattern of disturbance by fire and wind (Swanson et al. 1988). Within climatic - geomorphic
regions, water, plants, animals, soils, and topography interact to form ecosystems at Land Unit scales
(Pregitzer and Barnes 1984). Thus ecological systems exist at many spatial scales, from the global
ecosphere down to regions of microbial activity. The challenge of ecosystem classification and mapping is
to distinguish natural associations of ecological factors at different spatial scales, and to define ecological
types and map ecological units that reflect these different levels of organization.
And while the association of these multiple biotic and abiotic factors is all important in defining ecosystems
and ecological units, all factors are not equally important at all spatial scales. At coarse scales, the
important factors are largely abiotic, while at finer scales both biotic and abiotic factors are important.
Furthermore, the level of discernible detail, the number of factors contributing to ecosystems, and the
number of variables used to characterize these factors progressively increase at finer scales. Hence the
data and analysis requirements, as well as the investments for ecosystem classification and mapping,
also increase for finer-scaled activities.
The conditions and processes occurring across larger ecosystems affect and often override those of
smaller ecosystems, and the properties of smaller ecosystems emerge in the context of larger systems
(Rowe 1984). Moreover, environmental gradients that affect ecological patterns and processes change at
different spatial scales. Thus, it is useful to conceive of ecosystems and their underlying biophysical
environments as occurring in a nested geographic arrangement, with smaller ecosystems embedded in
larger ones (Allen and Starr 1982, O'Neill et al. 1986, Albert et al. 1986). This spatial hierarchy is
organized in decreasing orders of scale by the dominant factors affecting ecological systems. Ecosystems
become networked, however, when non-adjacent systems exhibit similar structure and function with
respect to specific biota (for example, sedentary plants as opposed to wide ranging animals) and various
processes; hence the networking of ecological systems is scale dependent (Allen and Hoekstra 1992).
Networking of ecosystems occurs most often at lower levels of the hierarchy, and depends upon
requirements, environmental tolerances, and dispersion mechanisms of biota, as well as other factors that
affect biotic-abiotic interactions within and across local, landscape and regional ecosystems.
Life and Environmental Interactions
Life-forms and environment have interacted and codeveloped at all spatial and temporal scales, one
modifying the other through feedback. Appreciating these interactions is integral to understanding
ecosystems. At a global scale, for example, scientists have theorized that the evolution of cyanobacteria,
followed by terrestrial plants capable of photosynthesis, carbon fixation and oxygen production converted
the earth's atmosphere from a hydrogen to an oxygen base and still sustain it today. At a continental
scale, the migration of species in response to climate change, and the interaction of their environmental
tolerances and dispersal mechanisms with landform controlled migration routes formed today's patterns in
the distributions of species. At a landscape scale, life-forms, environment, and disturbance regimes have
interacted to form patterns and processes. For example, pyrophilic communities tend to occupy droughty
soils in fire-prone landscape positions, produce volatile foliar substances, and accumulate litter, thereby
increasing their susceptibility to burning. At yet finer scales, vegetation has induced soil development over
time through carbon and nutrient cycling, enabling succession to proceed to communities with higher
fertility requirements.
In each of these examples, life forms and environment have modified one another through feedback to
form ecological patterns and processes. These types of relationships underscore the need to consider
both biotic and environmental factors while classifying, mapping and managing ecological systems.
Spatial and Temporal Variability
The structure and function of ecosystems change through space and time. Consequently, we need to
address both spatial and temporal sources of variability while evaluating, classifying, mapping, or
managing ecosystems (Delcourt et al. 1983, Forman and Godron 1986). At a land unit scale, for example,
the fertility of particular locations changes through space because of differences in soil properties or
hydrology, and at ecoregion scales, conditions vary from colder to warmer because of changes in
macroclimate. These relatively stable conditions favor certain assemblages of plants and animals while
excluding others because of biotic tolerances and such processes as competition. These environmental
conditions are classified as ecological types and mapped as ecological units.
Within ecological units, ecosystems may support vegetation that is young, mature, or old, and they may be
composed of communities that are early, mid-, or late successional. These relatively dynamic conditions
also benefit certain plant and animal species and assemblages. Conditions that vary temporally are
classified and mapped as existing vegetation, wildlife, water quality, and so forth.
These examples illustrate that ecological units do not provide all the information needed to classify, map
and manage ecosystems. Ecological units address the spatial distributions of relatively stable associations
of ecological factors that affect ecosystems. When combined with information on existing biotic conditions
and ecological processes, the National Hierarchy of Ecological Units provides a means of addressing
spatial and temporal variations that affect the structure, function and management potentials of
ecosystems. Adding our knowledge of processes to this information will enable us to evolve better
Ecological units provide basic information for natural resource planning and management. Ecological unit
maps may be used for activities such as delineating ecosystems, assessing resources, conducting
environmental analyses, and managing and monitoring natural resources.
Ecosystem Mapping
To map ecosystems, or places where life and environment interact, we need to combine two types of
maps: maps of existing conditions of biota that change readily through time, and maps of potential
conditions of environments that are relatively stable. Existing conditions change due to particular
processes that operate within the bounds of biotic and environmental, or ecological, potentials. Existing
conditions are inventoried as current vegetation, wildlife, water quality, and so forth. Potential conditions
are inventoried as ecological units. When these maps are combined, biotic distributions and ecological
processes can be evaluated, and results can be extrapolated to similar ecosystems. The integration of
multiple biotic and abiotic factors, then, provides the basis for defining and mapping ecosystems.
Fundamental base maps are key to mapping ecosystems and integrating resource inventories. These
maps include the primary base map series, showing topography, streams, lakes, ownership, political
boundaries, cultural features, and other layers in the cartographic features file. On this base, the next set
of layers could include terrestrial or aquatic ecological units. Next would be layers of information on
existing vegetation, wildlife populations, fish distribution, cultural resources, demographics, economic
data, and other information needed to delineate ecosystems to meet planning and analysis needs.
GIS will provide a tool for combining these separate themes of information, and representing the physical,
biological, and social dimensions to define and map ecosystems. But scientists and managers using this
technology must actually integrate information themes, comprehend processes, and formulate
management strategies. These tasks will not be accomplished mechanically.
Resource Assessments
The hierarchical framework of ecological units can provide a basis for assessing resource conditions at
multiple scales. Broadly defined ecological units (for example, ecoregions) can be used for general
planning assessments of resource capability. Intermediate scale units (for example, landtype
associations) can be used to identify areas with similar natural disturbance regimes (for example, mass
wasting, flooding, fire potential). Narrowly defined land units can be used to assess site specific conditions
including distributions of terrestrial and aquatic biota; forest growth, succession, and health; and various
physical conditions (for example, soil compaction and erosion potential, water quality).
High resolution information obtained for fine scale ecological units can be aggregated for some types of
broader scale resource assessments. Resource production capability, for example, can be estimated
based on potentials measured for landtype phases, and estimates can be aggregated to assess ranger
district, national forest, regional, and national capabilities.
Environmental Analyses
Ecological units provide a means of analyzing the feasibility and effects of management alternatives. To
discern the effects of management on ecosystems, we often need to examine conditions and processes
occurring above and below the level under consideration (Rowe 1980). For example, the effects of timber
harvesting are manifest not only at a land unit scale, but also at micro-site and landscape scales. Although
the direct effects of management are assessed at the land unit scale, indirect and cumulative effects take
place at different points in space or time, often at higher spatial scales. We can minimize conflicting
resource uses (for example, remote recreational experiences versus developed motorized recreation,
habitat management for area sensitive species versus edge species) if we consider the design and effects
of projects at several scales of analysis. Ecological units defined at different hierarchical levels will be
useful in conducting multi-scaled analyses for managing ecosystems and documenting environmental
effects (Brenner and Jordan 1991, Jensen et al. 1991).
Watershed Analysis
The national hierarchy provides a basis for evaluating the linkages between terrestrial and aquatic
systems. Because of the interdependence of geographic components, aquatic systems are linked or
integrated with surrounding terrestrial systems through the processes of runoff, sedimentation, and
migration of biotic and chemical elements. Furthermore, the context of water bodies affects their
ecological significance. A lake embedded within a landscape containing few lakes, for example, functions
differently from one embedded within a landscape composed of many lakes for wildlife, recreation for
people, and other ecosystem values. Aquatic systems delineated in this indirect way may have many
characteristics in common, including hydrology and biota (Frissell et al. 1986). Overlays of hierarchical
watershed boundaries on terrestrial ecological units are useful for many watershed analysis efforts. In this
case, the watershed becomes the analysis area, which is both superimposed by and composed of a
number of ecological units which affect important hydrologic processes such as water runoff and
percolation, water chemistry, and ecological function due to context.
Desired Future Conditions
Desired future conditions (DFC’s) portray the land or resource conditions expected if goals and objectives
are met. Ecological units will be useful in establishing goals and methods to meet DFC’s. When combined
with information on existing conditions, ecological units will help us project responses to various
Ecological units can be related to past, present, and future conditions. Past conditions serve as a model of
functioning ecosystems and provide insight into natural processes. It is unreasonable, for example, to try
to restore systems like oak savannas or old-growth forests in areas where they did not occur naturally.
Moreover, natural processes like disturbance or hydrologic regimes are beyond human control. Ecological
units will be helpful in understanding these processes and in devising DFC’s that cab attained and
Desired future conditions can be portrayed at several spatial scales. We can minimize conflicting resource
uses (for example, remote recreational experiences versus developed motorized recreation, habitat
management for area-sensitive species versus edge species) if we consider the effects of projects at
several spatial scales of analysis. Ecological units will be useful in delineating land units at relevant spatial
scales for planning DFC’s.
Resource Management
Information on ecological units will help establish management objectives and will support such
management activities as the protection of habitats of sensitive, threatened, and endangered species, or
the improvement of forest and rangeland health to meet conservation, restoration, and human needs. For
example, information on current productivity can be compared to potentials determined for landtype
phases, and areas producing less than their potential can be identified (Host et al. 1988). Furthermore,
long term sustained yield capability can be estimated based on productivity potentials measured for fine
scale ecological units.
Monitoring the effects of management requires baseline information on the condition of ecosystems at
different spatial scales. Through the ecological unit hierarchy, managers can obtain information about the
geographic patterns in ecosystems. They are thus in a position to design stratified sampling networks for
inventory and monitoring. Representative ecological units can be sampled and information can then be
extended to analogous unsampled ecological units, thereby reducing cost and time in inventory and
By establishing baselines for ecological units and monitoring changes, we can protect landscape,
community, and species level biological diversity; and other resource values such as forest productivity,
and air and water quality. The results of effectiveness and validation monitoring can be extrapolated to
estimate effects and set standards in similar ecological units.
Evaluation of air quality is an example of how the National Hierarchical Framework of Ecological Units can
be used for baseline data collection and monitoring. The Forest Service is developing a National Visibility
Monitoring Strategy that addresses protection of air quality standards as mandated by the Clean Air Act
(USDA Forest Service 1993). Key to this plan is stratification of the United States at the subregion level of
the national hierarchy into areas that have similar climatic, physiographic, cultural, and vegetational
characteristics. Other questions dealing with effects of specific airborne pollutants on forest health, such
as correlation of ozone with decline of ponderosa pine and other trees in mixed conifer forest ecosystems
in the San Bernardino Mountains of southern California, will require establishment of sampling networks in
smaller ecological units at landscape or lower levels.
Contemporary and Emerging Issues
The National Hierarchy of Ecological Units is based on natural associations of ecological factors. These
associations will be useful in responding to contemporary and emerging issues, particularly those that
cross administrative and jurisdictional boundaries. Concerns regarding biological diversity, for example,
can be addressed using the ecological unit hierarchy (Probst and Crow 1991). Conservation strategies
can be developed using landscape-level units as coarse filters, followed by detailed evaluations and
monitoring conducted to verify or adjust landscape designs. We can rehabilitate ecosystems and
dependent species that have been adversely affected by fire exclusion, fragmentation, or other results of
human activities if we grow to understand the natural processes that species and ecosystems
codeveloped with, and then mimic those processes through ecosystem management.
Species may become rare, threatened, or endangered because their habitat is becoming degraded,
because they are specialists endemic to a particular area, or because they are at the edge of their natural
range. In the first two instances, protection and recovery efforts are warranted. In the latter case, however,
it may be futile to try to maintain biota where they are predisposed to decline. At a minimum, populations
at the edge of their range can be evaluated for genetic diversity, and recovery programs can be
administered accordingly. Species and community distributions can often be related to ecological units,
which can be useful in their inventory and protection.
The emphasis on sustaining and restoring the integrity of ecosystems may aid in arresting the decline of
biological diversity and preempt the need for many future protection and recovery efforts Developing basic
information on the nature and distribution of ecosystems and their elements will enable us to better
respond to issues like global warming, forest health and sustainability, and biological diversity.
The hierarchical framework of ecological units was developed to improve our ability to implement
ecosystem management. This framework, in combination with other information sources, is playing an
important role in national, regional, and forest planning efforts; the sharing of information between forests,
stations, and regions; and interregional assessments of ecosystem conditions.
Regions and stations, with national guidance, are coordinating their design of ecological units at higher
levels of the national hierarchy. Development of landscape and land unit maps is being coordinated by
appropriate regional, station, forest and ranger district level staff. As appropriate, new technologies (for
example, remote sensing, GIS, expert systems) should be used in the design, testing and refinement of
ecological unit maps.
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only information, but also the concepts and tools traditionally used by various disciplines. The effort brings
together the biological and physical sciences that have too often operated independently. Specialists like
foresters, fishery and wildlife biologists, geologists, hydrologists, community ecologists and soil scientists
will need to work together to develop and implement this new classification and mapping system. The
results of these concerted efforts will then need to be applied in collaboration with planners, social
scientists, economists, archaeologists and the many other specialties needed to achieve a truly ecological
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... For example, De'ath and Fabricius, in 2000, used the tree technique to explore the analysis of complex ecological data with nonlinear relationships and high-order interaction. Traditionally, many studies and attempts to analyze the complex system of the land as dynamically organized and structured across the scales of space had assisted ecological researchers to solve population richness and dynamics (Allen et al. 2014), vegetation distributions (Hou 1983;Zhang and Zhou 1992) and ecosystem classification framework (Bailey 1995(Bailey , 1996aCleland et al. 1997;Wu et al. 2003a, b;Altert et al. 2015;Brodrick et al. 2019). ...
... Climatologists used relatively or multiple years' annual climate conditions to demonstrate the uniform climatic classifications and applied them to ecological regionalization study. However, the differences of the geology and geomorphology caused uncertain changes within Domain, Division, Province, and Section, where we had to solve the issues in the next level classification (O'Neill et al. 1986;Cleland et al. 1997;West et al. 2005;Brodrick et al. 2019). After Bailey (1983Bailey ( , 1995Bailey ( , 1996a classified upper levels of Ecosystem Classification of Land (Domain, Division, and Province), ECOMAP (1993) had been set up to present as the "top-down" approach of Ecosystem Classification of Land in the United States. ...
... Macroecosystem Groups of spatially related ecosystems can be considered as higher-order and commonly greater size, defined by Bailey (1983 (Creque et al. 1999), and provide essential information for natural resource planning and management. Ecological site maps may be used to delineate ecosystems, assess resources, conduct environmental analyses, and manage and monitor natural resources (Cleland et al. 1997). ...
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Background The ecosystem classification of land (ECL) has been studied for a couple of decades, from the beginning of the perfect organism system “top-down” approach to a reversed “bottom-up” approach by defining a micro-ecological unit. After comparing two cases of the ecosystem classification framework implemented in the different continental ecoregions, the processes were carefully examined and justified. Results Theoretically, Bailey’s upper levels of ECL (Description of the ecoregions of the United States, 2nd ed. Rev and expanded (1st ed. 1980). Misc. Publ. No. 1391 (Rev). Washington DC USDA Forest Service; 1995) were applied to the United States and world continents. For the first time, a complete ECL study was accomplished in Western Utah of the United States, with eight upper levels of ECOMAP (National hierarchical framework of ecological units. U.S. Department of Agriculture, Forest Service, Washington, DC. ; 1993) plus additional ecological site and vegetation stand. China’s Eco-geographic classification was most likely fitted into Bailey’s Ecosystem Classification upper-level regime. With a binary decision tree analysis, it had been validated that the Domains have an empty entity for 500 Plateau Domain between the US and China ecoregion framework. Implementing lower levels of ECL to Qinghai Province of China, based on the biogeoclimatic condition, vegetation distribution, landform, and plant species feature, it had classified the Section HIIC1 into two Subsections (labeled as i , ii ), and delineated iia of QiLian Mountain East Alpine Shrub and Alpine Tundra Ecozone into iia-1 and iia-2 Subzones. Coordinately, an Ecological Site was completed at the bottom level. Conclusions (1) It was more experimental processing by implementing a full ECL in the Western Utah of the United States based on the ECOMAP (1993). (2) The empty entity, named as Plateau Domain 500, should be added into the top-level Bailey’s ecoregion framework. Coordinately, it includes the Divisions of HI and HII and the Provinces of humid, sub-humid, semiarid, and arid for China's Eco-Geographic region. (3) Implementing a full ECL in a different continent and integrating the lower level's models was the process that could handle the execution management, interpreting the relationship of ecosystem, dataset conversion, and error correction.
... Relevé establishment (1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996).-Permanent vegetation plots (relevés) were first established and sampled from early June to early September from 1989 to 1996 as part of a national effort within the U.S. Forest Service to map terrestrial ecosystems at several different scales (Cleland et al., 1997). On the Chippewa National Forest, this was completed in cooperation with the Minnesota County Biological Survey within the Minnesota Department of Natural Resources, where relevé data are archived statewide (Aaseng et al., 2011). ...
Over the last century, nonnative earthworms have invaded forests of the Great Lakes region of North America. Although a growing body of scientific research has documented short-term changes associated with invasive earthworms, there is little research describing the effects of invasive earthworms over multiple decades. To investigate the long-term effects of invasive earthworms on forests, sites sampled in the past need to be classified as wormed or unwormed when originally sampled. However, this is often difficult to accomplish because field methods for sampling earthworms have only recently been developed, and the few historical permanent sites available for resampling largely do not have past information about earthworm presence or absence. Although historic sites lack data on invasive earthworm presence, many of these sites do have information about soil horizon thickness. Therefore, soil horizons can potentially be used as an indicator of the presence or absence of invasive earthworms. In this paper we developed a logistic regression generalized linear model to classify 40 sugar maple-basswood sites in the Chippewa National Forest of Northern Minnesota as wormed or unwormed (i.e., presence or relative absence of earthworms, respectively). A model using the thickness of the O horizon as a predictor variable correctly classified 93% of sites resampled in 2017 as wormed or unwormed. This result suggests we can predict which sugar maple-basswood stands in the Chippewa National Forest were wormed in the past. By comparing historic conditions to those present today, we can then analyze the long-term effects of invasive earthworms.
... Historic permanent vegetation plots (relevés) were first established and sampled from early June to early September from 1989 to 1996 as part of a national effort within the U.S. Forest Service to map terrestrial ecosystems at several different scales (Cleland et al., 1997). On the Chippewa National Forest, this effort was completed in cooperation with the Minnesota County Biological Survey of the Minnesota Department of Natural Resources (Aaseng et al., 2011). ...
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Abstract Nonnative European earthworms are invading hardwood forests of the Chippewa National Forest, MN. While effects on plant communities at the leading edge of invasion have been studied, little is known about longer‐term effects of invasive earthworms. We applied a model using historic O‐horizon soil thickness and a chronosequence approach to classify 41 hardwood sites in the Chippewa National Forest as “long‐term wormed” (wormed >2 decades), “short‐term wormed” or “unwormed/lightly wormed.” Graminoids, especially Carex pensylvanica, had the greatest mean percent cover in sites that had been wormed for over two decades. The families with the greatest negative change in mean percent cover after over two decades of earthworm invasion were Asteraceae, Violaceae, and Sapindaceae (specifically Acer species). Across all diversity metrics measured, long‐term wormed sites had the lowest understory plant species diversity, short‐term wormed sites had intermediate diversity, and unwormed/lightly wormed sites exhibited the highest diversity. Long‐term wormed sites had the lowest mean species richness across all sample scales (1–1024 m2). The greatest within‐group compositional dissimilarity occurred at sites that had been wormed for over two decades, suggesting that sites that had been wormed for over two decades have not reached a compositionally similar end‐state “wormed” community type. Our study suggests that understory diversity will decrease as hardwood forest stands become wormed over time. While our results support other findings that exotic earthworm invasion is associated with lower understory plant diversity in hardwood forests, our study was the first to use space‐for‐time substitution to document the effects after multiple decades of earthworm invasion.
... For the purposes of synthesizing data from the Pacific Northwest in an ecologically meaningful context, we defined the study area as all of the ecological sections present in Oregon and Washington, and in some cases extending into adjacent states (Figure 1). Ecological Sections tier beneath the province level in the U.S. Department of Agriculture-Forest Service (USDA-FS) ECOMAP hierarchical ecosystem classification system (Cleland et al., 1997;McNab et al., 2007). This definition of the study area includes a total of 17 sections, some of which extend into portions of CA, NV, and ID of the same topography and climate. ...
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Carbon (C)‐informed forest management requires understanding how disturbance and management influence soil organic carbon (SOC) stocks at scales relevant to landowners, forest policy and management professionals. The continued growth of datasets and publications allows powerful synthesis approaches to be applied to such questions at increasingly fine scales. Here, we report results from a synthesis that used meta‐analysis of published studies and two large observational databases to quantify disturbance and management impacts on SOC stocks. We conducted this, the third in a series of ecoregional SOC assessments, for the Pacific Northwest, which comprises ~8% of the land area but ~12% of the U.S. forest sector C sink. At the ecoregional level, our analysis indicated that fundamental patterns of vegetation, climate, and topography are far more important controls on SOC stocks than land use history, disturbance or management. However, the same patterns suggested that increased warming, drying, wildland fire, and forest regeneration failure pose significant risks to SOC stocks across the region. Detailed meta‐analysis results indicated that wildfires diminished SOC stocks throughout the soil profile, while prescribed fire only influenced surface organic materials and harvesting had no significant overall impact on SOC. Independent observational data corroborated the negative influence of fire on SOC derived from meta‐analysis, suggested that harvest impacts may vary sub‐regionally with climate or vegetation, and revealed that forests with agricultural uses (e.g., grazing) or legacies (e.g., cultivation) had smaller SOC stocks. We also quantified effects of a range of common forest management practices having either positive (organic amendments, nitrogen (N)‐fixing vegetation establishment, inorganic N fertilization) or no overall effects on SOC (other inorganic fertilizers, urea fertilization, competition suppression through herbicides). In order to maximize the management applications of our results, we qualified them with ratings of confidence based on degree of support across approaches. Lastly, similar to earlier published assessments from other ecoregions, we supplemented our quantitative synthesis results with a literature review to arrive at a concise set of tactics for adapting management operations to site‐specific criteria.
... For this study, we intersected their provisional seed zones with the province scale of the national ecoregion hierarchy developed by the USDA Forest Service [39] to account for edaphic factors. Provinces encompass regions similar in ecology, climate, soils, and potential natural vegetation [40,41]. This resulted in 474 provisional seed transfer zones that contained forest and were at least 100,000 ha in area (Figure 1a). ...
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Genetic diversity is essential because it provides a basis for adaptation and resilience to environmental stress and change. The fundamental importance of genetic variation is recognized by its inclusion in the Montréal Process sustainability criteria and indicators for temperate and boreal forests. The indicator that focuses on forest species at risk of losing genetic variation, however, has been difficult to address in a systematic fashion. We combined two broad-scale datasets to inform this indicator for the United States: (1) tree species occurrence data from the national Forest Inventory and Analysis (FIA) plot network and (2) climatically and edaphically defined provisional seed zones, which are proxies for among-population adaptive variation. Specifically, we calculated the estimated proportion of small trees (seedlings and saplings) relative to all trees for each species and within seed zone sub-populations, with the assumption that insufficient regeneration could lead to the loss of genetic variation. The threshold between sustainable and unsustainable proportions of small trees reflected the expectation of age–class balance at the landscape scale. We found that 46 of 280 U.S. forest tree species (16.4%) may be at risk of losing genetic variation. California and the Southeast encompassed the most at-risk species. Additionally, 39 species were potentially at risk within at least half of the seed zones in which they occurred. Seed zones in California and the Southwest had the highest proportions of tree species that may be at risk. The results could help focus conservation and management activities to prevent the loss of adaptive genetic variation within tree species.
... Another means of borrowing data that are proximate in space is to restrict the augmented sample to measurements (with appropriate postfire lag) from the same or similar ecological domains, regions or subsections (e.g., as delineated by Cleland et al. 1997). Nationwide availability of ecoregion designations of varied resolution would permit such restrictions. ...
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Wildfire activity in the western United States is expanding and many western forests are struggling to regenerate postfire. Accurate estimates of forest regeneration following wildfire are critical for postfire forest management planning and monitoring forest dynamics. National or regional forest inventory programs can provide vegetation data for direct spatiotemporal domain estimation of postfire tree density, but samples within domains of administrative utility may be small (or empty). Indirect domain expansion estimators, which borrow extra-domain sample data to increase precision of domain estimates, offer a possible alternative. This research evaluates domain sample sizes and direct estimates in domains spanning large geographic extents and ranging from 1 to 10 years in temporal scope. In aggregate, domain sample sizes prove too small and standard errors of direct estimates too high. We subsequently compare two indirect estimators—one generated by averaging over observations that are proximate in space, the other by averaging over observations that are proximate in time—on the basis of estimated standard error. We also present a new estimator of the mean squared error (MSE) of indirect domain estimators which accounts for covariance between direct and indirect domain estimates. Borrowing sample data from within the geographic extents of our domains, but from an expanded set of measurement years, proves to be the superior strategy for augmenting domain sample sizes to reduce domain standard errors in this application. However, MSE estimates prove too frequently negative and highly variable for operational utility in this context, even when averaged over multiple proximate domains.
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Evidence-based forest carbon (C) management requires identifying baseline patterns and drivers of soil organic carbon (SOC) stocks, and their responses to land use change and management, at scales relevant to landowners and resource professionals. The growth of datasets related to SOC, which is the largest terrestrial C pool, facilitates use of synthesis techniques to assess SOC stocks and changes at management-relevant scales. We report results from a synthesis using meta-analysis of published studies, as well as two large databases, in which we identify baseline patterns and drivers, quantify influences of land use change and forest management, and provide ecological context for distinct management regimes and their SOC impacts. We conducted this, the fourth in a series of ecoregional SOC assessments, for the South Atlantic States, which are disproportionately important to the national-scale forest C sink and forest products industry in the U.S. At the ecoregional level, baseline SOC stocks vary with climatic, topographic, and soil physical factors such as temperature and precipitation , slope gradient and aspect, and soil texture. Land use change and forest management modestly influence SOC stocks. Reforestation on previously cultivated lands increases SOC stocks, while deforestation for cultivation has the opposite effect; for continuously forested lands, harvesting is associated with SOC increases and prescribed fire with SOC declines. Effects of reforestation are large and positive for upper mineral soils (+30%) but not detectable in lower mineral soils. Negative effects of prescribed fire are due to significant C losses from organic horizons (-46%); fire and harvest have no impacts on upper mineral soils but both increase SOC in lower mineral soils (+8.2 and +46%, respectively, with high uncertainty in the latter). Inceptisols are generally more negatively impacted by prescribed fire or harvest than Ultisols, and covariance between inherent factors (including soil taxonomy) and management impacts indicates how interior vs. coastal physiographic sections differ in their management regimes and SOC trends. In the cooler, wetter, topographically rugged interior hardwood forests, which have larger baseline SOC stocks, prescribed fire and even light harvesting generally decrease SOC; in contrast, intensively managed coastal plain pine plantations begin with small initial SOC stocks, but exhibit rapid gains over even a single rotation. This covariance between place (physiography) and practice (management regime) suggests that distinct approaches to forest C management may be complementary to other ecological or production goals, when implemented as part of wider (e.g., state-level) forest C or climate policy.
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Currently, China’s forest ecosystem focus is shifting from a single management objective to multiple management objectives, aiming to improve forest quality and maximize the benefits of ecosystem services. Many difficulties and problems are encountered in the long-term development of most northern state-owned forest farms—for example, the fragmentation and degradation of forest landscapes caused by poor forest management and extensive land use—resulting in an ecosystem that is unable to provide optimal services. This research was conducted on the Fengning Grassland Forest Farm, which is based on the GEF project of state-owned forest farms. We applied lessons from international advanced concepts, such as landscape restoration, and combinecombined all types of existing data and supplementary survey data on forest farms. In addition, we used multivariate statistical analysis and geostatistical analysis methods to optimize spatial layout and forest landscape structure. Strategies of landscape restoration and optimization, forest quality improvement, and grassland ecological restoration were proposed. A forest growth model was established to predict the annual growth of forests, calculate sustainable levels of annual cutting, calculate biomass and carbon sequestration in the management period, and evaluate the value of the ecological service functions of forest ecosystems in forest farms. Finally, a set of forest management methods was developed to effectively improve the sustainable management level of state-owned forest farms and enhance the service function of forest ecosystems.
National forest inventories (NFI), such as the one conducted by the United States Forest Service Forest Inventory and Analysis (FIA) program, provide valuable information regarding the status of forests at regional to national scales. However, forest managers often need information at stand to landscape scales. Given various small area estimation (SAE) approaches, including design-based and model-based estimation, it may not be clear which is most appropriate for the user’s application. In this study, our objective was to assess the uncertainty in tree aboveground live carbon (ALC) estimates for differing modes of SAE across multiple scales to provide guidance for appropriate scales of application. We calculated means and variances for ALC with design-based (Horvitz-Thompson), model-assisted (generalized regression), and model-based (k-nearest neighbor synthetic) estimators for estimation units over a range of sizes for 30 subregions in California, United States. For larger areas (10,000–64,800 ha), relative efficiencies greater than one indicated that the generalized regression estimator (GREG) generated estimates with less error than the Horvitz-Thompson estimator (HT), while the bias-adjusted synthetic estimator relative efficiency compared to either the Horvitz-Thompson or model-assisted estimators exceeded one for areas 25,000 ha and smaller. Variance estimates from the unadjusted synthetic estimator underestimated the total error, because the estimator ignores bias and thus only addresses model variance. Across scales (250–64,800 ha, 0–27 plots per area of interest), 93% of the variation in the synthetic estimator’s relative standard error was explained by forest area, forest dominance, and regional variation in forest landscapes. Our results support model-assisted estimation use except for small areas where few plots (<10 in the current study) are available for generating estimates in spite of biases in estimates. However, users should exercise caution when interpreting model-based estimates of error as they may not account for model mis-specification, and thus induced bias. This research explored multiple scales of application for SAE procedures applied to NFI data regarding carbon pools, potentially supporting a multi-scale approach to forest monitoring. Our results guides users in developing defensible estimates of carbon pools, particularly as it relates to the limits of inference at a variety of spatial scales.
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While many forestry practitioners and ecologists have a general understanding of the USDA Forest Service, Forest Inventory and Analysis (FIA) program and the type of data collected, most non-expert users of FIA reports and basic data are unlikely to be familiar with the breadth of information available and the many potential uses of the data. We present four case studies from National Forests in the eastern United States, to highlight a variety of applications of FIA data, though similar case studies could also be identified on private lands. These include informing a model to help managers decide where to invest in oak management, quantifying habitat characteristics as part of the Endangered Species Act listing process, developing focal species for forest monitoring, and assessing the health of the black cherry population. In three of the cases, collaboration between scientists and managers was the key to unlocking the power of the FIA database to address management questions without the expense of collecting additional field data. These case studies illustrate the utility of FIA data to meet managers’ information needs and the importance of the linkages between research and management and serve as examples of potential applications of data collected by regional or nationwide forest inventory programs.
Variation in overstory biomass and mean annual biomacc increment (MABI) of upland forest stands was studied at 2 spatial scales: glacial landforms (1:250 000-1:1000 000) and ecological land units (1:10 ooo-1:80 000). Ecological land units were defined based on combinations of ground-flora vegetation, soil and physiography. Overstory biomass ranged from 105 t/ha (MABI = 1.5 t ha-1yr-1) on glacial outwash landforms to 208 t/ha (MABI = 3.2 t ha-1yr-1) on morainal landforms; 37% of the total variation in biomass was accounted for by landform. Analysis at the ecological land unit scale accounted for a higher proportion of variation c60% of the total. Overstory biomass among ecological land units ranged from 85 t/ha(MABI) = 1.3 t ha-1yr-1) for oak-dominated forests occurring on xeric sandy outwash sediments to almost 250 tHa(<MABI = 3.6 t ha-1yr-1) for sugar maple-red oak (Acer saccharum-Quercus rubra) forests occurring in mesic morainal positions. Variation in biomass appeared to be strongly related to differences in species composition and variation in soil moisture availability. A relatively strong association between ground-flora composition and productivity was indicated. -from Authors
Distinguished 11 ecosystem units that differed markedly in their vegetation as well as in their topographic and soil properties. Ecosystems were identified in the field using combinations of biophysical properties such as slope, aspect, soil texture, soil drainage and forest composition. Groups of indicator plants were especially helpful in distinguishing ecosystems in the field. Soils of ecosystems identified using the ecological method of classification differed significantly in their physical and chemical properties. Simultaneous use of topographic, soil, and vegetal factors proved most efficient in classifying these upland ecosystems. -from Authors
The uplands and wetlands were subjectively classified into 25 ecosystems by a method combining reconnaissance, plot sampling, data analysis, and ecosystem mapping. Each ecosystem was a characteristic combination of physiography, ecological species groups (ground vegetation), and soil. Discriminant analyses indicate that neither vegetation alone nor physiography and soil alone could be used with high reliability in classifying and mapping ecosystems. An additional discriminant analysis of the 3 ecosystem components indicated that the ecosystems could be distinguished by field characteristics without information from soil laboratory analyses. -from Authors
The cores and boundaries of land units are located by reference to relationships between climate, landform and biota in ecological land classification. This appeal to relationships, rather than to climate, or to geomorphology, or to soils, or to vegetation alone, provides the common basis for land classification.