United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-152

June 2005

Regional Data to Support 

Biodiversity Assessments: 

Terrestrial Vertebrate and Butterfly 

Data from the Southwest

Darren J. Bender, Curtis H. Flather, 

Kenneth R. Wilson, Gordon C. Reese

Perognathus amplus Aneides hardii
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Bender, Darren J.; Flather, Curtis H.; Wilson, Kenneth R.; Reese, Gordon C. 2005. Regional data to support 

biodiversity assessments: terrestrial vertebrate and butterfly data from the Southwest. Gen. Tech. Rep. 

RMRS-GTR-152. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 42 p. + DVD.

Abstract

Spatially explicit data on the location of species across broad geographic areas greatly facilitate effective conservation planning on lands managed for multiple uses. The importance of these data notwithstanding, our knowledge 

about the geography of biodiversity is remarkably incomplete. An important factor contributing to our ignorance 

is that much of the biodiversity data are not readily accessible because they are dispersed across many institutions and often have not been digitized. This report documents our efforts to address these conservation planning 

constraints. We have compiled extant data on predicted species distributions and more than 680,000 occurrence 

records for terrestrial vertebrates and butterflies into a single digital database for general use in conducting geographically broad biodiversity assessments across a two-state area (Arizona and New Mexico) that defines the 

Southwestern Region of the USDA, Forest Service. These data represent one of the most complete databases 

on species occurrence to be compiled for the Southwest. Our objectives are twofold: (1) to document the types, 

sources, and characteristics of the data comprising the biodiversity database; and (2) to illustrate the utility of 

the data in addressing applied conservation problems across the Southwestern Region. We report on three case 

studies that illustrate how the data can be used to generate simple distribution maps using both point locations 

and predicted ranges, describe the patterns of species richness for seletected taxa across the Southwest, and 

provide an example of how managers may use the data to identify where potential resource conflicts may be particularly important on National Forest System lands.

Keywords: butterflies, geographic information systems, geographic range, species associations with grazing, 

species diversity, species impacted by grazing, Southwestern biodiversity, terrestrial vertebrates

Authors

Darren J. Bender is with the Department of Geography, University of Calgary, Calgary, Alberta, Canada T2N 1N4.

Curtis H. Flather is with the USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80526.

Kenneth R. Wilson is with the Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, 

CO 80523.

Gordon C. Reese is with the Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, 

CO 80523.
Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Database Development and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Development of the Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Data Compilation of Species Observation Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Description of Environmental and Physical Data Layers . . . . . . . . . . . . . . . . . . . . . . . . .6

Vegetation/Land-Cover Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Natural Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Human-Made Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Description of Species Occurrence Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Predicted Range Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Species Observation Locations Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Data Layer Accuracy Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Data Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Species Observation Locations and Predicted Range Maps . . . . . . . . . . . . . . . . . . . .13

Regional Patterns of Biodiversity Across the Southwest . . . . . . . . . . . . . . . . . . . . . . . .13

Biodiversity and Domestic Grazing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Appendix A: DVD content description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Appendix B: Metadata documentation for the species observation GIS database . . . . . .32

USDA Forest Service RMRS-GTR-152. 2005 1

Introduction

Conservation of biological diversity on multipleuse lands is complicated by the inevitable conflicts that 

arise from varying, and often discrepant, land management objectives held by different segments of the 

public. Increasingly, resource managers need to account 

for these conflicts objectively when developing tenable 

conservation plans and resource management decisions 

(Mills and others 2001; Hansen and others 1993). The 

degree to which resource planners are effective in considering biodiversity in policy and management decisions 

is critically dependent on the availability of detailed 

knowledge about the taxonomy, distribution, abundance, 

and life history of the biota within the management area 

(Savage 1995; Wheeler 1995; Patrick 1997; Gioia and 

Pigott 2000).

Information about biodiversity contributes to conservation planning in many ways (for review see McNeely 

1995; Cracraft 2002; Funk and others 2002; Ferrier 

2002; Graham and others 2004). First, and perhaps 

most fundamentally, it provides a means of generating 

potentially accurate species lists for an area of interest. Second, when coupled with location information, 

the spatial distribution of biodiversity can be used to 

describe how characteristics of the biota (for example, 

species occurrence or species richness) vary geographically throughout a region of interest. Third, if data are 

collected periodically, then temporal changes in species 

occurrence and community composition can be documented. Finally, if biodiversity data are supplemented 

with ancillary information on the biotic and abiotic environment (for example, soils, elevation, vegetation 

types, weather), then researchers and planners can model biodiversity as a function of those ecological factors 

thought to determine its spatial pattern. Inferences from 

such modeling efforts will contribute to our understanding of why organisms currently occur where they do, 

and also permit predictions about how biodiversity may 

change in response to ongoing or proposed land management activities.

Although spatially explicit biodiversity data increase 

the effectiveness of conservation plans, our knowledge 

of biodiversity, both systematically and geographically, is woefully incomplete (Wilson 1988; Brown and 

Roughgarden 1990; Prance 1995; Simpson and Cracraft 

1995). Two problems with extant biodiversity data help 

characterize the nature of the limitations. First, biodiversity data collected to date contain notable biases, 

including taxonomic and geographic, focusing on easyto-study species (for example, vertebrates and vascular 

plants) in easily accessible places (Funk and Richardson 

2002; Graham and others 2004). Furthermore, collecting 

effort has declined over time, resulting in few recent records relative to the number of historical records and an 

emerging temporal bias in the data (Winker 1996; Smith 

and others 2000). Second, of the biodiversity data that 

have been collected, much are not readily accessible because they have not been digitized (Cracraft 2002) and 

because they are spread across many institutions (Pennisi 

2000)—being dispersed among the paper records of research ecologists, museum collections, or government 

agencies (Brown and Roughgarden 1990). Since biodiversity information is not readily accessible, it is often 

not considered, at least in a rigorous way, in policy or 

management decisions (Edwards and others 2000).

If financial and personnel constraints could be overcome, then one solution to the noted data biases could 

be to design a comprehensive (taxonomically and geographically) biodiversity inventory (Raven and Wilson 

1992; Prance 1995). However, the ideal inventory will 

likely never be available to answer our most pressing species conservation questions (Platnick 1992). 

Consequently, a feasible, if only partial, solution to biodiversity data limitations will have to address the second 

problem—that of data accessibility. Given that (1) resource decisions will be made whether comprehensive 

biodiversity data are available or not, and (2) an enormous amount of information has already been collected 

about where species occur (Edwards and others 2000), 

then it seems prudent to first invest in compiling existing biodiversity data so that this information (even with 

its inherent biases) can provide resource decision-makers with some insights on where resource-use conflicts 

with biodiversity may be particularly important. Such 

compilation efforts should not only consider specimens housed in formal museum collections, but also 

Regional Data to Support Biodiversity Assessments: 

Terrestrial Vertebrate and Butterfly Data From the Southwest

Darren J. Bender, Curtis H. Flather, 

Kenneth R. Wilson, Gordon C. Reese
2 USDA Forest Service RMRS-GTR-152. 2005

consider species occurrence information from published 

field studies, the notebooks of researchers and parataxonomists, and extant species monitoring programs such 

as the North American Breeding Bird Survey (Basset 

and others 2000; Smith and others 2000).

We take the latter approach in this report—namely 

we compile extant biodiversity and ancillary data into 

a single digital database for general use in conducting 

broad-scale biodiversity assessments across a twostate area (Arizona and New Mexico) that defines the 

Southwestern Region of the U.S. Forest Service. We 

have two primary objectives in compiling these data: (1) 

to document the types, sources, and characteristics of the 

data that contributed to the database, and (2) to illustrate 

the utility of the database in three applied conservation 

problems that relate to the simple description of regional 

biodiversity patterns and the evaluation of potential resource conflicts.

In addressing the first objective, we document the 

basic ancillary data that may be useful in establishing ecological relationships with various biodiversity 

attributes, as well as describe the species range and occurrence information that forms the core biodiversity 

data. We focused our search for extant biodiversity data 

on terrestrial vertebrates and butterflies. The choice of 

these taxonomic groups was determined, in part, by the 

opportunistic nature of this effort—those groups of species were expected to have the greatest collection effort 

and also represent a subset of species that we could feasibly compile given the funding level of the study.

In addressing the second objective, we report on three 

case studies that illustrate how these data could be used 

to address applied conservation problems. First, we show 

how the data can be used to generate simple distribution 

maps using both point locations and predicted distributions. Second, we document the general patterns of 

species richness across the Southwest noting, in particular, apparent relationships with ancillary environmental 

data and how the spatial patterns among taxonomic 

groups relate to one another. Lastly, we illustrate how 

these data can be used to initially examine the potential conflicts that may arise between biodiversity and 

extractive resource uses. Ultimately, the data compiled 

for this study could be used to assess potential conflicts 

with many resource management issues. However, the 

database’s proximate motivation concerned domestic 

livestock grazing (Memorandum of Understanding between USDA, Forest Service, Region 3-Southwestern 

Region and the USDA, Forest Service, Rocky Mountain 

Research Station, unpublished document); consequently, our third case study focuses on livestock grazing as 

the context within which to illustrate the value of such 

geographically extensive data in guiding multiple-use 

resource planning.

Although our discussions throughout this report focus on broad-scale assessment applications, we hope 

that those with more local perspectives will still find value in a regional biodiversity database. Because there is 

mounting evidence that understanding local patterns of 

biodiversity requires knowledge of regional diversity 

(Ricklefs 1987; Caley and Schluter 1997; Smith 2001), 

the data presented here can function to provide the regional context for more geographically focused research 

and management in the Southwest.

Database Development and 

Description

Development of the Database

Two main types of data were compiled to create this 

database. First, we collected data layers (hereafter referred to as base layers) that provide information on 

environmental conditions, land use/land cover, and administrative boundaries. These include information such 

as vegetation cover, soils, topographic information, 

natural features, road networks, and county and Forest 

Service grazing allotment boundaries. The second type 

of data in the database includes species occurrence data, 

both predicted and observed. Our focus in developing 

the occurrence data was at the species level of taxonomic 

resolution. However, we did maintain subspecies designations if the original location record or predicted range 

was recorded at this level. These data represent species 

range maps predicted from known habitat associations 

and documented locations where individuals were observed. All data were compiled and then imported into a 

geographic information system (GIS) to facilitate storage 

and retrieval, validation, and subsequent processing and 

analysis (see appendices for data descriptions [Appendix 

A] and metadata documentation [Appendix B]). The geospatial database that we compiled is designed to be used 

with GIS software produced by Environmental Systems 

Research Institute, Inc. (ESRI; Redlands, CA), namely 

ArcView GIS 3.x (1992-2002), and ArcGIS 8.0 or newer 

(1999-2002). The data are available on the accompanying DVD1

 in two ESRI file formats: ArcView shapefiles 

(locations of species observations, base layer maps) and 

1

 These data can be downloaded from the following ftp site: 

ftp://ftp2.fs.fed.us/incoming/ftcol/flather/R3_Biodiversity_Data.
USDA Forest Service RMRS-GTR-152. 2005 3

ArcInfo raster grids (predicted species occurrence maps, 

base layer maps).

Although we did not collect the original data included 

in this database, the utility of this database is that it brings 

together a comprehensive set of species occurrence data 

and associated base layer maps under a common spatial 

reference system and an interoperable file structure. All 

information in this database can be queried, viewed, and 

analyzed within one GIS software system. We anticipate 

that the comprehensive nature of these data will provide 

a biodiversity information system that can be used to 

support land management decisions within Arizona and 

New Mexico. It may also prove to be valuable for future research, especially as it relates to the predictions 

of species distributions (Reese and others 2005) and understanding macroecologic patterns of species richness 

(Shriner and others, in preparation). This section details 

how the database was developed and describes the contents of the database. A later section, Data Applications, 

provides examples of how we used this database to examine patterns in regional biodiversity and apply them 

to a potential threat to native biodiversity, namely livestock grazing.

Data Sources

Ancillary Data (Base Layers)—Many factors determine the distribution of a species including vegetation 

type, land use, elevation, and species interactions. 

Further, the management of biotic resources is influenced by jurisdictional boundaries. Thus, it is useful to 

have spatial data to provide a context for analysis and 

interpretation of species occurrence. We have compiled 

a number of GIS data layers that provide information 

on vegetation cover, soils, hydrology, elevation, natural features, human-made features, and administrative 

boundaries. All of these data are freely available and 

distributed to the public, so we have included them as 

ancillary data in our database.

The sources for the GIS base layers were generally 

federal and state government agencies. We have attached 

unmodified versions of these spatial layers (except that 

they have been converted to a common spatial reference 

system; see details below). A description of these files, 

including their original metadata, can be found on the 

accompanying DVD. Base map layers for Arizona and 

New Mexico are in the Basecovs directory.

Predicted Species Occurrence Data—The U.S. 

National Gap Analysis Program (GAP) has led an effort 

to model vertebrate species distributions for each state in 

the U.S. (Scott and others 1987, 1993; Kiester and others 

1996; Jennings 2000). The first generation GAP products for Arizona and New Mexico produced predicted 

occurrence maps in the 1990s that depict the range extents for extant vertebrate species. These maps are based 

on habitat suitability models and limits of the species’ 

known occurrence within the region (see Csuti and Crist 

2000). These maps are included in the Predicted directory on the accompanying DVD. The intent of GAP is to 

conduct a conservation assessment of biotic resources 

every 10 years. The second generation of GAP products for Arizona and New Mexico (as well as Colorado, 

Utah, and Nevada) is currently being conducted and is 

known as the Southwest Regional Gap Analysis Project. 

Second versions of the vertebrate distribution models are 

targeted to be complete in the spring of 2005 (Thomas, 

personal communication) and they should supersede 

those contained in this database.

Species Observation Data—For years, numerous individuals, organizations, and institutions have collected 

species observation data for the Southwest. Although 

they are not always available to the public or freely accessible, these data constitute an important source of 

information about the distribution of species. Sources 

for such information include museum collection records, established volunteer surveys (for example, the 

North American Breeding Bird Survey and the Audubon 

Christmas Bird Count), databases from state Natural 

Heritage Programs, biological atlases, reports from the 

primary literature, and private and academic collection 

records. 

We utilized all available data sources to build a comprehensive collection of species occurrence data for 

terrestrial vertebrates (birds, mammals, reptiles, and 

amphibians) and butterflies. Our general approach was 

to maximize the number of accumulated species occurrence records. Only occurrence records that were 

already georeferenced or could be georeferenced from 

the location supplied with the data source’s description 

for the observed occurrence were incorporated into the 

database. 

Many datasets were publicly available and some were 

distributed via the Internet. For example, bird observation data from the North America Breeding Bird Survey 

(BBS) and the Audubon Christmas Bird Count (CBC) 

were available online in a georeferenced digital format (see USGS 2001; National Audubon Society 2003). 

Other datasets were obtained from published materials, 

such as biological atlases of Arizona and New Mexico 

mammals by Hoffmeister (1986) and Findley and others (1975), respectively. We also conducted extensive 

literature searches in the primary literature, such as zoological journals, particularly regional journals. However, 

much of the data that we obtained was not published in 

print or on the internet. A wealth of species occurrence 
4 USDA Forest Service RMRS-GTR-152. 2005

data for Southwest fauna exists in museum, private, and 

academic collections, as well as observation databases 

of non-governmental organizations. We solicited these 

types of organizations for species observation data that 

could be used in our database, particularly if the organization had an office in the Southwest. A template of the 

letter that we used to solicit data is available on the DVD 

as the file “Request_template.doc”.

Overall, we obtained and compiled approximately 

one million species observation records into our database. However, the quality of some records were poor 

and they were excluded from the database (see subsection Accumulating Species Observation Records Into 

a Single Database below). The total number of suitable records in the database was 682,663. The largest 

proportion of our database (about 40 percent) is comprised of records originating from established volunteer 

surveys such as the BBS, which has been conducted in 

the Southwest for over 30 years (table 1). The various 

governmental and non-governmental organizations that 

maintain species observation record databases also provided a large portion of the database. For example, the 

Bird Banding Laboratory (USGS 2002) and the state 

Natural Heritage Programs provided nearly one-quarter 

of the species records. Established surveys and governmental/non-governmental observation databases were 

also most likely to deliver public data in electronic format, often via the Internet. Thus, the majority of our data 

was easily accessible, which is good news for those desiring to duplicate our efforts in other regions. Museum 

and other academic collections also provided a large 

number of records. These were often high quality datasets with respect to attribute information – providing, for 

example, detailed taxonomy, gender, and age information. However, considerable processing was required to 

extract location information from many of these records 

as discussed in the next section.

Data Compilation of Species Observation Data

At the outset of this project, only predicted species 

distribution maps and associated GIS base layer maps 

were available. Although much effort had been made 

to collect information on where species were being observed, no efforts had been made to accumulate and 

compile these data into a single database. A principal 

goal of this project was to compile as many species observation records as possible into a single GIS database, 

in part because we believed that having the most complete information on species occurrence was valuable 

for completing tasks such as identifying critical habitat or hotspots of biodiversity. Comparisons of predicted 

range maps with observation record locations are also 

valuable in identifying areas for targeted species surveys. For example, identification of areas where the 

species is predicted to occur but no location records currently exist could be used to site future inventory work 

to verify species occurrence within its predicted range. 

Conversely, geographic areas where point locations fall 

outside the predicted range could be used to further our 

understanding of the ecological factors affecting a species’ distribution; for example, the ecological attributes 

associated with such points might suggest that the model 

used to predict a species’ range should be modified.

Plotting Locations of Species Observations—Perhaps 

the greatest challenge in compiling this database was to 

accurately represent the location of each species observation within the GIS. Datasets came from very disparate 

sources and methods for expressing location and reference systems for those locations varied greatly. Much 

of the digital data (for example, all bird survey data) 

provided spatial location of observations in geographic 

coordinates. The process for bringing these data into the 

GIS was straightforward; we simply used these coordinates to define the location of each observation in the 

GIS directly. Where complete spatial reference information was not provided, we assumed that geographic 

coordinates followed the North American Datum of 

1927 (NAD 27). When our confidence in this assumption 

was low, we verified the locations visually by plotting 

them on 1:24,000 digital raster graphics (DRG) maps 

in NAD 27 to look for any positional inconsistencies 

(for example, points that fell in lakes or rivers; plotted 

Table 1—The number of suitable records obtained from the various sources of data that provided 

species observation location records. 

Description Count Percentage

Volunteer surveys 268,694 39.4

Government and Natural Heritage Programs 168,390 24.7

Museum and other academic collection records 133,827 19.6

Private collection records 61,837 9.1

Published materials 48,588 7.1

Non-government organizations 1,327 0.2

Total 682,663 100
USDA Forest Service RMRS-GTR-152. 2005 5

locations did not match text descriptions, if available). 

In cases where projected coordinates were provided (for 

example, UTM locations), the data source had to specify 

the exact map projection, coordinate system and datum 

that was used before those records were transformed to 

geographic coordinates (NAD 27) and compiled into the 

database.

A large proportion of the data did not have spatial 

coordinates associated with each species observation record. However, much of these data did contain a textual 

description of the collection/observation location for the 

species. In these cases, we manually plotted the location 

in the GIS using DRG maps referenced to NAD 27. Then, 

all attributes associated with that location (for example, 

species name, gender, age, date of collection) were manually entered into the database. This was a difficult and 

labor intensive process that sometimes involved subjective interpretation of the data. We employed numerous 

GIS operators to complete this task. To minimize potential operator bias and to standardize the data product, 

we implemented a standardized operating procedure for 

plotting textual locations. 

First, every operator was provided with DRG maps 

from the USGS standard topographic map series at the 

1:250,000 scale for all of Arizona and New Mexico. The 

site described in a species observation record could be 

further referenced with various large- and small-scale 

road atlases, topographic maps, county maps, and landuse/land cover maps. 

Second, operators were trained to judge the precision of each plotted location because textual location 

descriptions varied greatly in the quality of their spatial 

reference. For example, a record could describe a location as occurring at the intersection of two well-defined 

roads. These could be accurately located using road maps 

and plotted precisely on the GIS using on-screen digitizing from a roads layer. Alternatively, the description of 

the location could be somewhat vague, such as “10 miles 

northwest” of the intersection of the same two roads. In 

this case, the operator would plot the location exactly 10 

miles (16.1 km) northwest of the road intersection using 

on-screen digitizing tools in the GIS, but the precision 

of this plotted location must be considerably less than 

in the previous example. To distinguish varying degrees 

of positional precision in the database, we had operators 

make a qualitative judgment about whether or not the location estimate had accuracy within 5 miles (8 km).

Third, we verified the operator’s plots both automatically and manually. To ensure accuracy and consistency, 

one individual (G. C. Reese) was designated to oversee and validate all work of the operators. On a regular 

basis, each operator’s work was verified to ensure that 

gross plotting errors were not occurring and to check 

that the highest level of positional and attribute accuracy was being attained. To verify the operators’ GIS data, 

a number of checks were used: (1) Coarse positional 

accuracy was validated for all operators’ GIS data records where county names were provided as part of the 

species observation record in the original data. A spatial join operation was performed on the GIS data set 

between the plotted species observation locations and a 

GIS map of the county boundaries. If locations did not 

fall within the same county as was stated in their description, these records were further investigated and 

subsequently corrected or removed from the database. 

(2) A random-subset of records was selected from each 

operator’s work and checked for errors. If errors were 

found, either those individual records were re-plotted or, 

when a large number of errors was discovered during either error-checking procedure, the operator was required 

to check each record in their work set for accuracy or replot the entire set of records under regular supervision. 

Despite our best efforts to eliminate errors, it is certain 

that some positional or attribute errors were not detected 

in the compilation of this database. We encourage any 

user of these data to be conscious of this statement.

During the process of checking the species observation records, we became aware that a number of points 

were located outside the state boundaries of Arizona and 

New Mexico. Clearly erroneous data points were removed; however, a number of records were plotted with 

what we considered to be high accuracy (that is, actual 

location of observation was likely within 5 miles [8 km] 

of the plotted location in the database), and nevertheless, 

they fell outside the state boundaries. In total, there were 

about 900 anomalous records and all but one of these 

records came from the same data source (Bird Banding 

Lab records). We chose not to remove these records because we felt it was important to be consistent in the way 

we treated these data. We may have introduced a bias if 

we removed only the records that fell outside the state 

boundaries. For example, if there were geo-registration 

problems with the data source that caused a number of 

points to fall outside the state boundaries, then this represents a systematic error that may have affected all the 

point locations from that source. By leaving these points 

in the database, the users of the data can quickly see the 

problems and judge for themselves whether or not to 

use these data. Users also get a much better appreciation 

of the positional accuracy of these data by viewing the 

points that fell outside the state boundaries. 

Accumulating Species Observation Records Into a 

Single Database—Once each dataset had been imported into a GIS data layer, all datasets were converted into 
6 USDA Forest Service RMRS-GTR-152. 2005

a common spatial reference system. Each dataset was 

entered into a separate GIS data layer that followed either the spatial reference system specified by the data 

source or, when manually plotted, was in the Universal 

Transverse Mercator projection. To facilitate spatial 

analysis, we did a subsequent conversion of the entire 

compiled database to the Albers equal-area map projection. This was the final format of the spatial data and is 

used in the accompanying data DVD.

It was not always possible to plot a location in the 

GIS for every species observation record, nor was it always possible to determine to which species a record 

referred. These records were deemed incomplete and 

were eliminated from the final database that is provided 

in the accompanying data DVD.

Another challenge was standardization of the taxonomic nomenclature for individual species. Taxonomies 

are dynamic and some species names are used inconsistently across the time span over which the data were 

collected and recorded. To resolve these inconsistencies, we standardized all species names to conform to 

the Integrated Taxonomic Information System (ITIS 

2002). We used the ITIS system because it is the taxonomic standard that has been adopted by most North 

American federal agencies. We used the recorded scientific species name (or the common name, if no scientific 

name was provided) to attach the ITIS taxonomic serial 

number (TSN) to each record. We also used ITIS to resolve any apparent conflicts in taxonomic classification 

(for example, multiple named subspecies were lumped 

into a single parent species name). ITIS is still a work 

in progress, and occasionally their database did not contain information for a species in our database. When 

this occurred, we relied on a second taxonomic authority, the NatureServe Explorer (formerly the Association 

for Biodiversity Information; see NatureServe Explorer 

2002). The NatureServe taxonomic database is also recognized as an authoritative taxonomic source by U.S. 

federal agencies, such as the USDA Forest Service. 

Using either ITIS or NatureServe databases, we converted, where applicable, non-current species common 

names to currently accepted common names. Scientific 

names, common names, and taxonomic serial numbers 

(current to our last access of these databases in February 

2004) are available in the attached species observation 

database (on data DVD under the Observed directory).

Finally, we also standardized attribute information 

provided for each record. This was no easy task given 

that each data source had different collection and/or recording standards, and the information collected about 

each occurrence could have been for a different purpose. 

For example, museum collection records commonly 

included a number of attributes that provided detailed 

information on the biology of the species (for example, 

gender, age, reproductive status, and various morphometric measurements). From visual survey sources, such 

as the BBS, this level of detail was not available. We 

limited the number of attributes in the species observation records database to fundamental information that 

was generally common among all data sources. This included information about the date and time (if available) 

of the observation, the observation method, the gender 

and reproductive status of the individual (if observed), 

and, if provided, location descriptors that supplemented the coordinates. If additional, important information 

was provided in the original source record without an 

appropriate attribute field in the database, we moved the 

information into the Comments field of the database. 

Details regarding attribute fields and the database structure can be found in the attached metadata document (see 

Appendix A and B) and in the subsection describing the 

species observation location data that appears below.

Description of Environmental and 

Physical Data Layers

On the accompanying DVD, vector data (point, line 

and polygon features) are provided in ESRI shapefile format, whereas raster data are provided in ArcInfo 

GRID format. These data can also be imported into most 

other commercial GIS packages. The spatial reference 

system of each of these layers has been modified from 

its original system so that layers conform to a common 

projected coordinate system. This ensures that the data 

layers align correctly with each other and with the species occurrence data when displayed or analyzed on a 

GIS.

A summary of the geospatial data layers provided on 

the DVD is given in table 2, and in the contents.xls or

contents.txt files on the accompanying DVD. In addition 

to the species occurrence data, we provide geospatial 

data layers on vegetation/land cover, natural features, 

and human-made features. Each is described in further 

detail below.

Vegetation/Land-Cover Layers

Perhaps the most relevant GIS base layer to provide spatial context for viewing and analyzing species 

distributions is the vegetation/land-cover layer. We acquired the first generation vegetation/land-cover map 

produced in 1995 for the Arizona GAP. This coverage consisted of over 100 individual vegetation and 

land-cover categories. The New Mexico generalized 

vegetation map was produced by the first generation 
USDA Forest Service RMRS-GTR-152. 2005 7

New Mexico GAP in 1991 and consisted of 43 vegetation/land-cover categories. We provide derived versions 

of these two maps that follow a generalized 27 category classification system developed by Moir (unpublished 

paper) to ensure consistency between states. Conversion 

tables between the two original GAP classification 

schemes and our 27 category scheme are provided in the 

Basecovs directory as azvegxwalk.xls and nmvegxwalk.

xls (also provided as azvegxwalk.txt and nmvegxwalk.

txt). The vegetation maps on the accompanying DVD are 

stored as ArcInfo raster grids to facilitate overlay operations and other types of spatial analysis.

The second generation GAP land cover (vegetation) 

maps are expected to be available to the public by spring 

of 2005 (Thomas, personal communication). Land cover 

is being mapped as ecological systems, which are ecological and geographical aggregations of vegetation associations. Mapping is being done so as to minimize 

inconsistencies of land cover mapping across state 

administrative boundaries (Thomas, personal communication).

Natural Features

The accompanying DVD provides geospatial data 

layers for the following natural features: soils, hydrology (streams and springs), riparian vegetation polygons 

(AZ only), caves (NM only), and topography (through 

a digital elevation model). For each geospatial data layer, we have provided the original metadata file to that 

data layer in both ASCII text format (<layername>.txt

files) and Microsoft Word 8.0 format (<layername>.doc

files). These data were obtained through a number of 

Table 2—Description of the ancillary data (GIS base layers) provided with the database. The Arizona and New Mexico data can 

be found in Basecovs directory.

State File name File type Description Source

AZ azallot Shapefile USFS grazing allotment boundaries USDA Forest Service

 azcounty Shapefile County boundaries Arizona GAP

 azlnname Shapefile Index for named linear features U.S. Geological Survey

 azplaces Shapefile Census populated places AZ Land Resource Information System

 azptname Shapefile Index for named point features U.S. Geological Survey

 azroads Shapefile Major roadways AZ Land Resource Information System

 azstate Shapefile State boundary AZ Land Resource Information System

 aztrs Shapefile Township-range-section lines AZ Land Resource Information System

 aztwnrng Shapefile Township-range lines AZ Land Resource Information System

 azusfs Shapefile USFS national forest boundaries USDA Forest Service

 azsoils Shapefile Statewide soils AZ Land Resource Information System

 azspring Shapefile Springs U.S. Geological Survey

 azstream Shapefile Permanent and intermittent streams EPA BASINS database

 riparian Shapefile Riparian vegetation AZ Land Resource Information System

 azdem Arc Grid Elevation U.S. Geological Survey ESIC

 azveg Arc Grid Vegetation Arizona GAP

NM nmallot Shapefile USFS grazing allotment boundaries USDA Forest Service

 nmcounty Shapefile County boundaries New Mexico GAP

 nmlnname Shapefile Index for named linear features U.S. Geological Survey

 nmplaces Shapefile FIPS incorporated places NM Resource Geographic Information 

 System Program

 nmptname Shapefile Index for named point features U.S. Geological Survey

 nmroads Shapefile Major roadways NM Resource Geographic Information 

 System Program

 nmstate Shapefile State boundary New Mexico GAP

 nmtwnrng Shapefile Township-range lines U.S. Geological Survey ESIC

 nmusfs Shapefile USFS national forest boundaries USDA Forest Service

 nmcaves Shapefile Caves U.S. Geological Survey

 nmsoils Shapefile Statewide soils New Mexico GAP

 nmspring Shapefile Springs U.S. Geological Survey

 nmstream Shapefile Permanent and intermittent streams EPA BASINS database

 nmdem Arc Grid Elevation U.S. Geological Survey ESIC

 nmveg Arc Grid Vegetation New Mexico GAP
8 USDA Forest Service RMRS-GTR-152. 2005

public sources, and a breakdown of these sources can be 

found in the contents.xls or contents.txt that is in the root 

directory of the accompanying DVD. The natural features layers are useful ancillary data for interpreting the 

vertebrate species occurrence layers. Along with the vegetation/land cover layers, the natural features data can be 

used to help interpret plant community associations between different species and the locations at which they 

occur. For example, vegetation cover and elevation are 

typical constraints on the expected distribution of many 

species (for example, Scott and others 1993). Many species in the Southwest rely on narrow bands of riparian 

vegetation that follow water courses, so for these species the hydrological and riparian vegetation layers may 

be informative. Other species, such as bats, may rely on 

other natural features such as caves for roosts. Certainly, 

we have not captured all natural features that exist in the 

Southwest, but we did compile those features that we believed were useful from the available data. Data type, a 

short description of each layer, and data sources are listed in table 2 and metadata are provided in the Basecovs 

directory on the accompanying DVD.

Human-Made Features

For each state we also acquired spatial data layers 

that represent human-made features or potentially important jurisdictional boundaries. Included are layers 

of National Forests, grazing allotments within National 

Forests, state and county boundaries, township-range 

grid lines, U.S. boundaries delineating U.S. Census 

Bureau designated populated places, major roadways, 

and landscape features represented by either points or 

lines. These layers might be used for simple spatial reference or for addressing management questions. Data 

type, short descriptions, and data sources are listed in 

table 2 and metadata are provided in the Basecovs directory on the accompanying DVD.

Description of Species Occurrence Data

Predicted Range Maps

For terrestrial vertebrates, we have included the predicted habitat maps created by the first generation 

Arizona and New Mexico GAPs. These are binary maps 

that define a presumed limit or boundary for each species 

in each state and we treated these habitat maps as an estimate of the species’ potential distributional range across 

the region. Predicted range maps were available for 673 

unique terrestrial vertebrate species across the region. 

The predicted habitat maps were produced independently by each state using different inputs (for example, 

habitat suitability models and vegetation maps); consequently, many species ranges do not align at the border between Arizona and New Mexico. An example of 

this discrepancy is shown for the predicted ranges of the 

belted kingfisher (Ceryle alcyon) in Arizona and New 

Mexico (fig. 1). Arizona and New Mexico often differed 

in the detail and resolution at which riparian species 

were modeled. Procedures also varied within states, 

with Arizona modeling avian species at fine resolution, 

but herptile (amphibians and reptiles) species at coarse 

resolution, while New Mexico modeled avian species at 

coarse resolution and herptile species at fine resolution. 

This creates an effect where those species modeled at 

different resolutions appear to have smaller areal extents 

in the state that used fine resolution, when in fact this 

may not be true. 

Regardless of inconsistencies between the two states’ 

predicted habitat maps, the data are still useful for visualizing and analyzing coarse-scale predicted distributions 

of terrestrial vertebrates in the region. Range maps from 

multiple sources are commonly used to estimate the 

distribution of species richness and biodiversity across 

a region (for example, Scott and others 1987; Flather 

and others 1997; Reid 1998; Myers and others 2000). 

However, given the differences in the way habitat maps 

were generated within each state, intra-state comparisons of relative richness (for example, ranking sub-state 

regions from high to low number of species) are easier 

to interpret than inter-state comparisons.

Species Observation Locations Data

Perhaps the most valuable contribution of this database is the compilation of species occurrence data. The 

accompanying DVD contains both geospatial data layers 

and tabular data on over 680,000 known occurrences of 

birds, mammals, reptiles, amphibians and butterflies in 

the Southwest. More than 1,500 species and subspecies 

are represented in the database. These data represent one 

of the most complete databases on species occurrence to 

be compiled for the Southwest.

The geospatial database and the equivalent tabular 

database contain individual records that describe the 

observation of a particular species or subspecies (one 

or more individuals) at a single location on a particular date. The geospatial database is useful for plotting 

the locations of occurrences, which can be queried by 

species, location, time period or other species attributes 

(table 3). The tabular data are provided in a Microsoft 

Access 2000 database and in dBase IV format on the 

DVD. The tabular data provide the same information as 

the geospatial database, but they might be used as part of 

a relational database for advanced query and (non-spatial) statistical analysis.
USDA Forest Service RMRS-GTR-152. 2005 

9

Figure 1—Predicted species distributions from the Arizona and New Mexico GAPs illustrating how different modeling methods can produce different predicted range maps. 

The belted kingfisher (Ceryle alcyon) is shown; it is associated with habitats near water. The two versions of the predicted range maps for the same species reflect a different model definition of the term “near water.” New Mexico’s model reflects a greater distance to water body threshold than does the Arizona model.
10 USDA Forest Service RMRS-GTR-152. 2005

Table 3—A data dictionary for the species occurrence database that lists and describes the attributes available for the 

species observation records.

Field name Alias Attribute type 

CSUID CSU_UNIQUE_ID number (long int) 

SCI_NAME SCIENTIFIC_NAME text (100) 

COM_NAME COMMON_NAME text (100) 

TSN TAXONOMIC_SERIAL_NUMBER number (long int) 

TIME TIME time 

DATE DATE date 

DATE_ACC TIME_DATE_ACCURACY text (20) 

OBS_METH OBSERVATION_METHOD text (20) 

OBS_NAME OBSERVER_NAME text (50) 

OBS_QUAL OBSERVER_QUALIFICATIONS text (20) 

TOT_NUM TOTAL_NUMBER number (int) 

GENDER GENDER text (13) 

REPRO REPRODUCTIVE_STATUS text (35) 

GROUP GROUP_TYPE text (20) 

LAT LATITUDE number (double) 

LON LONGITUDE number (double) 

LOC_DESC LOCATION_DESCRIPTION text (memo) 

LOC_ACC LOCATION_ACCURACY number (long int) 

LOC_CONF LOCATION_CONFIDENCE text (10) 

COMMENTS COMMENTS text (memo) 

DATA_SRC DATA_SOURCE text (50) 

SRC_FILE SOURCE_FILE_NAME text (20) 

SRC_RID SOURCE_RECORD_ID number (long int) 

a

 Null refers to whether a field can be left blank. An entry of “not null” identifies those fields that must contain an entry, otherwise the 

entire record is ignored by the database.
USDA Forest Service RMRS-GTR-152. 2005 11

Description Attribute domain Nulla

CSU Access database specific unique identifier number. autonumber Not null

Taxonomic (genus, species, subspecies) species name, valid ITIS name

 as defined within ITIS.

Common name for species, as defined by ITIS. valid ITIS name Not null

ITIS taxonomic serial number. valid TSN Not null

The time of day that the species was collected. Values valid time

 are in local time.

The date of collection. valid date Not null

The precision of the time and date measurements. Day, Hour,

Month, Previous 

Year, Year

How the animal was detected or observed or if a Aural, Capture, 

 reproductive site for a specific animal was found. Visual, Visual

and Aural,

Voucher Specimen

The name of the person who observed or collected 

 the animal.

The qualifications of the observer to accurately identify Experienced, Limited

 the correct species associated with observation. Experience, Taxon

 Education and field experience generally define the level. Expert, Unknown

The number of individuals with which the detection > 0

 is associated. For example, if a single GIS point is 

 associated with a pair of owls, then “2” would be 

 entered into this field.

Sex of observed individual. Male, Female, 

Unknown, 

Mixed Group

The reproductive status of the animal detected, Non-Reproducing,

 if known. This could include labeling a pair as Reproducing, Unknown,

 reproductive, or that there are young present With Young

 (inferring that the pair is successfully reproducing).

The description of the size and relationship Family Group,

 of the animal group detected. Pair, Unknown

The latitude of the detection in decimal Not null

 degrees (NAD27 datum).

The longitude of the detection in decimal Not null

 degrees (NAD27 datum).

Description of the sighting/collection location.

Horizontal locational accuracy (meters), if known.

Identifies whether the location was plotted Yes, No, Unknown

 with confidence within 5 mi. of the observation.

The comments that the observer collected 

 during the detection, including key words. 

 A key word or key phrase could precede a 

 portion of text.

Original source of the data (collector/collection, Not null

 atlas, or survey name).

Name of the original shapefile from which the Not null

 data was consolidated.

Identification number from the original shapefile. Not null
12 USDA Forest Service RMRS-GTR-152. 2005

It is difficult to use all of the geospatial data simultaneously in a GIS because of the large volume of records. 

To facilitate easy display, query, and analysis in the GIS, 

we have sub-divided the geospatial species occurrence 

data into a number of individual GIS data layers organized by various themes. If one is interested in viewing 

the records by major taxonomic divisions (birds, mammals, herptiles, or butterflies), individual geospatial 

datasets are available for each group. We have further 

subdivided the data by state (AZ and NM) to reduce the 

volume of data in each geospatial layer. We have also 

provided another version of the species occurrence data 

that is further subdivided by the source from which we 

obtained the data. This version of the data is useful for 

those interested in viewing only the data from a specific source (for example, the Museum of Southwestern 

Biology, the North American Breeding Bird Survey). A 

complete list of the breakdown of each geospatial layer 

is provided on the accompanying DVD.

Data Layer Accuracy Documentation

Errors associated with the spatial representation of 

environmental attributes, species distributions, and observed species locations are an inherent characteristic 

of geospatial databases. Such errors can be caused by a 

misclassification of an attribute that is mapped (sometimes called “classification error,” “attribute error,” or 

“thematic error”), or a misregistration of its location 

(sometimes called “location error,” “spatial error,” or 

“positional error”). With increasing accessibility to geospatial data, there is a growing concern with accuracy 

and how to assess its effects in the decision-making process (Goodchild and Gopal 1989; Jansen and van der 

Wel 1994; Hunsaker and others 2001). Errors will never be eliminated, but they should be quantified to some 

degree to give the users of such databases a sense of the 

uncertainty associated with decisions based on these 

data (Flather and others 1997; Dean and others 1997).

Because our efforts to compile environmental and 

physical data layers (in the Basecovs directory) focused 

on existing data, we were unable to quantify accuracy 

independently. Rather, we relied on the metadata documentation associated with each data layer to provide 

the accuracy information, when available. To assist users in evaluating the uncertainty associated with these 

data, we have compiled a summary of the availability of 

accuracy information provided in the source metadata 

files contained on the DVD (see table 4). For the most 

part, comprehensive accuracy information was sparse—

about 30 percent of the files had metadata concerning 

both attribute and positional accuracy. Nearly 55 percent 

of the files had metadata on positional accuracy alone. 

In the absence of accuracy information, users of those 

particular data files will need to be cautious with their 

interpretation. We also recommend that users contact 

the original data providers to obtain the most recent accuracy information if accuracy issues are an important 

concern.

For the species observation data, we attempted to 

characterize the locational accuracy associated with 

each point in two ways (see description of LOCATION_

ACCURACY and LOCATION_CONFIDENCE in table 

2). First, and if known, the horizontal accuracy of the 

point location was recorded in meters. Second, and most 

commonly, we subjectively ranked our confidence that 

the location was plotted within 5 miles (8 km) of the true 

location. We did not attempt to characterize the attribute 

accuracy of the species location records. In this case, attribute accuracy refers to the likelihood that the species 

was identified correctly. Such an assessment could be 

accomplished by randomly selecting voucher specimens 

and confirming the identification, but such an evaluation 

was beyond what was budgeted for this effort. For those 

location records that did not actually collect a specimen, 

attribute accuracy can not be assessed. For these reasons, 

we assumed that all species identifications were recorded without error. We did verify species scientific names 

for nomenclature accuracy against two sources: the 

Integrated Taxonomic Information System (ITIS 2002) 

and NatureServe (NatureServe Explorer 2002). For additional details related to data quality information on the 

species location data see our metadata documentation in 

Appendix B under Attribute_Accuracy and Positional_

Accuracy.

Data Applications

The digital species database can be used to address 

a variety of management issues. The simplest applications could be descriptive in nature and could be used by 

managers to qualitatively or quantitatively assess species resources within an area. These descriptions could 

then be used to set the biodiversity context within which 

more complicated analyses could be conducted to address resource management issues that could potentially 

affect species conservation. We illustrate the use of the 

database with three examples: (1) the first involves the 

process of generating location information for a single 

species; (2) the second uses predicted species ranges to 

describe regional patterns of species richness across the 

Southwest; and (3) the last focuses on a specific, and 

contentious, resource management issue important to 
USDA Forest Service RMRS-GTR-152. 2005 13

species conservation policy (Fleischner 1994; Brown 

and McDonald 1995), namely the potential influence of 

domestic grazing on biodiversity.

Species Observation Locations and 

Predicted Range Maps

A simple and useful application of the species database is the geographic depiction of species point 

locations or the delineation of the species’ predicted 

range map. Here we provide examples from each taxon 

in the database. For selected terrestrial vertebrates, all 

observation locations within Arizona and New Mexico 

are presented simultaneously with the predicted vertebrate habitat map (fig. 2). Species point locations were 

mapped by querying the species observation locations 

data and plotting the point location coordinates for the 

species of interest. These were then overlayed on the 

predicted vertebrate habitat maps that were created by 

the Arizona and New Mexico GAPs. Although we have 

illustrated the utility of these data by plotting the actual predicted habitat and observation locations, potential 

users of these data should realize that summary maps of 

species occurrence linked to other geographic partitions 

such as counties, watersheds, and U.S. Forest Service 

Ranger Districts are also easily generated.

Federal land management agencies maintain lists of 

species that are of conservation concern (U.S. Forest 

Service 1995), including species listed as threatened 

or endangered under the Endangered Species Act (PL205, 87 Stat. 884, as amended). The geographic pattern 

of known and potential occurrence of such species (for 

example, see fig. 2a) is fundamental to setting conservation priorities and identifying areas where resource-use 

conflicts may occur. However, the utility of these maps 

goes beyond the simple description of where the species 

has records of occurrence or where the species may potentially occur. Such maps also give managers a sense 

of the uncertainty surrounding the potential occurrence 

of the species within a geographic area of interest. The 

density and distribution of points within the predicted range provide an indication of how well the species 

range has been represented in location samples. Some 

species, like the cactus mouse (Peromyscus eremicus), 

have a predicted distribution that appears to be well 

sampled based on the location of observation records, 

and few location records fall outside the predicted range 

(fig. 2b). Conversely, the distribution and point locations 

for the milk snake (Lampropeltis triangulum) indicate 

that significant portions of the predicted range have no 

recorded occurrences (fig. 2c), whereas for the Northern 

leopard frog (Rana pipiens) notable clusters of locations 

fall outside the predicted range of the species (fig. 2d). 

In these latter two cases our uncertainty about where the 

species occurs and its potential distribution is greater 

than for species where point locations are well-distributed throughout and within the predicted range. Spatially 

explicit depiction of these uncertainties can be useful 

in targeting future inventory efforts to verify the occurrence of species in a particular location (see Fleishman 

and others 2001: 1682), or in identifying environmental attributes (that is, information associated with the 

point locations that fall outside of the predicted range) 

that have not been captured in existing habitat-suitability models. 

Since we lacked predicted range maps for butterflies, 

we only display point locations for this taxonomic group. 

Observation locations for butterfly species suggest that 

the California sister (Adelpha bredowii) is wide-ranging (fig. 3a), whereas the sunrise skipper (Adopaeoides 

prittwitzi) has a more restricted range within Arizona 

and New Mexico (fig. 3b). Although we lack information on the potential distribution of butterfly species, 

these data highlight another use of generating speciesspecific location maps—namely the development of 

empirical distribution models that would allow managers to predict the geographic range of species. Point 

location data that are geographically associated with 

environmental factors thought to determine a species’ 

occurrence can be used to generate statistical models to 

predict species distributions (Peterson 2001; Hirzel and 

others 2002; Lehmann and others 2002; Reese and others 2005). Such approaches to predicting areas suitable 

for occupancy are essential if species that we know little about are to be adequately considered in biodiversity 

conservation planning (Fleishman and others 2001).

Regional Patterns of Biodiversity 

Across the Southwest

Distributions of species are inherently heterogeneous, 

and as such exhibit spatial structure on a landscape. 

One manifestation of this spatial structure is that some 

areas support more species than other areas. This pattern 

can be observed when all species, or a subset of species, 

within some locale share some attribute of conservation 

interest (for example, rare species, threatened species, or 

endemic species). Given spatial structure in species distributions, an obvious resource management question in 

the face of competing uses for a finite land base is which 

geographic areas should be managed to conserve biodiversity (Reid 1998).

One common approach to setting conservation priorities is to focus on those areas with high biological value 
14 USDA Forest Service RMRS-GTR-152. 2005

Table 4—Summary of data accuracy information provided in the source metadata documentation for data layers in 

Basecovs directory compiled in our geospatial database.

Data file Description Source 

azallot U.S. Forest Service grazing U.S. Forest Service 

 allotment boundaries 

azcounty County boundaries Arizona GAP 

azlnname Index for named linear features U.S. Geological Survey 

azplaces Census populated places AZ Land Resource Information System 

azptname Index for named point features U.S. Geological Survey 

azroads Major roadways AZ Land Resource Information System 

azstate State boundary AZ Land Resource Information System 

aztrs Township-range-section lines AZ Land Resource Information System 

aztwnrng Township-range lines AZ Land Resource Information System 

azusfs U.S. Forest Service national U.S. Forest Service 

 forest boundaries 

azsoils Statewide soils AZ Land Resource Information System 

azspring Springs U.S. Geological Survey 

azstream Permanent and intermittent EPA BASINS database 

 streams

riparian Riparian vegetation AZ Land Resource Information System 

azdem Elevation U.S. Geological Survey ESIC 

azveg Vegetation Arizona GAP 

nmallot U.S. Forest Service grazing U.S. Forest Service 

 allotment boundaries 

nmcounty County boundaries New Mexico GAP 

nmlnname Index for named linear features U.S. Geological Survey 

nmplaces FIPS incorporated places NM Resource Geographic Information 

 System Program

nmptname Index for named point features U.S. Geological Survey 

nmroads Major roadways NM Resource Geographic Information 

 System Program

nmstate State boundary New Mexico GAP 

nmtwnrng Township-range lines U.S. Geological Survey ESIC 

nmusfs U.S. Forest Service national U.S. Forest Service 

 forest boundaries 

nmcaves Caves NM Resource Geographic Information 

 System Program

nmsoils Statewide soils New Mexico GAP 

nmspring Springs U.S. Geological Survey 

nmstream Permanent and intermittent EPA BASINS database 

 streams

nmdem Elevation U.S. Geological Survey ESIC 

nmveg Vegetation New Mexico GAP 

a

 Yes—accuracy information is given in the source metadata documentation; No—accuracy information is unavailable.

b

 Metadata files also available in text format (*.txt).
USDA Forest Service RMRS-GTR-152. 2005 15

Attribute Positional Metadata

accuracya accuracya fileb Contact information

No No azallot.doc M. Candace Bogart, GIS Program Manager, Southwestern

Region, cbogart@fs.fed.us

No Yes azcounty.doc http://sdrsnet.srnr.arizona.edu/index.php

Yes Yes azlnname.doc http://geonames.usgs.gov/

No No azplaces.doc http://www.land.state.az.us/alris/

Yes Yes azptname.doc http://geonames.usgs.gov/

Yes Yes azroads.doc http://www.land.state.az.us/alris/

No Yes azstate.doc http://www.land.state.az.us/alris/

No Yes aztrs.doc http://www.land.state.az.us/alris/

No Yes aztwnrng.doc http://www.land.state.az.us/alris/

No No azusfs.doc M. Candace Bogart, GIS Program Manager, Southwestern

Region, cbogart@fs.fed.us

No No azsoils.doc Not available

No Yes azspring.doc http://geonames.usgs.gov/

No Yes azstream.doc http://www.epa.gov/docs/ostwater/BASINS/

No Yes riparian.doc http://www.land.state.az.us/alris/

Yes Yes azdem.doc http://edc.usgs.gov/products/elevation/dem.html

No No azveg.doc http://sdrsnet.srnr.arizona.edu/index.php

No No nmallot.doc M. Candace Bogart, GIS Program Manager, Southwestern

Region, cbogart@fs.fed.us

No No nmcounty.doc http://www.gap.uidaho.edu/ or http://rgis.unm.edu/

Yes Yes nmlnname.doc http://geonames.usgs.gov/

No No nmplaces.doc http://rgis.unm.edu/

Yes Yes nmptname.doc http://geonames.usgs.gov/

No Yes nmroads.doc http://rgis.unm.edu/

No No nmstate.doc http://www.gap.uidaho.edu/ or http://rgis.unm.edu/

Yes Yes nmtwnrng.doc http://geonames.usgs.gov/

No No nmusfs.doc M. Candace Bogart, GIS Program Manager, Southwestern

Region, cbogart@fs.fed.us 

No No nmcaves.doc http://rgis.unm.edu/

No No nmsoils.doc http://www.gap.uidaho.edu/ or http://rgis.unm.edu/

Yes Yes nmspring.doc http://geonames.usgs.gov/

No No nmstream.doc http://www.epa.gov/docs/ostwater/BASINS/

Yes Yes nmdem.doc http://edc.usgs.gov/products/elevation/dem.html

No No nmveg.doc http://www.gap.uidaho.edu/ or http://rgis.unm.edu/
16 USDA Forest Service RMRS-GTR-152. 2005 Figure 2—Species occurrence locations and predicted range map in Arizona and New Mexico for the (a) spotted owl (Strix occidentalis)1, (b) cactus mouse (Peromyscus ere- micus), (c) milk snake (Lampropeltis triangulum), and (d) northern leopard frog (Rana pipiens). Predicted range maps were created by the Arizona and New Mexico GAPs. 1 Note: The subspecies occurring in this region [S. o. lucida] is listed as threatened under the Endangered Species Act.
USDA Forest Service RMRS-GTR-152. 2005 17

Figure 3—Point locations for two butterfly species in Arizona and New Mexico: (a) California sister (Adelpha bredowii), and 

(b) sunrise skipper (Adopaeoides prittwitzi).
18 USDA Forest Service RMRS-GTR-152. 2005

(Kareiva and Marvier 2003). Myers and others (2000) argued that greater conservation benefits could be realized 

if efforts were focused in those areas where endemics 

were concentrated, areas that they termed “hotspots.” 

Since Myers’ specific usage with endemism, the term 

hotspot has become generalized to refer to an area, or 

set of areas, that rank high on any number of ecosystem 

attributes including species richness (Scott and others 

1993), threatened or endangered species (Dobson and 

others 1997; Flather and others 1998), imperiled species 

(Chaplin and others 2000), or indicators of ecosystem 

condition (Hof and others 1999).

For purposes of illustration we define hotspots using 

a species-richness criterion. This criterion has served 

as the basis for setting conservation priorities under an 

approach generally referred to as gap analysis (Burley 

1988) and has been formally implemented as part of the 

U.S. National GAP (Scott and others 1993). Using the 

Arizona and New Mexico GAP maps of predicted bird 

distributions, we present an example that extends the single-species focus of the previous case study to one that 

focuses on attributes associated with a collection of species. Arizona predicted species distribution maps were 

converted from vector (that is, polygon) to raster (that 

is, grid) format with cell resolutions of 100 m to correspond to the New Mexico GAP maps. Our approach was 

to simply overlay the distributions of all avian species 

in the database and compute the count of species whose 

range fell within each 100 x 100-m grid cell. Certainly 

other measures of biodiversity could have been computed and plotted (for example, counts of Forest Service 

Sensitive species, measures of biotic similarity between 

sites [see Cook and others 2000]), and we could have 

considered the relationship between biodiversity and 

other ancillary data (for example, topographic variation, 

land use, and land cover). Our choice to focus on species 

richness as a measure of biodiversity is not meant to limit the examination of other measures. Rather, we hope 

others will be motivated to explore alternative measures 

of biodiversity that can be estimated with species occurrence information (see Magurran 1988).

Avian species hotspots were defined as those grid cells 

falling within the upper 10th percentile of the frequency 

distribution of bird richness among all grid cells (that is, 

those 10 percent of cells with the greatest bird richness) 

within each state. Similarly, avian species coldspots 

were those grid cells falling within the lower 10th percentile. A plot of hotspot cells indicates a strong pattern 

of association between high bird richness and montane 

forest and riparian habitats, with the Mogollon Plateau 

being a prominent physiographic feature that is discernable in the avian hotspot range (fig. 4). It is interesting, 

although perhaps not surprising, that National Forest 

System lands intersect with 44 percent of the hotspot 

map, while only intersecting 5 percent of the coldspot 

map (fig. 4). This suggests that management of natural 

resources on Forest Service lands has the potential to affect a significant proportion of those areas supporting 

the greatest local richness of bird species. This pattern 

varies taxonomically, with other species groups showing 

the converse pattern, namely richness hotspots occurring 

predominantly outside National Forest lands and coldspots occurring in association with National Forests. For 

example, only 7 percent of herptile richness hotspots intersect with National Forest lands compared to nearly 20 

percent of the richness coldspots (fig. 5).

Although we have stated previously that our intent 

with these case studies is to be illustrative, we do want to 

point out that a number of approaches could have been 

used to explore regional patterns of diversity among 

Southwestern taxa. For example, we could have used 

the BBS data to empirically interpolate bird distributions (sensu Maurer 1994; Price and others 1995) as an 

alternative to the Arizona and New Mexico GAP habitat affinity models. However, since we were comparing 

across taxa in this case example (birds and herptiles), it 

was to our advantage to compare regional richness patterns based on a consistent methodology. Data like the 

BBS are, at the moment, unique to birds. However, it 

would be an interesting analysis to compare, within a 

single taxon, species richness patterns using alternative 

methodologies. Diversity patterns derived from different methodologies that matched would give resource 

managers confidence that the patterns observed from 

any one approach could not be dismissed as an artifact of 

the specific methodology used.

Biodiversity and Domestic Grazing

In this example, we use the biodiversity database to 

explore the relationship between the regional distribution 

of the biota (at least as reflected in terrestrial vertebrates) 

and management of domestic grazers. Our example is 

motivated by concern for the influence of ungulate 

grazing on biodiversity in arid southwestern ecosystems (Memorandum of Understanding between USDA, 

Forest Service, Region 3-Southwestern Region and 

the USDA, Forest Service, Rocky Mountain Research 

Station, unpublished document). Several investigators 

have reported on the high concentration of species in the 

Southwest that are of conservation concern (see Dobson 

and others 1997; Chaplin and others 2000) and grazing 

has been cited as a prominent factor contributing to the 

formal listing of species as threatened or endangered 
USDA Forest Service RMRS-GTR-152. 2005 19Figure 4—(a) Hot spots (upper 10 percent), and (b) cold spots (lower 10 percent) in avian species richness for Arizona and New Mexico based on predicted range maps created by the Arizona and New Mexico GAPs.
20 USDA Forest Service RMRS-GTR-152. 2005Figure 5—(a) Hot spots (upper 10 percent), and (b) cold spots (lower 10 percent) in herptile species richness for Arizona and New Mexico based on pre- dicted range maps created by the Arizona and New Mexico GAPs.
USDA Forest Service RMRS-GTR-152. 2005 21

in this region (Flather and others 1998). The effects of 

grazing, from both domestic and native herbivores, on 

biodiversity is an extremely complex issue. Some species and ecological processes clearly benefit from the 

disturbances associated with grazing while others do not 

(Brown and McDonald 1995). However, evidence from 

an extensive review of the literature suggests that many of 

the plant communities in the Southwest did not coevolve 

with large generalist herbivores (Milchunas, in press). 

This suggests that biotic communities in this region may 

be particularly sensitive to grazing and that sustainable 

use of forage resources will require careful management.

For this case study we focus on displaying biodiversity patterns among those species that are thought to 

respond in a negative way to grazing by domestic and 

native herbivores. We relied on the classification reported in Zwartjes and others (in press) to identify the list of 

species that expert panels judged to be negatively affected by grazing. Certainly, the process outlined in Zwartjes 

and others could be repeated for species thought to benefit from grazing. However, given the overall objective 

of reducing resource management conflicts between 

grazing and biodiversity conservation (Memorandum of 

Understanding between USDA, Forest Service, Region 

3-Southwestern Region and the USDA Forest Service, 

Rocky Mountain Research Station, unpublished document), our focus in this case study is on describing the 

geographic pattern of occurrence among those species 

whose distribution and abundance is thought to be harmed 

by grazing activities. Concentrations of species that are 

sensitive to grazing activities in certain geographic areas 

would allow managers to target where grazing management may require attention to reduce species impacts.

For each grazing allotment in the Southwestern 

Region (see Basecovs/HumanMade/AZ/azallot.shp and 

Basecovs/HumanMade/NM/nmallot.shp), we counted 

the number of species that were predicted to potentially occur within that allotment. We then cross-referenced 

this list of species against those species that are thought 

to respond negatively to grazing (see Zwartjes and others [in press]). From these two lists, we computed the 

percentage of species that potentially occur in each 

allotment that were thought to be negatively affected by 

grazing.

The distribution of grazing allotments that support a 

high percentage of terrestrial vertebrates thought to be 

negatively impacted by grazing shows no strong geographic pattern. There are clusters of allotments distributed 

throughout the Southwestern Region that support a high 

percentage of species believed to be negatively impacted 

by grazing (fig. 6a). There is some evidence that the likelihood of encountering a grazing allotment supporting a 

high percentage of vertebrate species judged to be grazing-impacted covaries with latitude, but the nature of that 

relationship depends on the state and taxonomic group. In 

Arizona, the high percentage classes of grazing-impacted 

species appear to occur more frequently in southern allotments (that is, negatively related to latitude) across all 

taxa (fig. 6b-d). Conversely, only New Mexico birds show 

a higher incidence of high percentage grazing-impacted classes in southern allotments (fig. 6b); New Mexico 

mammals (fig. 6c) and herptiles (fig. 6d) show a greater 

incidence of high percentage grazing-impacted classes in 

northern allotments. Estimated Spearman’s rank correlations2 (rs ) between latitude of each grazing allotment 

centroid and the percentage of vertebrates thought to be 

impacted by grazing confirm these visual patterns (table 

5). It is doubtful that latitude per se is the causative factor behind the manifestation of these patterns. Rather, 

other ecological gradients including precipitation, plant 

composition, productivity, and evolutionary history of 

grazing likely covary with latitude in complex ways (see 

Milchunas [in press] for a discussion of the interactions 

between these factors and biodiversity response to grazing) to affect how many species within the pool may be 

negatively impacted by domestic grazers.

It is also possible that the percentage of species judged 

to be negatively impacted by grazing is correlated with 

the overall number of species that could potentially occur within that allotment. Highly productive allotments 

may simultaneously support high numbers of species 

and also support higher densities of large ungulate herbivores—a combination that may predispose these areas 

to grazing/biodiversity conflicts. In Arizona, terrestrial 

vertebrates in general, and birds and mammals in particular, showed a positive relationship between overall 

taxon richness and the percentage of grazing-impacted 

species (table 5). We must note, however, that the magnitude of the correlations indicates a weak pattern of 

association. In New Mexico, overall taxon richness appears to be negatively correlated with the percentage of 

species thought to be sensitive to grazing (table 5). This 

negative relationship is particularly strong for herptile 

species (rs = -0.69). The fact that the magnitude and direction of the response changes between the two states 

likely reflects the complex interaction between the ecological factors that affect biodiversity response to grazing (see Milchunas in press).

This case study has shown that when basic distributional data are linked with other life history databases 

2

 Spearman’s rank correlation was estimated since it does not assume 

an underlying bivariate distribution and it offers a more general 

measure of association between two variables when that relationship is expected to be monotonic but not necessarily linear.
22 USDA Forest Service RMRS-GTR-152. 2005 Figure 6—The percentage of vertebrate species [(a) all terrestrial vertebrates, (b) avian, (c) mammal, and (d) herptile] thought to be negatively impacted by grazing ac- tivities (as estimated by Zwartjes and others [in press]) within U.S. Forest Service grazing allotments in the Southwestern Region. We assigned equal areas of grazing allotments to each percentage class based on an independent analysis of Arizona and New Mexico. This explains why the class cut-points vary between states (see 

legend). 
USDA Forest Service RMRS-GTR-152. 2005 23

like that represented in Zwartjes and others (in press), 

then simple, yet powerful, analyses can be accomplished. 

Our analysis was useful in identifying those grazing 

allotments where the potential conflicts between biodiversity conservation and grazing may be substantial and 

deserving of critical consideration in land management 

planning activities. Similar analyses could be completed 

on other resource management activities (for example, 

timber harvesting, fire, recreation) so long as reliable information on species responses to these activities exists 

or could be compiled.

Conclusions

Documenting biodiversity patterns over broad geographic areas as a basis for guiding natural resource 

management is a conceptually simple idea. This simplicity belies the challenges posed by inadequate 

species occurrence inventories for most groups of 

species (Lubchenco and others 1991; Lawler 2001). 

Certainly, data that could contribute to determining 

broad-scale distributional patterns exist for a subset of 

well-studied taxa; however, even these data are not readily available (Brown and Roughgarden 1990; Pennisi 

2000; Peterson 2001; Cracraft 2002). A common strategy for overcoming the dearth of taxonomic inventories 

has been to predict species distributions based on known 

or hypothesized habitat affinity relationships (Scott and 

others 1993; Fleishman and others 2001).

In this report we have compiled both kinds of information. We have consolidated species location records 

from a systematic search of state and federal agencies, 

institutions, and individual collections along with extant 

predictions of species ranges from state GAP databases. 

We used these data in three case examples to illustrate 

how basic occurrence information could be used to analyze the distributional patterns of selected species, 

to document geographic patterns of richness among 

suites of species, and to examine the occurrence pattern 

among species whose populations are thought to be negatively affected by grazing activities in the Southwest. 

Furthermore, we have compiled extensive layers of ancillary data that can be used in conjunction with species 

location information to further our understanding of the 

factors that limit the occurrence of species across broad 

geographic areas. Use of these data in this capacity was 

beyond the scope of this report, but we are involved in 

other efforts to further managers’ capacity to describe 

and analyze biodiversity (for example, Reese and others 

2005; Shriner and others, in preparation).

This effort represents an opportunistic compilation 

of extant data; there was no primary data collection on 

our part. For this reason, a number of caveats warrant 

remark. First, conservation managers and researchers 

must realize that this database should not be treated as 

the definitive source for biodiversity in the Southwest. 

New sources of location records will undoubtedly be 

discovered, and new collection and sighting records 

will be compiled. Thus, this database should be updated frequently and evolve as new information becomes 

Table 5—Spearman’s rank correlations (rs) between the percentage of species thought to be impacted 

by grazing (as estimated by Zwartjes and others [in press]), latitude, and overall taxon richness 

among grazing allotments in the Southwestern Region. The latitude of a grazing allotment was based 

on the geographic centroid of the allotment.

Taxonomic group Spearman’s correlation between latitude 

and percent grazing-impacted species

Arizona New Mexico

rs P rs P

Terrestrial vertebrates -0.120 0.001 0.143 <0.001

Birds -0.369 <0.001 -0.456 <0.001

Mammals -0.271 <0.001 0.224 <0.001

Herptiles -0.182 <0.001 0.335 <0.001

Spearman’s correlation between overall taxon richness 

and percent grazing-impacted species

Arizona New Mexico

rs P rs P

Terrestrial vertebrates 0.405 <0.001 -0.171 <0.001

Birds 0.403 <0.001 -0.062 0.083

Mammals 0.506 <0.001 0.350 <0.001

Herptiles 0.022 0.552 -0.690 <0.001
24 USDA Forest Service RMRS-GTR-152. 2005

available. As such, the database should best be treated 

as an adaptive source of biodiversity data with current 

patterns driving future inventories that will extend and 

verify our present understanding of biodiversity patterns 

across this two-state region.

Second, users of the database must recognize that 

there are multiple sources of error in the data. There is 

a tendency for users of geographic information to treat 

spatially explicit information as error free (Monmonier 

1991). However, specimens can be misidentified, location coordinates can be recorded incorrectly, and 

predicted distributions may be based on erroneous 

habitat affinities, all of which can lead to errors in geographically explicit conservation recommendations 

(Flather and others 1997). To a certain extent, the comparison of predicted species ranges with location records 

provides an initial indication of distributional uncertainty for each species as we have discussed. However, there 

is still a need for error analysis routines that will provide resource planners with an indication of how robust 

spatially explicit conservation recommendations will be 

under varying levels of error.

Finally, it is well-known that collection and sighting 

records are usually not based on well-designed sampling 

surveys across a landscape. Consequently, sampling biases can confound the development of tenable species 

distribution models. Although algorithms for predicting 

species distributions that are robust to such sampling biases are being investigated (Peterson 2001; Hirzel and 

others 2002; Reese 2003), use of this database to construct inferential models must explicitly consider effects 

associated with potentially biased sampling effort. 

These caveats notwithstanding, the information contained in this database is fundamental to any broad-scale 

study of biodiversity patterns. It represents the first generation of what should be an evolving database. We 

purposefully focused broadly in our compilation of species location data, predicted distributions, and ancillary 

environmental data, rather than restricting our efforts 

to that subset of species relevant to a specific resource 

management issue. For this reason, the data should be 

applicable to a broad set of resource management issues.

Acknowledgments

We would like to thank the U.S. Forest Service, 

Southwestern Region, the Wildlife, Fish, and Rare Plants 

Staff, and the National Science Foundation (Award #:

DBI-0109969) for funding this project. In particular, 

we would like to thank Don DeLorenzo, U.S. Forest 

Service, Southwestern Region, Director of the Wildlife, 

Fish, and Rare Plants Staff, and Richard Holthausen, 

National Wildlife Ecologist for facilitating funding and 

general administration of the project. 

A special thanks is due to numerous individuals and 

organizations that provided data sets including: Bruce 

Thompson, USGS, New Mexico Cooperative Fish 

and Wildlife Research Unit (now with New Mexico 

Department of Game and Fish); William Halvorson, 

USGS, Sonoran Desert Research Station; Kathryn 

Thomas, USGS, Colorado Plateau Research Station; 

Michael Bogan, USGS, Arid Lands Field Station; 

Cindy Ramotnik, USGS, Arid Lands Field Station; 

William Gannon, University of New Mexico, Museum 

of Southwestern Biology; Richard Bailowitz, Tucson, 

AZ; George Bradley, Department of Ecology and 

Evolutionary Biology, University of Arizona; Steve 

Cary, New Mexico Energy, Minerals and Natural 

Resources Department; Arthur Harris, University of 

Texas at El Paso; Joseph Bopp, New York State Museum; 

Eric Rickart, The University of Utah, Utah Museum of 

Natural History; Maria Rutzmoser, Harvard Museum 

of Natural History; Janet Braun and Laurie Vitt, Sam 

Noble Oklahoma Museum of Natural History; Philip 

Myers and Stephen Hinshaw, University of Michigan, 

Museum of Zoology; Bill Stanley, The Field Museum 

of Natural History, Chicago; George James Kenagy and 

John Rozdilsky, The Burke Museum of Natural History 

and Culture; Suzanne McLaren, Carnegie, Museum of 

Natural History; Britney Hager, The Dallas Museum 

of Natural History; Laurie Wilkins, Florida Museum of 

Natural History; Christine Mayer and Joyce Hofmann, 

Illinois Natural History Survey; Mark Hafner, Louisiana 

Museum of Natural History; Lisa Gatens and Mary Clark, 

North Carolina Museum of Natural Sciences; Jacques 

Gauthier and Lawrence Gall, Yale Peabody Museum; 

Dean Hendrickson and Jessica Rosales, Texas Memorial 

Museum; Rosanne Humphrey, University of Colorado, 

Museum of Natural History; The USGS Arizona and 

New Mexico GAPs; New Mexico Department of 

Game and Fish; and the New Mexico Natural Heritage 

Program. For their tireless data entry efforts we thank 

Jennifer Courtemanche, Kathy Flannigan, Amanda 

Byrd, Adrian Brown, Michael Woodruff, Patrick Burke, 

Marian Flanagan, Karen Weinbaum, Dan Neubaum, 

Gordon Mullen, Samantha Beckers, and Susan Shriner. 

We would also like to thank Rosamonde Cook, National 

Park Service, for her early efforts at database organization and data acquisition.

For their constructive comments and review of an 

earlier draft of this report we thank Andrew Hansen, 

Montana State University; Greg Hayward, USDA, Forest 
USDA Forest Service RMRS-GTR-152. 2005 25

Service, Rocky Mountain Region; Kathryn Thomas, 

USGS, Colorado Plateau Research Station, and Carolyn 

Hull Sieg, USDA, Forest Service, Rocky Mountain 

Research Station.

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Colorado Plateau Research Station.

U.S. Forest Service. 1995. Forest Service Manual FSM2670.5. 

Wildlife, Fish, and Sensitive Plant Habitat Amendment 

2500-96-6. Washington, D.C.: U.S. Forest Service.

United States Geological Survey. 2001. North American 

Breeding Bird Survey Home page [Online]. Available: 

http://www.mp2-pwrc.usgs.gov/bbs [23 November 2002].

United States Geological Survey. 2002. Bird Banding Lab 

web page, Patuxent Wildlife Research Center [Online]. 

Available: http://www.pwrc.usgs.gov/bbl/ [23 February 

2003].

Wheeler, Q. D. 1995. Systematics, the scientific basis for 

inventories of biodiversity. Biodiversity and Conservation. 

4: 476-489.

Wilson, E. O. 1988. The current state of biological diversity. 

In: Wilson, E. O., ed. Biodiversity. Washington, D.C.: 

National Academy Press: 3-18.
USDA Forest Service RMRS-GTR-152. 2005 27

Winker, K. 1996. The crumbling infrastructure of biodiversity: 

the avian example. Conservation Biology. 10: 703-707.

Zwartjes, P. W.; Cartron, J.–L. E.; Stoleson, P. L. L.; 

Haussamen, W. C.; Crane, T. E. [In press]. Assessment 

of native species and ungulate grazing in the Southwest: 

terrestrial wildlife. Gen. Tech. Rep. RMRS-GTR-000. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 00 p.
28 USDA Forest Service RMRS-GTR-152. 2005

The enclosed DVD data diskette contains the entire 

GIS database described in this report, plus associated 

tabular data. The DVD is organized into four major types 

of data: base coverages, observed species locations, predicted species ranges, and estimated species richness 

hotspot data, which are structured under the four main 

directories on the DVD (see fig. A1). A complete listing 

of the contents of each directory and its sub-directories 

is provided in table A1.

Data Formats

A number of data file formats have been used to represent the various geospatial and tabular datasets. The 

geospatial data are compatible with the ESRI family of GIS products, namely ArcView 3.x, ArcGIS 8.x, 

and ArcGIS 9. The tabular data are compatible with 

the Microsoft Office 2000 (or newer) suite of business 

software, namely Excel and Access. Where practical, 

tabular data are also provided in space-delimited ASCII 

text files to ensure compatibility for any user.

Two GIS file formats are used for the GIS datasets. First, all vector-based GIS data are provided in the 

widely used ArcView shapefile format. Second, all raster-based GIS data are provided in ArcInfo raster GRID 

layer format. Both formats can be accessed directly from 

the DVD.

Most of the base map layers (Basecovs directory) 

and all of the species observation point data (Observed

directory) have been provided in the shapefile format. 

The shapefile data structure actually uses multiple files 

to store the components of each individual GIS map 

layer (for example, geometric information is stored in 

a .shp file, tabular attribute data are stored in the associated .dbf file). For this reason, it is recommended 

that shapefiles should not be copied from the DVD individually using anything other than a GIS application. 

For the sake of convenience, the tabular attribute data 

for all the species observation data also have been provided in the Microsoft Access 2000 relational database 

format. Due to the large size of the database, the file 

Appendix A: DVD content description

had to be compressed (zip format) to fit on the DVD. 

The zip archive of the database can be found within the 

Observed/AccessDB directory as the file named spprecords.mdb.zip.

All of the predicted species distributions and species 

richness (for example, biodiversity hotspot) maps have 

been provided in the ArcInfo raster GRID format. The 

digital elevation data and the vegetation maps have also 

been provided in the GRID format. The ArcInfo GRID 

data structure uses multiple files (and directories) to 

represent a single raster map layer. As such, it is recommended that one should never attempt to copy a single 

raster layer from the DVD except with a GIS application. Otherwise, it is possible that the raster layer may 

become corrupted.

Metadata

A complete, FGDC compliant metadata document 

has been provided for the species observation GIS database in Appendix B. This document is also generally 

applicable to the equivalent non-spatial tabular version 

of the database (that is, the Microsoft Access database), 

but one should obviously be aware that the format of 

the data differs. The remainder of the GIS datasets were 

obtained through other sources and are provided in an 

unmodified form, except that each geospatial dataset 

was transformed into a common spatial reference system (Alber’s Equal Area projection, NAD27 datum). 

Accordingly, we provide the unmodified metadata document that was provided with these datasets when they 

were obtained. The metadata documents (if available) 

for each geospatial dataset are provided in the same directory as the geospatial layer (shapefile or grid) on the 

DVD. Note that we have made no attempt to verify the 

accuracy of these metadata documents nor have we attempted to update them in any way. The original sources 

for each geospatial dataset have been provided in table 

A1 and on the DVD, and users of this report are encouraged to contact the original data provider for updated 

datasets and/or metadata documentation.
USDA Forest Service RMRS-GTR-152. 2005 29

Data DVD

Basecov

Natural

AccessDB

Source

State

Taxon

Birds

Butterfly

Birds

Butterfly

Herptiles

Mammals

Birds Birds

Observed Predicted Richness

Humanmade

Herptiles Herptiles

Herptiles

Mammals

Mammals Mammals

Figure A1—Data structure of the DVD shown as a tree diagram. The four major types of data (base coverages, observed species locations, predicted species ranges, and estimated species richness hotspot data) are organized in the DVD under four 

main directories, which are depicted in the first row of the tree. Sub-directories to each main directory are shown below. Note 

that for the lowest level of each sub-directory above, the data files are stored within additional sub-directories labeled AZ 

(Arizona) and NM (New Mexico). The redundant AZ and NM sub-directory structure is omitted to keep the diagram simple.
30 USDA Forest Service RMRS-GTR-152. 2005 Table A1—Description of the contents of the enclosed data DVD. Directory Sub-directory File name File type Description Source1 . . contents Text and Excel spreadsheet Description of the contents of the DVD (this table) CSU Basecovs . azvegxwalk Text and Excel spreadsheet Cross-walk with GAP vegetation classification CSU . nmvegxwalk Text and Excel spreadsheet Cross-walk with GAP vegetation classification CSU HumanMade/Az azallot ArcView shapefile USFS grazing allotment boundaries USFS R3 HumanMade/Az azcounty ArcView shapefile County boundaries AZ GAP HumanMade/Az azlnname ArcView shapefile Index for named linear features USGS HumanMade/Az azplaces ArcView shapefile Census populated places ALRIS HumanMade/Az azptname ArcView shapefile Index for named point features USGS HumanMade/Az azroads ArcView shapefile Major roadways ESRI HumanMade/Az azstate ArcView shapefile State boundary ALRIS HumanMade/Az aztrs ArcView shapefile Township-range-section lines ALRIS HumanMade/Az aztwnrng ArcView shapefile Township-range lines ALRIS HumanMade/Az azusfs ArcView shapefile USFS national forest boundaries USFS R3 HumanMade/Nm nmallot ArcView shapefile USFS grazing allotment boundaries USFS R3 HumanMade/Nm nmcounty ArcView shapefile County boundaries NM GAP HumanMade/Nm nmlnname ArcView shapefile Index for named linear features USGS HumanMade/Nm nmplaces ArcView shapefile FIPS incorporated places NM GAP HumanMade/Nm nmptname ArcView shapefile Index for named point features USGS HumanMade/Nm nmroads ArcView shapefile Major roadways ESRI HumanMade/Nm nmstate ArcView shapefile State boundary NM GAP HumanMade/Nm nmtwnrng ArcView shapefile Township-range lines USGS ESIC HumanMade/Nm nmusfs ArcView shapefile USFS national forest boundaries USFS R3 Natural/Az azsoils ArcView shapefile Statewide soils ALRIS Natural/Az azspring ArcView shapefile Springs USGS Natural/Az azstream ArcView shapefile Permanent and intermittent streams EPA BASINS Natural/Az riparian ArcView shapefile Riparian vegetation ALRIS Natural/Az azdem ArcInfo grid Elevation USGS ESIC Natural/Az azveg ArcInfo grid Vegetation AZ GAP Natural/Nm nmcaves ArcView shapefile Caves USGS Natural/Nm nmsoils ArcView shapefile Statewide soils NM GAP Natural/Nm nmspring ArcView shapefile Springs USGS Natural/Nm nmstream ArcView shapefile Permanent and intermittent streams EPA BASINS Natural/Nm nmdem ArcInfo grid Elevation USGS ESIC Natural/Nm nmveg ArcInfo grid Vegetation NM GAP Observed AccessDB spprecords.mdb.zip Microsoft Access database All geo-referenced species records Various Source/Birds/Az birds ArcView shapefile Known locations of birds from unique sources in AZ Various Source/Birds/Nm birds ArcView shapefile Known locations of birds from unique sources in NM Various Source/Butterfly/Az butterfly ArcView shapefile Known locations of butterflies from unique sources in AZ Various Source/Butterfly/Nm butterfly ArcView shapefile Known locations of butterflies from unique sources in NM Various Source/Herps/Az herps ArcView shapefile Known locations of herps from unique sources in AZ Various Source/Herps/Nm herps ArcView shapefile Known locations of herps from unique sources in NM Various Source/Mammals/Az mammals ArcView shapefile Known locations of mammals from unique sources in AZ Various
USDA Forest Service RMRS-GTR-152. 2005 31 Source/Mammals/Az mammals ArcView shapefile Known locations of mammals from unique sources in NM Various State/Az azptobs ArcView shapefile Known locations of all terrestrial vertebrates and butterflies in AZ Various State/Az az_dates ArcView shapefile Species observations in AZ with various date formats or dates prior 1900 Various State/Nm nmptobs ArcView shapefile Known locations of all terrestrial vertebrates and butterflies in NM Various State/Nm nm_date ArcView shapefile Species observations in NM with various date formats or dates prior 1900 Various Taxon/Birds/Az azbirds ArcView shapefile Known locations of all birds in AZ Various Taxon/Birds/Nm nmbirds ArcView shapefile Known locations of all birds in NM Various Taxon/Butterfly/Az azbutfly ArcView shapefile Known locations of all butterflies in AZ Various Taxon/Butterfly/Nm nmbutfly ArcView shapefile Known locations of all butterflies in NM Various Taxon/Herps/Az azherps ArcView shapefile Known locations of all amphibians and reptiles in AZ Various Taxon/Herps/Nm nmherps ArcView shapefile Known locations of all amphibians and reptiles in NM Various Taxon/Mammals/Az azmammal ArcView shapefile Known locations of all mammals in AZ Various Taxon/Mammals/Nm nmmammal ArcView shapefile Known locations of all mammals in NM Various Predicted . GridNames Text and Excel spreadsheet Cross-walk with BISON codes, scientific and common names CSU Birds/Az g04* ArcInfo grid Predicted distributions for birds AZ GAP Birds/Nm g04* ArcInfo grid Predicted distributions for birds NM GAP Herps/Az g02* or g03* ArcInfo grid Predicted distributions for amphibians and reptiles AZ GAP Herps/Nm g02* or g03* ArcInfo grid Predicted distributions for amphibians and reptiles NM GAP Mammals/Az g05* ArcInfo grid Predicted distributions for mammals AZ GAP Mammals/Nm g05* ArcInfo grid Predicted distributions for mammals NM GAP Richness Birds/Az azbirds ArcInfo grid Hotspots distribution of all birds AZ GAP/CSU Birds/Nm nmbirds ArcInfo grid Hotspots distribution of all birds NM GAP/ CSU Herps/Az azherps ArcInfo grid Hotspots distribution of all amphibians and reptiles AZ GAP/CSU Herps/Nm nmherps ArcInfo grid Hotspots distribution of all amphibians and reptiles NM GAP/ CSU Mammals/Az azmamms ArcInfo grid Hotspots distribution of all mammals AZ GAP/CSU Mammals/Nm nmmamms ArcInfo grid Hotspots distribution of all mammals NM GAP/ CSU 1Key to Abbreviations used for Sources: ALRIS Arizona Land Resource Information System AZ Arizona CSU Colorado State University EPA BASINS Environmental Protection Agency (Better Assessment Science Integrating Point and Nonpoint Sources) ESRI Environmental Systems Research Institute GAP Gap Analysis Program NM New Mexico USFS R3 US Forest Service, Region 3 USGS ESIC US Geological Survey, Environmental Systems Information Center
32 USDA Forest Service RMRS-GTR-152. 2005

USFS/CSU Species Point Observation Records Database for AZ and NM

Identification_Information:

Citation:

Citation_Information:

Originator: USDA Forest Service, Rocky Mountain Research Station

Originator:

Colorado State University, Department of Fishery and Wildlife Biology

Publication_Date: Unpublished Material

Title: USFS/CSU Species Point Observation Records Database for AZ and NM

Edition: 1.00

Geospatial_Data_Presentation_Form: vector digital data

Series_Information:

Series_Name:

USDA Forest Service, Rocky Mountain Research Station, General Technical Reports Series

Issue_Identification: RMRS-GTR-152

Publication_Information:

Publication_Place: Fort Collins, CO

Publisher: USDA Forest Service, Rocky Mountain Research Station

Other_Citation_Details: Appendix to Gen. Tech. Rep. RMRS-GTR-152

Online_Linkage: ftp://ftp2.fs.fed.us/incoming/ftcol/flather/R3_Biodiversity_Data

Description:

Abstract:

This database contains point locations of species observations for terrestrial vertebrates (birds, mammals, 

amphibians and reptiles) and butterflies for Arizona and New Mexico. Each point observation is described 

by information such as the species name, date observed, details of the observer and method, animal 

information, and data quality. Source data was compiled from various sources, including museums, private 

collections, established bird surveys (North American Breeding Bird Survey and Audubon Christmas Bird 

Count Survey), and government and non-government organizations.

Purpose:

The purpose of this database was to provide a catalog of geospatially referenced species observations 

for Arizona and New Mexico. These data are valuable to scientists and managers for the purpose of 

mapping species distributions from known occurrences, and may also be used, in aggregate, to indicate 

the distribution of biodiversity resources in the region. When these data are accompanied by sufficient 

environmental GIS data, the species observation database is valuable for modeling observed species 

occurrence to predict the likely occurrence of species in areas that have been undersampled.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 1819/04/20

Ending_Date: 2000/12/10

Currentness_Reference: ground condition

Status:

Progress: Complete

Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -116.435036

East_Bounding_Coordinate: -102.554120

North_Bounding_Coordinate: 38.547881

South_Bounding_Coordinate: 30.405698

Appendix B: Metadata documentation for the species 

observation GIS database
USDA Forest Service RMRS-GTR-152. 2005 33

Keywords:

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: point observation

Theme_Keyword: species occurrence

Theme_Keyword: species sighting location

Theme_Keyword: terrestrial vertebrates

Theme_Keyword: butterflies

Theme_Keyword: birds

Theme_Keyword: mammals

Theme_Keyword: reptiles

Theme_Keyword: amphibians

Place:

Place_Keyword_Thesaurus: None

Place_Keyword: American Southwest

Place_Keyword: Arizona

Place_Keyword: New Mexico

Place_Keyword: USDA Forest Service Region 3

Access_Constraints:

Under review. Some data remains the property of individual donors and may not be accessible for public use. 

Data management is under review by the USDA Forest Service. Access can be negotiated by contacting the 

data distributor.

Use_Constraints: Under review by USDA Forest Service.

Point_of_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Dr. Curtis Flather

Contact_Organization: USDA Forest Service, Rocky Mountain Research Station

Contact_Position: Research Wildlife Biologist

Contact_Address:

Address_Type: mailing and physical address

Address: USDA Forest Service

Address: Rocky Mountain Research Station

Address: Natural Resources Research Center

Address: 2150 Centre Avenue, Building A

City: Fort Collins

State_or_Province: Colorado

Postal_Code: 80526-2098

Country: USA

Contact_Voice_Telephone: 970-295-5910

Contact_Facsimile_Telephone: 970-295-5959

Contact_Electronic_Mail_Address: cflather@fs.fed.us

Hours_of_Service: daytime hours, most weekdays

Data_Set_Credit:

The principle contributors to the compilation of this data set were Curtis Flather, Kenneth Wilson, 

Rosamonde Cook, Gordon Reese and Darren Bender.

Security_Information:

Security_Classification_System: UNDETERMINED

Security_Classification: Sensitive

Security_Handling_Description: Under review by USDA Forest Service

Native_Data_Set_Environment: ESRI ArcView 3.2a/ArcInfo 8.0.2; Microsoft Access 2000

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

No formal attribute accuracy assessment was conducted. All values were assumed to be accurate, as 

provided in the original data sources. Random samples of records (approximately 15%) were verified for 

transcription errors. If an operator was observed to have committed numerous transcription errors for a 
34 USDA Forest Service RMRS-GTR-152. 2005

block of entries (100 - 1000 records), that block was checked exhaustively for errors and/or re-transcribed 

entirely. Taxonomic data (species names) were also verified (see below).

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value: SCI_NAME

Attribute_Accuracy_Explanation:

Scientific (Latin) species names were verified for nomenclature accuracy and currentness against two 

sources. The first authority used was the Integrated Taxonomic Information System (ITIS) online 

at <http://www.itis.usda.gov>. If the species was not listed or unverified in ITIS, the Natureserve 

(formerly the Association for Biodiversity Information) online taxonomic database was used (<http://

www.natureserve.org/explorer/>). Verification was most recently done in March 2004 and 11 changes 

were deemed necessary and are current as of October 2004. The names changed were (1) Anthanassa 

texana to Phyciodes texana, (2) Anthocharis pima to Anthocharis cethura, (3) Ascia howarthi to 

Ganyra howarthi, (4) Celaenorrhinus fritzgaer to Celaenorrhinus fritzgaertneri, (5) Chlosyne nycteis 

to Chlosyne ismeria, (6) Euphilotes mohave to Euphilotes mojave, (7) Eurema boisduvalianum to 

Eurema boisduvaliana, (8) Heliopetes domicella to Heliopyrgus domicella, (9) Incisalia henrici to 

Callophrys henrici, (10) Nymphalis atalanta to Vanessa atalanta, and (11) Thomomys umbrinus 

grahamensis to Thomomys bottae mearnsi.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value: COM_NAME

Attribute_Accuracy_Explanation:

Common species names were verified for nomenclature accuracy and currentness against two 

sources. The first authority used was the Integrated Taxonomic Information System (ITIS) online 

at <http://www.itis.usda.gov>. If the species was not listed or unverified in ITIS, the Natureserve 

(formerly the Association for Biodiversity Information) online taxonomic database was used (<http://

www.natureserve.org/explorer/>).

Logical_Consistency_Report: not applicable

Completeness_Report: not applicable

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

No formal accuracy assessment has been completed. The digitizing operator subjectively plotted 

points from text descriptions, and labelled them as ‘accurate’ or ‘not accurate’. The term ‘accurate’ 

means that the operator was confident that the location described was within 5 miles (8 km) of 

the plotted point location in the GIS. This confidence in accuracy is provided in the LOC_CONF 

(LOCATIONAL_CONFIDENCE) field as a ‘yes’ (accurate) or ‘no’ (not accurate). Data that were 

provided in digital form with a quantitative locational reference (for example, latitude and longitude) 

were judged to be accurate to within 5 miles (8 km). When the county of each species collection/

observation point was recorded, the digitized points were cross-validated using a GIS map of 

counties to ensure that the plotted point actually fell within the named county. If not, these records 

were flagged as potential digitizing errors, and were verified or re-digitized. Random samples of 

records (approximately 15%) were also verified for digitizing errors. If an operator was observed to 

have committed numerous digitizing errors for a block of work (100 - 1000 records), that block was 

checked exhaustively for errors and/or re-digitized entirely.

Vertical_Positional_Accuracy:

Vertical_Positional_Accuracy_Report: none

Lineage:

Process_Step:

Process_Description:

All points were plotted/digitized manually and rechecked for transcription errors.

Process_Date: 2002/01/31

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: Albers Conical Equal Area

Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000
USDA Forest Service RMRS-GTR-152. 2005 35

Standard_Parallel: 45.500000

Longitude_of_Central_Meridian: -96.000000

Latitude_of_Projection_Origin: 23.000000

False_Easting: 0.000000

False_Northing: 0.000000

Geodetic_Model:

Horizontal_Datum_Name: North American Datum of 1927

Ellipsoid_Name: Clarke 1866

Semi-major_Axis: 6378206.400000

Denominator_of_Flattening_Ratio: 294.978698

Entity_and_Attribute_Information:

Detailed_Description:

Attribute:

Attribute_Label: FID

Attribute_Definition: Internal feature number.

Attribute_Definition_Source: ESRI

Attribute_Domain_Values:

Unrepresentable_Domain:

Sequential unique whole numbers that are automatically generated.

Attribute:

Attribute_Label: Shape

Attribute_Definition: Feature geometry.

Attribute_Definition_Source: ESRI

Attribute_Domain_Values:

Unrepresentable_Domain: Coordinates defining the features.

Attribute:

Attribute_Label: SCI_NAME

Attribute_Definition:

Taxanomic (genus, species, subspecies) species name, as defined within ITIS.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Codeset_Domain:

Codeset_Name: Standardized nomenclature.

Codeset_Source: Integrated Taxanomic Information System (ITIS)

Attribute:

Attribute_Label: SRC_UNIQUE

Attribute_Definition: [not implemented]

Attribute:

Attribute_Label: COM_NAME

Attribute_Definition: Common name for species, as defined by ITIS.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Codeset_Domain:

Codeset_Name: Standardized nomenclature.

Codeset_Source: Integrated Taxanomic Information System (ITIS)

Attribute:

Attribute_Label: TSN

Attribute_Definition: ITIS taxanomic serial number.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Codeset_Domain:

Codeset_Name: Taxanomic serial number

Codeset_Source: Integrated Taxanomic Information System (ITIS)

Attribute:

Attribute_Label: TIME

Attribute_Definition:

The time of day that the species was collected. Values are in local time.
36 USDA Forest Service RMRS-GTR-152. 2005

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Valid time (24-hr)

Attribute:

Attribute_Label: DATE_

Attribute_Definition: The date of collection.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Valid Gregorian calendar date

Attribute:

Attribute_Label: DATE_ACC

Attribute_Definition: The precision of the time and date measurements.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Exact

Enumerated_Domain_Value_Definition:

The time and date are exact to the minute for the detection.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Hour

Enumerated_Domain_Value_Definition:

Only the exact hour, day, month, and year are known.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Day

Enumerated_Domain_Value_Definition:

Only the exact day, month, and year are known.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Month

Enumerated_Domain_Value_Definition:

Only the exact month and year are known.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Year

Enumerated_Domain_Value_Definition: Only the exact year is known.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Previous Year

Enumerated_Domain_Value_Definition:

The detection can only be temporally fixed to some time previous to the entered year.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Attribute:

Attribute_Label: OBS_METH

Attribute_Definition:

How the animal was detected or observed or if a reproductive site for a specific animal was 

found.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Visual and Aural

Enumerated_Domain_Value_Definition:

The animal was detected by seeing it and hearing it.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Visual

Enumerated_Domain_Value_Definition: The animal was detected by seeing it.
USDA Forest Service RMRS-GTR-152. 2005 37

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Aural

Enumerated_Domain_Value_Definition: The animal was detected by hearing it.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Reproductive Site

Enumerated_Domain_Value_Definition:

A reproductive site for a species was detected (for example, nest, den).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Track

Enumerated_Domain_Value_Definition:

Evidence of the animal was found in the form of a track (foot print, drag pattern of a body 

part).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Excrement

Enumerated_Domain_Value_Definition:

Evidence of the animal was found in the form of excrement (for example, scat, 

regurgitated pellets).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Capture

Enumerated_Domain_Value_Definition:

The animal was detected by some type of physical capture (for example, netting, 

trapping).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Radio Telemetry

Enumerated_Domain_Value_Definition:

The animal was detected by the use of radio telemetry.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Voucher Specimen

Enumerated_Domain_Value_Definition:

A specimen collected for identification in a laboratory or for submission to a taxon 

expert. May or may not be retained in a voucher collection.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Other

Enumerated_Domain_Value_Definition:

Evidence of the animal or detection was by means other than those above (for example, 

hair snare, track plate, antler rub, plucking post).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Unknown

Enumerated_Domain_Value_Definition: Method of detection is unknown

Enumerated_Domain_Value_Definition_Source: CSU database team

Attribute:

Attribute_Label: OBS_NAME

Attribute_Definition:

The name of the person who detected the animal or sign of the animal.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Free text

Attribute:

Attribute_Label: OBS_QUAL
38 USDA Forest Service RMRS-GTR-152. 2005

Attribute_Definition:

The qualifications of the observer to accurately identify the correct species associated with 

observation or detection. Education and field experience generally define the level.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Taxon Expert

Enumerated_Domain_Value_Definition:

A person who has extensive field and research level experience with the species detected.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Expert

Enumerated_Domain_Value_Definition:

A person who has extensive field experience with the species detected.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Limited Experience

Enumerated_Domain_Value_Definition:

A person with limited field experience with the species detected. Capable of making 

positive identifications among similar species or subspecies, or other species.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: No Experience

Enumerated_Domain_Value_Definition:

A person with no field experience identifying the wildlife species. Positive species 

identification made from description.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Unknown

Enumerated_Domain_Value_Definition:

The experience level of the observer is unknown. Positive species identification is made 

from the description.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Attribute:

Attribute_Label: TOT_NUM

Attribute_Definition:

The number of individuals with which the detection is associated. For example, if a single GIS 

point is associated with a pair of owls, the number ‘2’ would be entered into this field.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0

Range_Domain_Maximum: 32767

Attribute:

Attribute_Label: GENDER

Attribute_Definition: Sex of observed individual.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Male

Enumerated_Domain_Value_Definition: Male animal(s)

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: Female

Enumerated_Domain_Value_Definition: Female animal(s)

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: Mixed group
USDA Forest Service RMRS-GTR-152. 2005 39

Enumerated_Domain_Value_Definition:

At least one male plus at least one female in a group of individuals described by one 

record.

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: Unknown

Enumerated_Domain_Value_Definition: Animal gender was not determined.

Enumerated_Domain_Value_Definition_Source: CSU database team

Attribute:

Attribute_Label: REPRO

Attribute_Definition:

The reproductive status of the animal detected, if known. This could include labeling a pair as 

reproductive, or that there are young present (inferring that the pair is successfully reproducing).

Attribute_Definition_Source: USFS NRIS Fauna

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Reproducing

Enumerated_Domain_Value_Definition:

The animal observed or nest/den found shows evidence of current season reproduction.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Non-reproducing

Enumerated_Domain_Value_Definition:

The animal observed or nest/den found shows positive evidence of non-reproduction in 

the current season.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Failed Reproduction

Enumerated_Domain_Value_Definition:

The animal observed or nest/den found shows evidence of reproduction that has failed 

(for example, dead young, abandoned eggs). Failure could be complete or partial loss of 

reproductive effort.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: With Young at Reproductive Site

Enumerated_Domain_Value_Definition:

The animal or group was observed with young on/at a nest or den.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: With Young

Enumerated_Domain_Value_Definition:

The animal or group was observed with young not at a nest or den.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Unknown

Enumerated_Domain_Value_Definition:

Reproduction status cannot be confirmed due to lack of evidence.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Not Applicable

Enumerated_Domain_Value_Definition:

The animal or group was observed outside of its reproductive season.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Attribute:

Attribute_Label: GROUP

Attribute_Definition:

The description of the size and relationship of the animal group detected.

Attribute_Definition_Source: USFS NRIS Fauna
40 USDA Forest Service RMRS-GTR-152. 2005

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Single

Enumerated_Domain_Value_Definition:

The animal observed was not part of a pair or family group. Only one animal was 

observed.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Pair

Enumerated_Domain_Value_Definition:

The animals observed were considered a pair due to protocol rules or professional 

judgment.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Family Group

Enumerated_Domain_Value_Definition:

The group of animals observed was a family group or part of one (more than two adults 

or two adults with young).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Group

Enumerated_Domain_Value_Definition:

The group of animals was a congregation of singles, pairs, family groups, or a mix of 

types (for example, herd, flock, swarm).

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Enumerated_Domain:

Enumerated_Domain_Value: Unknown

Enumerated_Domain_Value_Definition:

Group type could not be determined due to lack of evidence found.

Enumerated_Domain_Value_Definition_Source: USFS NRIS Fauna

Attribute:

Attribute_Label: LAT

Attribute_Definition: The latitude of the detection in decimal degrees

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Geographic coordinates using NAD27 datum

Attribute:

Attribute_Label: LON

Attribute_Definition: The longitude of the detection in decimal degrees

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Geographic coordinates using NAD27 datum

Attribute:

Attribute_Label: LOC_DESC

Attribute_Definition: Description of the sighting/collection location.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Free text

Attribute:

Attribute_Label: LOC_ACC

Attribute_Definition: Horizontal locational accuracy (meters), if known.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0

Range_Domain_Maximum: 32767

Attribute_Units_of_Measure: meteres
USDA Forest Service RMRS-GTR-152. 2005 41

Attribute:

Attribute_Label: LOC_CONF

Attribute_Definition:

Identifies whether the location was plotted with confidence within 5 miles (8 km) of the 

observation.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: Yes

Enumerated_Domain_Value_Definition: Locational confidence was achieved.

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: Y

Enumerated_Domain_Value_Definition: Locational confidence was achieved.

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: No

Enumerated_Domain_Value_Definition: Locational confidence was not achieved.

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: N

Enumerated_Domain_Value_Definition: Locational confidence was not achieved.

Enumerated_Domain_Value_Definition_Source: CSU database team

Enumerated_Domain:

Enumerated_Domain_Value: Unknown

Enumerated_Domain_Value_Definition: Locational confidence was not determined.

Enumerated_Domain_Value_Definition_Source: CSU database team

Attribute:

Attribute_Label: COMMENTS

Attribute_Definition:

The comments that the observer collected during the detection, including key words. A key word 

or key phrase could precede a portion of text.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Free text

Attribute:

Attribute_Label: DATA_SRC

Attribute_Definition:

Original source of the data (collector/collection, atlas, or survey name).

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Free text

Attribute:

Attribute_Label: SRC_FILE

Attribute_Definition:

Name of the original shapefile from which the data was consolidated.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Unrepresentable_Domain: Free text

Attribute:

Attribute_Label: SRC_RID

Attribute_Definition: Identification number from the original shapefile.

Attribute_Definition_Source: CSU database team

Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0

Range_Domain_Maximum: 2147483647
42 USDA Forest Service RMRS-GTR-152. 2005

Overview_Description:

Entity_and_Attribute_Overview:

See accompanying USFS General Technical Report regarding the development of this project and the 

Point Observation Records database.

Entity_and_Attribute_Detail_Citation: not cited

Distribution_Information:

Distribution_Liability: Under review.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Option:

Offline_Option:

Offline_Media: CD-ROM

Recording_Format: ISO 9660

Fees: TBD

Metadata_Reference_Information:

Metadata_Date: 20020214

Metadata_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Dr. Darren Bender

Contact_Organization: University of Calgary

Contact_Position: Assistant Professor of Geography

Contact_Address:

Address_Type: mailing address

Address: Department of Geography

Address: Univerity of Calgary

Address: 2500 University Dr NW

City: Calgary

State_or_Province: Alberta

Postal_Code: T2N 1N4

Country: Canada

Contact_Voice_Telephone: 403-220-6398

Contact_Facsimile_Telephone: 403-282-6561

Contact_Electronic_Mail_Address: dbender@ucalgary.ca

Hours_of_Service: daytime hours, most weekdays

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata

Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Time_Convention: local time

Metadata_Access_Constraints: none

Metadata_Use_Constraints: none

Metadata_Security_Information:

Metadata_Security_Classification_System: none

Metadata_Security_Classification: Unclassified

Metadata_Security_Handling_Description: none

Metadata_Extensions:

Online_Linkage: <http://www.esri.com/metadata/esriprof80.html>

Profile_Name: ESRI Metadata Profile

Metadata_Extensions:

Online_Linkage: <http://http://www.fgdc.gov/standards/status/sub5_2.html>

Profile_Name: Biological Data Profile
The Rocky Mountain Research Station develops scientific 

information and technology to improve management, protection, 

and use of the forests and rangelands. Research is designed 

to meet the needs of the National Forest managers, Federal 

and State agencies, public and private organizations, academic 

institutions, industry, and individuals.

Studies accelerate solutions to problems involving 

ecosystems, range, forests, water, recreation, fire, resource 

inventory, land reclamation, community sustainability, forest 

engineering technology, multiple use economics, wildlife and fish 

habitat, and forest insects and diseases. Studies are conducted 

cooperatively, and applications may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

*Station Headquarters, Natural Resources Research Center, 

2150 Centre Avenue, Building A, Fort Collins, CO 80526.

The U.S. Department of Agriculture (USDA) prohibits discrimination 

in all its programs and activities on the basis of race, color, national 

origin, sex, religion, age, disability, political beliefs, sexual orientation, 

or marital or family status. (Not all prohibited bases apply to all 

programs.) Persons with disabilities who require alternative means for 

communication of program information (Braille, large print, audiotape, 

etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice 

and TDD).

To file a complaint of discrimination, write USDA, Director, Office 

of Civil Rights, Room 326 W, Whitten Building, 1400 Independence 

Avenue, SW, Washington, D.C. 20250-9410 or call (202) 720-5964 

(voice and TDD). USDA is an equal opportunity provider and employer.

RMRS

ROCKY MOUNTAIN RESEARCH STATION

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