Front Cover

The following page provides a template for the required front cover.
Though some elements and margins are not shown per USGS requirements,
the template is provided for you to insert the appropriate text and
graphics. Your submission will be revised by the national office using a
graphics program. If the project logo style is the one you want to use,
we can build it for you; if you desire this, delete the graphic and
insert \"please provide project logo.\" You may provide an alternative
graphic if you wish. You may also wish to include graphics of plant
communities and animal species.

> For the remainder of this chapter, all text in italics are
> instructions, other text should appear in the report.
>
> **A GAP ANALYSIS OF**
>
> **XXX*(Insert State Name Here)***
>
> XXXX Final Report *(Insert date here)*

![](media/image1.png){width="5.998611111111111in" height="6.13125in"}

> **A GEOGRAPHIC APPROACH TO PLANNING FOR BIOLOGICAL DIVERSITY**
>
> U.S. Department of the Interior
>
> U.S. Geological Survey

*The following information should appear on the title page.*

THE *insert state name here*

GAP ANALYSIS PROJECT

**FINAL REPORT**

***insert date of final version here***

*insert names and affiliations of PI(s), staff, associated researchers
here*

**Contract Administration Through:**

*insert text*

**Submitted by:**

*insert principal investigator*

**Research Performed Under:**

Cooperative Agreement No. *Insert number*

Research Work Order No. *Insert number if applicable*

***Insert managing organization and location***

U.S. Geological Survey

Biological Resources Division

Gap Analysis Program

*Insert national GAP logo and USGS logo*

LIST OF PROJECT AFFILIATES

\*

\*

\*

United States Geological Survey Biological Resources Division

TABLE OF CONTENTS

LIST OF FIGURES

***You will need to develop a separate list for digital versus hard copy
if you are (most likely) providing only example printed maps versus all
maps available on the CD version.***

LIST OF MAPS

DEDICATION

***Insert state text here; see previous reports for suggestions.***

EXECUTIVE SUMMARY

***Insert state text here. See NM-GAP for example. As a style
guide\--the summary should be understandable by your congressperson; it
should be written to stand on its own and not be copied and pasted
sections from your report body. You may treat the report body in more
technical terms but it too should be understandable by someone with
basic knowledge in the various fields\--reserve the most highly
technical writing for published journal articles.***

INTRODUCTION

*Each item is to be followed by specific state text*

**How This Report is Organized**

This report is a summation of a scientific project. While we endeavor to
make it understandable for as general an audience as practicable, it
reflects the complexity of the project it describes. A glossary of terms
is provided to aid the reader in its understanding, and for those
seeking a detailed understanding of the subjects, the cited literature
should be helpful. The organization of this report follows the general
chronology of project development, beginning with the production of the
individual data layers and concluding with analysis of the data. It
diverges from standard scientific reporting by embedding results and
discussion sections within individual chapters. This was done to allow
the individual data products to stand on their own as testable
hypotheses and provide data users with a concise and complete report for
each data and analysis product.

We begin with an overview of the Gap Analysis Program mission, concept,
and limitations. We then present a synopsis of how the current
biodiversity condition of the project area came to be, followed by land
cover mapping, animal species distribution prediction, species richness,
and land stewardship mapping and categorization. Data development leads
to the Analysis section, which reports on the status of the elements of
biodiversity (natural community alliances and animal species), for
*insert project area name*. Finally, we describe the management
implications of the analysis results and provide information on how to
acquire and use the data.

**The Gap Analysis Program Mission**

The mission of the Gap Analysis Program is to prevent conservation
crises by providing conservation assessments of biotic elements (plant
communities and native animal species) and to facilitate the application
of this information to land management activities. This is accomplished
through the following five objectives:

1\) map actual land cover as closely as possible to the alliance level
(FGDC 1997).

2\) map the predicted distribution of those terrestrial vertebrates and
selected other taxa that spend any important part of their life history
in the project area and for which adequate distributional habitats,
associations, and mapped habitat variables are available.

3\) document the representation of natural vegetation communities and
animal species in areas managed for the long-term maintenance of
biodiversity.

4\) make all GAP project information available to the public and those
charged with land use research, policy, planning, and management.

5\) build institutional cooperation in the application of this
information to state and regional management activities.

To meet these objectives, it is necessary that GAP be operated at the
state or regional level but maintain consistency with national
standards. Within the state, participation by a wide variety of
cooperators is necessary and desirable to ensure understanding and
acceptance of the data and forge relationships that will lead to
cooperative conservation planning.

[State objectives for GAP]{.underline}: *optional section, insert state
text here*

**The Gap Analysis Concept**

The Gap Analysis Program (GAP) brings together the problem-solving
capabilities of federal, state, and private scientists to tackle the
difficult issues of land cover mapping, animal habitat characterization,
and biodiversity conservation assessment at the state, regional, and
national levels. The program seeks to facilitate cooperative development
and use of information. Throughout this report we use the terms \"GAP\"
to describe the national program, \"GAP Project\" to refer to an
individual state or regional project, and \"gap analysis\" to refer to
the gap analysis process or methodology.

Much of the following discussion was taken verbatim from Edwards et al.
1995, Scott et al. 1993, and Davis et al. 1995. The gap analysis process
provides an overview of the distribution and conservation status of
several components of biodiversity. It uses the distribution of actual
vegetation and predicted distribution of terrestrial vertebrates and,
when available, invertebrate taxa. Digital map overlays in a GIS are
used to identify individual species, species-rich areas, and vegetation
types that are unrepresented or underrepresented in existing management
areas. It functions as a preliminary step to the more detailed studies
needed to establish actual boundaries for planning and management of
biological resources on the ground. These data and results are then made
available to the public so that institutions as well as individual
landowners and managers may become more effective stewards through more
complete knowledge of the management status of these elements of
biodiversity. GAP, by focusing on higher levels of biological
organization, is likely to be both cheaper and more likely to succeed
than conservation programs focused on single species or populations
(Scott et al.1993).

Biodiversity inventories can be visualized as \"filters\" designed to
capture elements of biodiversity at various levels of organization. The
filter concept has been applied by The Nature Conservancy, which
established Natural Heritage Programs in all 50 states. The Nature
Conservancy employs a fine filter of rare species inventory and
protection and a coarse filter of community inventory and protection
(Jenkins 1985, Noss 1987). It is postulated that 85-90% of species can
be protected by the coarse filter without having to inventory or plan
reserves for those species individually. A fine filter is then applied
to the remaining 15-10% of species to ensure their protection. Gap
analysis is a coarse-filter method because it can be used to quickly and
cheaply assess the other 85-90% of species. GAP is not designed to
identify and aid protection of elements that are rare or of very
restricted distribution; rather it is designed to help \"keep common
species common\" by identifying risk far in advance of actual population
decline. These concepts are further developed below.

The intuitively appealing idea of conserving most biodiversity by
maintaining examples of all natural community types has never been
applied, although numerous approaches to the spatial identification of
biodiversity have been described (Kirkpatrick 1983, Margules and
Nicholls 1988, Pressey and Nicholls 1989, Nicholls and Margules 1993).
Furthermore, the spatial scale at which organisms use the environment
differs tremendously among species and depends on body size, food
habits, mobility, and other factors. Hence, no coarse filter will be a
complete assessment of biodiversity protection status and needs.
However, species that fall through the pores of the coarse filter, such
as narrow endemics and wide-ranging mammals, can be captured by the
safety net of the fine filter. Community-level (coarse-filter)
protection is a complement to, not a substitute for, protection of
individual rare species.

Gap analysis is essentially an expanded coarse-filter approach (Noss
1987) to biodiversity protection. The land cover types mapped in GAP
serve directly as a coarse filter, the goal being to assure adequate
representation of all native vegetation community types in biodiversity
management areas. Landscapes with great vegetation diversity often are
those with high edaphic variety or topographic relief. When elevational
diversity is very great, a nearly complete spectrum of vegetation types
known from a biological region may occur within a relatively small area.
Such areas provide habitat for many species, including those that depend
on multiple habitat types to meet life history needs (Diamond 1986, Noss
1987). By using landscape-sized samples (Forman and Godron 1986) as an
expanded coarse filter, gap analysis searches for and identifies
biological regions where unprotected or underrepresented vegetation
types and animal species occur.

More detailed analyses were not part of this project, but are areas of
research that GAP as a national program is pursuing. For example, a
second filter could combine species distribution information to identify
a set of areas in which all, or nearly all, mapped species are
represented. There is a major difference between identifying the richest
areas in a region (many of which are likely to be neighbors and share
essentially the same list of species) and identifying areas in which all
species are represented. The latter task is most efficiently
accomplished by selecting areas whose species lists are most different
or complementary. Areas with different environments tend to also have
the most different species lists for a variety of taxa. As a result, a
set of areas with complementary sets of species for one higher taxon
(e.g., mammals) often will also do a good job representing most species
of other higher taxa (e.g., trees, butterflies). Species with large home
ranges, such as large carnivores, or species with very local
distributions may require individual attention. Additional data layers
can be used for a more holistic conservation evaluation. These include
indicators of stress or risk (e.g., human population growth, road
density, rate of habitat fragmentation, distribution of pollutants) and
the locations of habitat corridors between wildlands that allow for
natural movement of wide-ranging animals and the migration of species in
response to climate change.

**General Limitations**

Limitations must be recognized so that additional studies can be
implemented to supplement GAP. The following are general project
limitations; specific limitations for the data are described in the
respective sections:

1\. GAP data are derived from remote sensing and modeling to make
general assessments about conservation status. Any decisions based on
the data must be supported by ground-truthing and more detailed
analyses.

2\. GAP is not a substitute for threatened and endangered species
listing and recovery efforts. A primary argument in favor of gap
analysis is that it is proactive: it seeks to recognize and manage sites
of high biodiversity value for the long-term maintenance of populations
of native species and communities before they become critically rare.
Thus, it should help to reduce the rate at which species require listing
as threatened or endangered. Those species that are already greatly
imperiled, however, still require individual efforts to assure their
recovery.

3\. GAP data products and assessments represent a snapshot in time
generally representing the date of the satellite imagery. Updates are
planned on a 5-10 year cycle, but users of the data must be aware of the
static nature of the products.

4\. GAP is not a substitute for a thorough national biological
inventory. As a response to rapid habitat loss, gap analysis provides a
quick assessment of the distribution of vegetation and associated
species before they are lost, and provides focus and direction for
local, regional, and national efforts to maintain biodiversity. The
process of improving knowledge in systematics, taxonomy, and species
distributions is lengthy and expensive. That process must be continued
and expedited, however, in order to provide the detailed information
needed for a comprehensive assessment of our nation\'s biodiversity.
Vegetation and species distribution maps developed for GAP can be used
to make such surveys more cost-effective by stratifying sampling areas
according to expected variation in biological attributes.

**The Study Area**

[A Brief Description of ***insert here state name***]{.underline}***.
Provide a brief description of the geologic, climatic, and human history
and processes that have resulted in the current landscape and
biodiversity condition. Tourist guides produced by states frequently
have a good brief description of the landscape and human history. This
should be 1-3 pages. See NM-GAP and PA-GAP reports for example. A map of
the state showing topography, major roads, rivers, and municipalities is
also suggested\--see WY-GAP for example.***

***insert state text here***

LAND COVER CLASSIFICATION AND MAPPING

**Introduction**

Mapping natural land cover requires a higher level of effort than the
development of data for animal species, agency ownership, or land
management, yet it is no more important for gap analysis than any other
data layer. Generally, the mapping of land cover is done by adopting or
developing a land cover classification system, delineating areas of
relative homogeneity (basic cartographic \"objects\"), then labeling
these areas using categories defined by the classification system. More
detailed attributes of the individual areas are added as more
information becomes available, and a process of validating both spatial
pattern and labels is applied for editing and revising the map. This is
done in an iterative fashion, with the results from one step causing
re-evaluation of results from another step. Finally, an assessment of
the overall accuracy of the data is conducted. The final assessment of
accuracy will show where improvements should be made in the next update
(Stoms,1994).

In its \"coarse filter\" approach to conservation biology (e.g., Jenkins
1985, Noss 1987), gap analysis relies on maps of dominant natural land
cover types as the most fundamental spatial component of the analysis
(Scott et al. 1993) for terrestrial environments. For the purposes of
GAP, most of the land surface of interest (natural) can be characterized
by its dominant vegetation.

Vegetation patterns are an integrated reflection of the physical and
chemical factors that shape the environment of a given land area
(Whittaker 1965). They also are determinants for overall biological
diversity patterns (Franklin 1993, Levin 1981, Noss 1990), and they can
be used as a currency for habitat types in conservation evaluations
(Specht 1975, Austin 1991). As such, dominant vegetation types need to
be recognized over their entire ranges of distribution (Bourgeron et al.
1994) for beta-scale analysis (*sensu* Whittaker 1960, 1977). These
patterns cannot be acceptably mapped from any single source of remotely
sensed imagery, therefore, ancillary data, previous maps, and field
surveys are used. The central concept is that the physiognomic and
floristic characteristics of vegetation (and, in the absence of
vegetation, other physical structures) across the land surface can be
used to define biologically meaningful biogeographic patterns. There may
be considerable variation in the floristics of subcanopy vegetation
layers (community association) that are not resolved when mapping at the
level of dominant canopy vegetation types (alliance), and there is a
need to address this part of the diversity of nature. As information
accumulates from field studies on patterns of variation in understory
layers, it can be attributed to the mapped units of alliances.

**Land Cover Classification**

Land cover classifications must rely on specified attributes, such as
the structural features of plants, their floristic composition, or
environmental conditions, to consistently differentiate categories
(Küchler and Zonneveld 1988). The criteria for a land cover
classification system for GAP are:

-   an ability to distinguish areas of different actual dominant
    vegetation;

-   a utility for modeling animal species habitats;

-   a suitability for use within and among biogeographic regions;

-   an applicability to Landsat Thematic Mapper (TM) imagery for both
    rendering a base map and from which to extract basic patterns (GAP
    relies on a wide array of information sources, TM offers a
    convenient meso-scale base map in addition to being one source of
    actual land cover information);

-   a framework that can interface with classification systems used by
    other organizations and nations to the greatest extent possible; and

-   a capability to fit, both categorically and spatially, with
    classifications of other themes such as agricultural and built
    environments.

For GAP, the system that fits best is referred to as the National
Vegetation Classification System (NVCS) (FGDC 1997). The origin of this
system was referred to as the UNESCO/TNC system (Lins and Kleckner 1996)
because it is based on the structural characteristics of vegetation
derived by Mueller-Dombois and Ellenberg (1974), adopted by the United
Nations Educational, Scientific, and Cultural Organization (UNESCO 1973)
and later modified for application to the United States by Driscoll et
al. (1983, 1984). The Nature Conservancy and the Natural Heritage
Network (Grossman et al. 1994) have been improving upon this system in
recent years with partial funding supplied by GAP. The basic assumptions
and definitions for this system have been described by Jennings (1993).

*Insert here and within the boiler plate variations in your project\'s
approach, particularly the differences in classification scheme, i.e.,
note if your project developed before the above scheme existed.
**Provide the full descriptive and hierarchical scheme with crosswalks
as necessary as an appendix.***

**Methods**

***insert any introductory state text here***

[Mapping Standards and Data Sources]{.underline}: *Describe here the
standards used to create the map applicable at the time of your project,
e.g., MMU, spatial and thematic accuracy, data source\--e.g., **describe
form of MRLC and any other imagery acquired including near-remote
sensing systems such as videography. Provide a map showing the outlines
of the actual imagery used\--see the WY-GAP report for example.***

[Land Cover Map Development]{.underline}: ***Insert state text here.
Provide a detailed but concise description of steps used in creating the
map that would allow a theoretical duplication of the effort. This
requires you to be scientifically accurate but understandable by
non-remote sensing specialists. Terms used that are not already in the
Glossary should be inserted there.***

[Special Feature Mapping]{.underline}: *insert state text here
(optional\-- describe process for mapping of any specialized features
that was conducted as a separate effort such as a riparian mapping
project).*

**Results**

***Insert state text here. Provide a map in maps section***

Table X. The land cover types mapped, their area mapped in the state in
square kilometers (multiply by 100 for hectares or 270 for acres), and
the percentage of the state\'s total area represented by the mapped
type.

Mapped Type NameTotal Area mapped % of state area

**Accuracy Assessment**

[Introduction]{.underline}: GAP land cover maps are primarily compiled
to answer the fundamental question in gap analysis: what is the current
distribution and management status of the nation\'s major natural land
cover types and wildlife habitats? Besides giving a measure of overall
reliability of the land cover map for Gap Analysis, the assessment also
identifies which general classes or which regions of the map do not meet
the accuracy objectives for the Gap Analysis Program. Thus the
assessment identifies where additional effort will be required when the
map is updated. We report the results of the accuracy assessment,
believing that the map is the best map currently available for the
project area.

The purpose of accuracy assessment is to allow a potential user to
determine the map\'s \"fitness for use\" for their application. It is
impossible for the original cartographer to anticipate all future
applications of a land cover map, so the assessment should provide
enough information for the user to evaluate fitness for their unique
purpose. This can be described as the degree to which the data quality
characteristics collectively suit an intended application. The
information reported includes details on the database\'s spatial,
thematic, and temporal characteristics and their accuracy.

Assessment data are valuable for purposes beyond their immediate
application to estimating accuracy of a land cover map. The reference
data is therefore made available to other agencies and organizations for
use in their own land cover characterization and map accuracy
assessments (see Data Availability for access information). The data set
will also serve as an important training data source for later updates.

Even though we have reached an endpoint in the mapping process where
products are made available to others, the gap analysis process should
be considered dynamic. We envision that maps will be refined and updated
on a regular schedule. The assessment data will be used to refine GAP
maps iteratively by identifying where the land cover map is inaccurate
and where more effort is required to bring the maps up to accuracy
standards. In addition, the field sampling may identify new classes that
were not identified at all during the initial mapping process.

[Methods:]{.underline}

*Insert state text here .Provide a thorough description of the methods
used to collect truth data, quality assess it, and analyze the map
accuracy. Provide maps and tables to document the truth data collection.
See also the reporting requirements in the Handbook chapter.*

[Results:]{.underline}

*Insert state text here. Some tables are provided below, interpretive
maps are also suggested.*

**Limitations and Discussion**

***insert state text here***

PREDICTED ANIMAL SPECIES DISTRIBUTIONS AND SPECIES RICHNESS

**Introduction**

All species range maps are predictions about the occurrence of those
species within a particular area (Csuti 1994). Traditionally, the
predicted occurrences of most species begin with samples from
collections made at individual point locations. Most species range maps
are small-scale (e.g., \>1:10,000,000) and derived primarily from point
data to construct field guides which are suitable, at best, for
approximating distribution at the regional level or counties for
example. The purpose of the GAP vertebrate species maps is to provide
more precise information about the current predicted distribution of
individual native species according to actual habitat characteristics
within their general ranges and to allow calculation of predicted area
of distributions and associations to specific habitat characteristics.

GAP maps are produced at a nominal scale of 1:100,000 or better and are
intended for applications at the landscape or \"gamma\" scale
(heterogeneous areas generally covering 1,000 to 1,000,000 hectares and
made up of more than one kind of natural community). Applications of
these data to site- or stand-level analyses (site\--a microhabitat,
generally 10 to 100 square meters; stand\--a single habitat type,
generally 0.1 to 1,000 ha; Whittaker 1977, see also Stoms and Estes
1993) will likely reveal the limitations of this process to incorporate
differences in habitat quality (e.g., understory condition) or necessary
microhabitat features such as standing dead trees.

Gap analysis uses the predicted distributions of animal species to
evaluate their conservation status relative to existing land management
(Scott et al. 1993). However, the maps of species distributions may be
used to answer a wide variety of management, planning, and research
questions relating to individual species or groups of species. In
addition to the maps, great utility may be found in the consolidated
specimen collection records and literature that are assembled into
databases used to produce the maps. Perhaps most importantly, as a first
effort in developing such detailed distributions, they should be viewed
as testable hypotheses to be confirmed or refuted in the field. We
encourage biologists and naturalists to conduct such tests and report
their findings in the appropriate literature and to the Gap Analysis
Program such that new data may improve future iterations.

Previous to this effort there were no maps available, digital or
otherwise, showing the likely present-day distribution of species by
habitat type across their ranges. Because of this, ordinary species
(i.e., those not threatened with extinction or not managed as game
animals) are generally not given sufficient consideration in land-use
decisions in the context of large geographic regions or in relation to
their actual habitats. Their decline, because of incremental habitat
loss can, and does, result in one threatened or endangered species
\"surprise\" after another. Frequently, the records that do exist for an
ordinary species are truncated by state boundaries. Simply creating a
consistent spatial framework for storing, retrieving, manipulating,
analyzing, and updating the totality of our knowledge about the status
of each animal species is one of the most necessary and basic elements
for preventing further erosion of biological resources.

***insert state text here***

**Methods**

***insert state text here, general statements about approach used.***

[Mapping Standards and Data Sources:]{.underline}

*Describe here the standards used to create the map applicable at the
time of your project, e.g., MMU, spatial and thematic accuracy, data
source\--e.g., **describe types of data used in modeling.***

[Mapping Range Extent:]{.underline}

*describe the process of mapping general geographic ranges, e.g., EMAP
hexagons.*

[Wildlife Habitat Relationships:]{.underline}

*describe the process of developing WHRMs*

[Distribution Modeling:]{.underline}

*Insert state text here. Describe the general process and if there are
significant differences among taxa worth noting, use optional section
below. Provide an appendix (suggest matrix style) of experts that helped
and their affiliation, taxonomic group they worked on, and task, e.g.,
model development, range extent attributing, map review.*

Table X. GIS coverages used in the animal species modeling process.
Refer to the metadata accompanying the digital data for more complete
descriptions. *please provide digital maps for each, printed copies in
the hardcopy version are optional.*

Coverage NameSource of AcquisitionDescription*insert file name, e.g.,
epariverWhere did you get itprovide brief description such as \"EPA
River Reach 3\--perennial rivers\"*

***Optional\--provide specific methods descriptions including: range
extent delineation method, habitat association development, and
particular modeling approaches.***

**Mammals:**

*insert state text here*

**Birds:**

*insert state text here*

**Reptiles:**

*insert state text here*

**Amphibians:**

*insert state text here*

***Add here other taxa if mapped***

**Results**

*Insert state text here, any general statements about particular success
or difficulties in the mapping, e.g., reptiles proved relatively simple
to map whereas amphibians were not because of lack of NWI, etc. Below
note the number of spp mapped per group and include example maps.*

[Mammals:]{.underline}

***insert state text here***

[Birds:]{.underline}

*i**nsert state text here***

[Reptiles:]{.underline}

***insert state text here***

[Amphibians:]{.underline}

***insert state text here***

***Add here other taxa if mapped***

**Species Richness**

GAP has often been associated with the mapping of species-rich areas or
\"hotspots.\" Richness maps identify where the same numbers of elements
co-occur in the same geographic locations. (In the case of our data,
where numbers of animal species are mapped for the same grid cells.)
These are color coded or shaded in intensity from the highest numbers of
co-occurrence (richness), to the lowest. While we continue to perform
this useful pattern analysis, it is only one of many that may be
conducted using the data. Richest areas may or may not indicate best
conservation opportunities. They may occur in already protected areas or
may represent mostly already protected species or those not at risk.
Still, they are often a useful starting point to examine conservation
opportunities in combination with other analyses described in this
report\'s Introduction and in the Analysis section. We also feel they
may be useful for other rewarding applications such as identifying
places of interest for wildlife observation and study.

Richness is depicted for all mapped taxa (figure X), and by taxonomic
groups (figures X-X). *Describe here more detail on map production,
color coding, and then results and interpretation. See OR-GAP for
example.*

*Insert figures of species richness*

**Accuracy Assessment**

Assessing the accuracy of the predicted vertebrate distributions is
subject to many of the same problems as assessing land cover maps, as
well as a host of more serious challenges related to both the behavioral
aspects of species and the logistics of detecting them. These are
described further in the Background section of the GAP Handbook on the
national GAP home page. It is, however, necessary to provide some
measure of confidence in the results of the gap analysis for species
collectively, if not individually or by taxonomic group (comparison to
stewardship and management status), and to allow users to judge the
suitability of the distribution maps for their own uses. We, therefore,
feel it is important to provide users with a statement about the
accuracy of GAP-predicted vertebrate distributions within the
limitations of available resources and practicalities of such an
endeavor. We acknowledge that distribution maps are never finished
products but are continually updated as new information is gathered.
This reflects not only an improvement over the modeling process, but
also the opportunity to map true changes in species distributions over
time. However, we feel that assessing the accuracy of the current maps
provides useful information about their reliability to potential users.

Our goal was to produce maps that predict distribution of terrestrial
vertebrates and from that, total species richness and species content
with an accuracy of 80% or higher. Failure to achieve this accuracy
indicates the need to refine the data sets and models used for
predicting distribution. There is a conscious effort in the GAP process,
however, to err on the side of commission. In other words, to attribute
species as possibly present when they are not. There are two primary
reasons for doing so: first, few species have systematic, unbiased known
ranges and we believe science is best served by identifying a greater
potential for sampling and investigation than a conservative approach
that may miss such opportunities; second, in conducting the analysis of
conservation representation (see the Analysis section), we believe it
most appropriate to identify a species that may need additional
conservation attention that is then refuted by further investigation
rather than identifying a species as sufficiently protected that is
discovered not to be by its subsequent loss.

The methods for validating and assessing the accuracy of the vertebrate
distribution maps are presented below along with the results.

[Methods:]{.underline}

***Insert state text here***

[Results:]{.underline}

***Insert state text here. insert tables per Handbook chapter.***

**Limitations and Discussion**

***Insert state text here***

LAND STEWARDSHIP

**Introduction**

To fulfill the analytical mission of GAP, it is necessary to compare the
mapped distribution of elements of biodiversity with their
representation in different categories of land ownership and management.
As will be explained in the Analysis section, these comparisons do not
measure viability, but are a start to assessing the likelihood of future
threat to a biotic element through habitat conversion\--the primary
cause of biodiversity decline. We use the term \"stewardship\" in place
of \"ownership\" in recognition that legal ownership does not
necessarily equate to the entity charged with management of the
resource, and that the mix of ownership and managing entities is a
complex and rapidly changing condition not suitably mapped by GAP. 0 At
the same time, it is necessary to distinguish between stewardship and
management status in that a single category of land stewardship such as
a national forest may contain several degrees of management for
biodiversity.

The purpose of comparing biotic distribution with stewardship is to
provide a method by which land stewards can assess their relative amount
of responsibility for the management of a species or plant community and
identify other stewards sharing that responsibility. This information
can reveal opportunities for cooperative management of that resource,
which directly supports the primary mission of GAP to provide objective,
scientific information to decision makers and managers to make informed
decisions regarding biodiversity. It also is not unlikely that a steward
that has previously borne the major responsibility for managing a
species may, through such analyses, identify a more equitable
distribution of that responsibility. We emphasize, however, that GAP
only identifies private land as a homogeneous category and does not
differentiate individual tracts or owners, unless the information was
provided voluntarily to recognize a long-term commitment to biodiversity
maintenance.

After comparison to stewardship, it is also necessary to compare biotic
occurrence to categories of management status. The purpose of this
comparison is to identify the need for change in management status for
the distribution of individual elements or areas containing high degrees
of diversity. Such changes can be accomplished in many ways that do not
affect the stewardship status. While it will eventually be desirable to
identify specific management practices for each tract, and whether they
are beneficial or harmful to each element, GAP currently uses a scale of
1 to 4 to denote relative degree of maintenance of biodiversity for each
tract. A status of \"1\" denotes the highest, most permanent level of
maintenance, and \"4\" represents the lowest level of biodiversity
management, or unknown status. This is a highly subjective area, and we
recognize a variety of limitations in our approach, although we maintain
certain principles in assigning the status level. Our first principle is
that land ownership is not the primary determinant in assigning status.
The second principle is that while data are imperfect, and all land is
subject to changes in ownership and management, we can use the intent of
a land steward as evidenced by legal and institutional factors to assign
status. In other words, if a land steward institutes a program backed by
legal and institutional arrangements that are intended for permanent
biodiversity maintenance, we use that as the guide for assigning status.

The characteristics used to determine status are as follows:

 Permanence of protection from conversion of natural land cover to
unnatural (human-induced barren, exotic-dominated, arrested succession).

 Relative amount of the tract managed for natural cover.

 Inclusiveness of the management, i.e., single feature or species
versus all biota.

 Type of management and degree that it is mandated through legal and
institutional arrangements.

The four status categories can generally be defined as follows (after
Scott et al. 1993, Edwards et al. 1995, Crist et al. 1995):

Status 1: An area having permanent protection from conversion of natural
land cover and a mandated management plan in operation to maintain a
natural state within which disturbance events (of natural type,
frequency, and intensity) are allowed to proceed without interference or
are mimicked through management.

Status 2: An area having permanent protection from conversion of natural
land cover and a mandated management plan in operation to maintain a
primarily natural state, but which may receive use or management
practices that degrade the quality of existing natural communities.

Status 3: An area having permanent protection from conversion of natural
land cover for the majority of the area, but subject to extractive uses
of either a broad, low-intensity type or localized intense type. It also
confers protection to federally listed endangered and threatened species
throughout the area.

Status 4: Lack of irrevocable easement or mandate to prevent conversion
of natural habitat types to anthropogenic habitat types. Allows for
intensive use throughout the tract. Also includes those tracts for which
the existence of such restrictions or sufficient information to
establish a higher status is unknown.

**Mapping Standards**

*Describe here the standards used to create the maps **and criteria for
including species** applicable at the time of your project.*

**Methods**

*Note, there is no accuracy assessment section for stewardship\--please
describe your quality control process within this section.*

[Stewardship Mapping]{.underline}:

***Insert state text here. Indicate sources and methods used to map
public land ownership and voluntarily provided private lands (unless
obtained from a public domain source). Be sure to document land types
identified but not included and reason, e.g., too small, did not qualify
for \>4 ranking, steward could not/would not provide boundaries.***

[Management Status Categorization]{.underline}:

***insert state text here*** *(if you used a flow chart or key of some
kind, provide description in appendix).*

**Results**

***Insert state text here. Provide maps for both stewardship and
management status.***

The following tables present summary statistics of area representation
of stewardship and management categories in the state. We begin by
comparing representation of various stewardship categories in management
status categories. Table 1 provides information on the proportional
make-up of management status categories by stewardship and vice-versa,
so that land stewards can see to what degree their lands generally
contribute to biodiversity maintenance.

Table X. *Insert table from Stewardship chapter. Note\-- the categories
provided are only an example. If the table will not fit on one page,
provide a generalized summary table here that includes only categories
that represent a fairly large area of the state or a large portion of
any one status category and lump all those not shown categorically into
a column that says \"other,\" to indicate that the list is incomplete.
Provide the complete table as an appendix..*

Table X. ***Insert status 1 &2 areas documentation table from
Stewardship Chapter of the Handbook.***

**Limitations and Discussion**

This map is a compilation of ownership maps provided by a variety of
sources that are individually responsible for their accuracy. It was
created solely for the purpose of conducting the analyses described in
this report and is not suitable for locating boundaries on the ground or
determining precise area measurements of individual tracts. ***insert
additional state text here***

ANALYSIS BASED ON STEWARDSHIP AND MANAGEMENT STATUS

**Introduction**

This chapter describes the method and results of the gap analysis as
used by the Gap Analysis Program. As described in the general
introduction to this report, the primary objective of GAP is to provide
information on the distribution and status of several elements of
biological diversity. Although GAP \"seeks to identify habitat types and
species not adequately represented in the current network of
biodiversity management areas\" (GAP Handbook, Preface, Version 1, p.
I), it is unrealistic to create a standard definition of \"adequate
representation\" for either land cover types or individual species (Noss
et al. 1995). A practical solution to this problem is to report both
percentages and absolute area of each element in biodiversity management
areas and allow the user to determine which types are adequately
represented in natural areas. There are many other factors that should
be considered in such determinations such as:

-   historic loss or gain in distribution,

-   nature of the spatial distribution,

-   immediate versus long term risk, and

-   degree of local adaptation among populations of the biotic elements
    that are worthy of individual conservation consideration.

Such analyses are beyond the scope of this project, but we encourage
their application coupled with field confirmation of the mapped
distributions.

Currently, land cover types and terrestrial vertebrates are the primary
focus of GAP\'s mapping efforts, however, other components of
biodiversity, such as aquatic organisms or selected groups of
invertebrates may be incorporated into GAP distributional data sets.
Where appropriate, GAP data may also be analyzed to identify the
location of a set of areas in which most or all land cover types or
species are predicted to be represented. The use of \"complementarity\"
analysis, that is, an approach that additively identifies a selection of
locations that may represent biodiversity rather than \"hot spots of
species richness\" may prove most effective for guiding biodiversity
maintenance efforts. Several quantitative techniques have been developed
recently that facilitate this process (see Pressey et al. 1993, Williams
et al. 1996, Csuti et al. 1997, for details). These areas become
candidates for field validation and may be incorporated into a system of
areas managed for the long-term maintenance of biological diversity.

The network of Conservation Data Centers (CDCs) and Natural Heritage
Programs (NHPs) established cooperatively by The Nature Conservancy and
various state agencies maintain detailed databases on the locations of
rare elements of biodiversity. GAP cooperatively uses these data to
develop predicted distributions of potentially suitable habitat for
these elements, which may be valuable for identifying research needs and
preliminary considerations for restoration or reintroduction.
Conservation of such elements, however, is best accomplished through the
fine-filter approach of the above organizations as described in the
introduction. It is not the role of GAP to duplicate or disseminate
Heritage Program or CDC Element Occurrence Records. Users interested in
more specific information about the location, status, and ecology of
populations of such species are directed to their state Heritage Program
or CDC.

**Methods**

The gap analysis is accomplished by first producing: maps of land cover
(see Figure X), predicted distributions for selected animal species (see
Figure X), and land stewardship and management status (see Figure X).
Intersecting the land stewardship and management map with the
distribution of the elements results in tables that summarize the area
and percent of total mapped distribution of each element in different
land stewardship and management categories. ***insert state text here to
provide more technical explanation of methods.***

**Results**

The data are provided in a format that allows users to carry out
inquires about the representation of each element in different land
stewardship and management categories as appropriate to their own
management objectives. This forms the basis of Gap\'s mission to provide
land owners and managers with the information necessary to conduct
informed policy development, planning, and management for biodiversity
maintenance.

As a coarse indicator of the status of the elements, we provide a
breakdown along five levels of representation (0-\<1%, 1-\<10%,
10-\<20%, and 20-\<50%; \>=50%). The \<1% level indicates those elements
with essentially none of their distribution in a protected status while
levels of 10%, 20%, and 50% have been recommended in the literature as
necessary amounts of conservation (Noss and Cooperrider 1994; Noss 1991;
Odum and Odum 1972; Specht et al. 1974; Ride 1975; Miller 1994).

[Land Cover Analysis]{.underline}

A summary of the analysis according to the thresholds described above is
shown in Table X below. The table found in Appendix X provides the area
in square kilometers (multiply by 100 for hectares, 247 for acres) of
each types\' mapped distribution by management status and land steward,
and the percent of the types\' total distribution in each category. For
example, a typical entry may indicate that a particular woodland type
has 35.23 km2 (3523 ha) in state lands that are ranked Status 2, which
represents 9.3% of that type\'s total distribution. *Projects\--please
replace this example with an actual example from your table. The table
format is found in the Analysis chapter. If it is small enough, you may
include it here rather than as an appendix.*

Table X. see example in analysis chapter of handbook.

As explained above, we provide results according to thresholds of
representation advocated in the literature to conserve biodiversity. The
values in the table will allow users to set any desirable threshold to
identify elements requiring more protection according to their criteria.
The following summaries highlight potential gaps and conservation needs;
Appendix X provides figures of stewardship representation for all land
cover types by land steward and management status.

***The narratives should summarize the stewardship condition
(representation among managing entities and GAP status) of select types
for each group below to explain the table results in a more informative
way. In particular, it is useful to compare the gap analysis results to
the current notions within the state about what needs more protection.
Consider if this analysis had been carried out several years ago, would
it have provided forewarning of current problems? WY and OR-GAP provide
a good qualitative model for this section.***

Land Cover with 0-\<1% representation in GAP status 1 and 2:

*insert state text here*

Land Cover with 1-\<10% representation in GAP status 1 and 2:

*insert state text here*

Land Cover with l0%-\<20% representation in GAP status 1 and 2:

*insert state text here*

Land Cover with 20% -\<50% representation in GAP status 1 and 2:

*insert state text here*

Land Cover with at least 50% representation in GAP status 1 and 2:

*insert state text here*

[Limitations and Discussion for Land Cover Analysis]{.underline}

Assessing the conservation status of natural land cover is limited by
several confounding factors:

GAP has typically found the accuracy of the mapped distributions of
natural communities at the floristic (e.g., alliance) level to be
substantially lower and more variable than that of animal distributions;

any aggregation of biotic units (e.g., above species) is a surrogate for
species or lower levels of biotic organization and will underrepresent
conservation need (Pressey and Logan 1995); and

for the most part we cannot distinguish the degree of natural condition
or value of the mapped units due to management manipulation, exotic
invasion, or spatial configuration. Considering an aggregation of
species such as we have mapped to be sufficiently represented in
existing conservation areas cannot be determined solely by the
percentage of the community represented because the aggregation has
unmapped variation in species composition that we could not measure.
Until individual plant species distributions can be mapped, it is not
possible to assure that the full range of vegetation biodiversity is
represented, and surrogates must be used.

***Insert state text here***

[Predicted Animal Species Distributions Analysis]{.underline}

A summary table is not provided due to the large number of species
analyzed, but some generalizations and examples of species results by
the various thresholds are provided below. The complete Animal Species
Distributions Analysis Table found in Appendix \_ provides the area in
square kilometers (multiply by 100 for hectares, 247 for acres) of the
species\' mapped distribution by management status and land steward, and
the percent of the species\' total distribution in each category. For
example, a typical entry may indicate that a particular species has
35.23 km2 (3523 ha) of potential habitat in state lands that are ranked
Status 2, which represents 9.3% of that species\' total distribution.
*Projects\--please replace this example with an actual example from your
table. The table format is found in the Analysis handbook chapter. If it
is small enough, you may include it here rather than as an appendix.*

***This section should summarize the stewardship condition of each
taxonomic group with additional narrative on species of interest.***

Species with 0-\<1% of predicted distribution in status 1 or 2:

***Insert state text here\--this is a list followed by explanatory
narrative of a few species***

Species with 1-\<10% of predicted distribution in status 1 or 2:

***Insert state text here***

Species with l0%-\<20% of predicted distribution in status 1 or 2:

***Insert state text here***

Species with 20% -\<50% of predicted distribution in status 1 or 2:

***Insert state text here***

Species with at least 50% representation in GAP status 1 and 2:

*insert state text here*

[Analysis of Important Statewide Species Assemblages]{.underline}:
***insert state text here***

[Analysis of State Endemics]{.underline}: ***insert state text here (if
applicable)***

[Special Features Analysis]{.underline}: ***insert state text here
(optional)***

[Ecoregional Analysis]{.underline}: *i**nsert state text here
(optional)***

[Limitations and Discussion]{.underline}

When applying the results of our analyses, it is critical that the
following limitations are considered: 1) the limitations described for
each of the component parts (land cover mapping, animal species mapping,
stewardship mapping) of the analyses, 2) the spatial and thematic map
accuracy of the components, and 3) the suitability of the results for
the intended application (see Appropriate and Inappropriate Use below).
***insert additional state text here***

CONCLUSIONS AND MANAGEMENT IMPLICATIONS

***Insert state text here. See previous project reports for examples;
this is not to be a summary of the report. Generally, this should have
statements about the condition of biodiversity in the state, the need
for additional data and analyses, and any obvious management
implications that don\'t overstep the information mission (versus
policy) that can help foster the additional work and cooperative
implementation activities. This is a chance to tell your cooperators
\"where we go from here.\"***

PRODUCT USE AND AVAILABILITY

**How to Obtain the Products**

It is the goal of the Gap Analysis Program and the USGS Biological
Resources Division (BRD) to make the data and associated information as
widely available as possible. Use of the data requires specialized
software called geographic information systems (GIS) and substantial
computing power. Additional information on how to use the data or obtain
GIS services is provided below and on the GAP home page (URL below).
While a CD-ROM of the data will be the most convenient way to obtain the
data, it may also be downloaded via the Internet from the national GAP
home page at:

http://www.gap.uidaho.edu/

The home page will also provide, over the long term, the status of our
state\'s project, future updates, data availability, and contacts.
Within a few months of this project\'s completion, CD-ROMs of the final
report and data should be available at a nominal cost\--the above home
page will provide ordering information. To find information on this
state GAP project\'s status and data, follow the links to \"Current
Projects\" and then to the particular state of interest.

***Insert state text here describing availability through your state GIS
library, directly from you, etc. including hardcopy volumes and any
associated products such as atlases.***

[Minimum GIS Required for Data Use]{.underline}: ***insert state text
here. Please indicate the minimum type of setup to use the data and a
lab in the state where someone can get GIS services conducted e.g.,
special cut-outs, analyses, packaged with other data, etc.***

**Disclaimer**

Following is the official Biological Resources Division (BRD) disclaimer
as of 29 January 1996, followed by additional disclaimers from GAP.
Prior to using the data, you should consult the GAP home page (see How
to Obtain the Data, above) for the current disclaimer.

Although these data have been processed successfully on a computer
system at the BRD, no warranty expressed or implied is made regarding
the accuracy or utility of the data on any other system or for general
or scientific purposes, nor shall the act of distribution constitute any
such warranty. This disclaimer applies both to individual use of the
data and aggregate use with other data. It is strongly recommended that
these data are directly acquired from a BRD server \[see above for
approved data providers\] and not indirectly through other sources which
may have changed the data in some way. It is also strongly recommended
that careful attention be paid to the content of the metadata file
associated with these data. The Biological Resources Division shall not
be held liable for improper or incorrect use of the data described
and/or contained herein.

These data were compiled with regard to the following standards. Please
be aware of the limitations of the data. These data are meant to be used
at a scale of 1:100,000 or smaller (such as 1:250,000 or 1:500,000) for
the purpose of assessing the conservation status of animals and
vegetation types over large geographic regions. The data may or may not
have been assessed for statistical accuracy. Data evaluation and
improvement may be ongoing. The Biological Resources Division makes no
claim as to the data\'s suitability for other purposes. This is writable
data which may have been altered from the original product if not
obtained from a designated data distributor identified above.

**Metadata**

Proper documentation of information sources and processes used to
assemble GAP data layers is central to the successful application of GAP
data. Metadata documents the legacy of the data for new users. The
Federal Geographic Data Committee (FGDC 1994, 1995) has published
standards for metadata and NBII (\<http://www.nbii.gov\>) has updated
those standards to include biological profiles. Executive Order 12906
requires that any spatial data sets generated with federal dollars will
have FGDC-compliant metadata.

Each spatial data layer submitted must be accompanied by its metadata
(\*.html file) in the same directory. You must also include an
additional directory (called \"meta_master\') which will include each
metadata file in three forms (\*.txt, \*.html, and \*.sgml). These are
readily created in MetaMaker
(\<http://www.nbii.gov/about/factsheet/factsheet5.html\>). The
redundancy in format is to provide one file for error checking (\*.txt),
one for presentation on the Internet (\*.html), and one for indexing
elements for the spatial data clearinghouse (\*.sgml). Remember,
metadata describes the development of the spatial data set being
documented. If there are companion files to the GIS data, use metadata
to reference (reports, spreadsheet, another GIS layer).

USGS personnel conduct metadata training to meet FGDC standards and to
include biological data. See the Internet site,
\<http://www.nbii.gov/metadata/training/index.html\> for more
information.

**Appropriate and Inappropriate Use of These Data**

*You may adapt this section freely to local conditions and the
resolution and confidence in your data.*

All information is created with a specific end use or uses in mind. This
is especially true for GIS data, which is expensive to produce and must
be directed to meet the immediate program needs. For GAP, minimum
standards were set (see A Handbook for Gap Analysis, Scott et al. 1993)
to meet program objectives. These standards include: scale or resolution
(1:100,000 or 100 hectare minimum mapping unit), accuracy (80% accurate
at 95% confidence), and format (ARC/INFO coverage tiled to the 30\' x
60\' USGS quadrangle). For complete project standards, refer to appendix
XX.

Recognizing, however, that GAP would be the first, and for many years
likely the only, source of statewide biological GIS maps, the data were
created with the expectation that they would be used for other
applications. Therefore, we list below both appropriate and
inappropriate uses. This list is in no way exhaustive but should serve
as a guide to assess whether a proposed use can or cannot be supported
by GAP data. For most uses, it is unlikely that GAP will provide the
only data needed, and for uses with a regulatory outcome, field surveys
should verify the result. In the end, it will be the responsibility of
each data user to determine if GAP data can answer the question being
asked, and if they are the best tool to answer that question.

[Scale:]{.underline} First we must address the issue of appropriate
scale to which these data may be applied. The data were produced with an
intended application at the ecoregion level, that is, geographic areas
from several hundred thousand to millions of hectares in size. The data
provide a coarse-filter approach to analysis, meaning that not every
occurrence of every plant community or animal species habitat is mapped,
only larger, more generalized distributions. The data are also based on
the USGS 1:100,000 scale of mapping in both detail and precision. When
determining whether to apply GAP data to a particular use, there are two
primary questions: do you want to use the data as a map for the
particular geographic area, or do you wish to use the data to provide
context for a particular area? The distinction can be made with the
following example: You could use GAP land cover to determine the
approximate amount of oak woodland occurring in a county, or you could
map oak woodland with aerial photography to determine the exact amount.
You then could use GAP data to determine the approximate percentage of
all oak woodland in the region or state that occurs in the county, and
thus gain a sense of how important the county\'s distribution is to
maintaining that plant community.

[Appropriate Uses]{.underline}: The above example illustrates two
appropriate uses of the data: as a coarse map for a large area such as a
county, and to provide context for finer-level maps. Specific case-study
examples are provided in appendix XX, but following is a general list of
applications:

-   Statewide biodiversity planning

-   Regional (Councils of Government) planning

-   Regional habitat conservation planning

-   County comprehensive planning

-   Large-area resource management planning

-   Coarse-filter evaluation of potential impacts or benefits of major
    projects or plan initiatives on biodiversity, such as utility or
    transportation corridors, wilderness proposals, regional open space
    and recreation proposals, etc.

-   Determining relative amounts of management responsibility for
    specific biological resources among land stewards to facilitate
    cooperative management and planning.

-   Basic research on regional distributions of plants and animals and
    to help target both specific species and geographic areas for needed
    research.

-   Environmental impact assessment for large projects or military
    activities.

-   Estimation of potential economic impacts from loss of biological
    resource-based activities.

-   Education at all levels and for both students and citizens.

[Inappropriate Uses:]{.underline} It is far easier to identify
appropriate uses than inappropriate ones, however, there is a \"fuzzy
line\" that is eventually crossed when the differences in resolution of
the data, size of geographic area being analyzed, and precision of the
answer required for the question are no longer compatible. Examples
include:

-   Using the data to map small areas (less than thousands of hectares),
    typically requiring mapping resolution at 1:24,000 scale and using
    aerial photographs or ground surveys.

-   Combining GAP data with other data finer than 1:100,000 scale to
    produce new hybrid maps or answer queries.

-   Generating specific areal measurements from the data finer than the
    nearest thousand hectares (minimum mapping unit size and accuracy
    affect this precision).

-   Establishing exact boundaries for regulation or acquisition.

-   Establishing definite occurrence or non-occurrence of any feature
    for an exact geographic area (for land cover, the percent accuracy
    will provide a measure of probability).

-   Determining abundance, health, or condition of any feature.

-   Establishing a measure of accuracy of any other data by comparison
    with GAP data.

-   Altering the data in any way and redistributing them as a GAP data
    product.

-   Using the data without acquiring and reviewing the metadata and this
    report.

LITERATURE CITED

Austin, M.P. 1991. Vegetation: Data collection and analysis. Pages 37-41
in C.R. Margules and M.P. Austin, editors, Nature conservation: Cost
effective biological surveys and data analysis. Australia CSIRO, East
Melbourne.

Bourgeron, P.S., H.C. Humphries, R.L. DeVelice, and M.E. Jensen. 1994.
Ecological theory in relation to landscape and ecosystem
characterization. Pages 58-72 in M.E. Jensen and P.S. Bourgeron,
editors, Ecosystem management: Principles and applications, Volume II.
Gen. Tech. Rep. PNW-GTR-318. USDA Forest Service, Pacific Northwest
Research Station, Portland, Oregon. 376 pp.

Crist, P., B. Thompson, and J. Prior-Magee. 1995. A dichotomous key of
land management categorization, unpublished. New Mexico Cooperative Fish
and Wildlife Research Unit, Las Cruces, New Mexico.

Csuti, B., S. Polasky, P.H. Williams, R.L. Pressey, J.D. Camm, M.
Kershaw, A.R. Kiester, B. Downs, R. Hamilton, M. Huso, and K. Sahr.
1997. A comparison of reserve selection algorithms using data on
terrestrial vertebrates in Oregon. *Biological Conservation* 80:83-97

Csuti, B. 1994. Methods for developing terrestrial vertebrate
distribution maps for Gap Analysis (version 1). In J.M. Scott and M.D.
Jennings, editors. A handbook for Gap Analysis. Idaho Cooperative Fish
and Wildlife Research Unit, University of Idaho, Moscow.

Davis, F.W., P.A. Stine, D.M. Stoms, M.I. Borchert, and A.D. Hollander.
1995. Gap Analysis of the actual vegetation of California - 1. The
southwestern region. *Madroño* 42:40-78.

Diamond, J. 1986. The design of a nature reserve system for Indonesian
New Guinea. Pages 485-503 in M.E. Soulé, editor. Conservation biology:
The science of scarcity and diversity. Sinauer Associates, Sunderland,
Massachusetts.

Driscoll, R.S., D.L. Merkel, D.L. Radloff, D.E. Snyder, and J.S.
Hagihara. 1984. An ecological land classification framework for the
United States. Miscellaneous Publication 1439. USDA Forest Service,
Washington, DC.

Driscoll, R.S., D.L. Merkel, J.S. Hagihara, and D.L. Radloff. 1983. A
component land classification for the United States: Status report.
Technical Note 360. U.S. Department of the Interior, Bureau of Land
Management, Denver, Colorado.

Edwards, T.C., Jr., C.H. Homer, S.D. Bassett, A. Falconer, R.D. Ramsey,
and D.W. Wight. 1995. Utah Gap Analysis: An environmental information
system. Technical Report 95-1, Utah Cooperative Fish and Wildlife
Research Unit, Utah State University, Logan, Utah.

Federal Geographic Data Committee (FGDC). 1994. Content standards for
digital geospatial metadata. 8 June 1994. Federal Geographic Data
Committee, Washington, DC.

Federal Geographic Data Committee. 1995. Content standards for digital
geospatial metadata workbook, FGDC, Washington, DC.

Federal Geographic Data Committee, Vegetation Subcommittee. 1997. FGDC
Vegetation Classification and Information Standards\--June 3, 1996
Draft. FGDC Secretariat, Reston, Virginia. 35 pp.

Forman, R.T.T., and M. Godron. 1986. Landscape ecology. John Wiley and
Sons, New York.

Franklin, J.F. 1993. Preserving biodiversity: Species, ecosystems, or
landscapes? *Ecological Applications* 3(2):202-205.

Gap Analysis Program. 2000. A handbook for conducting Gap Analysis. USGS
Gap Analysis Program, Moscow, Idaho.
http://www.gap.uidaho.edu/gap/handbook.

Grossman, D., K.L. Goodin, X. Li, C. Wisnewski, D. Faber-Langendoen, M.
Anderson, L. Sneddon, D. Allard, M. Gallyoun, and A. Weakley. 1994.
Standardized national vegetation classification system. Report by The
Nature Conservancy and Environmental Systems Research Institute for the
NBS/NPS Vegetation Mapping Program. National Biological Service, Denver,
Colorado.

Jenkins, R.E. 1985. Information methods: Why the Heritage Programs work.
*The Nature Conservancy News* 35(6):21-23.

Jennings, M.D. 1993. Natural terrestrial cover classification:
Assumptions and definitions. Gap Analysis Technical Bulletin 2. Idaho
Cooperative Fish and Wildlife Research Unit, University of Idaho,
Moscow.

Kirkpatrick, J.B. 1983. An iterative method for establishing priorities
for the selection of nature reserves: An example from Tasmania.
*Biological Conservation* 25:127-134.

Küchler, A.W., and I.S. Zonneveld, editors. 1988. Vegetation mapping.
Kluwer Academic Publishers, Dordrecht, The Netherlands. 635 pp.

Levin, S.A. 1981. The problem of pattern and scale in ecology. *Ecology*
73:1942-1968.

Lins, K.S., and R.L. Kleckner. 1996. Land cover mapping: An overview and
history of the concepts. Pages 57-65 in J.M. Scott, T.H. Tear, and F.
Davis, editors, Gap Analysis: A landscape approach to biodiversity
planning. American Society for Photogrammetry and Remote Sensing,
Bethesda, Maryland.

Margules, C.R., A.O. Nicholls, and R.L. Pressey. 1988. Selecting
networks of reserves to maximize biological diversity. *Biological
Conservation* 43:63-76

Miller, K.R. 1994. In J.A. McNeely and K.R. Miller, editors. National
parks conservation and development. Smithsonian Institution, Washington,
DC.

Mueller-Dombois, D., and H. Ellenberg. 1974. Aims and methods of
vegetation ecology. John Wiley & Sons, New York. 547 pp.

Nicholls, A.O., and Margules. 1993. An upgraded reserve selection
algorithm. *Biological Conservation* 64:165-169.

Noss, R.F. 1987. From plant communities to landscapes in conservation
inventories: A look at The Nature Conservancy (USA). *Biological
Conservation* 41:11-37.

Noss, R.F. 1990. Indicators for monitoring biodiversity: A hierarchical
approach. *Conservation Biology* 4:355-364.

Noss, R.F. 1991. Report to the Fund for Animals in Washington, DC.

Noss, R.F., and A.Y. Cooperrider. 1994. Saving nature\'s legacy. Island
Press, Washington, DC.

Noss, R.F., E.T. LaRoe III, and J.M. Scott. 1995. Endangered ecosystems
of the United States: A preliminary assessment of loss and degradation.
Biological Report 28, National Biological Service, Washington, DC.

Odum, E.D., and H.T. Odum. 1972. Proceedings of the North American
Wildlife and Natural Resources Conference 39:178.

Pressey, R.L., and A.O. Nicholls. 1989. Application of a numerical
algorithm to the selection of reserves in semi-arid New South Wales.
*Biological Conservation* 50:263-278.

Pressey, R.L., C.J. Humphries, C.R. Margules, R.I. Vane-Wright, and P.H.
Williams. 1993. Beyond opportunism: Key principles for systematic
reserve selection. Trends in Ecology and Evolution 8:124-128.

Pressey, R.L., and V.S. Logan. 1995. Reserve coverage and requirements
in relation to partitioning and generalization of land classes: analyses
for Western New South Wales*. Conservation Biology* 9(6):1506-1517.

Ride, W.L.D. 1975. In F. Fenner, editor. A national system of ecological
reserves in Australia. 64 pp.

Scott, J.M., F. Davis, B. Csuti, R. Noss, B. Butterfield, C. Groves, H.
Anderson, S. Caicco, F. D\'Erchia, T.C. Edwards, Jr., J. Ulliman, and G.
Wright. 1993. Gap analysis: A geographic approach to protection of
biological diversity. *Wildlife Monographs* 123.

Specht, R.L. 1975. The report and its recommendations. Pages 11-16 in:
F. Fenner, editor, A national system of ecological reserves in
Australia. Australian Academy of Sciences Report No. 19. Canberra,
Australia.

Specht, R.L., E.M. Roe, and V.H. Boughlon. 1974. Australian Journal of
Botany Supplement Series. Supplement No. 7.

Stoms, D., and J. Estes. 1993. A remote sensing research agenda for
mapping and monitoring biodiversity. *International Journal of Remote
Sensing* 14:1839-1860.

Stoms, D.M. 1994. Actual vegetation layer. In J.M. Scott and M.D.
Jennings, editors. A handbook for Gap Analysis. Idaho Cooperative Fish
and Wildlife Research Unit, University of Idaho, Moscow.

UNESCO. 1973. International classification and mapping of vegetation.
Paris.

Williams, P.H., D. Gibbons, C. Margules, A. Rebelo, C. Humphries, and R.
Pressey. 1996. A comparison of richness hotspots, rarity hotspots, and
complementary areas for conserving diversity of British birds.
*Conservation Biology* 10:155-174.

Whittaker, R.H. 1960. Vegetation of the Siskiyou mountains, Oregon and
California. *Ecological Monographs* 30(3):279-338.

Whittaker, R.H. 1965. Dominance and diversity in land plant communities.
*Science* 147: 250-259.

Whittaker, R.H. 1977. Species diversity in land communities.
*Evolutionary Biology* 10:1-67.

GLOSSARY

*insert current file from GAP Handbook*

GLOSSARY OF ACRONYMS

*insert current file from GAP Handbook and add any additions from your
text*

MAPS

APPENDICES

**Appendix \_ List of Example GAP Applications**

[Businesses and Non-government Organizations]{.underline}:

The following are some examples of applications of GAP data by the
private sector:

-   The Wyoming Natural Heritage Program (a private non-government
    organization) transformed the endangered and sensitive species
    database into a spatially referenced digital geographic information
    system using the GAP digital base map and other GAP spatial data.

-   Hughes Corp. is experimenting with the Utah and Nevada GAP digital
    base maps, simulating images to aid the development of new
    space-based remote sensing devices.

-   The Nature Conservancy used the Wyoming GAP data to develop a map of
    ecoregions of Wyoming.

-   Weyerhaeuser Corp. is using the Arkansas GAP data in managing their
    lands in Arkansas.

-   IBM Corp. is funding a project at the University of California-Santa
    Barbara that, in part, uses GAP data in the development of
    visualization software.

-   NM-GAP vegetation data is being used for an environmental assessment
    of a proposed spaceport, a state/private venture.

[County and City Planning]{.underline}:

Some other examples of the use of GAP by local governments are:

-   CA-GAP biological data were combined with the Southern California
    Association of Governments (SCAG) land ownership data to show which
    ownerships and jurisdictions were needed for joint conservation
    planning and management of a particular natural community or
    species, maximizing efficiency and minimizing the potential for yet
    another conservation crisis.

-   In California, county and city planners of several jurisdictions,
    wildlife agencies, developers of the 4S Ranch property, and the
    state Natural Communities Conservation Planning program used the GAP
    regional data, as well as more detailed information, to conserve
    1,640 acres of habitat within a 2,900-acre planned development.

-   Day-to-day county planning operations in Piute, Grande, and
    Washington counties, Utah.

-   County planners in Piute County, Utah, used GAP data to optimize the
    siting of a proposed sawmill for aspen with respect to the
    distribution of aspen stands.

-   Missoula County, Montana, used the GAP land cover map of the area as
    a base map for its comprehensive long-range plan.

-   Snohomish County, Washington, used the GAP land cover map in meeting
    state requirements for a growth management plan.

-   The City of Bainbridge Island, Washington, used GAP data to assist
    them in development of a watershed planning project.

[State Uses]{.underline}:

The following are some examples of uses of GAP data by state agencies.

-   The GAP database of species habitats was used by the Tennessee
    Wildlife Resources Agency (TWRA) to update its book \"Species in
    Need of Management.\"

-   Images of land cover derived from GAP TM data are used by TWRA for
    locating particular habitat types. Information on the locations of
    these habitat types is provided by TWRA to the public for a wide
    variety of public service functions, from education to cooperative
    resource management.

-   Early GAP data developed by TWRA were used to help identify an
    extremely important area of the state with high biodiversity that
    was subsequently purchased by the state for conservation.

-   Preliminary findings from GAP were used by TWRA to develop three
    resource management initiatives.

-   The Tennessee GAP project, which is being carried out primarily by
    TWRA, is the foundation of a multi-agency, long-term biodiversity
    program for Tennessee.

-   GAP data have been used by the Tennessee Forestry Stewardship
    Program to help develop a district program for nine conservation
    planning districts, outlining Best Management Practices (BMPs) for
    biological conservation on private lands.

-   GAP data are being used extensively by TWRA in the preparation of
    project proposals to the North American Waterfowl Conservation
    Program. These proposals require that biodiversity issues are
    addressed in specific detail. The use of GAP data on occurrence of
    land cover types and terrestrial vertebrates has made this possible.

-   The Wyoming Department of Fish and Game used GAP data to assist them
    in transforming the Wildlife Observation System database into a
    spatially referenced geographic information system.

-   The Utah Division of Wildlife Resources and the Bear River Water
    Conservancy District used the Utah GAP land cover map in a resource
    management assessment for mitigating conflicts between a proposed
    groundwater withdrawal project and the maintenance of an elk calving
    area in the Uinta Mountains.

-   The Utah Division of Wildlife Resources, the Rocky Mountain Elk
    Foundation, and Sheik Safari International used the Utah GAP land
    cover map to identify critical elk habitat. The environmental
    profile of these areas was then used to identify other similar areas
    for elk habitat enhancement.

-   The Utah Division of Wildlife Resources used the Utah GAP land cover
    map for a rapid ecological assessment of the Echo Henefer Wildlife
    Management Area.

-   The Washington Department of Fish and Wildlife used GAP data to
    develop a breeding bird atlas and an atlas of mammals of Washington
    State.

-   The Washington Department of Fish and Wildlife uses GAP data to
    operate an integrated landscape management program.

-   The Washington Department of Fish and Wildlife uses GAP data from
    Eastern Washington to assist with an innovative program that brings
    the forest products industry, state agency biologists,
    non-government organizations, and tribal biologists together in the
    field to jointly determine the appropriate management practices for
    any particular site of concern (Timber, Fish & Wildlife Program).

-   The Idaho Department of Fish and Game used GAP data to evaluate the
    impact from expanded military training activities on public lands in
    Southern Idaho.

-   The Idaho Department of Fish and Game uses GAP data for regional
    planning efforts on a regular basis.

[Statewide Planning]{.underline}:

Biodiversity planning programs or projects are now under way in Arizona,
California, Colorado, Maine, Missouri, Nevada, Oregon, and Tennessee. It
is likely that similar efforts will develop in other states. These
activities were the subject of the State Biodiversity Programs meeting
discussed on page \_ in this report. In some cases, these efforts grew
out of the state GAP project, however, in most cases, the GAP data are
being used to meet a previously defined need. In all cases, GAP data are
central to their development and operations. The goals of each of these
programs or projects are presented briefly below.

[Federal Agency Applications]{.underline}:

Some examples of applications of GAP data by federal agencies follow:

-   GAP data are being supplied to all military installations in the
    Great Basin ecoregion for integrated management of the natural
    resources. These installations constitute a very large amount of
    land area. Much of it is of high value for native species.

-   The Ouachita National Forest used the Arkansas GAP data to help them
    develop an ecosystem management plan.

-   The Wyoming GAP data were used by NASA to calibrate a model that
    predicts vegetation types based on climate and soil variables.

-   The potential contributions to biodiversity conservation of four
    different options proposed for new wilderness designation in Idaho
    were quantified by the Idaho Cooperative Fish and Wildlife Research
    Unit in cooperation with the Park Studies Unit.

-   The potential contributions to biodiversity conservation of four
    different options proposed for new national park designation in
    Idaho were quantified by the Idaho Cooperative Park Studies Unit.

-   The U.S. Forest Service in Booneville, Arkansas, used the Arkansas
    GAP data land cover maps in a 3-dimensional presentation to provide
    the public with a visual representation of the region and to enhance
    the public\'s involvement with the National Forest planning process.

-   The U.S. Fish and Wildlife Service regularly uses the GAP data for
    Southern California for habitat evaluation and management.

-   The U.S. Forest Service, Bureau of Land Management, and National
    Park Service are using the GAP data for a wide variety of natural
    resource management operations in Utah. For example, the entire Utah
    GAP database is directly linked with existing National Park Service
    databases for use by National Parks.

-   The Bureau of Land Management uses the Wyoming GAP data for managing
    the Buffalo Resource Area.

-   The U.S. Forest Service used the Utah GAP data to help assist them
    in evaluating human-induced impacts to forested lands surrounding
    ski resorts in central Utah.

-   The U.S. Fish and Wildlife Service in Delaware used GAP data to help
    identify potential habitat for the federally endangered Delmarva fox
    squirrel. These maps were displayed and served as a catalyst for
    bringing together people with a stake in the issue.

-   The U.S. Fish and Wildlife Service used the Indiana GAP data as part
    of a biological assessment for the base closure of the Jefferson
    Proving Grounds and its conversion to a National Wildlife Refuge.
    This 58,000-acre installation has restricted human access due to
    unexploded ordinance and contains some of the highest-quality
    natural habitat in Indiana.

-   The U.S. Fish and Wildlife Service in Louisiana used GAP data to
    avoid conflict over the designation of critical habitat of the
    federally endangered Louisiana black bear.

-   The NOAA Coastal Marine Sanctuary in Washington State uses GAP data
    for an educational display.

-   In Washington and New Mexico, digital land cover maps have been
    distributed to all National Forests.

-   The U.S. Natural Resources Conservation Service (NRCS) in New Mexico
    is using a GAP clustered imagery as a base for their land cover
    mapping activities.

```{=html}
<!-- -->
```
-   The Department of Defense is funding the development of an
    electronic environmental information system for the Mojave
    ecoregion, which would use GAP data as a foundation or base layer of
    information. The system will link 29 DoD installations to a common
    source of environmental information.

-   **begin state project appendices here**