2.0 Field Sampling Theory

Three areas of ecological theory should be given consideration in the
sampling design for vegetation classification and mapping: (1) the
nature of vegetation as a biotic component of ecosystems, (2) the nature
of the abiotic component, and (3) the vegetation/abiotic relationships.
The treatment of these issues will effect decision making for the
sampling design and the most appropriate measurements for
characterization and mapping of vegetation for the NPS/NBS vegetation
mapping program. Therefore, this section reviews specific issues in the
above three areas of ecological theory and their impact on vegetation
sampling.

2.1 Nature of Vegetation

Vegetation refers to the great diversity of plant species which occur in
repeating assemblages over the face of the earth. Before attempting to
inventory and map vegetation, it is necessary to understand certain
features of the nature of vegetation.

**2.1.1 Definitions**

It is beyond the scope and purpose of this document to provide a
glossary of terms and a comprehensive discussion of the many concepts
and terms embodied in community ecology. Instead, a few key terms and
concepts essential to the program are defined and discussed. A thorough
understanding of these concepts is critical to the sampling design and
vegetation survey.

In nature, plant species are always part of an assemblage, or community
of species populations living together in the same area. A general
definition of community is any assemblage of populations of living
organisms in a prescribed area or habitat. A more scientific definition
of plant community was given by Mueller-Dombois and Ellenberg (1974);
\"A plant community can be understood as a combination of plants that
are dependent on their environment and influence one another and modify
their own environment.\"

The concept of community has been central to plant ecology, or more
specifically, to plant sociology, all through this century;
consequently, a series of terms has been specially devised to describe
it. The fundamental unit of plant sociology is the plant association ---
a vegetation community of definite floristic composition.

Walter (1973) described the relationship between plants, community, and
vegetation as follows: \"Plant species are the building blocks of the
plant communities that together constitute the vegetation of the
different regions.\"

**2.1.2 Community vs. Continuum**

For many years, plant ecologists were divided in their opinions as to
whether vegetation consists of a series of distinct communities or
whether vegetation types grade into one another (i.e., vegetation is a
continuum). During the 1960s and 1970s, major debates resulted over the
fundamental issues of whether communities/associations were real or
abstract and whether a delimited community was a natural or artificial
construct. Debate focused around F.E. Clements\' (1936) view of
communities as discrete \"supra-organisms\" with characteristics of an
individual as opposed to H. Gleason\'s \"individualistic\" concept
proposing that species responded individually to environmental
gradients. Full treatment of this debate can be found in Whittaker
(1962) and Mueller-Dombois and Ellenberg (1974). Over time, however,
studies converged in supporting Gleason\'s continuum/gradient concept,
and it has received widespread acceptance. Recently, Austin and Smith
(1989) have reformulated the continuum concept and readdressed the
community/continuum from a new perspective. They emphasize that the two
ideas are not really dichotomous, but are based on incompatible frames
of reference. This is in agreement with the current widespread use of
both classification and gradient analysis (ordination) in a single
methodology informally known as complementary analysis (Kent and Coker
1992).

Vegetation is characterized by the link between individual species
distribution patterns, their occurrence in landscape features, and the
distribution of the landscape features. Therefore, various aspects of
both the continuum and the community views of vegetation complement
rather than exclude each other (Westhoff and Van der Maarel 1978, Austin
1991). Species can be individually distributed along gradients,
unidimensional or complex, following any of the possible models (Austin
1987, Austin and Smith 1989). The pattern of distribution of the
landscape features that control environmental factors constrain the
pattern of species combinations, their distribution in the landscape,
and their frequency.

**2.1.3 Vegetation Structure**

The overall appearance of the vegetation is called its physiognomy
(Kuchler and Zonneveld 1988). The physiognomy is used to describe the
broad features of the vegetation, such as the growth forms and/or the
life form of dominant species within a plant community. Life form

is a very important characteristic of vegetation and is used in many
vegetation classification systems.

The floristic composition of vegetation includes all species occurring
within a plant community. However, most plant communities consist of so
many species that it is not practical to discover all species within a
community. It is common to use dominant species in naming plant
communities and in legends with vegetation maps.

Vegetation profile is defined as the vertical aspect of the vegetation.
Stratification is the most obvious application of the study of
vegetation profile. The strata are not always horizontal, such as
epiphytic bryophyte and/or lichen layers in forests. The vertical
structures of vegetation are very important for the physiognomic aspect
of the standard NVCS as well as for photo interpretation.

A combination of physiognomy, floristic composition, and profile is
essential information to identify and describe plant communities.

**2.1.4 Parameters of Vegetation Description**

Community ecologists are often interested in obtaining information
pertaining to a large number of variables in a community. Much work in
community ecology is motivated by a desire to elucidate and describe
patterns in the data sets, rather than formally testing a priori
hypotheses (Green 1980). This is even more true for the NPS/NBS
vegetation mapping program. It is recommended that field teams only
collect the vegetation information necessary for characterization and
mapping of vegetation in parks. This objective leads to the following
brief descriptions of most common vegetation parameters recommended to
be used for this program.

***2.1.4.1 Species Occurrence***

As mentioned above, the species component is the fundamental structure
of a plant community. A species list is an essential part of all
vegetation survey activities.

***2.1.4.2 Frequency***

The frequency of a species is defined as the probability of finding it
within a plot when the plot is placed on the ground. The prime
requirement in estimating frequency is to use as large a sample size as
possible.

***2.1.4.3 Cover***

The cover of a species is defined as the proportion of ground occupied
by vertical projection. Cover is normally expressed as a percentage and
the maximum cover of any one species is 100 percent. For classification
purposes, the most common practice is estimation of cover in field.
There are a number of \"scales\" or ratings based on cover
(Mueller-Dombois and Ellenberg 1974, Causton 1988). Sampling of
percentage cover is very similar in principle to the sampling of
frequency. Thus, the recommendation of having a large sample size is
also applicable to cover.

**2.1.5 Vegetation Dynamics: Seasonal Variations and Succession**

Vegetation is a dynamic system in terms of species composition and
spatial structure. Even though the objective of the NPS/NBS vegetation
mapping program is only to describe existing vegetation patterns across
national parks in both documentation and maps, it is necessary to review
and discuss the dynamic characteristics of vegetation and consider the
dynamic features when developing the sampling methods.

***2.1.5.1 Seasonal Variations***

Vegetation follows the seasonal variation of the radiation climate. Of
particular interest here are phenological patterns over the course of a
year. Many plant communities have distinct seasonal peaks of growth and
flowering activity, and different components of the vegetation often
grow at different times of year. An extreme example of this phenomenon
is an annual grassland where live vegetation is present for only part of
the year. The seasonal variation can markedly affect spectral
reflectance. On the other hand, seasonal changes can be used to
differentiate between herbaceous vegetation and woody vegetation or
among different woody vegetation types with different phenological
patterns.

***2.1.5.2 Succession***

Succession is the dynamic process by which population of plants replace
one another over a period of time until relative community stability is
achieved. The series of successional communities can be recognized as
different stages, and each stage is treated as an existing vegetation
type once the structure and composition of the stage reaches a
relatively stable state.

**2.1.6 Ecotone**

Even those ecologists who favor the community idea of vegetation
recognize that boundaries of communities are indistinct. They coined the
word \"ecotone\" to describe the area of the boundary or the transition
zone. Very often the ecotone is found to be more species-rich than
either of the communities it separates, and the ecotone could really be
recognized as a community itself.

**2.1.7 Spatial Distribution of Vegetation: Implications for Sampling
Design**

At the landscape level, individual plant species distributions along
gradients combine to produce plant species assemblages. Austin (1991)
and Austin and Smith (1989) observed that the frequency of combinations
of species is often used as a criterion to recognize communities.
Therefore, the occurrence of a species combination is a consequence of a
particular landscape pattern; the frequency of species combinations is
also a function of the landscape pattern. As a result, the mapping of
these landscape patterns is central to a successful sampling design.

When defining landscape patterns, three types of environmental variables
or gradients need to be considered (Austin et al 1984, Austin 1985, and
Austin and Smith 1989):

> \(1\) Indirect factors that do not necessarily have an physiological
> influence on the species components of the ecosystems (e.g.,
> elevation)
>
> \(2\) Direct factors that have a direct physiological influence but
> are not consumed as a resource (e.g., temperature and pH)

\(3\) Resource gradients that can be used directly by species (e.g.,
nutrients)

In principle, sampling design for vegetation surveys should be based on
the most proximal variables that can be measured or estimated.

Theories and methodologies used to define the environment (indirect
factors, direct factors and resource gradients) fall into two major
categories:

\(1\) Delineation of landscape units containing recurrent patterns of
landform and/or

landscape characteristics (e.g., the ecological land units)

> \(2\) Identification of bioenvironments (i.e., classes in
> environmental variables that take into account key ecological
> interactions and processes)

The first approach has been to use broad environmental patterns alone or
broad correlations between vegetation and environment to describe and
delineate habitats of plant communities. Land classifications have been
developed using climatic attributes either alone or in conjunction with
other attributes (e.g., Austin and Yapp 1978, Omernik 1987, Walter 1979,
Bailey 1976, Bailey et al. 1993). A great variety of systems based on
combinations of soils, lithology, and landform have been used alone or
combined with vegetation data to produce classifications of biophysical
regions or natural landscape units. In Canada, Rowe and Sheard (1981)
detailed a landscape system for identifying units of lands that are
meaningful at the ecosystem level.

The strength of this approach is that it allows a direct analysis of the
spatial and temporal scales of landscape features (Delcourt and Delcourt
1988, Urban et al. 1987), which is necessary to match patterns and
processes (Levin 1992). For example, in a mountainous landscape in
Northwestern Montana, Lathrop and Peterson (1992) tested whether
watershed morphological characteristics and ecological processes
exhibited the same basic properties at various spatial scales
(self-similarity). They established structural self-similarity but did
not conclusively demonstrate self-similarity for ecological properties.
Establishing a relationship between landscape structure and vegetation
attributes across a range of spatial scales has important implications
for the proper scaling of vegetation sampling design at all relevant
scales.

On the other hand, the main weakness of this approach is that it relies
to heavily on indirect factors (soils, landform, etc.) without
explicitly stating the ecological relationships between vegetation and
environment. For example, landform patterns have often been used to
stratify areas into natural landscape units on the basis of a single
factor attribute. The units are argued to represent natural levels of
ecological integration with respect to environmental regimes and key
processes, and, indeed, there is compelling evidence that this is the
case (Swanson et al. 1988). Geomorphic pattern, through
erosion/sedimentation processes, has been shown to control carbon,
nitrogen, and phosphorus cycles in soils of riparian forests in southern
France (Pinay et al. 1992). However, the actual test of the strength of
the hypothesized relationship between vegetation and environment is not
always performed. For example, Kovalchik and Chitwood (1990) used
geomorphology in addition to a floristic classification of the
vegetation of riparian zones in central Oregon but did not explicitly
test the purported vegetation/geomorphological process relationship.

A second approach has recently been developed in Australia out of
concern for the problems outlined above. This approach recognizes the
need of the sampling design to be based on environmental units that
summarize the limited set of dominant environmental variables that
comprise the primary niche dimensions (Nix 1982) such as radiation,
thermal, moisture, mineral nutrient, and the biotic regimes (Nix 1982,
Mackey et al. 1988, 1989) to which species respond. The aim of the
methodology is to summarize environmental variability, identify the
distribution of major environmental gradients, and indicate where
significant shifts in ecological variability might occur (Mackey et al.
1988, 1989). Site-specific data are used to generate classes of sites
sharing similar ranges of values of the environmental variables (direct
and resource gradients). A map of these classes, or bioenvironments, can
be used alone in the assessment stage of an area for given purposes
(DeVelice et al. 1993), and/or in conjunction with vegetation data for
quantifying biotic--abiotic correlations (Mackey et al. 1989).

Sampling ecologically significant factors in the physical environment at
sufficient resolution ensures that key processes and interactions can be
taken into account (Mackey et al. 1988). Estimating the key attributes
involves the modeling of terrain--climatic interactions including simple
surface-fitting procedures, as well as models that take into account
known effects of physical processes. However, the accuracy of the
results is limited by the extent to which the processes and interactions
are known.

The consequences for vegetation sampling are as follows. Attempts to
describe patterns in abiotic factors or habitats for cost-effective and
efficient vegetation sampling design can be successful only if the
ecological meaning of the factors is understood. There is a difference
between natural landscape units and bioenvironments. The former refer to
geographical phenomena, the latter to an ecological space. An actual
combination of natural landscape features (for example, elevation class
x landform x geologic substrate) may be found in two different regions.
However, a change in regional climate may change the overall suitability
of the habitat for particular species. When characterizing and mapping
the physical environments of a region, it should not be assumed that all
occurrences of a mapped environment are ecologically identical. With
changes in climatic-terrain interactions occurring over large areas, the
values of the direct factors and resource gradients to which the biota
respond also change gradually. Therefore, similar physical environments
may correspond to different bioenvironments.

The accuracy and utility of describing and mapping natural landscape
features or bioenvironments for sampling vegetation patterns are
functions of the environmental variables selected, the relationship
between indirect and direct factors, the estimation procedures and
mapping scales used, and the strength of the purported relationship
between the vegetation and the selected environmental criteria.

2.2 Common Sampling Approaches

The complex spatial nature of the vegetation presents a number of
problems when designing optimal sampling schemes for a landscape. First,
there is a lack of useful information on the spatial characteristics of
the vegetation. Second, little attention has been given to extensions of
traditional sampling theory for two-dimensional sampling of vegetation.

**2.2.1 Sampling Scale Issue**

Vegetation is a multilevel phenomenon, that is to say the scale of
pattern ranges from the region of influence of an individual plant to
the ecoregion. The choice of scale for observation is of great
importance in vegetation inventory. Hierarchies of observation scale
present different issues because scale can be changed in a continuous
manner, while the hierarchies of vegetation classification systems
emphasize the importance of distinguishing types. It is important to
understand this to ensure that different types are not mixed by using
various observation scales.

The relation of vegetation and environment and ecological processes are
also scale dependent phenomena. Several studies (Allen and Starr 1982,
Weir and Wilson 1988, Reed et al. 1993) indicated that multiple scales
of observation in ecological investigations can be critical for
understanding vegetation patterns and processes. However, due to the
objectives of the field inventory for the NPS/NBS vegetation mapping
program, it is recommended that observation scales vary only among
vegetation types and not within vegetation types.

**2.2.2 Ecological Gradients**

Plant communities may be distributed over large areas, sometimes in
several regions (for example, the spruce fir forests of the Rocky
Mountains). However, in designing sampling procedures for vegetation
classification, all occurrences of a community should not be assumed to
have identical properties. With numerous species in a community, the
individual distribution of species ensures gradual intracommunity
changes along regional gradients. Occurrences of the same community may
have different overall composition, frequency, and size of areas,
resulting in different properties relevant for vegetation classification
and landscape evaluation. It is likely that the same community found at
different locations along a regional gradient or in different climatic
regions would respond differently to a specific conservation-management
practice. Therefore, species distributions need to be established with
data spanning the range of environmental variability along which they
are distributed. Communities need to be defined using data preferably
covering the range of environmental variability in all landscapes in
which they occur. Effective sampling for vegetation classification and
mapping requires adequate replication within communities to allow for
and detect geographical variability and gradient relations.

**2.2.3 Random vs. Representative Sampling**

The primary goal of vegetation surveys is to characterize as many
vegetation patterns as possible within the study area(s). The recovery
of vegetation pattern is not necessarily accomplished by the usual
statistical sampling procedures. Sampling theory emphasizes
randomization in order to provide a probability structure for
statistical analysis or to give credibility to the statistical model
used (Gillison and Brewer 1985). Gillison and Brewer (1985) argue that
randomization procedures may be counterproductive to the intent of
ecological surveys, especially where the occurrence of natural pattern
is known to be nonrandom. Data sets need to be representative of the
full range of variability in biological patterns in response to
variability in the environment.

In vegetation surveys, two aspects of pattern recognition should be
considered: (1) the recognition of the pattern itself (e.g., a specific
forest type) and (2) the frequency and distribution of patches of the
pattern (i.e., spatial distribution, number and size of forest stands)
(Godron and Forman 1983, Gillison and Brewer 1985). In landscapes,
vegetation patch frequency and distribution vary as a scale-sensitive
function of environmental complexity and the level of resolution of the
vegetation classifications used to characterize the pattern (Gillison
and Brewer 1985). This variability in landscape level vegetation
configuration should be analyzed in terms of the driving variables (the
abiotic factors) controlling the vegetation.

**2.2.4 Stratified Random Sampling**

In standard sampling design, each spatial point in the landscape is
given an equal probability of being sampled. Random placement of sample
sites will not accurately reflect the full range of variability of the
biotic and abiotic components of ecosystems at regional scales unless
the sampling intensity is very high (Gauch 1982, Orloci 1978, Pielou
1984).

To alleviate the shortcomings of standard random sampling, stratified
sampling schemes have been used to provide both accuracy in the recovery
of patterns and statistical validity. Stratified sampling divides a
study area into compartments and locates samples randomly within
compartments. This approach has been used successfully over large
heterogeneous areas with mostly unknown patterns. For example, a nested
stratified random sampling design by landform and ecoregions was used in
southern Yukon, Canada, to characterize vegetation pattern and its
underlying environmental gradients (Orloci and Stanek 1979). The results
of the study indicate that the selected stratifying variables accounted
for a large part of the regional variation in vegetation.

**2.2.5 Gradient-Oriented Transect (Gradsect) Sampling**

Orloci and Stanek\'s (1980) sampling design is very similar to a
methodology known as gradient directed transect (gradsect) sampling.
Gradsect sampling is a variant of stratified random sampling schemes.
This approach, first described by Gillison and Brewer (1985), is based
on the distribution of patterns along environmental gradients. The
gradsect sampling design (Gillison and Brewer 1985, Austin and Heyligers
1989) is intended to provide a description of the full range of biotic
variability (e.g., vegetation) in a region by sampling along the full
range of environmental variability. Transects that contain the strongest
environmental gradients in a region are selected in order to optimize
the amount of information gained in proportion to the time and effort
spent during the vegetation survey (Austin and Heyligers 1989). In
addition, sampling sites are deliberately located to minimize travel
time. The method has been shown statistically to capture more
information than standard designs (Gillison and Brewer 1985).

Helman (1983) and Austin and Heyligers (1989, 1991) have expanded the
gradsect methodology to include levels of environmental stratification
within each gradsect. The procedure, as modified, thus becomes a
two-stage sampling design: (1) gradsects are chosen; (2) adequate
environmental stratification and replication are performed within
gradsects.

The relative efficiency of gradsect sampling compared to other sampling
techniques has been analyzed (Gillison and Anderson 1981, Gillison and
Brewer 1985, Austin and Adomeit 1991), where efficiency is assessed with
respect to the ability to recover ecological patterns. A gradsect sample
was found to have greater logistical efficiency than a systematic grid
sample of vegetation in a 7 hectare small mammal study, while providing
adequate data for vegetative classification and prediction of small
mammal distributions (Gillison and Anderson 1981). In another study, an
existing vegetation map derived from high-density point sampling of a
424 km^2^ area was used as the test base for comparing gradsects to
random transects (Gillison and Brewer 1985). Gradsects were found to be
27 percent shorter on average than random transects and logistically
better located. They also were on the average 21 percent richer in
vegetation types than random transects of the same length.

A simulation study was conducted to evaluate several traditional
statistical sampling methods as well as gradsect sampling (Austin and
Adomeit 1991). The simulated data set used for the tests was based on an
actual landscape, with realistic biotic/environment relationships
(Belbin and Austin 1991). Austin and Adomeit (1991) provided \"informed
guesses\" of sampling costs for the various methods. Among the three
statistical sampling methods compared, random sampling recovered more
species than systematic sampling or transect sampling at intermediate
cost. Transect sampling was found to have the highest costs and detected
the fewest species. Gradsect sampling methods were compared to random
sampling. Various rules for gradsect sampling within a grid cell were
included in the test. These pertained to resampling within a grid cell
and choice of samples from topographic units. In general, gradsects
detected more species than random sampling and some gradsect sampling
rules achieved this result at lower cost.

The results of tests of efficiency show that gradsects are generally
more efficient than traditional statistical techniques in recovering the
greatest amount of ecological pattern per sampling effort. The gradsect
method allows placement of samples in logistically more accessible areas
than statistical techniques such as systematic or random sampling, thus
improving cost-effectiveness. Costs can also be reduced and
effectiveness maximized when stratifying variables are carefully chosen
using existing information (Austin and Adomeit 1991).

2.3 Relationships Between Classification and Mapping

The types identified in a vegetation classification are not mapping
units. Kuchler (1988) described a vegetation map as an application of a
given vegetation classification. A map assumes that the vegetation
categories are established in a manner that can be visualized. The
intimate relationship between classification and mapping (Kuchler 1988)
is that the choice of a classification strongly affects a map and the
purpose of a map determines the classification approach that should be
used.

The vegetation classification system that has been developed by The
Nature Conservancy can be used for pure vegetation maps (sensu Kuchler
1988), or as a basis for ecological maps (Zonneveld 1988) or maps of
land units (Zonneveld 1989). The use of a pure vegetation map will be
limited by the lack of predictability of vegetation dynamics unless
mapping is repeated over a certain period of time (see discussion in
Kuchler 1988).

**2.3.1 Scale Considerations**

Other important factors to consider in surveys for vegetation
classification and mapping include the notion of scales (spatial and map
scales). Complexity and scale complicate the relationship between
classification and mapping. Plant communities form complex patterns on
the landscape, each community representing a different combination of
environmental conditions. At the scale of a small area, a map of plant
associations can be used to characterize vegetation within the area
itself. Even at this fine scale, plant communities may form complex
mosaics composed of various phases. In terms of classification, the
choice between recognizing one plant community for the entire mosaic or
one plant community per phase is essentially a matter of philosophy of
classification, although ad hoc tests exist for making a decision. In
terms of mapping, it is essentially a matter of scale. Communities that
can be mapped independently at the 1:24,000 scale cannot be mapped
1:100,000 and smaller. At these scales (such as a midscale of
1:250,000), vegetation needs to be mapped as complexes. In this case, it
is even more important to relate vegetation patterns to the ecological
factors that shape them in order to be able to interpret the map units.
Tests of homogeneity of mapping units exist (see Task 4).

2.4 Characteristics of a Successful Sampling Approach

There are a number of factors that must be addressed in the development
of a successful sampling approach, particularly for the NBS/NPS
Vegetation Mapping Program.

**2.4.1 Flexibility**

Vegetation types differ in their relationship to numerous variables. The
sample areas will be of different sizes and levels of biological
complexity, and the prior knowledge of areas will differ. Therefore, all
aspects of the sampling design need to be flexible to fit area-specific
ecological and programmatic conditions.

**2.4.2 Replicability (Explicit and Consistent Sampling Procedures)**

The notion that vegetation surveys are objective and/or void of
assumptions about the phenomena the data seek to explain is unrealistic.
Scientists have an implicit mental model of the relationships between
the different components of the systems that they study (Austin 1991).
This knowledge is used in the routine procedures followed by the field
practitioner, whether this is explicitly stated or implicit. Therefore,
it is necessary to explicitly state assumptions made in all stages of
the design. Once the assumptions are tested, they should be consistent
across the entire methodology. Furthermore, all steps in the design need
to be applied consistently during implementation. Replicability and
flexibility of sampling procedures need not be antagonistic criteria
and, in fact, should be met simultaneously.

**2.4.3 Cost-Effectiveness**

An important feature of a vegetation survey, and of its associated
sampling design, is its cost-effectiveness. Cost-effectiveness can be
defined as the recovery of all vegetation patterns found in an area with
the smallest number of samples, smallest sampling crew, and shortest
amount of time. To achieve cost-effectiveness, the domain and grain of
the data set need to match the domain and grain of the ecological system
(i.e., vegetation) under study. Therefore, cost-effective procedures for
data collection need to take into account ecological interactions at
various scales (Austin 1991, Mackey et al. 1989, Gillison and Brewer
1985, Bourgeron et al. 1993). Therefore, a few basic theoretical
concepts of vegetation structure, composition and function influence
every step of the process of vegetation characterization and mapping,
from data collection to interpretation (Austin 1987). These theoretical
concepts need to be clearly defined and continually reassessed in the
light of new developments (see Section 3).

**2.4.4 Integrated Field Methods to Support Multiple Objectives**

There are multiple objectives to be met through the acquisition,
analysis, and presentation of field data. The methods to support these
objectives must be fully integrated to succeed.

***2.4.4.1 Correlation of Vegetation and the Environment***

In order to use a vegetation map for conservation planning and/or
management of any element of the vegetation, a correlation must be
established between the vegetation and the environment. To be of optimal
use, the analysis should be expanded to include the use of regional
gradients in the field inventory. It should be noted that if a
particular plant community occurs over a range of environmental
conditions (i.e. along an environmental gradient), the inventoried areas
should encompass the full range of variability of environmental
conditions encountered along the gradient in order to sample a
representative set of individual species populations, species
assemblages, and ecological processes. The process of sampling specific
areas to determine the patterns of vegetation and associated ecological
processes calls for integrated inventories and assessments of vegetation
and key environmental variables at the same time.

***2.4.4.2 Classification***

The goal of any classification is to elucidate pattern and reduce a
large number of objects to a smaller number. Thus classification reduces
the dimensionality of systems to those that are functionally important
for the objectives of classification. In ecological classification
systems the objective is to identify vegetation and/or land units with
similar characteristics and to use those units to understand ecosystem
relationships (Larson and Schlatterer 1984, Nelson et al. 1984). A
complementary step to the classification itself is the identification of
the agents of formation (sensu Urban et al. 1987) of the pattern (in
this case the vegetation types). Vegetation types are characterized by
the link between individual species distributions and their
co-occurrence with a set of environmental factors (e.g., physical,
biological, and disturbance factors). In a given area the distribution
pattern of the landscape features controls the environmental factors
constraining species distributions. At a local scale, then, vegetation
types are a function of landscape features (e.g., landform, soils,
geologic substrate) and the distribution of these landscape features
(Bourgeron and Engelking 1993).

It is essential to integrate sampling of vegetation and relevant
environmental/ecological attributes in a common methodology. The
objectives of an integrated field methodology for vegetation
classification and mapping are

> \(1\) To describe vegetation types and establish their regional
> patterns of distribution
>
> \(2\) To identify the fundamental relations of the vegetation types to
> the ecological processes that act upon them
>
> \(3\) To analyze the role of processes at regional and landscape
> scales on local vegetation structure
>
> \(4\) To use the identified classification units and processes in
> characterizing and evaluating landscapes in terms of their vegetation
> patterns

***2.4.4.3 Characterization***

The fourth objective listed above is central to the successful mapping
of vegetation. A three-step process is involved in the characterization
of a vegetation type or complex of types found in a given location. This
characterization would include the description of relationships with
environmental variables that shape a vegetation type and its response to
disturbance factors or management scenarios. First, a classification is
needed to identify the type itself. Second, the environmental
conditions, or habitat factors, need to be delineated, leading to the
mapping unit boundaries. Indeed, plant communities are characterized as
recurrent patterns of species co-occurring in similar landscape
features. Third, as a result of the individual distribution of species
(the continuum concept), gradual intracommunity changes along regional
environmental gradients need to be quantified. Examples of the same
vegetation type found in different areas will more likely express
different characteristics of that type.

In terms of sampling design, the consequences are (1) the objective of
defining communities should be extended to determining their range of
distribution, and (2) the specific overall species composition of each
individual occurrence of a community needs to be documented and related
to environmental/ecological constraints. The fact that communities and
mapping units are not invariant is important to acknowledge for
conservation planning because occurrences of a given community differ in
their contribution to local/regional patterns of diversity and could
respond differently to a specific conservation-management scenario
depending upon their locations along regional gradients (Bourgeron and
Engelking 1994).

2.5 Variability of Existing Information

A necessary step in conducting regional surveys is determining the types
of data already available and whether they may be useful and
cost-effective for a specific purpose even if biases exist (Austin
1991b). Maps of vegetation, soils, geology and climate exist in many
cases. These maps could be used as initial surrogates for field data.
However, data layers at different scales can add to the initial
uncertainty and error rate inherent in each map. This approach has been
used successfully by various investigators. For example, in Australia,
maps of land systems (Christian and Stewart 1953) were used to build a
database to describe the range of natural environments in the study
region. This database was later used to sample the range of natural
variability contained in various areas in the region (Pressey and
Nicholls 1991).

Remote sensing (digital) imagery is readily available over North
America. This may be a cost effective approach for broadscale mapping of
land cover. However, there are significant challenges in producing maps
from remote sensing data that pertain to the scale of measurement of the
data (Davis et al. 1991). There has been a recent trend to acquire
ground and remote sensing data over a range of scales (Sellers et al.
1988, Davis et al. 1991). Davis et al. (1991) concluded that some of the
problems in remote sensing relate to understanding spatial scale
dependence of processes and patterns. This search for multiscaled
patterns and processes (Levin 1992, Milne 1993, Minshall 1993, Turner et
al. 1993) requires, in turn, the collection of ground data at the
appropriate intensity and frequency.

Another source of existing data is herbarium and museum records.
Databases have been developed that compile this kind of information. The
work of the Natural Heritage Programs and The Nature Conservancy
(Jenkins 1976) with the Biodiversity and Conservation Database (BCD)
software provides an example. Herbarium and museum records for specific
plant and animal species, plant communities, and/or ecosystems
(identification, location, ownership, and any relevant biological and
management information) are collected and entered on the BCD for each
state. Although the database is descriptive, extremely cost-effective
use is made of these existing data for many purposes. Another example is
the work of the U.S. Fish and Wildlife Service Gap Analysis Project
(Scott et al. 1987) or GAP. State GAP programs combine species
distribution information with vegetation maps. New survey costs can be
minimized by using existing data because old records may suggest
historically suitable areas for specific species or communities that
have not been visited in decades. Repeat visits of such areas have
yielded important information on changes in grassland and forest
ecosystems in the western United States. (Fisser 1980, Gruell 1983).

A final source of existing data is plot data collected during previous
vegetation surveys. If these data are incorporated into a standardized
database, a very cost-effective tool for vegetation classification and
landscape evaluation can be created. One limitation in the use of
existing survey data is that the same biotic or abiotic information is
rarely collected across surveys. However, if a minimum list of
attributes common to all surveys is established, data can be used for a
variety of purposes (Austin 1991b, Jensen et al. 1993).

Such databases can be used to determine which areas and/or which part of
environmental gradients have not yet been sampled. By systematically
reexamining existing data, duplication of survey effort is avoided and
maximum use of resources can be made. Disadvantages (cost of
establishing a database; limitation to a minimum list of attributes at
the regional scale) are offset by potential cost reduction and
increasing effectiveness of new surveys and by providing an initial
regional overview of the information.